Automatic detection of the topographical distribution of EEG rhythms based on an iterative adjustment of the averaged reference potential

Electroencephalogram (EEG) interpretation is important for the investigation of brain diseases. In this study, an automatic technique was developed to detect the topographical distribution of EEG rhythms. In order to obtain the topographical distribution, the amplitude of the EEG rhythm was analyzed based on the referential derivation where the reference potential was adjusted iteratively. The result of the automatic detection of the topographical distribution was helpful in highlighting the EEG rhythms of interest for automatic EEG interpretation. The technique developed has application significance for real clinics.     

Simulation studies of inverted pendulum based on PID controllers

The inverted pendulum problem is one of the most important problems in control theory and has been studied excessively in control literatures. When a control system have more than two PID controllers, the adjustment of PID parameters is not an easy problem. In this paper, PID controllers are applied to the stabilization and tracking control of three types of inverted pendulum. The way how to design the PID controllers is given step by step in this paper. Simulation results prove that the way to design of PID controllers is very simple and effective. The system design not only can realize stabilization and tracking control of three types of inverted pendulum, but also have robustness to outer large and fast disturbances.     

交叉偶极阵列声波测井仪控制和处理电路的设计与实现

针对交叉偶极阵列声波测井信号的特点,设计了一种以DSP+FPGA架构为核心的控制和处理电路。详细介绍了电路的硬件结构和软件设计流程,重点描述了测井数据通信接口和逻辑控制模块的实现,并给出了一种首波提取算法。实验结果表明,该电路在井下高温高压恶劣环境中能够稳定可靠地长时间工作,完全满足工业生产作业的需要。     

Optimization of optical lens-controlled scanning electron microscopic resolution using generalized regression neural network and genetic algorithm

A scanning electron microscope (SEM) is a sophisticated equipment employed for fine imaging of a variety of surfaces. In this study, prediction models of SEM were constructed by using a generalized regression neural network (GRNN) and genetic algorithm (GA). The SEM components examined include condenser lens 1 and 2 and objective lens (coarse and fine) referred to as CL1, CL2, OL-Coarse, and OL-Fine. For a systematic modeling of SEM resolution (R), a face-centered Box–Wilson experiment was conducted. Two sets of data were collected with or without the adjustment of magnification. Root-mean-squared prediction error of optimized GRNN models are GA 0.481 and 1.96×10^-12 for non-adjusted and adjusted data, respectively. The optimized models demonstrated a much improved prediction over statistical regression models. The optimized models were used to optimize parameters particularly under best tuned SEM environment. For the variations in CL2 and OL-Coarse, the highest R could be achieved at all conditions except a larger CL2 either at smaller or larger OL-Coarse. For the variations in CL1 and CL2, the highest R was obtained at all conditions but larger CL2 and smaller CL1.     

基于NiosII的便携式超声波流量计设计

介绍了便携式超声波流量计的工作原理和系统硬件结构,分析了系统收发电路各个模块的设计,着重介绍了基于FPGA软核NiosII的便携式超声波流量计的数字电路部分设计。试验结果表明,系统工作稳定,能够满足测量精度要求,并且减小了便携式超声波流量计的体积,降低了产品成本。     

Observer design for nonlinear discrete‐time systems: Immersion and dynamic observer error linearization techniques

This paper focuses on the observer design for nonlinear discrete‐time systems by means of nonlinear observer canonical form. At first, sufficient and necessary conditions are obtained for a class of autonomous nonlinear discrete‐time systems to be immersible into higher dimensional observer canonical form. Then a method called dynamic observer error linearization is developed. By introducing a dynamic auxiliary system, the augmented system is shown to be locally equivalent to the generalized observer form, whose nonlinear terms contain auxiliary states and output of the system. A constructive algorithm is also provided to obtain the state coordinate transformation. These results are an extension of their counterparts of nonlinear continuous‐time systems to nonlinear discrete‐time systems (Syst. Control Lett. 1986; 7 :133–142; SIAM. J. Control Optim. 2003; 41 :1756–1778; Int. J. Control 2004; 77 :723–734; Automatica 2006; 42 :321–328; IEEE Trans. Automat. Control 2007; 52 :83–88; IEEE Trans. Automat. Control 2004; 49 :1746–1750; Automatica 2006; 42 :2195–2200; IEEE Trans. Automat. Control 1996; 41 :598–603; Syst. Control Lett. 1997; 31 :115–128). Copyright © 2009 John Wiley & Sons, Ltd.     

Partner selection in a virtual enterprise under uncertain information about candidates

Partner selection is a major issue in the formation of a virtual enterprise. In practice, in the partner selection process, the information about the candidates and their performances are incomplete and uncertain. Vague sets theory is one of the methods used to deal with uncertain information. In this paper, a new method based on vague sets is proposed to deal with the partner selection problem in the formation of a virtual enterprise while the factors of satisfaction degree, due date, cost and the precedence of tasks are taken into account. On the basis of the agreement index of satisfaction degree, the formulated partner selection problems are interpreted so as to maximize the minimum agreement index. To solve the problem, an improved particle swarm optimization (PSO) algorithm is proposed. Finally, the simulation of a numerical example demonstrates that the method is effective.     

Maximum contrast analysis for nonnegative blind source separation

In this paper, we propose a maximum contrast analysis (MCA) method for nonnegative blind source separation, where both the mixing matrix and the source signals are nonnegative. We first show that the contrast degree of the source signals is greater than that of the mixed signals. Motivated by this observation, we propose an MCA-based cost function. It is further shown that the separation matrix can be obtained by maximizing the proposed cost function. Then we derive an iterative determinant maximization algorithm for estimating the separation matrix. In the case of two sources, a closed-form solution exists and is derived. Unlike most existing blind source separation methods, the proposed MCA method needs neither the independence assumption, nor the sparseness requirement of the sources. The effectiveness of the new method is illustrated by experiments using X-ray images, remote sensing images, infrared spectral images, and real-world fluorescence microscopy images.     

An observer-based approach for the projection onto a 2d-curve under movement

Standard path control laws of autonomous vehicles use the shortest distance between the vehicle’s position and the path as a control error. In order to determine this distance, the projection point onto the path needs to be determined continuously. This requires fast algorithms that feature high numerical reliability in the field of vehicle application.This paper presents two different observer-based approaches for the projection problem. The identity observer reconstructs all states of interest for path control. The second one, a reduced observer, only possesses the curve parameter as a state and calculates the other values by algebraic formulas. Both algorithms consider the continuous movement of the vehicle, the run of the curve, and work without any approximation of the curve. Furthermore, they are applicable for arbitrary parameterized smooth curves, guarantee the required numerical stability, have short calculating time, and show good statistical properties. The performance is shown in several simulations as well as under real conditions.