G-P算法在脑学习机能研究中的应用

运用非线性动力学理论,分析大脑信息处理运动的过程.由混沌动力学吸引子关联维数的物理意义出发,论证了关联维数值与神经元群电发放之间的关系.采用连续加算作业法,同步采集EEG信号,应用分形论中的G-P算法,获取EEG信号的关联维数.实验结果表明:连续加算作业法的下半时EEG信号的关联维数明显小于上半时EEG信号的关联维数.因此,基于G-P算法的关联维数可以作为脑学习机能的评价指数.     

Modeling multibody systems with uncertainties. Part II: Numerical applications

This study applies generalized polynomial chaos theory to model complex nonlinear multibody dynamic systems operating in the presence of parametric and external uncertainty. Theoretical and computational aspects of this methodology are discussed in the companion paper “Modeling Multibody Dynamic Systems With Uncertainties. Part I: Theoretical and Computational Aspects”.In this paper we illustrate the methodology on selected test cases. The combined effects of parametric and forcing uncertainties are studied for a quarter car model. The uncertainty distributions in the system response in both time and frequency domains are validated against Monte-Carlo simulations. Results indicate that polynomial chaos is more efficient than Monte Carlo and more accurate than statistical linearization. The results of the direct collocation approach are similar to the ones obtained with the Galerkin approach. A stochastic terrain model is constructed using a truncated Karhunen-Loeve expansion. The application of polynomial chaos to differential-algebraic systems is illustrated using the constrained pendulum problem. Limitations of the polynomial chaos approach are studied on two different test problems, one with multiple attractor points, and the second with a chaotic evolution and a nonlinear attractor set.The overall conclusion is that, despite its limitations, generalized polynomial chaos is a powerful approach for the simulation of multibody dynamic systems with uncertainties.     

Internet访问时间的混沌性解析

以CAIDA组织授权的海量数据为样本空间,利用相空间重构技术以及G-P算法,对时间维的Internet访问时间进行相空间重构,得到其混沌吸引子饱和关联维数为2.8308,证实Internet访问时间的演化过程具有混沌特征.在此基础上,根据关联维数及奇怪吸引子在相空间混沌轨道运动的特性,提出了三维微分方程组的Internet访问时间长期预测模型.     

Effect of tool vibrations on surface roughness during lathe dry turning process

Choice of optimized cutting parameters is very important to control the required surface quality. In fact, the difference between the real and theoretical surface roughness can be attributed to the influence of physical and dynamic phenomena such as: built-up edge, friction of cut surface against tool point and vibrations. The focus of this study is the collection and analysis of surface roughness and tool vibration data generated by lathe dry turning of mild carbon steel samples at different levels of speed, feed, depth of cut, tool nose radius, tool length and work piece length. A full factorial experimental design (288 experiments ) that allows to consider the three-level interactions between the independant variables has been conducted. Vibration analysis has revealed that the dynamic force, related to the chip-thickness variation acting on the tool, is related to the amplitude of tool vibration at resonance and to the variation of the tool's natural frequency while cutting. The analogy of the effect of cutting parameters between tool dynamic forces and surface roughness is also investigated. The results show that second order interactions between cutting speed and tool nose radius, along with third-order interaction between feed rate, cutting speed and depth of cut are the factors with the greatest influence on surface roughness and tool dynamic forces in this type of operation and parameter levels studied. The analysis of variance revealed that the best surface roughness condition is achieved at a low feed rate (less than 0.35 mnt/rev), a large tool nose radius (1.59 mm) and a high cutting speed (265 m/min and above). The results also show that the depth of cut has not a significant effect on surface roughness, except when operating within the built-up edge range. It is shown that a correlation between surface roughness and tool dynamic force exist only when operating in the built-up edge range. In these cases, built-u edge formation deteriorates surface roughness and increases dynamic forces acting on the tool. The effect of built-up edge formation on surface roughness can be minimized by increasing depth of cut and increasing tool vibration. Key words:design of experiments, lathe dry turning operation, full factorial design, surface roughness, measurements, cutting parameters, tool vibrations.     

Identification and verification of chaotic dynamics in a missile system from experimental time series

This article is concerned with the chaotic dynamics in a missile system. Five channels of acceleration signals were measured at different locations and/or orientations of the missile during a test flight. Based on these data, the existence of chaotic behaviour is determined using common techniques for nonlinear time series analysis, such as phase-space reconstruction, Poincaré map, correlation dimension and maximum Lyapunov exponent. It is found that the vibration behaviour of the missile system represents high-order (eight dimensional) chaos. Chaotic dynamics exist in three (out of the five) channels of acceleration signals. As typical in experimental time series, the acceleration signals are contaminated with random noises. In order to determine whether deterministic chaos dominates in the three acceleration signals, a sequence of two statistical tests, the BDS test and the Kaboudan test, is applied. The BDS test rules out the possibility that the three acceleration signals are purely random. The subsequent Kaboudan test indicates that deterministic chaotic dynamics indeed dominate in two acceleration signals where the seeker is located.     

Improved prediction of stability lobes in milling process using time series analysis

In this paper, a new method is presented for prediction of cutting forces, surface texture and stability lobes in end milling operation based on time series analysis. In the approach, an equivalent damping ratio is defined for the cutting zone while the damping ratio of non-cutting zone is determined by experimental modal analysis. Using correlation dimension criterion, the simulation and experimental force signals are compared to anticipate the value of process damping by assessing the variation of correlation dimension for both signals. The effect of cutter deflections and run out are taken into account. Moreover, the stability lobes are predicted by considering the variation of process damping with cutting conditions. The feasibility of the proposed algorithm is verified experimentally for machining of Aluminum 7075-T6. Comparison of experiment results against simulation results indicates that the improved model can accurately predict cutting forces, surface texture and stability lobes for low radial immersion.     

Visual analysis of image collections

Multidimensional Visualization techniques are invaluable tools for analysis of structured and unstructured data with variable dimensionality. This paper introduces PEx-Image—Projection Explorer for Images—a tool aimed at supporting analysis of image collections. The tool supports a methodology that employs interactive visualizations to aid user-driven feature detection and classification tasks, thus offering improved analysis and exploration capabilities. The visual mappings employ similarity-based multidimensional projections and point placement to layout the data on a plane for visual exploration. In addition to its application to image databases, we also illustrate how the proposed approach can be successfully employed in simultaneous analysis of different data types, such as text and images, offering a common visual representation for data expressed in different modalities.     

Tool wear monitoring and selection of optimum cutting conditions with progressive tool wear effect and input uncertainties

One of the big challenges in machining is replacing the cutting tool at the right time. Carrying on the process with a dull tool may degrade the product quality. However, it may be unnecessary to change the cutting tool if it is still capable of continuing the cutting operation. Both of these cases could increase the production cost. Therefore, an effective tool condition monitoring system may reduce production cost and increase productivity. This paper presents a neural network based sensor fusion model for a tool wear monitoring system in turning operations. A wavelet packet tree approach was used for the analysis of the acquired signals, namely cutting strains in tool holder and motor current, and the extraction of wear-sensitive features. Once a list of possible features had been extracted, the dimension of the input feature space was reduced using principal component analysis. Novel strategies, such as the robustness of the developed ANN models against uncertainty in the input data, and the integration of the monitoring information to an optimization system in order to utilize the progressive tool wear information for selecting the optimum cutting conditions, are proposed and validated in manual turning operations. The approach is simple and flexible enough for online implementation.     

Revealing oscillators calculation of the dimension of attractors

The monitoring of various natural phenomena yields data sets sustaining the dynamics of usually very complex systems. The question then is: how to reveal the characteristics and the nature of a system? In approaching this question, the calculation of the dimension of its attractor may give an indication of its complexity. The present review is an account of the analyses of the calculation of attractor dimension. Various quasi-periodic systems with different degrees of freedom are simulated and, by adding noise and nonstationarity, experimental conditions are mimicked. By increasing the complexity of the system an overestimation of attractor dimension is profound, even on noise-free signals. The presented analysis points out that for experimental data sets, resulting from systems already having more than one degree of freedom, the calculated value of attractor dimension cannot be used to determine whether the system dynamics is chaotic. It may be firmly determined whether it is deterministic or not, since in the case of a deterministic system its surrogate has a higher dimension than the original signal.     

基于Web Service/SOAP的远程信号分析工具

本文用Delphi6设计了一个基于WebService/SOAP的远程信号分析工具,并论述了此工具的主要类及体系结构。通过车削力和车床主轴箱振动的测量实验,验证了此分析工具的正确性。     

Computer observer for in-process measurement of lathe tool wear

In the laboratory, tool wear is measured by direct visual observation of flank and crater wear dimensions using a tool maker's microscope. On the shop floor, the journeyman machinist uses chip appearance, sound, vibration, and surface finish to access tool condition. More precise information can be provided by between pass measurements of work piece dimensions. Although there is a body of research directed toward in-process measurement of tool wear, none has found practical application on the shop floor. This, despite the demands of unattended operation of machine tools in the automated factory. Two of the authors have developed a state space model of metal cutting on a lathe. Operation of that model was experimentally accessed using the Advanced Continuous Simulation Language (ACSL) [1]. The state space model embodied in the simulation is comprised of six state variables. In an actual lathe, four of those variables (spindle speed and torque, and cutting speed and force) would be directly measurable. The other two variables, flank and crater tool wear, would not be measurable. This paper describes a linear observer that reconstructs the two tool wear state variables based on system inputs and the four measurable state variables. In actual use the observer would be implemented using a microcomputer dedicated to the lathe. In this study, the previously developed ACSL model was substituted for the lathe, and the linear observer was incorporated as an extension to the simulation. Operation of the observer, including response to initial errors, is demonstrated.     

Tool condition classification using Hidden Markov Model based on fractal analysis of machined surface textures

The texture of a machined surface generated by a cutting tool, with geometrically well-defined cutting edges, carries essential information regarding the extent of tool wear. There is a strong relationship between the degree of wear of the cutting tool and the geometry imparted by the tool on to the workpiece surface. The monitoring of a tool’s condition in production environments can easily be accomplished by analyzing the surface texture and how it is altered by a cutting edge experiencing progressive wear and micro-fractures. This paper discusses our work which involves fractal analysis of the texture of surfaces that have been subjected to machining operations. Two characteristics of the texture, high directionality and self-affinity, are dealt with by extracting the fractal features from images of surfaces machined with tools with different levels of tool wear. The Hidden Markov Model is used to classify the various states of tool wear. In this paper, we show that fractal features are closely related to tool condition and HMM-based analysis provides reliable means of tool condition prediction.     

Advanced process monitoring—a major step towards adaptive control

Adaptive Control (AC) of machine tools requires many kinds of measured input data. The more information about the complex metal cutting process that can be obtained, the better the process can be controlled.

The paper describes an Adaptive Control Optimization (ACO) system for turning operations. The system continuously chooses Optimal Cutting Data (OCD), taking into account both economical criteria and technical limitations.

The system operates at three different levels:

• • Advanced Process Monitoring

• • Adaptive Control Constraint (ACC)

• • Adaptive Control Optimization (ACO).

Two commercial monitoring systems perform process monitoring. In addition, five independent measurement systems have been developed.

A dedicated vision system has been installed in the lathe to measure the tool flank wear between cuts. The flank wear data are utilized to predict the tool life. Based upon these predictions economical optimum cutting data can be calculated at the ACO level.

To obtain in-process real-time control of the metal cutting process the cutting forces are measured during machining. The forces are measured with conventional piezoelectric force transducers which are located between the turret housing and the cross-slide. The measured force signals are processed by a dedicated microcontroller at the ACC level and cutting data adjustments are fed back to the machine control.

A vibration measurement system, which either can be connected to an accelerometer or use the dynamic force signal from the piezoelectric force transducer, is part of a vibration control module at the ACC level. An ultra-fast signal processor performs the signal analysis.

The remaining two measurement systems—a high frequency tool signal analysis system and a power spectra analysis system—are mentioned in the paper but not further discussed.

Finally, the paper deals with how the strategies at the three different levels will be combined, in order to form an AC system. The monitoring tasks will always reside in the background and be activated if any failure occurs. The ACO subsystem will act as a path-finder and suggest cutting data. The active control tasks will, however, be carried out at the ACC level.     

Investigation of cutting parameter effects on surface roughness in lathe boring operation by use of a full factorial design

The main objective of this study is to investigate cutting parameter effects of surface roughness in a lathe dry boring operation. A full factorial design was used to evaluate the effect of six (6) independent variables (cutting speed, feed rate, depth of cut, tool nose radius, tool length and type of boring bar) and their corresponding two-level interactions. In this experiment, the dependant variable was the resulting fast cut surface roughness (R,). In order to perform all possible variable combinations, a total of 216 cuts were.

The results revealed that using short tool length always provide good surface roughness and that only slight improvement on surface roughness can be achieved by properly controlling the cutting parameters and/or the type of boring bar used. The results also revealed that using a long tool length may results in vibration that could be efficiently controlled by the use of a damped boring bar. With such a long tool length, the cutting variables become important factors to control in order to significantly improve surface roughness results with both types of boring bars. A prediction model is proposed for each types of boring bar. Both models are highly significant, p<0.00001, with coefficients of determination of 0.56 and 0.57 for a standard boring bar and a damped boring bar, respectively.     

Component-based analysis of embedded control applications

The widespread use of embedded systems requires the creation of industrial software technology that will make it possible to engineer systems being correct by construction. That can be achieved through the use of validated (trusted) components, verification of design models, and automatic configuration of applications from validated design models and trusted components. This design philosophy has been instrumental for developing COMDES—a component-based framework for distributed embedded control systems. A COMDES application is conceived as a network of embedded actors that are configured from instances of reusable, executable components—function blocks (FBs). System actors operate in accordance with a timed multitasking model of computation, whereby I/O signals are exchanged with the controlled plant at precisely specified time instants, resulting in the elimination of I/O jitter. The paper presents an analysis technique that can be used to validate COMDES design models in SIMULINK. It is based on a transformation of the COMDES design model into a SIMULINK analysis model, which preserves the functional and timing behaviour of the application. This technique has been employed to develop a feasible (light-weight) analysis method based on runtime observers. The latter are conceived as special-purpose actors running in parallel with the application actors, while checking system properties specified in Linear Temporal Logic. Observers are configured from reusable FBs that can be exported to SIMULINK in the same way as application components, making it possible to analyze system properties via simulation. The discussion is illustrated with an industrial case study—a Medical Ventilator Control System, which has been used to validate the developed design and analysis methods.     

SD振子中内部激变现象的研究

应用广义胞映射图论方法(GCMD)研究SD(smooth and discontinuous systems)振子的内部激变现象,通过对SD常微分方程系统的全局分析发现周期解通向混沌的内部激变现象是由于周期吸引子与在其吸引域内部的混沌鞍碰撞产生的,混沌鞍是胞空间中的瞬态自循环胞集,周期吸引子与混沌鞍发生碰撞后,混沌鞍转...     

A new chaos detector

This paper proposes a new detector for chaos. It is simpler and numerically less intensive than previous methods. It is more robust than previous methods. It works well even with short data sets (200 time scalar points), in the presence of noise (as low as 4 dB signal to noise ratio in stationary additive white Gaussian noise), and with severe data level quantization (data quantized to five bits). The theory behind the new detector is given, and examples of its application to the Logistic, Henon, and Lozi Maps are shown. The new chaos detector is briefly compared to existing methods such as the estimation of the largest Lyapunov exponent, the correlation or attractor dimension, and the entropy of the data set. Conventional electrical engineering signal analysis tools (such as the Discrete Fourier Transform) fail to help in the detection of chaos. This failure is explored. Sinusoids and several types of noise are input to the detector and the outputs are recorded. The basis for a binary signaling scheme which uses chaotic sequences and the new chaos detector is briefly described.     

Chaos Theory Applied to the Caloric Response of the Vestibular System

Developments in the field of nonlinear dynamics has given us a new conceptual framework for understanding the mechanisms involved in the regulation of complex nonlinear systems. This concept, called "chaos" or "deterministic chaos," has been applied to EKG, EEG, and other physiological signals, but not yet to the ENG signal. The underlying geometrical structure in chaotic dynamics is fractal (noninteger dimension), and calculating the fractal dimension of the electronystagmographic recording from caloric testing gave a dimension ranging from 3.3 to 7.7. This result demonstrates that the multidimensional vestibular system, with its numerous neurological pathways, can somehow reduce the degrees of freedom and give rise to an irregular dynamic low-dimensional behavior, which is associated with deterministic chaos.     

神经元网络奇怪吸引子的计算机模拟

为模拟神经元网络的混沌现象,阐述了相空间重构技术,介绍了由一维可观察量计算系统的最大Lyapunov指数和关联维数的方法。利用Lyapunov指数作判据,构造了3层反馈神经元网络的奇怪吸引子,分析了奇怪吸引子的运动特征并计算了奇怪吸引子的关联维数。研究表明混沌神经元网络具有复杂的动力学特征,同时存在各种吸引子,不仅有不动点、极限环、环面,而且有奇怪吸引子。     

Dimensional characteristics of diffusion chaos

The phenomenon of multimode diffusion chaos is considered. For a number of examples, it is shown by an extended numerical experiment that the Lyapunov dimension of the attractor of a distributed evolutionary dynamic system increases when the diffusion coefficient tends to zero.