Design of neural network-based estimator for tool wear modeling in hard turning

Hard turning with cubic boron nitride (CBN) tools has been proven to be more effective and efficient than traditional grinding operations in machining hardened steels. However, rapid tool wear is still one of the major hurdles affecting the wide implementation of hard turning in industry. Better prediction of the CBN tool wear progression helps to optimize cutting conditions and/or tool geometry to reduce tool wear, which further helps to make hard turning a viable technology. The objective of this study is to design a novel but simple neural network-based generalized optimal estimator for CBN tool wear prediction in hard turning. The proposed estimator is based on a fully forward connected neural network with cutting conditions and machining time as the inputs and tool flank wear as the output. Extended Kalman filter algorithm is utilized as the network training algorithm to speed up the learning convergence. Network neuron connection is optimized using a destructive optimization algorithm. Besides performance comparisons with the CBN tool wear measurements in hard turning, the proposed tool wear estimator is also evaluated against a multilayer perceptron neural network modeling approach and/or an analytical modeling approach, and it has been proven to be faster, more accurate, and more robust. Although this neural network-based estimator is designed for CBN tool wear modeling in this study, it is expected to be applicable to other tool wear modeling applications.     

一种维类转换为维层次的方法

分析维层次的结构和定义对数据仓库聚集操作的重要作用,确定了某些维类也可能产生类似维层次的分析要求,提出维类转换为维层次的CDCDH方法.给出维类和维层次的定义,并进一步提出显性维层次和隐性维层次的定义.对于表现为维类的隐性维层次,则确定其向显性维层次转换的条件和转换的步骤.同时结合一个具体的实例,说明了CDCDH方法的实施过程.     

综合电子系统嵌入式计算机技术

随着信息技术的发展,传统的机械化战争模式正向信息化战争方向发展.武器平台的信息化成为综合电子系统嵌入式计算机发展的强大推动力.该文通过研究,给出综合电子系统嵌入式计算机的体系结构、高速交换网络、模块结构、供电网络、实时分布操作系统、冗余设计和功能重组等.     

串行RapidIO验证模型

片上系统设计中大量使用IP核,其验证是整个系统设计的关键.串行RapidIO(SRIO)定义了器件间的全双工串行链路,物理上每个方向使用单向差分信号,因此SRIO核的验证存在一定的难度.该文基于PCI-RapidIO桥的设计与实现,建立了SRIO的验证模型,包括功能仿真模型、硬件验证模型和互操作性验证模型,为SRIO核的验证提供了思路,并建立了SRIO的仿真环境平台和FPGA硬件验证平台.     

BP神经网络算法预测及其在飞参数据分析中的应用

针对飞机喘振参数受随机因素影响的特点,提出了用人工神经网络中BP网络对飞机发动机喘振预测.通过对喘振数据及机组实际振动数据的预测结果检验,证明该神经网络预测有利于飞机发动机喘振状态的预测精度.     

一种小型嵌入式Linux Boot Loader的设计与实现

提出了一种小型嵌入式Linux Boot Loader的设计方案,并详细介绍该方案在Arm-Linux平台上的实现过程.方案大大简化了特定应用平台下Linux Boot Loader的设计,并且大大减小了生成的可执行文件的大小,针对特定应用平台自行设计小型的Boot Loader具有一定的参考价值.     

Dynamical characteristics of software trustworthiness and their evolutionary complexity

Developing trusted softwares has become an important trend and a natural choice in the development of software technology and applications, and software trustworthiness modeling has become a prerequisite and necessary means. To discuss and explain the basic scientific problems in software trustworthiness and to establish theoretical foundations for software trustworthiness measurement, combining the ideas of dynamical system study, this paper studies evolutionary laws of software trustworthiness and the dynamical mechanism under the effect of various internal and external factors, and proposes dynamical models for software trustworthiness, thus, software trustworthiness can be considered as the statistical characteristics of behaviors of software systems in the dynamical and open environment. By analyzing two simple examples, the paper explains the relationship between the limit evolutionary behaviors of software trustworthiness attributes and dynamical system characteristics, and interprets the dynamical characteristics of software trustworthiness and their evolutionary complexity. Supported partially by the National Basic Research Program of China (Grant No. 2005CB321900) and the National Natural Science Foundation of China (Grant No. 60473091)     

Sampled-data based average consensus with measurement noises: convergence analysis and uncertainty principle

In this paper, sampled-data based average-consensus control is considered for networks consisting of continuous-time first-order integrator agents in a noisy distributed communication environment. The impact of the sampling size and the number of network nodes on the system performances is analyzed. The control input of each agent can only use information measured at the sampling instants from its neighborhood rather than the complete continuous process, and the measurements of its neighbors’ states are corrupted by random noises. By probability limit theory and the property of graph Laplacian matrix, it is shown that for a connected network, the static mean square error between the individual state and the average of the initial states of all agents can be made arbitrarily small, provided the sampling size is sufficiently small. Furthermore, by properly choosing the consensus gains, almost sure consensus can be achieved. It is worth pointing out that an uncertainty principle of Gaussian networks is obtained, which implies that in the case of white Gaussian noises, no matter what the sampling size is, the product of the steady-state and transient performance indices is always equal to or larger than a constant depending on the noise intensity, network topology and the number of network nodes.     

The generalization of a class of impulse stochastic control models of a geometric Brownian motion

Recently, international academic circles advanced a class of new stochastic control models of a geometric Brownian motion which is an important kind of impulse control models whose cost structure is different from the others before, and it has a broad applying background and important theoretical significance in financial control and management of investment. This paper generalizes substantially the above stochastic control models under quite extensive conditions and describes the models more exactly under more normal theoretical system of stochastic process. By establishing a set of proper variational equations and proving the existence of its solution, and applying the means of stochastic analysis, this paper proves that the generalized stochastic control models have optimal controls. Meanwhile, we also analyze the structure of optimal controls carefully. Besides, we study the solution function of variational equations in a relatively deep-going way, which constitutes the value function of control models to some extent. Because the analysis methods of this paper are greatly different from those of original reference, this paper possesses considerable originality to some extent. In addition, this paper gives the strict proof to the part of original reference which is not fairly well-knit in analyses, and makes analyses and discussions of the model have the exactitude of mathematical sense. Supported by the National Natural Science Foundation of China (Grant No. 19671004)     

Stability and control of nonlinear systems described by retarded functional equations: a review of recent results

This paper reports on recent results in a series of the work of the authors on the stability and nonlinear control for general dynamical systems described by retarded functional differential and difference equations. Both internal and external stability properties are studied. The corresponding Lyapunov and Razuminkhin characterizations for input-to-state and input-to-output stabilities are proposed. Necessary and sufficient Lyapunov-like conditions are derived for robust nonlinear stabilization. In particular, an explicit controller design procedure is developed for a new class of nonlinear time-delay systems. Lastly, sufficient assumptions, including a small-gain condition, are presented for guaranteeing the input-to-output stability of coupled systems comprised of retarded functional differential and difference equations.