^239Puα粒子的微剂量谱和径向剂量分布

用大容积（６０ｍｍ×８０ｍｍ）的圆柱型流气式组织等效正比计数器测定了２３９Ｐｕα粒子的微剂量谱和径向剂量分布。α粒子束在无窗的情况下直接穿入正比计数器，由离子穿过而发生能量沉积的机率Ｒｂ为１。测量结果证实：沿α粒子径迹由单次事件沉积的剂量平均比能ｚ１Ｄ（Ｇｙ）与距径迹核径向距离ｂ（μｍ）之间完全符合ｚ１Ｄ＝ａｂ－２的函数关系，同时给出了求出最大径向距离（ｐｅｎｕｍｂｒａ）的方法。     

KNOWLEDGE-BASED FEATURE DISCOVERY FOR EVALUATION FUNCTIONS

Since Samuel's work on checkers over thirty years ago, much effort has been devoted to learning evaluation functions. However, all such methods are sensitive to the feature set chosen to represent the examples. If the features do not capture aspects of the examples significant for problem solving, the learned evaluation function may be inaccurate or inconsistent. Typically, good feature sets are carefully handcrafted and a great deal of time and effort goes into refining and tuning them. This paper presents an automatic knowledge-based method for generating features for evaluation functions. The feature set is developed iteratively: features are generated, then evaluated, and this information is used to develop new features in turn. Both the contribution of a feature and its computational expense are considered in determining whether and how to develop it further.
This method has been applied to two problem-solving domains: the Othello board game and the domain of telecommunications network management. Empirical results show that the method is able to generate many known features and several novel features and to improve concept accuracy in both domains.     

连续体结构的参数化水平集统一优化

针对传统分步式结构优化设计的不足,提出一种同时进行结构拓扑、形状和尺寸统一优化的设计方法.首先采用水平集函数描述统一的结构优化模型和几何尺寸边界,通过引入紧支径向插值基函数将结构拓扑优化变量、形状优化变量和尺寸优化变量变换为基函数的扩展系数;然后取该扩展系数为设计变量,借助一种参数的变化表达3种优化要素对结构性能的影响,将复杂的多变量优化问题变换为相对简单的参数优化问题,有利于与相对成熟的优化算法相结合提高求解效率;进一步用R函数将其融合为一个整体,构造出统一优化模型,并用最优化准则法进行求解.最后通过数值案例证明了该方法的有效性和精确性.     

基于改进麻雀搜索算法的服务功能链优化映射算法

为了实现5G网络场景下服务功能链的低成本高效率部署,提出了一种基于改进麻雀搜索算法的服务功能链优化映射算法。在服务功能链映射过程中,采用双层编码方式,将服务请求的组链方案和映射方案进行混合编码,然后使用改进离散麻雀搜索算法进行求解,得到该请求的服务功能链部署方案,并计算其映射权重。当同一时间片内存在多个请求类型时,按照映射权重对所有服务功能链进行排序,优先映射权重高的请求,以此减少延迟开销,提高节点计算资源利用率和链路资源利用率。仿真结果表明,该算法可以有效减少部署开销,提高资源利用率。     

Evaluating a color-based active basis model for object recognition

Wu and coworkers introduced an active basis model (ABM) for object recognition in 2010, in which the learning algorithm tends to sketch edges in textures. A grey-value local power spectrum was used to find a common template and deformable templates from a set of training images and to detect an object in new images by template matching. In this paper, we propose a color-based active basis model (color-based ABM for short), which incorporates color information. We adopt the framework of Wu et al. in the learning, detection, and classification of the color-based ABM. However, in order to improve the performance in object recognition, we modify the framework of Wu et al. by using different color-based features in both the learning and template matching algorithms. In this color-based ABM approach, two types of learning (i.e., supervised learning and unsupervised learning) are also explored. Moreover, the usefulness of the color-based ABM for practical object recognition in computer vision applications is demonstrated and its significant improvement in recognizing objects is reported.     

A critical review of the most popular types of neuro control

In this review article, the most popular types of neural network control systems are briefly introduced and their main features are reviewed. Neuro control systems are defined as control systems in which at least one artificial neural network (ANN) is directly involved in generating the control command. Initially, neural networks were mostly used to model system dynamics inversely to produce a control command which pushes the system towards a desired or reference value of the output (1989). At the next stage, neural networks were trained to track a reference model, and ANN model reference control appeared (1990). In that method, ANNs were used to extend the application of adaptive reference model control, which was a well‐known control technique. This attitude towards the extension of the application of well‐known control methods using ANNs was followed by the development of ANN model‐predictive (1991), ANN sliding mode (1994) and ANN feedback linearization (1995) techniques. As the first category of neuro controllers, inverse dynamics ANN controllers were frequently used to form a control system together with other controllers, but this attitude faded as other types of ANN control systems were developed. However, recently, this approach has been revived. In the last decade, control system designers started to use ANNs to compensate/cancel undesired or uncertain parts of systems' dynamics to facilitate the use of well‐known conventional control systems. The resultant control system usually includes two or three controllers. In this paper, applications of different ANN control systems are also addressed. Copyright © 2011 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

An on-line learning neural controller for helicopters performing highly nonlinear maneuvers

This paper presents an on-line learning adaptive neural control scheme for helicopters performing highly nonlinear maneuvers. The online learning adaptive neural controller compensates the nonlinearities in the system and uncertainties in the modeling of the dynamics to provide the desired performance. The control strategy uses a neural controller aiding an existing conventional controller. The neural controller is based on a online learning dynamic radial basis function network, which uses a Lyapunov based on-line parameter update rule integrated with a neuron growth and pruning criteria. The online learning dynamic radial basis function network does not require a priori training and also it develops a compact network for implementation. The proposed adaptive law provides necessary global stability and better tracking performance. Simulation studies have been carried-out using a nonlinear (desktop) simulation model similar to that of a BO105 helicopter. The performances of the proposed adaptive controller clearly shows that it is very effective when the helicopter is performing highly nonlinear maneuvers. Finally, the robustness of the controller has been evaluated using the attitude quickness parameters (handling quality index) at different speed and flight conditions. The results indicate that the proposed online learning neural controller adapts faster and provides the necessary tracking performance for the helicopter executing highly nonlinear maneuvers.     

基于西门子S7-300 PLC装卸机械自控系统设计

主要介绍了装卸机械控制系统的构成、系统在运行过程中的控制要求、以及如何实现机械自动操作的功能,利用S7-300PLC的功能模块化程序设计思想,设计出手动控制逻辑功能和自动控制逻辑功能及组织块程序,大大提高了工作效率和生产安全性。     

Correlation properties of combiners with memory in stream ciphers

For pseudo-random generators where one or several LFSRs are combined by a memoryless function, it is known that the output sequences are correlated to certain LFSR-sequences whose correlation coefficients c _t satisfy the equation _i c ₂ ⁱ = 1. In this paper it is proved that a corresponding result also holds for generators whose LFSRs are connected to a combiner with memory.If correlation probabilities are conditioned on side information, e.g., on known output digits, it is shown that new or stronger correlations may occur. This is exemplified for the summation cipher with only two LFSRs where such correlations can be exploited in a known plaintext attack. A cryptanalytic algorithm is given which is shown to be successful for LFSRs of considerable length and with arbitrary feedback connection.A preliminary version of this paper was presented at Eurocrypt '90, May 21–24, Århus, Denmark, and has appeared in the proceedings, pp. 204–213.     

项目驱动式教学法在计算机基础课程中的应用

项目驱动式教学做为一种新兴的教学模式越来越多的应用于各门学科的教学应用当中。计算机基础这门课程由于其理论知识更新速度快,实践操作要求高的特点,将之与项目驱动式教学法相结合可以起到很好的教学效果。本文将传统教学方法和项目驱动式教学法进行对比,针对当前教学过程中存在的问题提出相应的解决方法。