砂姜黑土小麦分蘖成穗及其调控研究

采用盆栽试验和小区试验相结合的方法 ,对砂姜黑土上不同小麦品种的分蘖成穗特性及其调控进行了研究 .结果表明 ,兰考 90 6分蘖发生快、多且衰亡也最快 ,分蘖成穗率较低 ,主要是由于二、三级分蘖发生较多 ,越冬期具有 4张以上叶片的分蘖才能成穗 .温麦 6号分蘖发生平稳 ,成穗率最高 ;百农 64分蘖力中等 ,增加一定的施肥量能明显提高分蘖成穗率 .不同生长调节剂对兰考 90 6分蘖成穗有明显的调控作用 ,以 6 BAt和 2 ,4 D控制分蘖成穗效果最为明显     

求解多集分裂可行问题的一种共轭梯度法

基于求解多集分裂可行问题与非线性最优化问题的等价性,考虑Jinling Zhao and Qingzhi Yang在[1]中提出的求解SFP的共轭梯度法和Censor等在[2]中提出的梯度投影法,尝试运用共轭梯度法求解多集分裂可行问题;并且证明了所构造算法的收敛性．提出的新算法克NT求矩阵逆的缺点．初步的数值结果表明新算法对于不同的问题都能够有较快的收敛速度,具有良好的稳定性和可行性,在问题维数增大时表现得越发明显．     

Modelling,control, and stability analysis of non-linear systems using generalized fuzzy neural networks

This paper presents an adaptive fuzzy neural controller (AFNC) suitable for modelling and control of MIMO non-linear dynamic systems. The proposed AFNC has the following salient features: (1) fuzzy neural control rules can be generated or deleted dynamically and automatically; (2) uncertain MIMO non-linear systems can be adaptively modelled on line; (3) adaptation and learning speed is fast; (4) expert knowledge can be easily incorporated into the system; (5) the structure and parameters of the AFNC can be self-adaptive in the presence of uncertainties to maintain a high control performance; and (6) the asymptotical stability of the system is established using the Lyapunov approach. Simulation studies on a two-link robot manipulator show that the performance of the proposed controller is better than that of some existing fuzzy/neural methods.     

Classification of EEG signals using neural network and logistic regression

Epileptic seizures are manifestations of epilepsy. Careful analyses of the electroencephalograph (EEG) records can provide valuable insight and improved understanding of the mechanisms causing epileptic disorders. The detection of epileptiform discharges in the EEG is an important component in the diagnosis of epilepsy. As EEG signals are non-stationary, the conventional method of frequency analysis is not highly successful in diagnostic classification. This paper deals with a novel method of analysis of EEG signals using wavelet transform and classification using artificial neural network (ANN) and logistic regression (LR). Wavelet transform is particularly effective for representing various aspects of non-stationary signals such as trends, discontinuities and repeated patterns where other signal processing approaches fail or are not as effective. Through wavelet decomposition of the EEG records, transient features are accurately captured and localized in both time and frequency context. In epileptic seizure classification we used lifting-based discrete wavelet transform (LBDWT) as a preprocessing method to increase the computational speed. The proposed algorithm reduces the computational load of those algorithms that were based on classical wavelet transform (CWT). In this study, we introduce two fundamentally different approaches for designing classification models (classifiers) the traditional statistical method based on logistic regression and the emerging computationally powerful techniques based on ANN. Logistic regression as well as multilayer perceptron neural network (MLPNN) based classifiers were developed and compared in relation to their accuracy in classification of EEG signals. In these methods we used LBDWT coefficients of EEG signals as an input to classification system with two discrete outputs: epileptic seizure or non-epileptic seizure. By identifying features in the signal we want to provide an automatic system that will support a physician in the diagnosing process. By applying LBDWT in connection with MLPNN, we obtained novel and reliable classifier architecture. The comparisons between the developed classifiers were primarily based on analysis of the receiver operating characteristic (ROC) curves as well as a number of scalar performance measures pertaining to the classification. The MLPNN based classifier outperformed the LR based counterpart. Within the same group, the MLPNN based classifier was more accurate than the LR based classifier.     

时变时滞不确定系统的鲁棒无记忆控制

针对控制时滞系统存在时变时滞及参数摄动的情况,通过构造适当形式的Lyapunov函数,求得无记忆反馈控制律.给出单滞后系统稳定化控制,并讨论具有指定衰减度的控制器设计,进而将结论推广到多重滞后系统.     

使用会话期上下文检查的RBAC模型：RBAC CCS

在大型信息系统的设计中,访问控制一直是一项复杂的工作。基于角色的访问控制（RBAC）被推荐来代替传统的访问控制模型。应用到信息系统中时,需要解决RBAC的控制粒度问题。我们需要一个能够基于上下文进行细粒度访问控制的RBAC模型,即基于对象实例上下文上的访问控制。对现有的解决方案进行综述和比较,提出了使用会话期上下文检查的RBAC模型：RBACCCS。RBACCCS中用了参数化权限,在权限检查时用上下文变量实例化这些参数化权限,这样就解决了上下文的表示和上下文作用机制问题。最后对RBACCCS模型进行     

复式河道的桥壅水计算

本文应用复式河道的桥梁壅水实验资料对拱桥法进行了验证,发现拱桥法计算值往往过高.提出了可用于复式河道的边滩等价河宽的概念和计算方法,并与实测资料进行了对比.     

真空热处理工艺参数的选取

通过多种材料、多种工件、多批次的加工，对常见真空热处理工艺参数的确定，摸索出一些经验公式，并在实际生产中取得很好效果。