The Application of Security Control in Investment

根据我国投资机制的特点,建立“钓鱼”型投资模型,用警戒控制器设计方法求出使国内生产总值达到期望值波动幅度最小的控制策略。A "fishing"investment model is established according to characteristics of the investment mechanism in China. By using design method of security control,a control strategy under which gross products at home fluctuates within the narrowest range is obtained

A Study of The Application in Image De-noising

本文的目的是讨论现有的几种图像去噪方法，寻找既能有效去除噪声又不使图像边缘模糊的方法。 小波变换可以将信号或图像分成不同的频率区域而不损失信号或图像的空间域信息，这一特点使得我们可以根据信号或图像的局部特征来调节滤波器参数，这对傅里叶变换来说是无能为力的。本文以较多的篇幅讨论了基本阀值去噪方法的参数选择问题，但这种方法有两个缺点：首先，它没有自动调节阀值的机制，阀值一般通过实验来选择，所选的阀值要能够有效地去除噪声但又不能使重构图像的边缘模糊；其次，因为含噪图像经多尺度小波分解后，噪声的能量主要分布在细尺度层的高频系数上，将这些系数置为零或减小其幅度能够有效地去除噪声，但这些系数中一部分是由...The purpose of this thesis is to discussing existing techniques of noise filtering in image processing application and find a method for filtering noise without blurring images. Since the wavelet transform can split a signal or a image into different bands without losing the spatial domain information that the Fourier transform does not, this feature enable us to adjust the filtering paramete...学位：工学硕士院系专业：计算机与信息工程学院自动化系_控制理论与控制工程学号：20013100

A Comparison Study of Two Kinds of Wavelet Thresholding De-Nois ing

传统的小波去噪算法是对含噪信号的正交小波分解系数硬取阀值或软取阀值(该文提出软取的阀值应大约为硬取阀值的一半),这种算法可能会使重构信号在奇异点邻域产生人为的振荡,即Gibbs现象。能有效消除或减弱振荡的一个有效方法是对含噪信号进行在某个范围内所有可能的循环平移(实际上反向平移小波),然后用阀值估计信号,并对逆平移后的估计取平均得到重构信号。该文研究了平均平移算法的实现及平移范围与去噪效果的关系。De-Noising with traditional wavelet transform is applying the soft or hard thresholding to the coefficients of the noisy signal transformed into an orthogonal wavelet domain(this paper proposes that the soft thresholding should be a half of the hard thresholding).It may produce artifacts on discontinuities of the signal(Gibbs phenomena).One effective method to suppress such artifacts is,for a range of shifts,one shifts the data(in practice people shift the wavelet basis),De-Noises the shifted data,and then unshifts the de-noised data.Doing this for each of a range of shifts and averaging all the results so obtain a reconstruction signal.This paper also studies the realization of averaging shift algorithm as well as the relationship between shift range and de-noising effect.国家自然科学基金(编号:79970079

The Application of Fuzzy Neural Networks in Stock Price Forecasting

讨论模糊神经网络在股价预测中的应用,模糊神经网络克服模糊规则产生对专家的依赖性及模糊集的非自适应性,隶属函数的自适应和模糊规则的自组织通过神经网络的自学习和竞争获得。通过一个股价预测实例验证了该方法的有效性。The main purpose of this paper is to investigate the application of the fuzzy neural network for stock price forecasting. It has overcome the limitation such as the dependency on experts for fuzzy rule and no self-adaptation for fuzzy set.　The self-regulation of membership functions and the self-organization of fuzzy rule are realized by the self- learning of the neural networks. This approach has been proven by experiments.国家自然科学基金资助项目(79970079

Repurposing an online tutor training resource

This paper presents a reflective case study that illustrates the challenges associated with repurposing, for the human health sciences, an existing high-quality staff development and tutor resource website originally developed by the Faculty of Veterinary Sciences at the University of Sydney. The discussion focuses on the experience of negotiating, planning, and executing repurposing the site for staff development and tutor support in postgraduate programs offered by the Faculties of Health Sciences and Medicine. Benefits and challenges associated with repurposing this resource within the same overall university context are considered

JUNO Sensitivity on Proton Decay p→νˉK+p\to \bar\nu K^+ Searches

The Jiangmen Underground Neutrino Observatory (JUNO) is a large liquid scintillator detector designed to explore many topics in fundamental physics. In this paper, the potential on searching for proton decay in $p\to \bar\nu K^+$ mode with JUNO is investigated.The kaon and its decay particles feature a clear three-fold coincidence signature that results in a high efficiency for identification. Moreover, the excellent energy resolution of JUNO permits to suppress the sizable background caused by other delayed signals. Based on these advantages, the detection efficiency for the proton decay via $p\to \bar\nu K^+$ is 36.9% with a background level of 0.2 events after 10 years of data taking. The estimated sensitivity based on 200 kton-years exposure is $9.6 \times 10^{33}$ years, competitive with the current best limits on the proton lifetime in this channel

JUNO sensitivity on proton decay p → ν K + searches*

The Jiangmen Underground Neutrino Observatory (JUNO) is a large liquid scintillator detector designed to explore many topics in fundamental physics. In this study, the potential of searching for proton decay in the $p\to \bar{\nu} K^+$ mode with JUNO is investigated. The kaon and its decay particles feature a clear three-fold coincidence signature that results in a high efficiency for identification. Moreover, the excellent energy resolution of JUNO permits suppression of the sizable background caused by other delayed signals. Based on these advantages, the detection efficiency for the proton decay via $p\to \bar{\nu} K^+$ is 36.9% ± 4.9% with a background level of $0.2\pm 0.05({\rm syst})\pm$$0.2({\rm stat})$ events after 10 years of data collection. The estimated sensitivity based on 200 kton-years of exposure is $9.6 \times 10^{33}$ years, which is competitive with the current best limits on the proton lifetime in this channel and complements the use of different detection technologies