新课改理念下化学愉悦学习观的培养策略

愉悦学习观是新课程改革理念体现的一种学习观念。学生具有了这种学习观,可以维持其学习兴趣,提高其学习效率,具有热爱科学、热爱社会和生活的情感,科学素养得到全面地发展。化学教学中教师可以利用多种积极因素,挖掘教材美感、利用化学实验、联系生产实际、选用多媒体技术和进行多样化评价等策略来培养学生的愉悦学习观。     

化学制药工艺学在线教学实践与思考

以化学制药工艺学课程为例,从在线教学技术、教学设计等方面探讨了以学生为中心的在线教学模式与方法,对比分析了在线教学与线下教学的优缺点.实践表明,与线下教学相比,以学生为中心的在线教学模式更有利于学生自主学习能力的培养和课程目标的达成.     

The Dynamics of Organizational Learning

This paper presents a dynamical theory of organizational learning that explicitly accounts for both the introduction of new routines into the manufacturing process and improvements in the selection of which procedures to follow. Besides producing a power law of organizational learning with a rate that depends on the effectiveness of the decision procedure, the theory also accounts for observed anomalies characterized by price increases with cumulative output.     

自适应复信道均衡的一种新的神经网络方法

近年来,神经网络已经广泛地应用到许多信号处理问题中.自适应信道均衡是数字通信系统中的一个重要问题.在本文中我们提出一种基于复数赫布类型算法的自适应信道均衡器.计算机模拟表明,无论在线性还是非线性信道中,所提出的均衡器均表现出良好的性能,这为自适应复信道均衡提供了一种新的方法.     

一种针对加密流量中邮箱异常通信行为的检测技术

随着网络安全防范意识增强,加密技术在网络空间中迅速普及,这一切正在改变网络安全形势,而如何检测和监管加密流量中的电子邮件的异常通信行为成为挑战.先对网络加密流量分析的基本概念、研究进展进行了论述,然后提出了一种基于网络行为的加密流量邮件攻击行为分析系统.该系统通过提取流量中相关特征信息,实现邮件行为的识别和异常行为的判...     

A Novel Conflict Management Method Based on Uncertainty of Evidence and Reinforcement Learning for Multi-Sensor Information Fusion

Dempster–Shafer theory (DST), which is widely used in information fusion, can process uncertain information without prior information; however, when the evidence to combine is highly conflicting, it may lead to counter-intuitive results. Moreover, the existing methods are not strong enough to process real-time and online conflicting evidence. In order to solve the above problems, a novel information fusion method is proposed in this paper. The proposed method combines the uncertainty of evidence and reinforcement learning (RL). Specifically, we consider two uncertainty degrees: the uncertainty of the original basic probability assignment (BPA) and the uncertainty of its negation. Then, Deng entropy is used to measure the uncertainty of BPAs. Two uncertainty degrees are considered as the condition of measuring information quality. Then, the adaptive conflict processing is performed by RL and the combination two uncertainty degrees. The next step is to compute Dempster’s combination rule (DCR) to achieve multi-sensor information fusion. Finally, a decision scheme based on correlation coefficient is used to make the decision. The proposed method not only realizes adaptive conflict evidence management, but also improves the accuracy of multi-sensor information fusion and reduces information loss. Numerical examples verify the effectiveness of the proposed method.     

Performance of Organizational Design Models and Their Impact on Organization Learning

The performance of various organizational structures is an essential parameter in the reengineering of organizations, particularly in the current rapidly changing, competitive and information technology-dependent environment. This situation has generally resulted in a smaller workforce confronted with voluminous information-processing requirements. Naturally, the focus has been on the design of organzations that will support effective and efficient learning processes. This specific study is on the contingency modely by Ouksel et al. (1997) on the impact of organizational structure on organizational learning and performance. It expands the empirical evaluation of the model to test its robustness. This is done by conducting extensive simulations with (a) a larger number of variables in the decision tasks, and (b) a larger set of numbers for each of the numeric variables, than has been used in the past. Overall, the validity of previous results achieved has been confirmed, but the selection of the most appropriate design, that which will indeed improve learning, is even more nuanced than past studies have concluded.     

基于注意力学习的正则化相关滤波跟踪算法

边界效应是制约相关滤波跟踪性能的一个重要因素.目前大多数方法只是简单地采用先验知识,如逆高斯分布,预设掩模等,或者分割前景目标作为正则化项,进行约束求解,并没有考虑目标的空时域特性.针对这一问题,本文提出一种基于注意力学习的正则化相关滤波跟踪算法.该方法考虑目标在空间中的分布特性,利用注意力机制学习目标的特定空间权重,适应目标在空域中的变化;同时利用目标在时域中的连续性,通过对注意力权重矩阵的约束来间接调整滤波器;最后通过交替方向乘子（ADMM）算法迭代优化模型.我们在标准的数据库上进行大量实验,结果表明本文算法能实时跟踪目标,并且在精确度和成功率上都有了一定的提升.     

基于监督学习的协作宽带压缩频谱检测方案

宽带压缩频谱检测存在信号稀疏度未知和次用户检测开销过大的问题.因此,本文提出一种高效的协作宽带压缩频谱检测方案.首先,推导了一种基于学习的稀疏度自适应预测模型.其次,设计了一种宽带频谱筛选算法.最后,提出一种协作宽带压缩频谱检测方案.仿真结果表明,自适应预测模型的拟合效果优于现有预测模型,并且所提检测方案也有效地降低了次用户采样率和频谱重构时延.     

Computation of Kullback–Leibler Divergence in Bayesian Networks

Kullback–Leibler divergence KL(p,q) is the standard measure of error when we have a true probability distribution p which is approximate with probability distribution q. Its efficient computation is essential in many tasks, as in approximate computation or as a measure of error when learning a probability. In high dimensional probabilities, as the ones associated with Bayesian networks, a direct computation can be unfeasible. This paper considers the case of efficiently computing the Kullback–Leibler divergence of two probability distributions, each one of them coming from a different Bayesian network, which might have different structures. The paper is based on an auxiliary deletion algorithm to compute the necessary marginal distributions, but using a cache of operations with potentials in order to reuse past computations whenever they are necessary. The algorithms are tested with Bayesian networks from the bnlearn repository. Computer code in Python is provided taking as basis pgmpy, a library for working with probabilistic graphical models.