Prediction of landslide displacement with controlling factors using extreme learning adaptive neuro-fuzzy inference system (ELANFIS)

Landslide is a major geo-environmental hazard which imparts serious threat to lives and properties. The slope failures are due to adverse inherent geological conditions triggered by an external factor. This paper proposes a new method for the prediction of displacement of step-like landslides, by accounting the controlling factors, using recently proposed extreme learning adaptive neuro-fuzzy inference system (ELANFIS) with empirical mode decomposition (EMD) technique. ELANFIS reduces the computational complexity of conventional ANFIS by incorporating the theoretical idea of extreme learning machines (ELM). The rainfall data and reservoir level elevation data are also integrated into the study. The nonlinear original landslide displacement series, rainfall data, and reservoir level elevation data are first converted into a limited number of intrinsic mode functions (IMF) and one residue. Then decomposed displacement data are predicted by using appropriate ELANFIS model. Final prediction is obtained by the summation of outputs of all ELANFIS sub models. The performance of proposed the technique is tested for the prediction Baishuihe and Shiliushubao landslides. The results show that ELANFIS with EMD model outperforms other methods in terms of generalization performance.     

开放的大学英语网络教学运行环境和模式研究

通过对大学英语网络自主学习运行环境和教学模式的研究,介绍了现有网络学习运行环境包含的各种网络结构、软件架构、学习和考核方法等几个方面,讨论了各种环境因素在实际应用中的优劣与取舍原因,给出了开放教学理念下的网络运行环境研究结果。在研究中能给学生提供充分思考的空间、促进学生在学习中的独立思考和探索精神,进而达到学生素质全面发展的目的。     

高职电子技术课程的兴趣教学法

高职院校学生学习基础相对较差,对于传统的课堂教育难以适应,提出在电子技术教学中实施兴趣教学法,提高学生主动学习的积极性,也有助于提高教学质量。电子技术课实践性强,几乎每堂课都会涉及到实验,这样可以长期在实验室上课或者在教室做演示实验,每次课前教师列一个清单让学生提前到实验室准备好实验器材,学生在准备器材的过程中就会带着疑问：这是做什么用的呢？学生有了疑问就会有学习的好奇心,进而也就激发了对该课程的学习兴趣。     

Admission control for a responsive distributed middleware using decision trees to model run-time parameters

The software in modern systems has become too complex to make accurate predictions about their performance under different configurations. Real-time or even responsiveness requirements cannot be met because it is not possible to perform admission control for new or changing tasks if we cannot tell how their execution affects the other tasks already running. Previously, we proposed a resource-allocation middleware that manages the execution of tasks in a complex distributed system with real-time requirements. The middleware behavior can be modeled depending on the configuration of the tasks running, so that the performance of any given configuration can be calculated. This makes it possible to have admission control in such a system, but the model requires knowledge of run-time parameters. We propose the utilization of machine-learning algorithms to obtain the model parameters, and be able to predict the system performance under any configuration, so that we can provide a full admission control mechanism for complex software systems. In this paper, we present such an admission control mechanism, we measure its accuracy in estimating the parameters of the model, and we evaluate its performance to determine its suitability for a real-time or responsive system.     

健壮性学习算法

使用统计方法可以对汉英机器翻译的词性标注和句法语义分析阶段产生的歧义进行消歧,在估计过程中往往使用最大可能方法,但是并不是在所有的情况下取最大值都是正确的。为了从所有候选结果中取到正确的结果,本文使用健壮性学习算法。使用这个算法,当正确的候选结果评分不是最高时,仍能通过健壮性算法来调整正确结果的评分,使之最大,并且降低不正确候选的评分。而且,由于训练集与测试集存在不同,使训练集中的错误率最小不能保证测试集中的错误率也最小。因此当考虑训练语料库和测试语料库存在统计变化时,应该使用健壮性学习算法。     

Prediction-based Iterative Learning Control (PILC) for Uncertain Dynamic Nonlinear Systems Using System Identification Technique

Prediction-based Iterative Learning Control (PILC) is proposed in this paper for a class of time varying nonlinear uncertain systems. Convergence of PILC is analyzed and the uniform boundedness of tracking error is obtained in the presence of uncertainty and disturbances. It is shown that the learning algorithm not only guarantees the robustness, but also improves the learning rate despite the presence of disturbances and slowly varying desired trajectories in succeeding iterations. The effectiveness of the proposed PILC is presented by simulations.     

任务型的教学模式在高职计算机教学中的应用

该文对任务型的教学模式的本质进行了研究,对它加以改进、深化,并将之用于职业教育的计算机教学,取得了好的教学效果。在实践的基础上,该文从基于任务型的教学模式的概念、教学结构和职业教育的现状特点和这一模式具体的实施步骤方面进行了理论上的总结。     

知识获取技术及其在网络智能故障诊断中的应用

基于遗传算法提出了一种新型的机器学习方法,它利用遗传算法的全局寻优手段,为解决知识获取这一瓶颈问题另辟捷径。论述了该方法的原理及其实现过程,并且给出了网络故障诊断应用实例,证明了它的有效性。     

军校营造良好学风的几点思考

针对当前军校学风建设存在的问题,从加强思想教育、完善教学管理制度、发挥队干部良好的导向作用、加强教风建设及落实教学管理制度等方面提出了军队院校营造良好学风的对策.