强化词块训练 提高英语听说能力

以词块教学理论为依据,分析在听说教学中进行词块训练的可行性。研究词块训练在听说课堂中的具体实施过程,并通过问卷调查和访谈结果,论证了听说课上引入词块理念能提高英语学习者听说能力从而提出词块学习对语言习得的有效性。     

The automated prediction of solar flares from SDO images using deep learning

In the last few years, there has been growing interest in near-real-time solar data processing, especially for space weather applications. This is due to space weather impacts on both space-borne and ground-based systems, and industries, which subsequently impacts our lives. In the current study, the deep learning approach is used to establish an automated hybrid computer system for a short-term forecast; it is achieved by using the complexity level of the sunspot group on SDO/HMI Intensitygram images. Furthermore, this suggested system can generate the forecast for solar flare occurrences within the following 24 h. The input data for the proposed system are SDO/HMI full-disk Intensitygram images and SDO/HMI full-disk magnetogram images. System outputs are the “Flare or Non-Flare” of daily flare occurrences (C, M, and X classes). This system integrates an image processing system to automatically detect sunspot groups on SDO/HMI Intensitygram images using active-region data extracted from SDO/HMI magnetogram images (presented by Colak and Qahwaji, 2008) and deep learning to generate these forecasts. Our deep learning-based system is designed to analyze sunspot groups on the solar disk to predict whether this sunspot group is capable of releasing a significant flare or not. Our system introduced in this work is called ASAP_Deep. The deep learning model used in our system is based on the integration of the Convolutional Neural Network (CNN) and Softmax classifier to extract special features from the sunspot group images detected from SDO/HMI (Intensitygram and magnetogram) images. Furthermore, a CNN training scheme based on the integration of a back-propagation algorithm and a mini-batch AdaGrad optimization method is suggested for weight updates and to modify learning rates, respectively. The images of the sunspot regions are cropped automatically by the imaging system and processed using deep learning rules to provide near real-time predictions. The major results of this study are as follows. Firstly, the ASAP_Deep system builds on the ASAP system introduced in Colak and Qahwaji (2009) but improves the system with an updated deep learning-based prediction capability. Secondly, we successfully apply CNN to the sunspot group image without any pre-processing or feature extraction. Thirdly, our system results are considerably better, especially for the false alarm ratio (FAR); this reduces the losses resulting from the protection measures applied by companies. Also, the proposed system achieves a relatively high scores for True Skill Statistics (TSS) and Heidke Skill Score (HSS).     

浅谈在编译原理教学中促进学生的学习迁移

学习迁移规律是指导教育和学习的一种实用理论,根据对迁移的不同类型及其特点和影响学习迁移的各种要素的分析,结合编译原理课程的特点,按照促进学习迁移的教学原则,在编译原理教学中促进积极学习迁移,有助于培养学生的创新思维和解决问题的能力,有利于培养学生的良好学习品德.     

浅谈大学语文学习动机的激发和维持

本文从外在动机和内在动机两方面提出了在大学语文教学中激发和维持学生语文学习动机的若干方法及其重要性.     

高职生英语学习焦虑的成因与对策分析

高职学生在英语学习过程中普遍存在着焦虑情绪,一定程度上阻碍了学生英语水平的提高.对焦虑情绪对英语学习的影响、成因和如何预防及解决学生的焦虑情绪进行分析,以期对解决学生的焦虑情绪提供一些建议.     

组合动力运载器上升段轨迹智能优化方法

针对组合动力水平起飞可重复使用运载器，开展了上升段轨迹优化模型设计与轨迹优化方法研究。首先，针对跨大空/速域飞行须采用多种动力形式协调工作这一问题，考虑动力/气动/轨迹/指标间的复杂耦合关系，建立了运载器动力和气动模型。其次，为降低轨迹优化问题的求解难度，设计了一种全新的飞行剖面，实现了关键优化参数的提取和攻角约束的自动满足，减少了优化算法需要处理的约束数量。然后，提出了一种改进的粒子群优化(PSO)算法完成求解；在收敛性分析的基础上，引入强化学习机制对PSO寻优过程进行自主智能控制，从本质上提升了PSO算法的求解效率。最后通过数学仿真验证了方法的正确性和有效性。     

现代化视域下的高校师德建设路径

鉴于高校教师师德建设的重要性研究,在现代化视域下限定其论域边界,分析其与其他范畴的联系和区别,高校教师师德建设的具体路径。     

Docking control for probe-drogue refueling: An additive-state-decomposition-based output feedback iterative learning control method

Designing a controller for the docking maneuver in Probe-Drogue Refueling (PDR) is an important but challenging task, due to the complex system model and the high precision requirement. In order to overcome the disadvantage of only feedback control, a feedforward control scheme known as Iterative Learning Control (ILC) is adopted in this paper. First, Additive State Decomposition (ASD) is used to address the tight coupling of input saturation, nonlinearity and the property of NonMinimum Phase (NMP) by separating these features into two subsystems (a primary system and a secondary system). After system decomposition, an adjoint-type ILC is applied to the Linear Time-Invariant (LTI) primary system with NMP to achieve entire output trajectory tracking, whereas state feedback is used to stabilize the secondary system with input saturation. The two controllers designed for the two subsystems can be combined to achieve the original control goal of the PDR system. Furthermore, to compensate for the receiver-independent uncertainties, a correction action is proposed by using the terminal docking error, which can lead to a smaller docking error at the docking moment. Simulation tests have been carried out to demonstrate the performance of the proposed control method, which has some advantages over the traditional derivative-type ILC and adjoint-type ILC in the docking control of PDR.     

Sunspot cycle prediction using Warped Gaussian process regression

Solar cycle prediction is a key activity in space weather research. Several techniques have been employed in recent decades in order to try to forecast the next sunspot-cycle maxima and time. In this work, the Gaussian process, a machine-learning technique, is used to make a prediction for the solar cycle 25 based on the annual sunspot number 2.0 data from 1700 to 2018. A variation known as Warped Gaussian process is employed in order to deal with the non-negativity constraint and asymmetrical data distribution. Tests using holdout data yielded a root mean square error of 10.0 within 5 years and 25.0–35.0 within 10 years. Simulations using the predictive distribution were performed to account for the uncertainty in the prediction. Cycle 25 is expected to last from 2019 to 2029, with a peak sunspot number about 117 (110 by the median) occurring most likely in 2024. Thus our method predicts that solar Cycle 25 will be weaker than previous ones, implying a continuing trend of declining solar activity as observed in the past two cycles.     

基于流形学习的涡轮泵海量数据异常识别算法

为了获取海量试车数据中的信息以分析涡轮泵的健康状态,提出一种基于流形学习的海量数据异常识别算法.该算法将反映涡轮泵状态的振动数据重构到高维空间中,利用扩散映射方法直接对其进行学习,提取出数据内在的低维流形特征,以可视化的方式直观地识别出涡轮泵数据中的异常状态.仿真与试车数据验证结果表明了所提算法的可行性和有效性.该算法克服了传统方法解决非线性问题不足的缺点,为试车后涡轮泵的健康分析提供了一条新的途径.