对大学生高等数学学习困难的思考

针对大学生高等数学课程学习困难的原因进行分析 ,结合教学实际提出搞好初、高等数学的衔接 ,加强高等数学概念教学和帮助学生树立正确的学习观 ,是改变学生学习高等数学困难的有效途径     

Unmixing method for hyperspectral data based on sub-space method with learning process

An unmixing method for hyperspectral Earth observation satellite imagery data is proposed. It is based on a sub-space method with learning process. The proposed method utilizes a sub-space for feature space during unmixing. It is used to be done in a feature space which consists of spectral bands of observation vectors. As the results from the experiments with airborne based hyperspectral imagery data, AVIRIS, it is found that the proposed unmixing is superior to the other existing method in terms of decomposition accuracy and the process time required for the decompositions.     

基于元胞自动机的车队散布模型参数变化规律的研究

车队散布模型是干线信号优化研究中的重要模型,但是散布模型的参数均由经验确定,模型的准确性很难得到保证。本文使用改进后的元胞自动机模型对路段进行微观交通流仿真,并利用仿真所得的统计数据分析车队散布参数,得到散布模型的参数与道路长度和车辆密度之间的定性定量关系,经VISSIM系统验证,结果吻合较好。根据该关系可以在干线信号协调优化过程中,对车队散布模型的参数进行实时校正,使优化结果更加准确。     

Optimization of One versus All-SVM using AdaBoost algorithm for rainfall classification and estimation from multispectral MSG data

In this paper, we implement the AdaBoost algorithm to optimize the classifications results of precipitations intensities carried out by One versus All strategy using Support Vector Machine (OvA-SVM). The model developed which combines the AdaBoost algorithm with a multiclass SVM is applied to images from the MSG (Meteosat Second Generation) satellite. Other variants to build multiclass SVMs, such as the OvO-SVM (One versus One SVM), SBT-SVM (Slant Binary Tree SVM) and DDAG-SVM (Decision Directed Acyclic Graph) are also implemented on which we tested the AdaBoost algorithm. The study showed that the AdaBoost algorithm performed better in the case of the OvA-SVM variant compared to the other variants.In order to evaluate the elaborated model, some classification techniques, such as the ECST Enhanced Convective Stratiform Technique (ECST), the SART where the Support vector machine, Artificial neural network and Random forest classifiers are combined, the Convective/Stratiform Rain Area Delineation Technique (CS-RADT) and the Random Forest technique (RFT) are applied. The classification results obtained show that AdaBoost with OvA-SVM (AdaOvA-SVM) presents very interesting performances where the evaluation parameters POD, POFD, FAR, BIAS, CSI and PC indicate the values 95.2%, 12.4%, 14.7%, 0.9, 88.1% and 96.5% respectively. Indeed, the AdaOvA-SVM technique has surpassed the CS-RADT, ECST and RFT techniques. As for the comparison with the SART, we noted that OvA-SVM presents very close results. The same trend was also observed when estimating precipitation. At the end of this study, it is shown that the AdaBoost algorithm performs better on a weak classifier or on a strong classifier operating in an unfavorable environment.     

学伴用随原则视野下公共英语教材网络化意义研究

外语学习的学伴用随对外语学习以及教学具有非常重要的指导意义。将学伴用随原则贯彻于公共英语教材的网络化,实现英语网络自主学习平台功用的合理化,有利于弥补英语习得过程中的语境缺失,激发学生的交际需要,最大化语言接触量,实现真正意义上的灵活互动的英语网络学习环境。     

从高职生工作后适应性学习方式谈教学改革

高职高专毕业生毕业后往往还要经过适应性的学习，才能进入工作角色。适应性的学习方式不仅是高职高专毕业生生存和发展的必然，也为高职高专院校现行的教学方法提供了改革的依据。     

一种自信学习自动机

提出一种二次不动—惩罚变结构随机自动机模型（Q(IP)）。较之于其线性形式（L(IP)）,新模型的学习带有一定的自信（当然,有时也可能是自负）。特别,跟传统自动机不同的是,新算法的极限行为同时兼具吸收壁和遍历性。     

基于UPPAAL软件的实时系统设计研究

UPPAAL是丹麦Aalborg和瑞士Uppsala大学联合开发,具有世界先进水平的实时系统模拟,校核的软件,基于对实时系统严密逻辑,真实时间的抽象,而构建时间状态机动态模型网络,模拟,校核系统,检测潜在失败,保证设计可靠性,雷达传感器内存接口实时系统是航天雷达系统重要组成部分。本文UPPAAL软件对此系统精确的动态数学模型,模拟,校核系统,覆盖各设计阶段,保证实时系统设计可靠性。     

A novel visual attention method for target detection from SAR images

Synthetic Aperture Radar(SAR) imaging systems have been widely used in civil and military fields due to their all-weather and all-day abilities and various other advantages. However, due to image data exponentially increasing, there is a need for novel automatic target detection and recognition technologies. In recent years, the visual attention mechanism in the visual system has helped humans effectively deal with complex visual signals. In particular, biologically inspired top-down attention models have garnered much attention recently. This paper presents a visual attention model for SAR target detection, comprising a bottom-up stage and top-down process.In the bottom-up step, the Itti model is improved based on the difference between SAR and optical images. The top-down step fully utilizes prior information to further detect targets. Extensive detection experiments carried out on the benchmark Moving and Stationary Target Acquisition and Recognition(MSTAR) dataset show that, compared with typical visual models and other popular detection methods, our model has increased ability and robustness for SAR target detection, under a range of Signal to Clutter Ratio(SCR) conditions and scenes. In addition, results obtained using only the bottom-up stage are inferior to those of the proposed method, further demonstrating the effectiveness and rationality of a top-down strategy. In summary, our proposed visual attention method can be considered a potential benchmark resource for the SAR research community.     

基于信任域策略优化的末制导控制量学习算法CSCD

近年来,深度强化学习在解决序列决策问题上取得了很大进展,无模型强化学习算法在与环境不断交互的过程中学习策略,不需要提前对环境建模,使其适用于许多问题。针对以往使用强化学习进行末制导策略学习的训练不稳定问题,使用信任域策略优化算法直接学习末制导控制量,同时设计了一种新颖的奖励函数,可以提高训练稳定性和算法性能。在二维环境下进行了实验,结果表明,该算法具有良好的训练稳定性,并可以达到很好的命中效果。