Residual attention-based tracking-by-detection network with attention-driven data augmentation

Tracking-by-detection (TBD) is a significant framework for visual object tracking. However, current trackers are usually updated online based on random sampling with a probability distribution. The performance of the learning-based TBD trackers is limited by the lack of discriminative features, especially when the background is full of semantic distractors. We propose an attention-driven data augmentation method, in which a residual attention mechanism is integrated into the TBD tracking network as supplementary references to identify discriminative image features. A mask generating network is used to simulate changes in target appearances to obtain positive samples, where attention information and image features are combined to identify discriminative features. In addition, we propose a method for mining hard negative samples, which searches for semantic distractors with the response of the attention module. The experiments on the OTB2015, UAV123, and LaSOT benchmarks show that this method achieves competitive performance in terms of accuracy and robustness.     

“电路分析”课程混合式教学探索与实践

本文以“电路分析”课程为例，借助云班课平台，采用“SPOC”与“BOPPPS”相结合的混合式教学模式，开展探究式、个性化、参与式教学。形成了“学生中心，产出导向，持续改进”闭环。经过近年来的不断改进，课程建设已初步完成。通过对三届学生的教学实践，发现采用“SPOC+BOPPPS”的线上线下混合式教学模式，学生学习积极性、主动性明显增强，同时学习效果也得到了显著提升。     

Cognitive and affective processes for learning science in immersive virtual reality

As immersive virtual reality (IVR) systems proliferate in classrooms, it is important to understand how they affect learning outcomes and the underlying affective and cognitive processes that may cause these outcomes. Proponents argue that IVR could improve learning by increasing positive affective and cognitive processing, thereby supporting improved performance on tests of learning outcome, whereas opponents of IVR contend that it could hurt learning by increasing distraction, thereby disrupting cognitive learning processes and leading to poorer learning outcomes. In a media comparison study, students viewed a biology lesson either as an interactive animated journey in IVR or as a slideshow on a desktop monitor. Those who viewed the IVR lesson performed significantly worse on transfer tests, reported higher emotional arousal, reported more extraneous cognitive load and showed less engagement based on EEG measures than those who viewed the slideshow lesson, with or without practice questions added to the lessons. Mediational analyses showed that the lower retention scores for the IVR lesson were related to an increase in self-reported extraneous cognitive load and emotional arousal. These results support the notion that immersive environments create high affective and cognitive distraction, which leads to poorer learning outcomes than desktop environments.     

基于改进Faster R-CNN及YOLO的大角度车牌识别算法

随着大数据时代的到来,智慧城市等一系列概念的提出,人工智能开始在城市各个角落得到广泛应用。其中,车牌识别作为城市道路交通重要的一环,也取得了重大突破。基于深度学习的车牌识别一经提出,各类算法应用于车牌检测及识别上,对比于传统的车牌识别,大大提高了识别的速率和准确率。然而,在非限制条件下,如大角度车牌识别准确率仍有提升的空间。提出了一种改进的FasterR-CNN与YOLO的深度学习车牌识别算法,将对大角度下的车牌识别准确率达到了99.7%。由于是轻量型的框架,为后续的移动设备部署工作提供了便利。     

Towards artificial intelligence at scale in the chemical industry

In the Industry 4.0 era, the chemical industry is embracing broad adoption of artificial intelligence (AI) and machine learning (ML) methods. This article provides a holistic view of how the industry is transforming digitally towards AI at scale. First, a historical perspective on how the industry used AI to aid humans in better decision-making is shown. Then state-of-the-art AI research addressing industrial needs on reliability and safety, process optimization, supply chain, material discovery, and reaction engineering is highlighted. Finally, a vision of the plant of the future is illustrated with critical components of AI-ready culture, model life cycle management, and renewed role of humans in chemical manufacturing.     

Improving Properties of Poly(vinylidene fluoride) by Adding Expanded Graphite without Surface Modification via Water‐Assisted Mixing Extrusion

Expanded graphite (EG) is introduced into poly(vinylidene fluoride) (PVDF) by melt mixing extrusion with water injection. The results demonstrate that the unfunctionalized EG in composite prepared with water injection exbibits better dispersion than that in the one prepared without water injection due to the promoting role of water during extrusion. Thus, the PVDF/EG composite with loading of 4 wt% prepared by water‐assisted mixing extrusion (WAME) exhibits electrical conductivity of about three orders of magnitude higher than the neat PVDF and one order of magnitude higher than the one prepared without water injection. Comparing to the neat PVDF, the thermal conductivity of the composites prepared with and without water injection is increased by 101.5% and 75.0%, respectively. The introduced EG leads to increased Young’s modulus and tensile strength especially for the composite prepared by WAME. The present work indicates that WAME can promote the dispersion of EG in PVDF matrix without any extra functionalization.     

Effects of fibers and conventional reinforcement contents on fibers scaterring in prismatic bars

This paper presents results from a second stage of an experimental study of the dependence of steel fibers distribution along RFC prismatic specimens on the conventional reinforcement ratio and on the total amount of fibers in the concrete mix. The experimental program included two types of prismatic specimens with 30- and 60- kg/m³ of hooked-end steel fibers. Each specimen was sawed into equal segments and the numbers of fibers appearing at the cross-sections were counted and used for a further statistical analysis. This analysis comprised calculations of the average value and standard deviation of a non-dimensional variable, which represents the distribution of the total steel along the specimen. They were used to calibrate a theoretical model, which had been previously proposed by the authors. The test results showed reasonable to good agreement with the theoretical model. A comparison between the results of the 30- and 60-kg/m³ fibers shows that as the conventional reinforcement ratio increases, the standard deviations for the different mixtures approach each other.     

基于深度学习的番茄叶部病害识别模型

番茄叶病种类多、成因复杂,其预防和识别难度较大。传统基于机器学习的方法多靠人工识别,需要一定的专家经验,且具有主观性强、识别准确率不高等缺点。为实现番茄叶病特征的自动提取,并提高识别准确率,提出一种基于深度学习的番茄叶病识别模型。该模型基于卷积神经网络对番茄叶部病害特征进行自动提取,获得高维特征后,采用PCA降维算法去除冗余特征;从增大类间距离并减小类内距离的角度改进了softmax分类器,提高了识别准确率。将该模型在CrowdAI提供的数据集上进行了仿真验证,结果表明,该模型能够对番茄叶部常见10种病害进行自动提取特征和识别,综合识别准确率达到95%以上。     

基于主动学习的离群点集成挖掘方法研究

离群点检测任务通常缺少可用的标注数据，且离群数据只占整个数据集的很小一部分，相较于其他的数据挖掘任务，离群点检测的难度较大，尚没有单一的算法适合于所有的场景。因此，结合多样性模型集成和主动学习思想，提出了一种基于主动学习的离群点集成检测方法OMAL（Outlier Mining based on Active Learning）。在主动学习框架指导下，根据各种基学习器的对比分析，选择了基于统计的、基于相似性的、基于子空间划分的三个无监督模型作为基学习器。将各基学习器评判的处于离群和正常边界的数据整合后呈现给人类专家进行标注，以最大化人类专家反馈的信息量；从标注的数据集和各基学习器投票产生的数据集中抽样，基于GBM（Gradient BoostingMachine）训练一个有监督二元分类模型，并将该模型应用于全数据集，得出最终的挖掘结果。实验表明，提出方法的AUC有了较为明显的提升，且具有良好的运行效率，具备较好的实用价值。     

机器人系统有限时间自适应迭代学习控制

研究任意初态下，机器人系统的有限时间自适应迭代学习控制方法。引入初始修正吸引子的概念，构造一个含有初始修正项的误差变量。针对定常机器人系统和时变机器人系统，采用Lyapunov-like方法，分别设计迭代学习控制器处理系统中不确定性。并且，采用未含/含限幅学习机制，保证闭环系统各变量的一致有界性和误差变量在整个作业区间一致收敛性。藉以实现跟踪误差在预先指定区间的完全跟踪。仿真结果验证所设计控制方法的有效性。