基于信息化教学平台的“即时互动教学”模式应用——以“报关业务技能”课程为例

“报关业务技能”是报关与国际货运专业的一门专业课程,政策性和实践性都非常强,合适的教学模式对学生专业技能的掌握至关重要。随着“互联网+”越来越多地应用于教育领域,“即时互动”也在教学中成为一种趋势。文章以“超星学习通”作为信息化教学平台,探索即时互动教学模式,并将其应用于教学实践,不断提升教学成效。     

A framework for cloned vehicle detection

Rampant cloned vehicle offenses have caused great damage to transportation management as well as public safety and even the world economy. It necessitates an efficient detection mechanism to identify the vehicles with fake license plates accurately, and further explore the motives through discerning the behaviors of cloned vehicles. The ubiquitous inspection spots that deployed in the city have been collecting moving information of passing vehicles, which opens up a new opportunity for cloned vehicle detection. Existing detection methods cannot detect the cloned vehicle effectively due to that they use the fixed speed threshold. In this paper, we propose a two-phase framework, called CVDF, to detect cloned vehicles and discriminate behavior patterns of vehicles that use the same plate number. In the detection phase, cloned vehicles are identified based on speed thresholds extracted from historical trajectory and behavior abnormality analysis within the local neighborhood. In the behavior analysis phase, consider the traces of vehicles that uses the same license plate will be mixed together, we aim to differentiate the trajectories through matching degreebased clustering and then extract frequent temporal behavior patterns. The experimental results on the real-world data show that CVDF framework has high detection precision and could reveal cloned vehicles’ behavior effectively. Our proposal provides a scientific basis for traffic management authority to solve the crime of cloned vehicle.     

基于AECOsimBD建立建筑三维信息模型中对象定义的方法

AECOsimBD是Bentley BIM解决方案的核心产品,它的作用是建立建筑专业的三维信息模型。而对象是三维信息建模的基本要素,文章介绍了基于AECOsimBD对象类型和对象样式的三维信息模型对象的定义方法。     

Safe system approach to reducing serious injury risk in motorcyclist collisions with fixed hazards

Collisions with fixed objects in the roadway environment account for a substantial proportion of motorcyclist fatalities. Many studies have identified individual roadway environment and/or motorcyclist characteristics that are associated with the severity of the injury outcome, including the presence of roadside barriers, helmet use, alcohol use and speeding. However, no studies have reported the cumulative benefit of such characteristics on motorcycling safety. The safe system approach recognises that the system must work as a whole to reduce the net injury risk to road users to an acceptable level, including the four system cornerstone areas of roadways, speeds, vehicles and people. The aim of the present paper is to consider these cornerstone areas concomitantly, and quantitatively assess the serious injury risk of motorcyclists in fixed object collisions using this holistic approach. A total of 1006 Australian and 15,727 (weighted) United States motorcyclist-fixed object collisions were collected retrospectively, and the serious injury risks associated with roadside barriers, helmet use, alcohol use and speeding were assessed both individually and concomitantly. The results indicate that if safety efforts are made in each of the safe system cornerstone areas, the combined effect is to substantially reduce the serious injury risk of fixed hazards to motorcyclists. The holistic approach is shown to reduce the serious injury risk considerably more than each of the safety efforts considered individually. These results promote the use of a safe system approach to motorcycling safety.     

基于双重金字塔网络的视频目标分割方法

针对复杂视频场景中难以分割特定目标的问题，提出一种基于双重金字塔网络（DPN）的视频目标分割方法。首先，通过调制网络的单向传递让分割模型适应特定目标的外观。具体而言，从给定目标的视觉和空间信息中学习一种调制器，并通过调制器调节分割网络的中间层以适应特定目标的外观变化。然后，通过基于不同区域的上下文聚合的方法，在分割网络的最后一层中聚合全局上下文信息。最后，通过横向连接的自左而右结构，在所有尺度中构建高阶语义特征图。所提出的视频目标分割方法是一个可以端到端训练的分割网络。大量实验结果表明，所提方法在DAVIS2016数据集上的性能与较先进的使用在线微调的方法相比，可达到相竞争的结果，且在DAVIS2017数据集上性能较优。     

面向分布式环境的仿真部署工具设计

仿真环境的构建是大规模分布式仿真系统中最为繁琐的问题,易受到人的主观因素影响，存在着环境重构难、自动化程度低和数据资源难以管理等多个方面的问题。本文设计一种基于客户端/服务器架构的面向分布式环境的仿真部署工具，通过定义规范化的对象系统、标准化的交互接口和统一化的数据资源调度方式，实现分布式环境下仿真资源的集中管理，优化了部署环境，简化了部署流程。仿真实验表明使用本文提出的方法可快速实现部署环境的重构。     

基于改进SSD的高效目标检测方法

为改善一阶段目标检测算法检测精度较差的缺陷，提出一种基于SSD的高效多目标定位检测算法FSD。该算法主要从两个方面对一阶段目标检测算法进行改进：设计了一个更高效的密集残差网络，即R-DenseNet，通过采用一种更窄的密集网络结构形式，在保持特征提取容量的同时降低了计算复杂度，从而提高了算法的检测和收敛性能；改进了损失函数，通过抑制易分样本在损失函数中的权重，提高算法的鲁棒性，改善了目标检测中样本失衡的现象。采用Tensorflow深度学习框架部署网络，并在搭载Nvidia Titan X的Ubuntu上开展实验，实验表明FSD在COCO和PASCAL VOC这两个目标检测数据集上上都取得了最高的检测精度，其中FSD300D的检测精度相比SSD300有3.7%提升，检测相率比SSD有10.87%提升。     

基于并行跟踪检测框架与深度学习的目标跟踪算法

在空地协同背景下，地面目标的移动导致其在无人机视角下外观会发生较大变化，传统算法很难满足此类场景的应用要求。针对这一问题，提出基于并行跟踪和检测（PTAD）框架与深度学习的目标检测与跟踪算法。首先，将基于卷积神经网络（CNN）的目标检测算法SSD作为PTAD的检测子处理关键帧获取目标信息并提供给跟踪子；其次，检测子与跟踪子并行处理图像帧并计算检测与跟踪结果框的重叠度及跟踪结果的置信度；最后，根据跟踪子与检测子的跟踪或检测状态来判断是否对跟踪子或检测子进行更新，并对图像帧中的目标进行实时跟踪。在无人机视角下的视频序列上开展实验研究和对比分析，结果表明所提算法的性能高于PTAD框架下最优算法，而且实时性提高了13%，验证了此算法的有效性。     

A methodology for applying students' interactive task performance scores from a multimedia-based performance assessment in a Bayesian Network

Computer-based simulations are increasingly being used in educational assessment. In most cases, the simulation-based assessment (SBA) is used for formative assessment, which can be defined as assessment for learning, but as research on the topic continues to grow, possibilities for summative assessment, which can be defined as assessment of learning, are also emerging. The current study contributes to research on the latter category of assessment. In this article, we present a methodology for scoring the interactive and complex behavior of students in a specific type of SBA, namely, a Multimedia-based Performance Assessment (MBPA), which is used for a summative assessment purpose. The MBPA is used to assess the knowledge, skills, and abilities of confined space guard (CSG) students. A CSG supervises operations that are carried out in a confined space (e.g., a tank or silo). We address two specific challenges in this article: the evidence identification challenge (i.e., scoring interactive task performance), and the evidence accumulation challenge (i.e., accumulating scores in a psychometric model). Using expert ratings on the essence and difficulty of actions in the MBPA, we answer the first challenge by demonstrating that interactive task performance in MBPA can be scored. Furthermore, we answer the second challenge by recoding the expert ratings in conditional probability tables that can be used in a Bayesian Network (a psychometric model for reasoning under uncertainty and complexity). Finally, we validate and illustrate the presented methodology through the analysis of the response data of 57 confined space guard students who performed in the MBPA.