基于开放编译的面向对象程序静态分析器*

采用反射和开放编译技术,设计并实现了一个面向对象程序静态分析器,对开放编译器OpenC++进行了扩展与改进。通过实验研究表明,该程序静态分析器可以得到全面的系统依赖信息,可以有效地辅助程序理解。     

基于标值点过程的遥感影像道路网络提取

针对高分辨率遥感影像道路网络的特点,采用基于贝叶斯理论的全自动方法从遥感影像中提取道路.根据道路的局部和全局特征,使用标值点过程对道路建模,采用结合可逆跳跃马尔可夫链蒙特卡罗算法的模拟退火算法优化求得全局最优解.提出新的预处理方法得到道路的位置和方向信息,提出基于预处理的生灭转移核以降低算法的搜索空间,提出基于连接的移动转移核以加快算法的收敛速度.实验结果表明.该方法可以快速、有效地从不同的遥感影像(光学、SAR)提取道路网络.     

关于扩充转移网络的研究

随着因特网的普及,计算机整体性能已经大幅提高,信息处理量也出现爆炸式增长,在这种情况下,要求计算机对我们的母语（自然语言）理解日益迫切。自然语言理解的主流技术一直是句法--语义分析。本文研究了扩充转移网络的描述语言的语法,给出了扩充转移网络的分析算法,并通过实例解析了这种算法的特点。     

Dual geometric perception for cross-domain road segmentation

Road segmentation plays an important role in navigation systems and autonomous driving. However, many methods in road segmentation are based on supervised learning and suffer from performance degradation in the real world. There is a certain domain gap (distribution shift problem) between the source domain (training data) and the target domain (testing data). In this paper, we propose a Dual-Geometric Perception (DGP) approach for cross-domain road segmentation, which jointly uses semantic and dual-geometric information to learn the domain-invariant feature for road segmentation. First, we propose an RGB-N dual stream network structure, which effectively fuses normal vector information and RGB information to reduce domain gap. Moreover, a dual geometric adversarial learning strategy is proposed to utilize depth-aware and normal vector features to perform better domain alignment. Furthermore, a self-training learning strategy is used to further improve the model’s generalizability in the target domain. Extensive experiments demonstrate that our proposed DGP achieves superior performance on lane-to-lane and lane-to-sidewalk road domain adaptation tasks.     

视觉Transformer识别任务研究综述

Transformer模型在自然语言处理领域取得了很好的效果,同时因其能够更好地连接视觉和语言,也激发了计算机视觉界的极大兴趣。本文总结了视觉Transformer处理多种识别任务的百余种代表性方法,并对比分析了不同任务内的模型表现,在此基础上总结了每类任务模型的优点、不足以及面临的挑战。根据识别粒度的不同,分别着眼于诸如图像分类、视频分类的基于全局识别的方法,以及目标检测、视觉分割的基于局部识别的方法。考虑到现有方法在3种具体识别任务的广泛流行,总结了在人脸识别、动作识别和姿态估计中的方法。同时,也总结了可用于多种视觉任务或领域无关的通用方法的研究现状。基于Transformer的模型实现了许多端到端的方法,并不断追求准确率与计算成本的平衡。全局识别任务下的Transformer模型对补丁序列切分和标记特征表示进行了探索,局部识别任务下的Transformer模型因能够更好地捕获全局信息而取得了较好的表现。在人脸识别和动作识别方面,注意力机制减少了特征表示的误差,可以处理丰富多样的特征。Transformer可以解决姿态估计中特征错位的问题,有利于改善基于回归的方法性能,还减少了三维估计时深度映射所产生的歧义。大量探索表明视觉Transformer在识别任务中的有效性,并且在特征表示或网络结构等方面的改进有利于提升性能。     

Traffic accidents involving fatigue driving and their extent of casualties

The rapid progress of motorization has increased the number of traffic-related casualties. Although fatigue driving is a major cause of traffic accidents, the public remains not rather aware of its potential harmfulness. Fatigue driving has been termed as a “silent killer.” Thus, a thorough study of traffic accidents and the risk factors associated with fatigue-related casualties is of utmost importance. In this study, we analyze traffic accident data for the period 2006–2010 in Guangdong Province, China. The study data were extracted from the traffic accident database of China's Public Security Department. A logistic regression model is used to assess the effect of driver characteristics, type of vehicles, road conditions, and environmental factors on fatigue-related traffic accident occurrence and severity. On the one hand, male drivers, trucks, driving during midnight to dawn, and morning rush hours are identified as risk factors of fatigue-related crashes but do not necessarily result in severe casualties. Driving at night without street-lights contributes to fatigue-related crashes and severe casualties. On the other hand, while factors such as less experienced drivers, unsafe vehicle status, slippery roads, driving at night with street-lights, and weekends do not have significant effect on fatigue-related crashes, yet accidents associated with these factors are likely to have severe casualties. The empirical results of the present study have important policy implications on the reduction of fatigue-related crashes as well as their severity.     

Factors contributing to young moped rider accidents in Denmark

Young road users still constitute a high-risk group with regard to road traffic accidents. The crash rate of a moped is four times greater than that of a motorcycle, and the likelihood of being injured in a road traffic accident is 10–20 times higher among moped riders compared to car drivers. Nevertheless, research on the behaviour and accident involvement of young moped riders remains sparse.Based on analysis of 128 accident protocols, the purpose of this study was to increase knowledge about moped accidents. The study was performed in Denmark involving riders aged 16 or 17. A distinction was made between accident factors related to (1) the road and its surroundings, (2) the vehicle, and (3) the reported behaviour and condition of the road user. Thirteen accident factors were identified with the majority concerning the reported behaviour and condition of the road user. The average number of accident factors assigned per accident was 2.7. Riding speed was assigned in 45% of the accidents which made it the most frequently assigned factor on the part of the moped rider followed by attention errors (42%), a tuned up moped (29%) and position on the road (14%). For the other parties involved, attention error (52%) was the most frequently assigned accident factor. The majority (78%) of the accidents involved road rule breaching on the part of the moped rider.The results indicate that preventive measures should aim to eliminate violations and increase anticipatory skills among moped riders and awareness of mopeds among other road users. Due to their young age the effect of such measures could be enhanced by infrastructural measures facilitating safe interaction between mopeds and other road users.     

基于随机前沿模型对"一带一路"沿线国家电力投资潜力研究

"一带一路"能源安全合作背景下,基于随机前沿模型,测算并分析了2003—2014年中国对"一带一路"沿线国家能源电力投资效率和潜力。研究表明:中国对"一带一路"沿线国家的电力投资效率比较显著,而且呈逐年递增的趋势,其投资潜力能够得到较好的实现,但高投资效率的国家较少,特别是亚洲地区国家,电力投资效率都不超过0.8,说明"一带一路"能源电力投资潜力巨大。     

Design by framework completion

An object-oriented framework in essence defines an architecture for a family of applications or subsystems in a given domain. Every application in the family obeys these architectural restrictions. Such frameworks are typically delivered as collections of inter-dependent abstract classes, together with their concrete subclasses. The abstract classes and their interdependencies implicitly realize the architecture. Developing a new application reusing classes of a framework requires a thorough understanding of the framework architecture.We introduce an approach called Design by Framework Completion, in which an exemplar (an executable visual model for a minimal instantiation of the architecture) is used for documenting frameworks. We propose exploration of exemplars as a means for learning the architecture, following which new applications can be built by replacing selected pieces of the exemplar. For the piece to be replaced, the inheritance lattice around its class provides the space of alternatives, one of these classes may be suitably adapted (say, by sub-classing) to create the new replacement.Design by Framework Completion proposes a paradigm shift when designing in presence of reusable components: It enables a much simpler top-down approach for creating applications, as opposed to the prevalent search for components and assemble them bottom-up strategy. We believe that this paradigm shift is essential because components can only be fitted together if they all obey the same architectural rules that govern the framework.     

A real-time versatile roadway path extraction and tracking on an FPGA platform

This paper presents an algorithm for roadway path extraction and tracking and its implementation in a Field Programmable Gate Array (FPGA) device. The implementation is particularly suitable for use as a core component of a Lane Departure Warning (LDW) system, which requires high-performance digital image processing as well as low-cost semiconductor devices, appropriate for the high volume production of the automotive market. The FPGA technology proved to be a proper platform to meet these two contrasting requirements. The proposed algorithm is specifically designed to be completely embedded in FPGA hardware to process wide VGA resolution video sequences at 30 frames per second. The main contributions of this work lie in (i) the proper selection, customization and integration of the main functions for road extraction and tracking to cope with the addressed application, and (ii) the subsequent FPGA hardware implementation as a modular architecture of specialized blocks. Experiments on real road scenario video sequences running on the FPGA device illustrate the good performance of the proposed system prototype and its ability to adapt to varying common roadway conditions, without the need for a per-installation calibration procedure.