结构特征下的可撤销人脸识别

目的 相对于其他生物特征识别技术,人脸识别具有非接触、不易察觉和易于推广等特点,在公共安全和日常生活中得到广泛应用。在移动互联网时代,云端人脸识别可以有效地提高识别精度,但是需要将大量的人脸数据上传到第三方服务器。由于人的面部特征是唯一的,一旦数据库泄露就会面临模板攻击和假冒攻击等安全威胁。为了保证人脸识别系统的安全性并提高其识别率,本文提出一种融合人脸结构特征的可撤销人脸识别算法。方法 首先,对原始人脸图像提取结构特征作为虚部分量,与原始人脸图像联合构建复数矩阵并通过随机二值矩阵进行置乱操作。然后,使用2维主成分分析方法将置乱的复数矩阵映射到新的特征空间。最后,采用基于曼哈顿距离的最近邻分类器计算识别率。结果 在4个不同人脸数据库上的实验结果表明,原始人脸图像和结构特征图像经过随机二值矩阵置乱后,人眼无法察觉出有用的信息且可以重新生成,而且融合方差特征后,在GT （Georgia Tech）、NIR （Near Infrared）、VIS （Visible Light）和YMU （YouTuBe Makeup）人脸数据库上,平均人脸识别率分别提高了4.9%、2.25%、2.25%和1.98%,且平均测试时间均在1.0 ms之内,表明该算法实时性强,能够满足实际应用场景的需求。结论 本文算法可在不影响识别率的情况下保证系统的安全性,满足可撤销性。同时,融合结构特征丰富了人脸信息的表征,提高了人脸识别系统的识别率。     

基于活跃熵的序列图像脆弱性增强仿真

针对传统序列图像未对相邻脉冲图像进行精确对准,导致序列图像脆弱性的增强效果较差等问题,提出基于活跃熵的序列图像脆弱性增强模拟方法。利用对均匀运动目标多个相邻脉冲的时延时间伸缩分布图的特征提取,获取序列图像的运动参数;通过传统的相关成像原理获取到目标散射点的时延时间分布像,将时延伸缩分布像转换为序列图像的几何像;再通过图像熵技术对多个相邻脉冲图像进行精确对准,以达到序列图像脆弱性增强的目的。为了验证序列图像增强效果进行仿真,充分验证了所提方法的实用性以及优势。     

建筑机电系统物理连接关系自动修复方法

建筑机电设备逻辑关系自动提取成为建筑信息模型(BIM)从施工向运维阶段应用的 关键技术要点之一。针对实际工程项目 BIM 模型中设备、管线之间的物理连接错误、缺漏较多、 自动化提取设备逻辑连接关系难度高等问题,提出了建筑机电系统物理连接关系自动修复和效 率优化的方法。针对跨文件连接断点、错位连接、连接方向错误等 3 类问题进行准确定义,提 出自动检查和修复方法,能够自动快速修复现有 BIM 模型中常见的物理连接大量缺失的问题。 该方法在工程的实施支持施工 BIM 模型向运维 BIM 模型的快速转化,有利于实现基于 BIM 的 机电系统运维维护管理。     

The water–energy nexus: energy use for water supply in China

China’s strategy to meet increasing water demand is to increase the use of inter-basin transfer water and unconventional water. This study evaluates the energy requirements of this strategy by disaggregating and quantifying the regional energy use for water supply in 2020 and 2030; the energy use in 2014 is calculated to represent the present situation. We find that the energy use for water supply in 2014 amounted to 81 billion kWh. This value is expected increase to 90 and 109 billion kWh by 2020 and 2030, respectively. In 2030, the urban domestic sector will overtake the agricultural sector as the most energy-intensive sector, with major contribution from inter-basin transfer water.     

基于图论的建筑机电设备逻辑关系自动提取方法

随着建筑信息模型(BIM)在建筑施工及运维阶段的深入应用,建筑机电设备的逻辑 关系自动提取成为进一步应用的瓶颈。针对建筑机电系统信息模型应用过程中逻辑连接关系应 用需求高、判断复杂的问题,提出了一种基于图论的建筑机电设备逻辑关系自动提取方法。基 于 BIM,将机电系统抽象为无向连通图,连接器抽象为图的边,机电设备、管道、管道附件等 抽象为图的节点,将一片管道抽象为管道团,将设备与大量管道的复杂连接转换为设备到几个 管道团的简单连接,从而将机电系统逻辑关系自动生成的问题转换为无向连通图求解的问题, 建立了机电构件物理连接关系提取方法、设备逻辑连接关系自动生成和设备连接路径计算方法, 实现了建筑机电系统逻辑关系快速、准确、智能的提取。该方法在工程中的实施有利于基于 BIM 的机电系统运营维护管理,有利于实现建筑的全生命期信息管理。     

基于多通道自注意力机制的刑事案件量刑技术

近年来,司法领域存在着"案多人少"的问题,为解决此问题该文以司法量刑为目的,参照中国"依法量刑"的原则,设计并实现了一种基于多通道自注意力机制的刑事案件辅助量刑模型.该模型对案件事实描述、被告人基本信息以及法律法规3方面进行潜在表征学习,同时将上述信息结合,从而达到准确量刑的目的.该文在裁判文书数据集上进行实验,通过与其它基线模型的实验对比可见该模型具有更好的刑期预测性能.     

一种针对路口监控图像的区域分割方法

针对智能交通系统中对交通路口场景理解的需求,提出一种基于线特征先验和凸包损失函数的空间分割网络,目标是对斑马线以及斑马线所围路口区域进行精确检测和分割。利用公安交通管理系统平台采集并标注路口数据集;引入线特征先验,将RGBL图像作为网络输入,为深度学习实例分割提供显著的物体边缘特征以加强深度网络对图像特征学习的针对性;在分割网络中引入SCNN网络结构,构成空间分割网络以增强网络对空间结构的学习;引入凸包二值交叉熵动态损失函数来优化网络的输出精度。实验结果表明,该空间分割网络对斑马线及路口区域的检测正确率和分割完整度和精确度都有了显著的提升。     

基于压电效应的振荡水柱发电系统CFD分析

"透明海洋"开发需要为远洋检测传感器提供可持续电源,基于压电效应的振荡水柱发电原理是采集波能驱动压电功能材料形变实现能量转换,系统结构简单,稳定性好。基于线性波浪理论,建立振荡水柱(OWC)二维数值波浪水槽。流体动力学(CFD)分析结果表明,造波稳定,波浪在气室内形成的空气压强在时域上呈现一定周期性,经数据处理拟合为正弦曲线。应用ANSYS软件系统的参数化设计语言(APDL)功能开发空气驱动压电材料形变输出能量的仿真模型,分析产生电压变化特性。     

Studying software logging using topic models

Software developers insert logging statements in their source code to record important runtime information; such logged information is valuable for understanding system usage in production and debugging system failures. However, providing proper logging statements remains a manual and challenging task. Missing an important logging statement may increase the difficulty of debugging a system failure, while too much logging can increase system overhead and mask the truly important information. Intuitively, the actual functionality of a software component is one of the major drivers behind logging decisions. For instance, a method maintaining network communications is more likely to be logged than getters and setters. In this paper, we used automatically-computed topics of a code snippet to approximate the functionality of a code snippet. We studied the relationship between the topics of a code snippet and the likelihood of a code snippet being logged (i.e., to contain a logging statement). Our driving intuition is that certain topics in the source code are more likely to be logged than others. To validate our intuition, we conducted a case study on six open source systems, and we found that i) there exists a small number of “log-intensive” topics that are more likely to be logged than other topics; ii) each pair of the studied systems share 12% to 62% common topics, and the likelihood of logging such common topics has a statistically significant correlation of 0.35 to 0.62 among all the studied systems; and iii) our topic-based metrics help explain the likelihood of a code snippet being logged, providing an improvement of 3% to 13% on AUC and 6% to 16% on balanced accuracy over a set of baseline metrics that capture the structural information of a code snippet. Our findings highlight that topics contain valuable information that can help guide and drive developers’ logging decisions.     

SharkDB: an in-memory column-oriented storage for trajectory analysis

The last decade has witnessed the prevalence of sensor and GPS technologies that produce a high volume of trajectory data representing the motion history of moving objects. However some characteristics of trajectories such as variable lengths and asynchronous sampling rates make it difficult to fit into traditional database systems that are disk-based and tuple-oriented. Motivated by the success of column store and recent development of in-memory databases, we try to explore the potential opportunities of boosting the performance of trajectory data processing by designing a novel trajectory storage within main memory. In contrast to most existing trajectory indexing methods that keep consecutive samples of the same trajectory in the same disk page, we partition the database into frames in which the positions of all moving objects at the same time instant are stored together and aligned in main memory. We found this column-wise storage to be surprisingly well suited for in-memory computing since most frames can be stored in highly compressed form, which is pivotal for increasing the memory throughput and reducing CPU-cache miss. The independence between frames also makes them natural working units when parallelizing data processing on a multi-core environment. Lastly we run a variety of common trajectory queries on both real and synthetic datasets in order to demonstrate advantages and study the limitations of our proposed storage.