城市洪涝试验研究进展

城市洪涝物理试验与原型试验是开展数值模型验证及提高预报预警精度的重要手段。从城市产流过程、地表洪水演进以及地表径流与管网水流交互三方面,综述了近年来国内外城市洪涝试验的研究进展。针对目前城市洪涝试验研究中存在的不足,指出今后应完善现有城市产汇流过程相关机理方面的试验研究,深入研究地表与管网垂向交互过程,加强与城市洪涝期间的流动行为密切相关过程的定量试验研究。     

Influence of rainfall on the performance of bioretention systems modified with activated carbon and biochar

The use of bioretention areas is common in urban stormwater management, but their performance varies significantly depending on rainfall characteristics and design conditions. In this study, a pilot experiment using bioretention columns with different media (commercial activated carbon and river sediment-derived biochar) investigated the influence of rainfall on bioretention performance. The results indicated that the runoff volume retention ratio (R_v), which included the runoff purified and discharged at the bottom of the column, and the runoff retained in media during rainfall event, decreased significantly with increases in the rainfall event return period (p < 0.05). The R_v of the activated carbon and biochar columns decreased with a 2-yr return period and then fell further with a 50-yr return period. Porous material has been shown to improve the water-holding capacity of bioretention media, but it did not result in an improved R_v under heavy rain that exceeded the 2-yr return period. With the increase of the return period from two to 50 yr, the mass removal efficiency (R_L) of total phosphorus and phosphate illustrated a clear decreasing trend in all columns. The total nitrogen, ammonia and nitrate removal did not show a clear trend with return periods because of transformations among different forms of nitrogen and similar saturation periods during the different rainfall events. The influence of the return period on chemical oxygen demand (COD) removal was related to whether the inflow COD reached maximum COD removal capacity of the bioretention media. Under a rainfall event with a specific return period, there were no significant differences in the R_L of all nitrogen species and COD among the different columns (p > 0.05). The addition of adsorptive material, such as activated carbon and biochar, may not be the key factor for improving nitrogen and COD removal under heavy rain that exceeds the 2-yr return period. The bioretention performance of phosphorus removal from urban stormwater runoff could be improved by replacing or adding media with high adsorption capacity, but these improvements would not be significant under heavy rain that exceeds the 2-yr return period. The results provide some reference for evaluating bioretention performance and optimizing bioretention design in the future.     

基于胰岛素基础率估计的人工胰腺系统自抗扰控制

胰岛素基础率是人工胰腺系统实现人体血糖闭环控制的基准, 但该变量在临床治疗中难以准确确定. 针对这一问题, 本文设计了一种基于胰岛素基础率动态估计的人工胰腺自抗扰控制方法, 通过扩张状态观测器(Extended state observer, ESO)实时估计血糖代谢过程中的内部与外界干扰, 构建具备参数自适应能力的反馈控制律和胰岛素注射安全约束, 实现血糖闭环调控能力的有效改善. 在此基础上, 本文设计了基于移动设备和蓝牙模块的人工胰腺软件系统, 并通过美国食品药品监督管理局(Food and Drug Administration, FDA)接受的UVA/Padova T1DM仿真平台完成算法的比较仿真与功能测试. 本文的工作将为后续人工胰腺临床试验的开展提供方法基础和技术支持, 也为我国糖尿病患者血糖管理的改善提供精准医学治疗手段.     

A novel deep learning scheme for multi-condition remaining useful life prediction of rolling element bearings

The remaining useful life (RUL) prediction of a rolling element bearing is important for more reasonable maintenance of machinery and equipment. Generally, the information of a failure can hardly be acquired in advance while running and the degradation process varies in terms of different faults. Thus, fault identification is indispensable for a multi-condition RUL prediction, where, however, the fault identification and RUL prediction are separated in most studies. A new hybrid scheme is proposed in this paper for the multi-condition RUL prediction of rolling element bearings. The proposed scheme contains both classification and regression, where the 2D-DCNN based classifier and predictors are built concerning typical fault conditions of a bearing. For the online prediction, the raw signals are spanned in the time-frequency domain and then transferred into images as the input of the scheme. The classifier is used to monitor the vibration of rolling bearings for online fault recognition and excite the corresponding predictor for RUL prediction once a fault is detected. The output from the predictor is amended by the proposed adaptive delay correction method as the final prediction results. A demonstration is performed based on the XJTU-SY datasets and the results are compared with those from the state-of-the-art methods, which proves the superiority of the proposed scheme in improving the accuracy and linearity of RUL prediction. The time cost of the proposed online prediction scheme is also investigated and the results indicate high time effectiveness.     

A graph-based QoS prediction approach for web service recommendation

Applied Intelligence - With the development of the Internet, the recommendation based on Quality of Service(QoS) is proven to be an efficient way to deal with the ever-increasing web services in...     

一种基于多项式的代理保护代理签名体制研究

文章首先分析了张青坡等人中提出的多项式形式的E1Gamal签名体制的安全缺陷,然后基于有限域上多项式的性质,提出了有限域上多项式形式代理保护代理签名方案;新的签名方案中,利用多项式进行签名权利的委托,并由改进的有限域上的多项式形式的E1Gamal签名体制生成代理签名。新方案的安全性基于离散对数的难解性。     

操作系统中基于安全封装的访问控制实现方法

针对敏感客体的访问控制问题,文章提出了操作系统中基于安全封装的访问控制实现方法。设计了针对敏感客体访问的统一访问接口,定义了相关接口,描述了访问接口的通信协商过程;提出了类C权限描述语言,给出了该语言的形式化描述及谓词含义;基于程序调用栈,实现了程序状态与权限调用的绑定。最终,实现了对系统中敏感客体的安全访问控制。     

基于FPGA的高速多路视频数据采集系统

针对同时处理高速多路视频数据的需求,以NiosII软核CPU为核心,通过在FPGA上构建可编程片上系统（Sys—ternOnProgrammableChip,SOPC）,利用SOPC系统自定义外设接口,配合DMA技术,完成对A／D转换后的多路视频数据的同时解码采集。视频解码模块采用滑动窗法快速检测定时基准信号。FPGA可重构的特性可以使系统根据实际应用需要在原方案基础上扩展、裁减功能模块,并根据资源情况重构系统,达到资源与效率的最优匹配。     

PUF-enhanced offline RFID security and privacy

RFID (Radio Frequency IDentification) based communication solutions have been widely used nowadays for mobile environments such as access control for secure system, ticketing systems for transportation, and sport events. These systems usually depend on readers that are not continuously connected to a secure backend system. Thus, the readers should be able to perform their duties even in offline mode, which generally requires the management by the readers of the susceptible data. The use of RFID may cause several security and privacy issues such as traceability of tag owner, malicious eavesdropping and cloning of tags. Besides, when a reader is compromised by an adversary, the solution to resolve these issues getting worse. In order to handle these issues, several RFID authentication protocols have been recently proposed; but almost none of them provide strong privacy for the tag owner. On the other hand, several frameworks have been proposed to analyze the security and privacy but none of them consider offline RFID system.Motivated by this need, in this paper, we first revisit Vaudenay's model, extend it by considering offline RFID system and introduce the notion of compromise reader attacks. Then, we propose an efficient RFID mutual authentication protocol. Our protocol is based on the use of physically unclonable functions (PUFs) which provide cost-efficient means to the fingerprint chips based on their physical properties. We prove that our protocol provides destructive privacy for tag owner even against reader attacks.     

Graffiti: graph-based classification in heterogeneous networks

We address the problem of multi-label classification in heterogeneous graphs, where nodes belong to different types and different types have different sets of classification labels. We present a novel approach that aims to classify nodes based on their neighborhoods. We model the mutual influence of nodes as a random walk in which the random surfer aims at distributing class labels to nodes while walking through the graph. When viewing class labels as “colors”, the random surfer is essentially spraying different node types with different color palettes; hence the name Graffiti of our method. In contrast to previous work on topic-based random surfer models, our approach captures and exploits the mutual influence of nodes of the same type based on their connections to nodes of other types. We show important properties of our algorithm such as convergence and scalability. We also confirm the practical viability of Graffiti by an experimental study on subsets of the popular social networks Flickr and LibraryThing. We demonstrate the superiority of our approach by comparing it to three other state-of-the-art techniques for graph-based classification.