Leveraging stimuli responsive hydrogels for on/off control of mixing

Microfluidic mixers are an important component in microfluidic devices. This paper presents a micromixer which can control mixing with responsive hydrogel actuators to modulate mixing between two adjacent fluids dependant on the chemistries of the fluid. This is achieved by the responsive hydrogels swelling or contracting under different stimuli, which alters the mixing between the two fluids. We present this device using standard pH responsive hydrogels for two different device designs and demonstrate altered mixing profiles based on the pH of the fluid streams. For the T-shaped device an increase in mixing efficiency from 18.3% to 34.5% is observed between the contracted and expanded hydrogel states. For the modified T-shaped device mixing efficiency in the contracted state is 25.0% while in the expanded state efficiency increases to 72.9%. This can be used as a mixer that has on/off functionality of an active mixer, based on the pH of the mixing streams, with the advantages a passive mixer offers. Other responsive hydrogel chemistries could be substituted into the device to achieve many different triggers for mixing.     

Coarse iris classification using box-counting to estimate fractal dimensions

This paper proposes a novel algorithm for the automatic coarse classification of iris images using a box-counting method to estimate the fractal dimensions of the iris. First, the iris image is segmented into sixteen blocks, eight belonging to an upper group and eight to a lower group. We then calculate the fractal dimension value of these image blocks and take the mean value of the fractal dimension as the upper and the lower group fractal dimensions. Finally, all the iris images are classified into four categories in accordance with the upper and the lower group fractal dimensions. This classification method has been tested and evaluated on 872 iris cases, and the proportions of these categories in our database are 5.50%, 38.54%, 21.79%, and 34.17%. The iris images are classified with two algorithms, the double threshold algorithm, which classifies iris images with an accuracy of 94.61%, and the backpropagation algorithm, which is 93.23% accurate. When we allow for the border effect, the double threshold algorithm is 98.28% accurate.     

Steganalysis and payload estimation of embedding in pixel differences using neural networks

In this paper a steganalysis technique is proposed for pixel-value differencing method. This steganographic method, which is immune against conventional attacks, performs the embedding in the difference of the values of pixel pairs. Therefore, the histogram of the differences of an embedded image is different as compared with a cover image. A number of characteristics are identified in the difference histogram that show meaningful alterations when an image is embedded. Five distinct multilayer perceptrons neural networks are trained to detect different levels of embedding. Every image is fed to all networks and a voting system categorizes the image as stego or cover. The implementation results indicate 88.6% success in correct categorization of the test images that contained more than 20% embedding. Furthermore, using a neural network an estimator is presented which gives an estimate of the amount of the MPVD embedding in an image. Implementation of the estimator showed an average accuracy of 88.3% in the estimation of the amount of embedding.     

An improved hybrid method for forward kinematics analysis of parallel robots

This paper combines a new structure of artificial neural networks (ANNs) with a 3rd-order numerical algorithm and proposes an improved hybrid method for solving forward kinematics problem (FKP) of parallel manipulators. In this method, an approximate solution of the FKP is first generated by the neural network. This solution is next considered as an initial guess for the 3rd-order numerical technique which solves the nonlinear forward kinematics equations and obtains the answer with a desired level of accuracy. To speed up the method, a new structure is proposed for designing the ANN which is called Same Class One Network. In this structure, the outputs of the ANN are classified into classes of similar variables with an individual network designed for each class. The proposed method is then applied to a planar 3-RPR parallel manipulator and a spatial 3-PSP parallel robot. The results show that using this method will lead to a 55% reduction in required iterations and a 20% reduction in the FKP analysis time, while maintaining a high level of solution accuracy.     

Auditing file system permissions using association rule mining

Identifying irregular file system permissions in large, multi-user systems is challenging due to the complexity of gaining structural understanding from large volumes of permission information. This challenge is exacerbated when file systems permissions are allocated in an ad-hoc manner when new access rights are required, and when access rights become redundant as users change job roles or terminate employment. These factors make it challenging to identify what can be classed as an irregular file system permission, as well as identifying if they are irregular and exposing a vulnerability. The current way of finding such irregularities is by performing an exhaustive audit of the permission distribution; however, this requires expert knowledge and a significant amount of time. In this paper a novel method of modelling file system permissions which can be used by association rule mining techniques to identify irregular permissions is presented. This results in the creation of object-centric model as a by-product. This technique is then implemented and tested on Microsoft’s New Technology File System permissions (NTFS). Empirical observations are derived by making comparisons with expert knowledge to determine the effectiveness of the proposed technique on five diverse real-world directory structures extracted from different organisations. The results demonstrate that the technique is able to correctly identify irregularities with an average accuracy rate of 91%, minimising the reliance on expert knowledge. Experiments are also performed on synthetic directory structures which demonstrate an accuracy rate of 95% when the number of irregular permissions constitutes 1% of the total number. This is a significant contribution as it creates the possibility of identifying vulnerabilities without prior knowledge of how to file systems permissions are implemented within a directory structure.     

文本聚类在自动文摘中的应用研究

针对当前自动文摘方法的不足,提出了基于文本聚类的自动文摘实现方法。将文本聚类引入自动文摘中,能实现多文档的自动文摘。实现了面向“塑料”行业的基于文本聚类的自动文摘系统TCAAS,其单文档自动文摘的正确率和召回率在80%以上,多文档自动文摘的正确率和召回率在75%以上。实验表明该方法可行,对自动文摘系统的设计具有借鉴意义和深入研究的价值。     

Effect of a Traffic Speed Based Cruise Control on an Electric Vehicle’s Performance and an Energy Consumption Model of an Electric Vehicle

This paper proposes a cruise control system (CCS) to improve an electric vehicle’s range, which is a significant hurdle in market penetration of electric vehicles. A typical driver or a conventional adaptive cruise control (ACC) controls an electric vehicle (EV) such that it follows a lead vehicle or drives close to the speed limit. This driving behaviour may cause the EV to cruise significantly above the average traffic speed. It may later require the EV to slow down due to the traffic ripples, wasting a part of the EV’s kinetic energy. In addition, the EV will also waste higher speed dependent dissipative energies, which are spent to overcome the aerodynamic drag force and rolling resistance. This paper proposes a CCS to address this issue. The proposed CCS controls an EV’s speed such that it prevents the vehicle from speeding significantly above the average traffic speed. In addition, it maintains a safe inter-vehicular distance from the lead vehicle. The design and simulation analysis of the proposed CCS were in a MATLAB simulation environment. The simulation environment includes an energy consumption model of an EV, which was developed using data collected from an electric bus operation in London. In the simulation analysis, the proposed system reduced the EV’s energy consumption by approximately 36.6% in urban drive cycles and 15.4% in motorway drive cycles. Finally, the experimental analysis using a Nissan e-NV200 on two urban routes showed approximately 30.8% energy savings.      

对称三进制在椭圆曲线标量乘法中的应用

把对称三进制引入到椭圆曲线密码体制标量乘法中,对k进行重新编码,直接计算kP,以改进标量乘法的运算效率。给出将k重新编码为对称三进制串的算法,提出对称三进制标量乘法算法。相对于二进制标量乘法算法,平均效率提升5.4%。当进行预计算时,相对于二进制算法和二进制预计算算法,平均效率分别提升73.18%、15.58%,并且能减少需要存储的点数。     

Valence extraction using EM selection and co-occurrence matrices

This paper discusses two new procedures for extracting verb valences from raw texts, with an application to the Polish language. The first novel technique, the EM selection algorithm, performs unsupervised disambiguation of valence frame forests, obtained by applying a non-probabilistic deep grammar parser and some post-processing to the text. The second new idea concerns filtering of incorrect frames detected in the parsed text and is motivated by an observation that verbs which take similar arguments tend to have similar frames. This phenomenon is described in terms of newly introduced co-occurrence matrices. Using co-occurrence matrices, we split filtering into two steps. The list of valid arguments is first determined for each verb, whereas the pattern according to which the arguments are combined into frames is computed in the following stage. Our best extracted dictionary reaches an F-score of 45%, compared to an F-score of 39% for the standard frame-based BHT filtering.     

Gait flow image: A silhouette-based gait representation for human identification

In this paper, we propose a novel gait representation—gait flow image (GFI) for use in gait recognition. This representation will further improve recognition rates. The basis of GFI is the binary silhouette sequence. GFI is generated by using an optical flow field without constructing any model. The performance of the proposed representation was evaluated and compared with the other representations, such as gait energy image (GEI), experimentally on the USF data set. The USF data set is a public data set in which the image sequences were captured outdoors. The experimental results show that the proposed representation is efficient for human identification. The average recognition rate of GFI is better than that of the other representations in direct matching and dimensional reduction approaches. In the direct matching approach, GFI achieved an average identification rate 42.83%, which is better than GEI by 3.75%. In the dimensional reduction approach, GFI achieved an average identification rate 43.08%, which is better than GEI by 1.5%. The experimental result showed that GFI is stronger in resisting the difference of the carrying condition compared with other gait representations.     

一种消息恢复型数字签名方案的改进

研究一种消息恢复型数字签名方案,该方案不需要hash函数和消息冗余就能抵抗伪造攻击。讨论已有的攻击方法和一种能够抵抗这些攻击的改进方案。在保持改进方案安全性的基础上,进行进一步的性能优化。在典型参数设置情况下( , ),优化方案的签名长度仅为原先的44%,计算效率提高了61%,传输效率提高了56%。     

分段复合多尺度模糊熵和IGWO-SVM的脑电情感识别

为提高脑电的情感识别率,提出了分段复合多尺度模糊熵算法,采用分段粗粒化和计算复合多尺度模糊熵的策略,使提取特征较好地解决了数据缺失和计算不准确的问题;同时构造了应用余弦非线性收敛因子和动静态位置更新的灰狼算法优化支持向量机分类模型。为证明所提两种算法的有效性,进行了仿真实验验证,并在公开DEAP数据库下与几种常见的支持向量机优化模型比较脑电的情感识别率,结果表明在提出的模型下,效价、唤醒度、优势度、喜欢度的平均识别率分别为87.27%、87.81%、89.06%、87.58%,均高于其他算法。另外对比了高/低喜欢度下效价和唤醒度的分类,实验表明喜欢度低时情感识别率较高。     

基于Bi-SSD的小目标检测算法

针对目前目标检测技术中小目标检测困难问题, 提出了一种基于SSD (Single Shot multibox Detector) 改进的小目标检测算法Bi-SSD (Bi-directional Single Shot multibox Detector). 该算法为SSD的浅层特征设计了小目标特征提升模块, 在网络的分类和回归部分结合多尺度特征融合方法和BiFPN (Bi-directional Feature Pyramid Network) 结构, 设计了6尺度BiFPN分类回归子网络. 实验结果表明, 在PASCAL VOC和MS COCO目标检测数据集上Bi-SSD相比原始的SSD算法有更好的检测性能. 其中VOC2007+2012上Bi-SSD算法的mAP指标达到了78.47%相较SSD算法提升了1.34%, 在COCO2017上Bi-SSD算法的mAP达到26.4%提升了接近2.4%.     

一种稳健快速的椒盐噪声抑制算法

针对图像中椒盐噪声的特点,提出了一种细节保持的椒盐噪声的滤波算法.算法分两步进行:第一步先对噪声图像进行自适应中值滤波,而后使用逻辑判断的方法对可能噪声点进行标识;第二步采用了一种新的逐次修复的方法,利用当前噪声点周围的非噪声点或已修复的噪声点的信息对当前的噪声点进行修复.我们对密度在10%~95%的各种噪声强度的情况进行了仿真,结果显示本文提出的算法能在有效地抑制噪声的同时完好地保持图像的细节信息,尤其在高噪声密度条件下显示了良好的噪声抑制效果和细节保持能力,显示了算法的稳健性.文中还给出了一种相当有效的优化策略,极大地降低了算法的运算复杂度,使算法适合于实时实现,有实际应用价值.     

采用自适应扰动观察法的光伏并网系统最大功率点跟踪

提出了一种自适应扰动观察（P＆O）算法,用于在不同天气条件下太阳能光伏（PV）并网系统的最大功率点跟踪（MPPT）控制策略。该策略对于从太阳能光伏电池板中,获取最大的功率输出是十分重要的。利用一种依赖于功率变化的可变的扰动步长,提出了改进的自适应扰动观察算法。最后将通过仿真所得到的数据与传统的扰动观察算法进行了比较,结果表明所提出MPPT算法的收敛值和速度得到了改善,稳定时间缩短25%,稳态值提高20%以上,在太阳能光伏并网系统的最大功率点跟踪时是有效而实用的。     

信息物理系统资源能力建模

新词自动检测是中文信息处理的重要基础,但中文字符极强的构词能力给新词检测带来了巨大困难。提出一种新词检测的形式化描述模型,用以建立特征和新词检测结果之间的统计联系。在此基础上提出应用统计学习模型作为框架来整合不同类型的可用特征,以充分发挥特征之间的组合作用,进一步改善新词检测效果。实验表明,统计框架方法的性能明显地优于特征的简单叠加,能有效提高新词检测效果,开放实验和封闭实验的F值分别为49.72%和69.83%,达到了目前的较好水平。     

基于网络挖掘的上下文相关词汇级复述研究

词汇级复述研究旨在为词汇获取复述.词汇级复述是上下文相关的,即对同一个词在不同上下文中应获取不同的复述词.提出了一种获取上下文相关词汇级复述的方法.该方法包括两部分:基于网络挖掘的候选复述词获取以及基于二元分类的复述词确认.在《人民日报》语料库上的实验结果表明:(1) 基于网络挖掘的候选复述词获取方法是切实可行的,平均为每个待复述词在每个给定的上下文句子中获取2.3 个正确复述词;(2) 利用二元分类的方法进行复述确认是有效的,其F 值达到0.6023;(3) 利用该方法抽取得到的复述中,有75.11%和98.31%无法通过两种常用的上下文无关方法,即基于辞典和基于聚类的方法来获得.这证明了所提出的上下文相关复述方法可以有效地补充传统的上下文无关方法.     

基于最大熵模型的英文名词短语指代消解

提出了一种新颖的基于语料库的英文名词短语指代消解算法,该算法不仅能解决传统的代词和名词／名词短语间的指代问题,还能解决名词短语间的指代问题。同时,利用最大熵模型,可以有效地综合各种互不相关的特征,算法在MUC7公开测试语料上F值达到了60．2％,极为接近文献记载的该语料库上F值的最优结果61．8％。     

基于非凸约束信息的传感器网络节点定位方法

节点定位是无线传感器网络中的关键性问题,大多数定位方法无法评估每个节点的定位精度。该文提出一种无需测距的定位算法,将传感器节点的真实位置限定于一个区域中,使用该区域的面积评估传感器节点的位置精确度,利用网络中的非凸约束提高定位精度。仿真结果表明,在使用非凸约束的情况下,对于节点总数为250,20%为已知位置节点的传感器网络来说,90%的节点能较好地被定位。     

一种基于形式化描述的测试序列生成改进方法

动生成测试序列始终是软件工程中一项极为困难的工作．国际标准化组织（ＩＳＯ）一直致力于协议一致性测试方法与形式化描述技术的研究．本文讨论了基于形式化描述的协议测试序列生成方法中的问题,特别是提出了“部分Ｔ序列叠加算法”对使用ＵＩＯ序列生成测试序列的方法做了进一步改进,大大减少了测试开销．