Semantic weak signal tracing

The weak signal concept according to Ansoff has the aim to advance strategic early warning. It enables to predict the appearance of events in advance that are relevant for an organization. An example is to predict the appearance of a new and relevant technology for a research organization. Existing approaches detect weak signals based on an environmental scanning procedure that considers textual information from the internet. This is because about 80% of all data in the internet are textual information. The texts are processed by a specific clustering approach where clusters that represent weak signals are identified. In contrast to these related approaches, we propose a new methodology that investigates a sequence of clusters measured at successive points in time. This enables to trace the development of weak signals over time and thus, it enables to identify relevant weak signal developments for organization’s decision making in strategic early warning environment.     

从假性紫罗兰酮合成β-紫罗兰酮中主要副产物(I)的分离鉴定

假性紫罗兰酮在浓硫酸及 - 2 0°C低温条件下反应 ,主要生成 β-紫罗兰酮 ,少量的 α-及 γ-紫罗兰酮 ,同时生成副产物 ( I)及 ( II)。用 IR、1H NMR及 MS光谱分析方法 ,对副产物 ( I)进行了结构鉴定 ,结构为 3,4,4a,5 ,8,8a-六氢 - 2 ,5 ,5 ,8a-四甲基 - 2 H - 1 -苯并吡喃     

Estimation of rheological parameters using velocity measurements

In the present investigation, we develop a method for estimating rheological parameters of viscoelastic fluids using velocity measurement in a square straight channel. It is believed that a somewhat complicated patterns of secondary flows due to the non-zero second normal stress difference are more useful than the simple viscometric flows traditionally adopted in the determination of rheological parameters. The inverse problem of determining the rheological parameters from a set of velocity measurements is solved using a conjugate gradient method. When applied to a general constitutive equation encompassing the UCM model, the Oldroyd-B model and the PTT model, the present method is found to yield a reasonably accurate estimation of five rheological parameters simultaneously even with noisy velocity measurements.     

PRIVACY LEAKAGE IN HEALTH SOCIAL NETWORKS

Members of health social networks may be susceptible to privacy leaks by the amount of information they leave behind. The threat to privacy increases when members of these networks reuse their pseudonyms in other social networks. The risk of re‐identifying users from such networks requires quantitative estimates to evaluate its magnitude. The estimates will enable managers and members of health social communities to take corrective measures. We introduce a new re‐identification attack, the social network attack, that takes advantage of the fact that users reuse their pseudonyms. To demonstrate the attack, we establish links between MedHelp and Twitter (two popular social networks) based on matching pseudonyms. We used Bayesian networks to model the re‐identification risk and used stylometric techniques to identify the strength of the links. On the basis of our model 7‐11. 8% of the MedHelp members in the sample population who reused their pseudonyms in Twitter were re‐identifiable compared with 1% who did not. The risk estimates were measured at the 5% risk threshold. Our model was able to re‐identify users with a sensitivity of 41% and specificity of 96%. The potential for re‐identification increases as more data is accumulated from these profiles, which makes the threat of re‐identification more serious.     

13C-NMR分析技术在改性聚苯醚剖析中的应用

应用^13C核磁共振分析技术，参照Sadtler标准^13C核磁共振谱图，对国外公司的五种改性聚苯醚样品进行了剖析。结果表明，有两种改性聚苯醚样品系由聚苯醚、聚苯乙烯和磷酸三苯酯组成，该类材料具有优良的阻燃性能；有两种系由聚苯醚、聚苯乙烯和聚顺丁二烯共混而成，具有较强的冲击性能；有一种系由聚苯醚、聚苯乙烯和聚乙烯构成，具有突出的润滑性能和耐磨性能。     

Bootstrapping a robot’s kinematic model

We present a system that is able to autonomously build a 3D model of a robot’s hand, along with a kinematic model of the robot’s arm, beginning with very little information. The system starts by using exploratory motions to locate and centre the robot’s hand in the middle of its field of view, and then progressively builds the 3D and kinematic models. The system is flexible, and easy to integrate with different robots, because the model building process does not require any fiducial markers to be attached to the robot’s hand. To validate the models built by the system we perform a number of experiments. The results of the experiments demonstrate that the hand model built by the system can be tracked with a precision in the order of 1 mm, and that the kinematic model is accurate enough to reliably position the hand of the robot in camera space.     

Esterification of acrylic acid with methanol by reactive chromatography: Experiments and simulations

The esterification of acrylic acid with methanol using Amberlyst 15 as a stationary phase has been investigated using a chromatographic reactor. Several experimental runs at various operating conditions have been conducted on a batch column. A classical reactive chromatography model including lumped kinetics, a linear driving force transport model and a heterogeneous kinetic model for the catalytic reaction has been developed. The additional dispersion of concentration fronts due to density gradient effects has been accounted for in the model. The model parameters have been determined in a fast and reliable way by directly fitting the batch column experiments. In general, a good agreement between experimental and calculated results is obtained. The evaluation of the covariance of the fitted model parameters reveals important insights about the system behavior.Based on the detailed batch column model, a complete model of a simulated-moving-bed reactor has been implemented and its optimal point of operation for the synthesis of methyl acrylate from acrylic acid has been determined. Particularly when considering the low-operating temperature, we can regard this process as a possible competition for current technologies.     

Fault identification for process monitoring using kernel principal component analysis

In this research, we develop a new fault identification method for kernel principal component analysis (kernel PCA). Although it has been proved that kernel PCA is superior to linear PCA for fault detection, the fault identification method theoretically derived from the kernel PCA has not been found anywhere. Using the gradient of kernel function, we define two new statistics which represent the contribution of each variable to the monitoring statistics, Hotelling's T²and squared prediction error (SPE) of kernel PCA, respectively. The proposed statistics which have similar concept to contributions in linear PCA are directly derived from the mathematical formulation of kernel PCA and thus they are straightforward to understand. The main contribution of this work is that we firstly suggest a fault identification method especially applicable to process monitoring using kernel PCA. To demonstrate the performance, the proposed method is applied to two simulated processes, one is a simple nonlinear process and the other is a non-isothermal CSTR process. The simulation results show that the proposed method effectively identifies the source of various types of faults.     

一种新型指纹锁电路设计

市场上主流的电子锁是基于密码设计的。密码锁的最大的缺陷是密码容易被他人窃取、猜测及遗忘。随着生物技术的发展,越来越多的活体技术应用到识别系统中,如指纹、掌纹、人脸、虹膜等。相对于其它的活体识别技术,指纹识别系统以其可实现性强,成本相对低廉,同时又具备较高的安全性,被越来越多的应用到各种场合。文章给出了一种新型的指纹锁架构,并详细论述了系统的各个组成部分以及指纹识别算法的实现流程。文章对降低系统功耗和增加保密性都提出了独特的方法。     

基于神经网络的轮胎胎号字符识别

介绍了采用BP神经网络来进行轮胎胎号字符识别的一种尝试性方法。用Matlab来模拟用神经网络进行胎号数字识别这一过程，用投影—变换系数法进行特征提取，确定特征输入、隐含层的神经元和输出后，经训练后可识别胎号，识别率尚可。