电子鼻在危险爆炸物检测中的应用研究

利用由18个纳米氧化锌厚膜气敏传感器组成的阵列对硝铵、矿山炸药、苦味酸、2,4二硝基甲苯(DNT)这4种典型爆炸物样品进行了测量.采用动、静态相结合的采样方法考察了传感器阵列的检测能力,在动态实验中通过提取不同的特征值并利用主元分析(PCA)和聚类分析(CA)方法对数据进行了分析和识别.静态实验结果表明传感器阵列在不同浓度上对4种典型爆炸物均有不同程度的响应,该方法能检测到硝铵、矿山炸药、苦味酸的浓度低至3.34 μg/L,DNT为83.3 μg/L;动态实验结果表明提取极值为特征值对阵列进行PCA、CA分析,可使4种典型爆炸物在毫克级上能完全区分.以上结果说明电子鼻技术在危险爆炸物检测中是一种很有发展前途的实用技术.     

电子对抗系统网络入侵检测技术优化研究

近年来,随着互联网技术的飞速发展,网络入侵防御技术成为互联网安全研究领域中的重要课题。针对现有电子对抗系统存在的网络后台安全逻辑欠缺,导致系统安全度降低、外端数据监测机制断裂的问题,提出电子对抗系统网络入侵检测技术优化研究方法。采用网络电子数据动态交互流特征定向技术、数据溢出监测算法与数据完整度监测机制三大模组对现有问题进行针对性解决。从问题产生根源对电子对抗系统网络入侵检测技术进行优化,通过仿真实验测试表明,提出电子对抗系统网络入侵检测技术优化研究方法具有入侵源监测响应速度快、准确度高、扩展性强、应用性好的特点。     

拉曼光谱快速鉴别品牌药与仿制药的研究

本文用拉曼光谱结合随机森林法很好的区分了相同药品不同厂家之间差异,为拉曼光谱用于快速区分仿制药和品牌药奠定基础。选用了1个品牌药厂家和6个仿制药厂家生产的卡托普利片作为实验研究对象,经过截取拉曼波长范围、光谱平滑、背景扣除、中心化等预处理,优化参数后,用随机森林法和主成分分析投影判别分别建立分析模型。计算结果表明:随机森林法所建立的分类模型可以区分卡托普利片的品牌药与仿制药厂家间的差异性,结果优于的主成分分析降维后不同主成分数下的投影判别分析效果。因此结合随机森林法能为拉曼光谱在药品快检中的高效、快速、无损分析提供有力保障。     

Designing an electronic performance support system for technology-rich environments

This study aims to design and validate an electronic performance support system that enables educators to use instructional technologies effectively and efficiently. The study was designed and conducted using developmental research methods and with the participation, at different stages, of educators and educational technology and software experts. The tangible product of the designed system is a session plan independent of the content. The system’s activity database contains 44 activities. The system works as follows. Based on the learning outcome, student, educator, and environment characteristics, activities not deemed suitable are eliminated, the most suitable activities are presented for the educator to select, and the educator makes a selection. The educator then accesses a session plan produced by the system and can edit this plan as desired. The system has additional features that were created based on the opinions of educators and educational technology experts. These features are tools for supporting academic, technical, administrative, and professional cooperation. To validate the designed system, the views and recommendations of educators and software and educational technology experts were collected. All three groups confirmed the validity of the design. Nevertheless, based on the feedback received, improvements were made before giving the design its final shape.     

人工嗅觉系统关键技术研究进展

回顾了人工嗅觉系统的发展历史,在介绍了人类嗅觉机理、人工嗅觉仿生系统的原理与组成的基础上,详细阐述了关键技术如气体传感器技术、信号预处理技术和模式识别技术等的研究现状,剖析总结了人工嗅觉系统存在的问题,并对未来发展进行了展望.     

Semi-quantitative and quantitative analysis of soft drinks using an electronic tongue

A potentiometric electronic tongue with 36 cross-sensibility lipo/polymeric membranes was built and applied for semi-quantitative and quantitative analysis of non-alcoholic beverages. A total of 16 commercial fruit juices (e.g., orange, pineapple, mango and peach) from five different brands were studied. In the semi-quantitative approach, the signal profiles recorded by the device were used together with a stepwise linear discriminant analysis to differentiate four beverage groups with different fruit juice contents: >30%, 14-30%, 6-10% and <4%. The model, with two discriminant functions based on the signals of only four polymeric membranes, explained 99% of the total variability of experimental data and was able to classify the studied samples into the correct group with an overall sensibility and specificity of 100% for the original data, and greater than 93% for the cross-validation procedure.The signals were also used to obtain MLR and PLS calibration models to estimate and predict the concentrations of fructose and glucose in the soft drinks. The linear models established were based on the signals recorded by 16 polymeric membranes and were able to estimate and predict satisfactorily (cross-validation) the concentrations of the two sugars (R² greater than 0.96 and 0.84, respectively).     

Combined electronic nose and tongue for a flavour sensing system

We present a novel, smart sensing system developed for the flavour analysis of liquids. The system comprises both a so-called “electronic tongue” based on shear horizontal surface acoustic wave (SH-SAW) sensors analysing the liquid phase and a so-called “electronic nose” based on chemFET sensors analysing the gaseous phase. Flavour is generally understood to be the overall experience from the combination of oral and nasal stimulation and is principally derived from a combination of the human senses of taste (gustation) and smell (olfaction). Thus, by combining two types of microsensors, an artificial flavour sensing system has been developed. Initial tests conducted with different liquid samples, i.e. water, orange juice and milk (of different fat content), resulted in 100% discrimination using principal components analysis; although it was found that there was little contribution from the electronic nose. Therefore further flavour experiments were designed to demonstrate the potential of the combined electronic nose/tongue flavour system. Consequently, experiments were conducted on low vapour pressure taste-biased solutions and high vapour pressure, smell-biased solutions. Only the combined flavour analysis system could achieve 100% discrimination between all the different liquids. We believe that this is the first report of a SAW-based analysis system that determines flavour through the combination of both liquid and headspace analysis.     

An integrated automatic face detection and recognition system

This paper proposes an integrated system for unconstrained face recognition in complex scenes. The scale and orientation tolerant system comprises a face detector followed by a recognizer. Given a color input image of a person, the face detector encloses the face from the complex scene within a circular boundary, and locates the position of the nose. A radial grid mapping centered on the nose is then performed to extract a feature vector within the boundary. The feature vector is input to a radial basis function neural network classifier for face identification. The proposed face detector achieved an average detection rate of 95.8% while the face recognizer achieved an average recognition rate of 97.5% on a database of 21 persons with variations in scale, orientation, natural illumination and background. The two modules were combined to form an automatic face recognition system that was evaluated in the context of a security system using a video database of 21 users and 10 intruders, acquired in an unconstrained environment. A recognition rate of 93.5% with 0% false acceptance rate was achieved.     

The beta test of an electronic supermarket

This paper presents findings of a beta test of an electronic supermarket. It first reports qualitative information elicited during interviews and focus groups conducted with the beta test user group. A total of 57 users who had tested the system participated in the qualitative component of the study. Interviews and focus groups addressed what the users considered the best and worst features of the system and what things they would do differently if they were the management of the electronic supermarket. Results generated a number of suggestions for improvement and concerns. Interestingly, it became apparent that the electronic supermarket, rather than being considered an impersonal form of shopping, was considered a very personalized form of shopping, akin to the corner grocery market. The paper then reports the results of a quantitative survey that aimed to predict intentions to use the electronic supermarket are reported. Construct measurement was loosely based on the technology acceptance model, a derivative of the theory of reasoned action, and further supplemented by industry specific relevant constructs. Constructs tapped, therefore, consisted of perceived usefulness, enjoyment, peer-group norms, usability and perceptions of the electronic shopping experience. Biographical and situational data were also recorded. Analysis was conducted on 64 completed questionnaires. Results indicated a high level of intention to use the system in the future. Not surprisingly, many of the hypothesised predictors of intention to use the system were inter-correlated. Multivariate regression analyses revealed, however, that two variables contributed significantly to the intention to use the system in the future: enjoyment in using the system and peer-group norms, together accounting for 61% of the variance in intentions to use the system in the future. The implications of the research for theory and practice are discussed. The paper concludes by presenting a theoretical model of the factors identified as being important in the use of electronic supermarkets.     

Adaptive online prediction method based on LS-SVR and its application in an electronic system

Health trend prediction has become an effective way to ensure the safe operation of highly reliable systems,and online prediction is always necessary in many real applications.To simultaneously obtain better or acceptable online prediction accuracy and shorter computing time,we propose a new adaptive online method based on least squares support vector regression(LS-SVR).This method adopts two approaches.One approach is that we delete certain support vectors by judging the linear correlation among the samples to increase the sparseness of the prediction model.This approach can control the loss of useful information in sample data,improve the generalization capability of the prediction model,and reduce the prediction time.The other approach is that we reduce the number of traditional LS-SVR parameters and establish a modified simple prediction model.This approach can reduce the calculation time in the process of adaptive online training.Simulation and a certain electric system application indicate preliminarily that the proposed method is an effective prediction approach for its good prediction accuracy and low computing time.     

电子式互感器现场校验系统的误差分析研究

电子式互感器作为智能变电站关键设备,以更安全、节能、环保的优点逐步取代传统的电磁式互感器,而电子互感器校验系统研究却相对滞后.本文简要介绍了电子互感器校验的工作原理和误差的定义,并根据IEC60044-8标准,分析得出了系统的11个误差来源所引起的不确定度,最后给出了尽可能消除这些误差的方法,为电子式互感器现场校验装置的研制提供了强有力的理论支持.     

Geostatistical and local cluster analysis of high resolution hyperspectral imagery for detection of anomalies

This paper describes a new methodology to detect small anomalies in high resolution hyperspectral imagery, which involves successively: (1) a multivariate statistical analysis (principal component analysis, PCA) of all spectral bands; (2) a geostatistical filtering of noise and regional background in the first principal components using factorial kriging; and finally (3) the computation of a local indicator of spatial autocorrelation to detect local clusters of high or low reflectance values and anomalies. The approach is illustrated using 1 m resolution data collected in and near northeastern Yellowstone National Park. Ground validation data for tarps and for disturbed soils on mine tailings demonstrate the ability of the filtering procedure to reduce the proportion of false alarms (i.e., pixels wrongly classified as target), and its robustness under low signal to noise ratios. In almost all scenarios, the proposed approach outperforms traditional anomaly detectors (i.e., RX detector which computes the Mahalanobis distance between the vector of spectral values and the vector of global means), and fewer false alarms are obtained when using a novel statistic S₂ (average absolute deviation of p-values from 0.5 through all spectral bands) to summarize information across bands. Image degradation through addition of noise or reduction of spectral resolution tends to blur the detection of anomalies, increasing false alarms, in particular for the identification of the least pure pixels. Results from a mine tailings site demonstrate the approach performs reasonably well for highly complex landscape with multiple targets of various sizes and shapes. By leveraging both spectral and spatial information, the technique requires little or no input from the user, and hence can be readily automated.     

A general theory of a class of linear neural nets for principal and minor component analysis

This paper presents a unified theory of a class of learning neural nets for principal component analysis (PCA) and minor component analysis (MCA). First, some fundamental properties are addressed which all neural nets in the class have in common. Second, a subclass called the generalized asymmetric learning algorithm is investigated, and the kind of asymmetric structure which is required in general to obtain the individual eigenvectors of the correlation matrix of a data sequence is clarified. Third, focusing on a single-neuron model, a systematic way of deriving both PCA and MCA learning algorithms is shown, through which a relation between the normalization in PCA algorithms and that in MCA algorithms is revealed. This work was presented, in part, at the Third International Symposium on Artificial Life and Robotics, Oita, Japan, January 19–21, 1998     

Use of an artificial model of monitoring data to aid interpretation of principal component analysis

An artificial data matrix of element concentrations at sampling locations was created which included six simulated gradients of correlated variables (Ca+Mg, Ni+V, Pb+Cu+Zn, Cd, Fe and K), representing a simplified model of a National survey. The data matrix model was used to explore the efficiency with which Principal Components Analysis (PCA), without and with Varimax rotation, could derive the imposed gradients. The dependence of PCA on outliers was decreased by log-transformation of data. The Components derived from non-rotated PCA were confounded by bipolar clusters and oblique gradients, both resulting in superimposition of two independent gradients on one Component. Therefore, erroneous interpretation of results could result from assessment of variable loadings on Components, without assessment of coupled independent gradients. Varimax rotation greatly improved the results, by rotation of too few Components led to the same problems, and rotation of too many Components led to fragmentation of correlated variables onto single-element Components. The best configuration matching the original model could be selected after investigation of element concentrations superimposed on sample ordinations.     

Combining the spectral PCA and spatial PCA fusion methods by an optimal filter

High correlation among the neighboring pixels, both spectrally and spatially in a multispectral image makes it indispensable to use relevant data transformation approaches, before performing image fusion. The principal component analysis (PCA) method has been a popular choice for the spectral transformation. To propose a new consistent data transformation method in spatial domain, this paper applies the PCA transform to the spatial information of the neighboring pixels. Owing to the fact that the coefficients of PCA are obtained from statistical properties of data, they are adaptive and robust. Then, a new hybrid algorithm is proposed combining the spectral PCA and spatial PCA methods, by an optimal filter to make the synthesized result more similar to what the corresponding multisensors would observe at the high-resolution level. The evaluation of the pan-sharpened images, using global validation indexes, reveals that the proposed approach improves the fusion quality compared with six state of the art fusion methods.     

Visual learning and recognition of 3D objects using two-dimensional principal component analysis: A robust and an efficient approach

Inspired by the conviction that the successful model employed for face recognition [M. Turk, A. Pentland, Eigenfaces for recognition, J. Cogn. Neurosci. 3(1) (1991) 71-86] should be extendable for object recognition [H. Murase, S.K. Nayar, Visual learning and recognition of 3-D objects from appearance, International J. Comput. Vis. 14(1) (1995) 5-24], in this paper, a new technique called two-dimensional principal component analysis (2D-PCA) [J. Yang et al., Two-dimensional PCA: a new approach to appearance based face representation and recognition, IEEE Trans. Patt. Anal. Mach. Intell. 26(1) (2004) 131-137] is explored for 3D object representation and recognition. 2D-PCA is based on 2D image matrices rather than 1D vectors so that the image matrix need not be transformed into a vector prior to feature extraction. Image covariance matrix is directly computed using the original image matrices, and its eigenvectors are derived for feature extraction. The experimental results indicate that the 2D-PCA is computationally more efficient than conventional PCA (1D-PCA) [H. Murase, S.K. Nayar, Visual learning and recognition of 3-D objects from appearance, International J. Comput. Vis. 14(1) (1995) 5-24]. It is also revealed through experimentation that the proposed method is more robust to noise and occlusion.     

基于SVM分类机的移动通信欺诈检测系统

设计并实现了一种基于SVM分类机(支持向量机)的入侵检测系统,它搜集移动通信客户的产品信息、呼叫行为信息(CDR)、背景资料、交欠费信息等特征,采用主成份分析(PCA)的属性归约技术对数据进行预处理,然后使用SVM分类机对测试数据进行分类,从而识别出新用户的欺诈类型。在此基础上,讨论了SVM分类机与通常的所挖掘分类技术的差别、SVM核函数的选择和RBF核函数参数的选择对分类效果的影响。实验结果表明,该系统能有效地检测出新客户的欺诈行为类型,具有很高的分类准确率。     

Agent and data mining based decision support system and its adaptation to a new customer-centric electronic commerce

Recently, as the Internet has become more widely used, Electronic Commerce (EC) has emerged and has developed a high-level business environment. The customer-centric EC model is important for the success of EC and this study presents a new customer-centric EC model in make-to-order (MTO) semiconductor manufacturing environment. In this study we proposed the EC model providing the process transparency of process sampling method that can provide online semiconductor customers with the performance information of available process sampling methods which can be used at all manufacturing process steps for their own products in MTO manufacturing environment, and then the capability to select a desirable one among them based on their purchase situations on EC web site. In the proposed EC model the customer can select a process sampling method that is most suitable to him/her according to the customer's purchase situation. In this model the use of intelligent decision support system called customized sampling decision support system (CSDSS) that can autonomously generate available customized sampling methods and provide the performance information of those methods to EC system is requisite. We implemented an Internet-based prototype of CSDSS which had an architecture based on intelligent agent technology and also the successful integration of data mining process for the generation of optimal sampling method into DSS framework by means of applying that technology.