Template identification technology of nuclear warheads and components

Template identification technology （TIT） is designed for the scenarios where a batch of disarmed nuclear weapons or components would be dismantled to observe a nuclear disarmament treaty. The core function played by the TIT is to make a judgment on whether the verified item belongs to a certain kind of nuclear weapons or component （NW/NC） or to which kind the verified item belongs. This paper analyses the functions played by the TIT in the process of NW/NC dismantlement, and proposes that two phases would be followed when applying the TIT： firstly to establish NW/NC templates with a sample of size n drawn from a certain kind of disarmament NW; secondly to authenticate NW/NC by means of the TIT. This paper also expatiates some terms related to the concept of the TIT and investigates on the development status of NW/NC TIT based on radiation signatures. The study concludes that the design of template structure is crucial to the establishment of an effective TIT and that starting from different research angles and aiming at the same goal of classification different template structures and corresponding template identification methods can be built up to meet specific identification requirements.

Identification of Volatile Oil Components of Nardostachys jatamansi DC. Root and Rhizome,Herb

[Objectives] Volatile oil components of traditional drug use site " root and rhizome" of N. jatamansi and herb were identified and contrasted,which aimed to provide the basis for rationally using wild N. jatamansi resource. [Methods]Volatile oil components from different sites of N. jatamansi were identified and isolated by GC-MS. [Results] There were 56 kinds of volatile oil components from different sites of N. jatamansi,in which 39 components from herb of N. jatamansi,39 components from root and rhizome of N. jatamansi,and there were 22 common components. But 1 component in higher concentration had obvious difference. [Conclusions] The herb of N. jatamansi could not completely replace " root and rhizome" of N. jatamansi as the medicine,which was consistent with prior detection result of each physicochemical index from different sites of N. jatamansi. The research could provide the reference for making quality standard of N. jatamansi medicine and rational basis for its further research and development.

A deep kernel method for lithofacies identification using conventional well logs

How to fit a properly nonlinear classification model from conventional well logs to lithofacies is a key problem for machine learning methods.Kernel methods(e.g.,KFD,SVM,MSVM)are effective attempts to solve this issue due to abilities of handling nonlinear features by kernel functions.Deep mining of log features indicating lithofacies still needs to be improved for kernel methods.Hence,this work employs deep neural networks to enhance the kernel principal component analysis(KPCA)method and proposes a deep kernel method(DKM)for lithofacies identification using well logs.DKM includes a feature extractor and a classifier.The feature extractor consists of a series of KPCA models arranged according to residual network structure.A gradient-free optimization method is introduced to automatically optimize parameters and structure in DKM,which can avoid complex tuning of parameters in models.To test the validation of the proposed DKM for lithofacies identification,an open-sourced dataset with seven con-ventional logs(GR,CAL,AC,DEN,CNL,LLD,and LLS)and lithofacies labels from the Daniudi Gas Field in China is used.There are eight lithofacies,namely clastic rocks(pebbly,coarse,medium,and fine sand-stone,siltstone,mudstone),coal,and carbonate rocks.The comparisons between DKM and three commonly used kernel methods(KFD,SVM,MSVM)show that(1)DKM(85.7%)outperforms SVM(77%),KFD(79.5%),and MSVM(82.8%)in accuracy of lithofacies identification;(2)DKM is about twice faster than the multi-kernel method(MSVM)with good accuracy.The blind well test in Well D13 indicates that compared with the other three methods DKM improves about 24%in accuracy,35%in precision,41%in recall,and 40%in F1 score,respectively.In general,DKM is an effective method for complex lithofacies identification.This work also discussed the optimal structure and classifier for DKM.Experimental re-sults show that(m_(1),m_(2),O)is the optimal model structure and linear svM is the optimal classifier.(m_(1),m_(2),O)means there are m KPCAs,and then m2 residual units.A workflow to determine an optimal classifier in DKM for lithofacies identification is proposed,too.

Modal identification of multi-degree-of-freedom structures based on intrinsic chirp component decomposition method

Modal parameter identification is a mature technology.However,there are some challenges in its practical applications such as the identification of vibration systems involving closely spaced modes and intensive noise contamination.This paper proposes a new time-frequency method based on intrinsic chirp component decomposition(ICCD)to address these issues.In this method,a redundant Fourier model is used to ameliorate border distortions and improve the accuracy of signal reconstruction.The effectiveness and accuracy of the proposed method are illustrated using three examples:a cantilever beam structure with intensive noise contamination or environmental interference,a four-degree-of-freedom structure with two closely spaced modes,and an impact test on a cantilever rectangular plate.By comparison with the identification method based on the empirical wavelet transform(EWT),it is shown that the presented method is effective,even in a high-noise environment,and the dynamic characteristics of closely spaced modes are accurately determined.

Chaos Identification Based on Component Reordering and Visibility Graph

The identification between chaotic systems and stochastic processes is not easy since they have numerous similarities. In this study, we propose a novel approach to distinguish between chaotic systems and stochastic processes based on the component reordering procedure and the visibility graph algorithm. It is found that time series and their reordered components will show diverse characteristics in the ＇visibility domain＇. For chaotic series, there are huge differences between the degree distribution obtained from the original series and that obtained from the corresponding reordered component. For correlated stochastic series, there are only small differences between the two degree distributions. For uncorrelated stochastic series, there are slight differences between them. Based on this discovery, the well-known Kullback Leible divergence is used to quantify the difference between the two degree distributions and to distinguish between chaotic systems, correlated and uncorrelated stochastic processes. Moreover, one chaotic map, three chaotic systems and three different stochastic processes are utilized to illustrate the feasibility and effectiveness of the proposed method. Numerical results show that the proposed method is not only effective to distinguish between chaotic systems, correlated and uncorrelated stochastic processes, but also easy to operate.

Identification of Mine Water Inrush Source Based on PCA-BP Neural Network

It is of great significance to analyze the chemical indexes of mine water and develop a rapid identification system of water source, which can quickly and accurately distinguish the causes of water inrush and identify the source of water inrush, so as to reduce casualties and economic losses and prevent and control water inrush disasters. Taking Ca2+, Mg2+, Na+ + K+, , , Cl-, pH value and TDS as discriminant indexes, the principal component analysis method was used to reduce the dimension of data, and the identification model of mine water inrush source based on PCA-BP neural network was established. 96 sets of data of different aquifers in Panxie mining area were selected for prediction analysis, and 20 sets of randomly selected data were tested, with an accuracy rate of 95%. The model can effectively reduce data redundancy, has a high recognition rate, and can accurately and quickly identify the water source of mine water inrush.

Robust Principal Component Test in Gross Error Detection and Identification

Principle component analysis （PCA） based chi-square test is more sensitive to subtle gross errors and has greater power to correctly detect gross errors than classical chi-square test. However, classical principal com- ponent test （PCT） is non-robust and can be very sensitive to one or more outliers. In this paper, a Huber function liked robust weight factor was added in the collective chi-square test to eliminate the influence of gross errors on the PCT. Meanwhile, robust chi-square test was applied to modified simultaneous estimation of gross error （MSEGE） strategy to detect and identify multiple gross errors. Simulation results show that the proposed robust test can reduce the possibility of type Ⅱ errors effectively. Adding robust chi-square test into MSEGE does not obviously improve the power of multiple gross error identification, the proposed approach considers the influence of outliers on hypothesis statistic test and is more reasonable.

Investigation on Chemical Components of Higher Fungus Phellinus rhabarbarinus(Berk.) G. Cunn.

Five compounds including 2 benzene derivatives, 2 terpenoids and 1 sterol were separated by various separation methods such as positive silica gel, Sephadex LH-20 and Rp-18 gel from higher fungi Phellinus rhabarbarinus （Berk.） G. Cunn. Their structures were elucidated on the basis of nuclear magnetic resonance （NMR） spectroscopy and other methods. All the compounds were separated from this higher fungus for the first time.

Identification and spatial patterns of coastal water pollution sources based on GIS and chemometric approach

Comprehensive and joint applications of GIS and chemometric approach were applied in identification and spatial patterns of coastal water pollution sources with a large data set （5 years （2000-2004）, 17 parameters） obtained through coastal water monitoring of Southern Water Control Zone in Hong Kong. According to cluster analysis the pollution degree was significantly different between September-next May （the 1st period） and June-August （the 2nd period）. Based on these results, four potential pollution sources, such as organic/eutrophication pollution, natural pollution, mineral/anthropic pollution and fecal pollution were identified by factor analysis/principal component analysis. Then the factor scores of each monitoring site were analyzed using inverse distance weighting method, and the results indicated degree of the influence by various potential pollution sources differed among the monitoring sites. This study indicated that hybrid approach was useful and effective for identification of coastal water pollution source and spatial patterns.

Near-Infrared Spectroscopy Combined with Partial Least Squares Discriminant Analysis Applied to Identification of Liquor Brands

The identification of liquor brands is very important for food safety. Most of the fake liquors are usually made into the products with the same flavor and alcohol content as regular brand, so the identification for the liquor brands with the same flavor and the same alcohol content is essential. However, it is also difficult because the components of such liquor samples are very similar. Near-infrared (NIR) spectroscopy combined with partial least squares discriminant analysis (PLS-DA) was applied to identification of liquor brands with the same flavor and alcohol content. A total of 160 samples of Luzhou Laojiao liquor and 200 samples of non-Luzhou Laojiao liquor with the same flavor and alcohol content were used for identification. Samples of each type were randomly divided into the modeling and validation sets. The modeling samples were further divided into calibration and prediction sets using the Kennard-Stone algorithm to achieve uniformity and representativeness. In the modeling and validation processes based on PLS-DA method, the recognition rates of samples achieved 99.1% and 98.7%, respectively. The results show high prediction performance for the identification of liquor brands, and were obviously better than those obtained from the principal component linear discriminant analysis method. NIR spectroscopy combined with the PLS-DA method provides a quick and effective means of the discriminant analysis of liquor brands, and is also a promising tool for large-scale inspection of liquor food safety.

Effect of the target positions on the rapid identification of aluminum alloys by using filament-induced breakdown spectroscopy combined with machine learning

Filament-induced breakdown spectroscopy(FIBS)combined with machine learning algorithms was used to identify five aluminum alloys.To study the effect of the distance between focusing lens and target surface on the identification accuracy of aluminum alloys,principal component analysis(PCA)combined with support vector machine(SVM)and Knearest neighbor(KNN)was used.The intensity and intensity ratio of fifteen lines of six elements(Fe,Si,Mg,Cu,Zn,and Mn)in the FIBS spectrum were selected.The distances between the focusing lens and the target surface in the pre-filament,filament,and post-filament were 958 mm,976 mm,and 1000 mm,respectively.The source data set was fifteen spectral line intensity ratios,and the cumulative interpretation rates of PC1,PC2,and PC3 were 97.22%,98.17%,and 95.31%,respectively.The first three PCs obtained by PCA were the input variables of SVM and KNN.The identification accuracy of the different positions of focusing lens and target surface was obtained,and the identification accuracy of SVM and KNN in the filament was 100%and 90%,respectively.The source data set of the filament was obtained by PCA for the first three PCs,which were randomly selected as the training set and test set of SVM and KNN in 3:2.The identification accuracy of SVM and KNN was 97.5%and 92.5%,respectively.The research results can provide a reference for the identification of aluminum alloys by FIBS.

Research Progress of Pharmacognostical Identification and Chemical Composition of Pholidota cantonensis Rolfe.

Research progress of pharmacognostical identification and chemical composition of Pholidota cantonensis Rolfe. was reviewed. This paper will facilitate assurance of the safety of clinical treatment.

Fault Identification of Power Grid Based on Wide-Area Differential Current and K-Means Clustering

A new method of fault domain identification is proposed based on K-means clustering analysis theories using the wide-area information of power grid. In the method, the node Intelligent Electronic Device (IED) associated domain is defined, and the relationship of positive sequence current fault component for the association domain boundaries is sought, then the conception of positive sequence fault component differential current for node IED association domains is introduced. The information of the positive sequence fault component differential current gathered by node IEDs is selected as the object of K-means clustering. The node IEDs of fault associated domains can be classified into one category, and the node IEDs of non-fault associated domains are classified into another category. With the fault area minimum principle, the group of node IEDs about fault associated domains can be obtained. The overlap of fault associated domains for different nodes is the fault area. A large number of simulations show that the algorithm proposed can identify fault domains with high accuracy and no influence by the operating mode of the system and topological changes.

Text-Independent Algorithm for Source Printer Identification Based on Ensemble Learning

Because of the widespread availability of low-cost printers and scanners,document forgery has become extremely popular.Watermarks or signatures are used to protect important papers such as certificates,passports,and identification cards.Identifying the origins of printed documents is helpful for criminal investigations and also for authenticating digital versions of a document in today’s world.Source printer identification(SPI)has become increasingly popular for identifying frauds in printed documents.This paper provides a proposed algorithm for identifying the source printer and categorizing the questioned document into one of the printer classes.A dataset of 1200 papers from 20 distinct(13)laser and(7)inkjet printers achieved significant identification results.A proposed algorithm based on global features such as the Histogram of Oriented Gradient(HOG)and local features such as Local Binary Pattern(LBP)descriptors has been proposed for printer identification.For classification,Decision Trees(DT),k-Nearest Neighbors(k-NN),Random Forests,Aggregate bootstrapping(bagging),Adaptive-boosting(boosting),Support Vector Machine(SVM),and mixtures of these classifiers have been employed.The proposed algorithm can accurately classify the questioned documents into their appropriate printer classes.The adaptive boosting classifier attained a 96%accuracy.The proposed algorithm is compared to four recently published algorithms that used the same dataset and gives better classification accuracy.

IMPROVED COVARIANCE DRIVEN BLIND SUBSPACE IDENTIFICATION METHOD

An improved covariance driven subspace identification method is presented to identify the weakly excited modes. In this method, the traditional Hankel matrix is replaced by a reformed one to enhance the identifiability of weak characteristics. The robustness of eigenparameter estimation to noise contamination is reinforced by the improved Hankel matrix, in combination with component energy index （CEI） which indicates the vibration intensity of signal components, an alternative stabilization diagram is adopted to effectively separate spurious and physical modes. Simulation of a vibration system of multiple-degree-of-freedom and experiment of a frame structure subject to wind excitation are presented to demonstrate the improvement of the proposed blind method. The performance of this blind method is assessed in terms of its capability in extracting the weak modes as well as the accuracy of estimated parameters. The results have shown that the proposed blind method gives a better estimation of the weak modes from response signals of small signal to noise ratio （SNR）and gives a reliable separation of spurious and physical estimates.

MODELING INTRAPERSONAL DEFORMATION SUBSPACE USING GMM FOR PALMPRINT IDENTIFICATION

In this paper, an efficient model of palmprint identification is presented based on subspace density estimation using Gaussian Mixture Model (GMM). While a few training samples are available for each person, we use intrapersonal palmprint deformations to train the global GMM instead of modeling GMMs for every class. To reduce the dimension of such variations while preserving density function of sample space, Principle Component Analysis (PCA) is used to find the principle differences and form the Intrapersonal Deformation Subspace (IDS). After training GMM using Expectation Maximization (EM) algorithm in IDS, a maximum likelihood strategy is carried out to identify a person. Experimental results demonstrate the advantage of our method compared with traditional PCA method and single Gaussian strategy.

Classification and Identification of Nuclear, Biological or Chemical Agents Taken from Remote Sensing Image by Using Neural Network

In the context of new risks and threats associated to nuclear, biological and chemical （NBC） attacks, and given the shortcomings of certain analytical methods such as principal component analysis （PCA）, a neural network approach seems to be more accurate. PCA consists in projecting the spectrum of a gas collected from a remote sensing system in, firstly, a three-dimensional space, then in a two-dimensional one using a model of Multi-Layer Perceptron based neural network. It adopts during the learning process, the back propagation algorithm of the gradient, in which the mean square error output is continuously calculated and compared to the input until it reaches a minimal threshold value. This aims to correct the synaptic weights of the network. So, the Artificial Neural Network （ANN） tends to be more efficient in the classification process. This paper emphasizes the contribution of the ANN method in the spectral data processing, classification and identification and in addition, its fast convergence during the back propagation of the gradient.

<i>vicK</i>Gene as Potential Identification Marker for <i>Staphylococcus aureus</i>

Staphylococcus aureus is an important human pathogen frequently detected in hospital community and has emerged as an important health concern in human medicine. Identification of S. aureus from clinical specimens by phenotypic methods may produce variable characteristics leading to ambiguity. Hence, a rapid and reliable method for identification of S. aureus is required which could expedite appropriate antibiotic therapy. This study aimed to evaluate the specificity of polymerase chain reaction (PCR) targeting a signal transduction gene, vicK, among S. aureus isolates of Hospital Sultanah Nur Zahirah, Kuala Terengganu, Malaysia. A total of 118 bacterial isolates were screened, which consisted of one hundred S. aureus isolates, ten Staphylococcus spp. and eight non-Staphylococci. Results indicated that PCR targeting vicK was able to identify 98% of S. aureus isolates with high sensitivity and specificity, while the remaining isolates of Staphylococcus spp. and non-Staphylococci did not yield any amplification of the gene. vicK thus, is highly specific within interspecies and intraspecies, which is potential to be used as a molecular identification marker for S. aureus.

基于电流模分量特性的多端柔性直流配电网线路纵联保护

多端柔性直流配电网因在供电可靠性方面极具优势而受到广泛的关注,然而对于直流线路保护中故障的快速、可靠识别成为其急速发展所需解决的关键问题之一。因此,设计了一种利用电流模分量特性的直流线路纵联保护方法。该方法利用故障时正极电流线模分量方向来判别区内外故障,以不同故障条件下故障点正极电流零模分量平均值的差异来构造保护判据,从而判别故障类型,形成保护策略。最后,在PSCAD/EMTDC4.5上搭建了多端柔性直流配电网模型,对保护判据及关键影响要素进行了仿真分析。结果表明,该保护方法能够有选择性地快速、可靠识别区内外故障及故障类型,且具有较好的耐过渡电阻与抗噪声能力。

基于电成像的孔洞型碳酸盐岩储层连通性评价

碳酸盐岩储层非均质性强,孔隙结构复杂,相较于常规碎屑岩储层,其孔隙度、渗透率关系的规律及特征不明显。碳酸盐岩储层的连通性与孔洞特征具有强相关性,且连通性是影响储层渗透率的重要因素,因此,对碳酸盐岩孔洞连通性的评价是孔洞型碳酸盐岩储层评价的关键问题。以伊拉克东南部B油田FQ-1井M组MB21亚油组孔洞型碳酸盐岩储层为例,基于地层微电阻率扫描成像(FMI)测井的高分辨率动态图像,利用最大类间方差算法(OTSU)进行二次迭代分割,识别电成像图像孔洞;根据8连通域标记法标记连通孔洞;提取每个连通孔洞的特征孔径、内部最长通道距离、内部最长通道与水平方向夹角、平均通道宽度这4个几何参数,最终构建孔洞型碳酸盐岩储层连通指数。连通指数在伊拉克东南部B油田FQ-1井M组MB21亚油组孔洞型碳酸盐岩储层的应用表明,利用此方法建立的连通指数与FQ-1井碳酸盐岩储层岩心渗透率相关系数达0.8437,并且连通指数曲线的趋势符合岩心渗透率在储层不同深度的分布情况,证明了连通指数可以指示储层孔隙结构较好、溶蚀孔洞发育的深度段,并可以划分储层中连通性较好的深度区间,该参数对于寻找碳酸盐岩储层渗透层具有重要意义。