基于MRA信息补偿的图像识别算法

目前的图像识别方法对各类提取等量的特征信息,不可避免的存在过识别与不可分类问题。利用小波分解的信号自然降维特性,及多分辨率分析(MRA)空间的正交互补性,本文提出了一种通过构造信息补偿树(ICT)进行识别的新方法,实现了对各类自适应得提取不同量的特征信息,有效解决了上述问题。针对3类脸部图像进行实验,结果表明该算法是有效的且复杂性低。

上市公司财务报告舞弊的典型迹象识别

近年来我国上市公司财务报告舞弊案时有发生,且其舞弊手段越来越隐秘。本文试图通过识别上市公司三个典型的非财务舞弊迹象,以帮助大众从大体上把握上市公司财务报告存在舞弊的可能性。

指纹识别芯片

在如今这个安全至上的时代,一种称为指纹扫描芯片的新型半导体设备充当了一门名为“生物测定学”的科学的先锋,这种芯片能够通过识别指纹来确认电脑设备用户的身份。“生物测定学”是旨在让数据库窃取、骇客入侵和其他电脑犯罪行为难以得逞的学科,目前。

基于感知模式纹理的人脸识别

稀疏表示分类法和局部二值模式是目前广泛运用的人脸表示方法.虽然稀疏表示分类法通过重构算法能很好的稀疏表示人脸,但在人脸的局部特征表示上不够理想.局部二值模式是基于局部纹理特征表示人脸,其提取的局部纹理简单、不连续、不光滑,造成纹理损失严重.提出局部纹理特征的感知模式纹理的概念,克服了稀疏表示分类法在局部特征表现不佳的缺点,同时又比局部二值模式纹理光滑、鲁棒.利用感知模式纹理特征在Yale和ORL数据库取得了较好的识别效果.

正版光盘保护神CD—COPS

目前，如何保护自己的正版光盘产品不受盗版侵害，是广大软件开发商及光盘出版商所关心的大问题。近日，以生产加密产品闻名的北京金天地公司和丹麦ＬＩＮＫＤａｔａＳｅｃｕｒｉｔｙ公司合作，在中国市场上推出一种全新的针对光盘软件的加密产品：“光盘狗”ＣＤ－ＣＯＰＳ。“光盘狗”通过识别光盘上的特征码来区分原版盘和盗版盘。对于开发商来说，只需要在压盘前利用金天地公司的加密工具对主执行程序进行加密，并进行测试就可以了。在光盘压制后，再由金天地通过测试得到光盘的运行码，供用户使用。当用户在使用光盘时，“光盘狗”会进行合法性检测，以确认是否为原版盘。ＣＤ－ＣＯＰＳ不需要制作特殊的母盘，既缩短了加密时间。

An early warning method for water inrush in Dongjiahe coal mine based on microseismic moment tensor

Dongjiahe Coal Mine belongs to the Carboniferous Permian coal field which has a high degree of karst and fissure development.This paper takes the working face of Dongjiahe Coal Mine as an example;through the microseismic(MS)monitoring system arranged on the working face,the moment tensor theory was used to invert the focal mechanism solution of the anomalous area of the floor MS event;combining the numerical simulation and field data,the underlying floor faults were identified by the stress inversion method.The results show that:1)Moment tensors were decomposed into three components and the main type of rupture in this area is mixed failure according to the relative criterion;2)The hidden fault belongs to the reversed fault,its dip angle is approximately 70°,and the rupture length is 21 m determined by the inversion method of the initial dynamic polarity and stress in the focal mechanism;3)The failure process of the fault is divided into three stages by numerical simulation method combined with the temporal and spatial distribution of MS events.The results can provide a reference for early warning and evaluation of similar coal mine water inrush risks.

A dynamic size-based time series feature and application in identification of zinc flotation working conditions

Conventional feature description methods have large errors in froth features due to the fact that the image during the zinc flotation process of froth flotation is dynamic,and the existing image features rarely have time series information.Based on the conventional froth size distribution characteristics,this paper proposes a size trend core feature(STCF)considering the froth size distribution,i.e.,a feature centered on the time series of the froth size distribution.The core features of the trend are extracted,the inter-frame change factor and the inter-frame stability factor are given and two calculation methods of the feature factors are proposed.Meanwhile,the STCF feature algorithm was established based on the core features by adding the inter-frame change factor and the inter-frame stability factor.Finally,a flotation condition recognition model based on BP neural network was established.The experiments show that the recognition model has achieved excellent results,proving that the method proposed effectively overcomes the limitation of the lack of dynamic information in the existing traditional size distribution features and the introduction of the two factors can improve the classification accuracy to varying degrees.

A review on Gaussian Process Latent Variable Models

Gaussian Process Latent Variable Model （GPLVM）, as a flexible bayesian non-parametric modeling method, has been extensively studied and applied in many learning tasks such as Intrusion Detection, Image Reconstruction, Facial Expression Recognition, Human pose estimation and so on. In this paper, we give a review and analysis for GPLVM and its extensions. Firstly, we formulate basic GPLVM and discuss its relation to Kernel Principal Components Analysis. Secondly, we summarize its improvements or variants and propose a taxonomy of GPLVM related models in terms of the various strategies that be used. Thirdly, we provide the detailed formulations of the main GPLVMs that extensively developed based on the strategies described in the paper. Finally, we further give some challenges in next researches of GPLVM.

Postcolonialism and the Feminine in Paulina Chiziane＇s O alegre canto da perdiz

In this study we present the novel O alegre canto da perdiz （2008）, by Paulina Chiziane, focusing on the path of the characters Delfina and Maria das Dores, pointing to the construction of a female speech denouncing the state to which the Mozambican woman was subjected, especially during colonization, a trauma still present in Africa. By telling the saga of these two women （mother and daughter）, the novel also makes a reinterpretation of the origin and history of the peoples of Africa. Beyond the issues that mark the secular submission of women to the world of man in certain African societies, Paulina Chiziane also leads us to confront the issue of reductionism practiced by those who look from outside Africa and seeks to present its history and its literature as if the African continent were a single country, as reported by the Nigerian novelist Chimamanda Adichie in her speech against ＂the danger of listening and repeating a single story, the winners＇ story＂ （Adichie, 2009）. We aim to identify aspects of the unique feminine of Paulina Chiziane by rescuing legends of matriarchy in the course of the characters. We will also do a reading of colonialism and post-colonialism objectifying the female of writing Paulina Chiziane. The critical placement of the text allows us to analyze it with the contribution of Spivak （2010）, Said （1978）, Bonnici （2000）, among others.

Phase Analysis and Identification Method for Multiphase Batch Processes with Partitioning Multi-way Principal Component Analysis (MPCA) Model

Multi-way principal component analysis (MPCA) is the most widely utilized multivariate statistical process control method for batch processes. Previous research on MPCA has commonly agreed that it is not a suitable method for multiphase batch process analysis. In this paper, abundant phase information is revealed by way of partitioning MPCA model, and a new phase identification method based on global dynamic information is proposed. The application to injection molding shows that it is a feasible and effective method for multiphase batch process knowledge understanding, phase division and process monitoring.

Iterative identification of discrete-time output-error model with time delay

The problem of discrete-time model identification of industrial processes with time delay was investigated.An iterative and separable method is proposed to solve this problem,that is,the rational transfer function model parameters and time delay are alternately fixed to estimate each other.The instrumental variable technique is applied to guarantee consistent estimation against measurement noise.A noteworthy merit of the proposed method is that it can handle fractional time delay estimation,compared to existing methods commonly assuming that the time delay is an integer multiple of the sampling interval.The identifiability analysis for time delay is addressed and correspondingly,some guidelines are provided for practical implementation of the proposed method.Numerical and experimental examples are presented to illustrate the effectiveness of the proposed method.

An outlier recognition approach in surveying data based on information entropy

A novel outlier recognition method in surveying data is presented based on Shannon information entropy. The probability distribution of surveying data does not need to be known or hypothesized in this method, and it is not only accurate but also convenient to calculate in this method compared with statistical recognition method.

Monitoring and Recognition of Debris Flow Infrasonic Signals

Low frequency infrasonic waves are emitted during the formation and movement of debris flows, which are detectable in a radius of several kilometers, thereby to serve as the precondition for their remote monitoring.However, false message often arises from the simple mechanics of alarms under the ambient noise interference.To improve the accuracy of infrasound monitoring for early-warning against debris flows, it is necessary to analyze the monitor information to identify in them the infrasonic signals characteristic of debris flows.Therefore, a large amount of debris flow infrasound and ambient noises have been collected from different sources for analysis to sum up their frequency spectra, sound pressures, waveforms, time duration and other correlated characteristics so as to specify the key characteristic parameters for different sound sources in completing the development of the recognition system of debris flow infrasonic signals for identifying their possible existence in the monitor signals.The recognition performance of the system has been verified by simulating tests and long-term in-situ monitoring of debris flows in Jiangjia Gully,Dongchuan, China to be of high accuracy and applicability.The recognition system can provide the local government and residents with accurate precautionary information about debris flows in preparation for disaster mitigation and minimizing the loss of life and property.

Fracture detecting based on Ant Colony Algorithm

Image processing plays an important role in engineering treatment. The authors mainly introduced the feature recognition of borehole image process based on Ant Colony Algorithm (ACA). The most important geological structure-fracture on the borehole image was identified, and quantitative parameters were obtained by HOUGH transform. Several case studies show that the method is feasible.

Advanced Identification Methods for Industrial Processes with Big Time Delays

This paper presents an advanced method for system identification of industrial processes with big time delays. Identification methods based on neural networks, tree partitioning and wavelet networks are presented and analyzed. The obtained results are compared and the tree partitioning method is selected as most appropriate identification method for the water treatment process. The decision was made based on a thorough analysis on the overall fit between the measured data and the results of the simulated model. At the end, we propose possibilities for further research in this area.

识别优质大米

目前在市场上,大米品种繁多,但质量参差不齐。经常会有不法商贩拿陈米、油米等劣质米以次充好,欺骗消费者。常用的简便易行的识别方法有:辨色、捻摸、闻大米的气味。想买到好大米,要掌握以下四个技巧:

气味识别系统——电子鼻

韩国科学技术院生物工程研究中心张溶根和金钟得教授领导的研究小组开发出一种能识别甲烷、丙烷等九种挥发性物质的气味识别系统,准确率达百分之九十以上。这预示着人类进入电子鼻时代已为期不远了。所谓“气味识别系统”就是通过识别空气中散发气味的物质的分子,将其转为电子信号,然后根据信号的特性来区分是什么气味。

Complex network theory-based condition recognition of electromechanical system in process industry

In order to recognize the different operating conditions of a distributed and complex electromechanical system in the process industry,this work proposed a novel method of condition recognition by combining complex network theory with phase space reconstruction.First,a condition-space with complete information was reconstructed based on phase space reconstruction,and each condition in the space was transformed into a node of a complex network.Second,the limited penetrable visibility graph method was applied to establish an undirected and un-weighted complex network for the reconstructed condition-space.Finally,the statistical properties of this network were calculated to recognize the different operating conditions.A case study of a real chemical plant was conducted to illustrate the analysis and application processes of the proposed method.The results showed that the method could effectively recognize the different conditions of electromechanical systems.A complex electromechanical system can be studied from the systematic and cyber perspectives,and the relationship between the network structure property and the system condition can also be analyzed by utilizing the proposed method.

A Case Study on Intermediate CSL Learners' Word Recognition Processes and Strategies in Contextual Reading Settings

This study investigates word recognition processes and strategies of intermediate learners of Chinese as a Second Language （CSL） in contextual reading settings. Two intermediate CSL learners were chosen as research participants, and think-aloud methods and retrospective interviews were used to collect data. The data were analyzed by using Moustakas＇ data analysis procedure, CresweU＇s three steps and Bogdon and Biklen＇s data analysis methods. Results indicated that intermediate CSL learners go through different processes of word recognition as it might be automatic, based on context, pronunciation, previous knowledge and the meaning of characters, or, in case of word recognition failure, skipping the words or skipping them but reading them again later; and their word recognition strategies in contextual reading settings mainly include cognitive strategies and self-regulatory strategies. Among these strategies, cognitive strategies consist of direct transformation, translation, interpretation, guessing, inferring and finding key words; and self-regulatory strategies include metacognitive strategies, behavior regulating strategies, emotion regulating strategies and motivation regulating strategies. A model of intermediate CSL learners＇ word recognition strategies can be constructed based on the results. The present study provides both theoretical and pedagogical implications in the field of CSL vocabulary acquisition and teaching.

Identification and analysis of mouse non-coding RNA using transcriptome data

Transcripts are expressed spatially and temporally and they are very complicated, precise and specific; however, most studies are focused on protein-coding related genes. Recently, massively parallel c DNA sequencing(RNA-seq) has emerged to be a new and promising tool for transcriptome research, and numbers of non-coding RNAs, especially linc RNAs, have been widely identified and well characterized as important regulators of diverse biological processes. In this study, we used ultra-deep RNA-seq data from 15 mouse tissues to study the diversity and dynamic of non-coding RNAs in mouse. Using our own criteria, we identified totally 16,249 non-coding genes(21,569 non-coding RNAs) in mouse. We annotated these non-coding RNAs by diverse properties and found non-coding RNAs are generally shorter, have fewer exons, express in lower level and are more strikingly tissue-specific compared with protein-coding genes. Moreover, these non-coding RNAs show significant enrichment with transcriptional initiation and elongation signals including histone modifications(H3K4me3, H3K27me3 and H3K36me3), RNAPII binding sites and CAGE tags. The gene set enrichment analysis(GSEA) result revealed several sets of linc RNAs associated with diverse biological processes such as immune effector process, muscle development and sexual reproduction. Taken together, this study provides a more comprehensive annotation of mouse non-coding RNAs and gives an opportunity for future functional and evolutionary study of mouse non-coding RNAs.