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森林资源信息分类及编码体系研究 被引量:7
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作者 白降丽 彭道黎 杨馥宁 《浙江林学院学报》 CAS CSCD 北大核心 2007年第3期326-330,共5页
按照科学性、系统性、一致性、简单性、唯一性、可扩充性、合理性和规范性等原则,采用线分类法和面分类法结合的方法,将森林资源信息分为基础类、监测类、管理类和标准类等4个基本类型,在此基础上再分若干小类。同时,将森林资源信息数... 按照科学性、系统性、一致性、简单性、唯一性、可扩充性、合理性和规范性等原则,采用线分类法和面分类法结合的方法,将森林资源信息分为基础类、监测类、管理类和标准类等4个基本类型,在此基础上再分若干小类。同时,将森林资源信息数据的所有属性因子分为23类。在此基础上采用线分类法与面分类法相结合的方法对森林资源信息进行编码,编码结构由标志码、要素类代码即森林资源信息大类下的小类码和属性代码等3部分组成。 展开更多
关键词 森林经理学 森林资源 信息分类 信息编码 线分类法 分类法
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机械制造企业ERP系统中物料代码的编制 被引量:3
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作者 翁卫东 高文金 肖行波 《石油机械》 北大核心 2005年第12期62-65,共4页
物料代码是人和计算机使用所有其它数据元素的基础,它的编制是企业实施ERP不可缺少的重要工作。物料代码在系统中主要作为物料的标识符,而不是一种描述符,因此物料代码并不追求一定要带有含义,但纯粹的无含义码也是缺乏实用性的,必要的... 物料代码是人和计算机使用所有其它数据元素的基础,它的编制是企业实施ERP不可缺少的重要工作。物料代码在系统中主要作为物料的标识符,而不是一种描述符,因此物料代码并不追求一定要带有含义,但纯粹的无含义码也是缺乏实用性的,必要的有含义的分类码有助于物料的查询和统计。以江汉第四石油机械厂ERP项目实施中物料代码的编制过程为依据,提出机械制造企业物料代码既要简单实用,又要科学合理的编制原则和方法。 展开更多
关键词 ERP系统 物料代码 编码规则 分类 隶属码 分类法 线分类法
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Particle swarm optimization based RVM classifier for non-linear circuit fault diagnosis 被引量:5
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作者 高成 黄姣英 +1 位作者 孙悦 刁胜龙 《Journal of Central South University》 SCIE EI CAS 2012年第2期459-464,共6页
A relevance vector machine (RVM) based fault diagnosis method was presented for non-linear circuits. In order to simplify RVM classifier, parameters selection based on particle swarm optimization (PSO) and preprocessi... A relevance vector machine (RVM) based fault diagnosis method was presented for non-linear circuits. In order to simplify RVM classifier, parameters selection based on particle swarm optimization (PSO) and preprocessing technique based on the kurtosis and entropy of signals were used. Firstly, sinusoidal inputs with different frequencies were applied to the circuit under test (CUT). Then, the resulting frequency responses were sampled to generate features. The frequency response was sampled to compute its kurtosis and entropy, which can show the information capacity of signal. By analyzing the output signals, the proposed method can detect and identify faulty components in circuits. The results indicate that the fault classes can be classified correctly for at least 99% of the test data in example circuit. And the proposed method can diagnose hard and soft faults. 展开更多
关键词 non-linear circuits fault diagnosis relevance vector machine particle swarm optimization KURTOSIS ENTROPY
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Comparison of wrist motion classification methods using surface electromyogram 被引量:1
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作者 JEONG Eui-chul KIM Seo-jun +1 位作者 SONG Young-rok LEE Sang-min 《Journal of Central South University》 SCIE EI CAS 2013年第4期960-968,共9页
The Gaussian mixture model (GMM), k-nearest neighbor (k-NN), quadratic discriminant analysis (QDA), and linear discriminant analysis (LDA) were compared to classify wrist motions using surface electromyogram (EMG). Ef... The Gaussian mixture model (GMM), k-nearest neighbor (k-NN), quadratic discriminant analysis (QDA), and linear discriminant analysis (LDA) were compared to classify wrist motions using surface electromyogram (EMG). Effect of feature selection in EMG signal processing was also verified by comparing classification accuracy of each feature, and the enhancement of classification accuracy by normalization was confirmed. EMG signals were acquired from two electrodes placed on the forearm of twenty eight healthy subjects and used for recognition of wrist motion. Features were extracted from the obtained EMG signals in the time domain and were applied to classification methods. The difference absolute mean value (DAMV), difference absolute standard deviation value (DASDV), mean absolute value (MAV), root mean square (RMS) were used for composing 16 double features which were combined of two channels. In the classification methods, the highest accuracy of classification showed in the GMM. The most effective combination of classification method and double feature was (MAV, DAMV) of GMM and its classification accuracy was 96.85%. The results of normalization were better than those of non-normalization in GMM, k-NN, and LDA. 展开更多
关键词 Gaussian mixture model k-nearest neighbor quadratic discriminant analysis linear discriminant analysis electromyogram (EMG) pattern classification feature extraction
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Least squares weighted twin support vector machines with local information 被引量:1
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作者 花小朋 徐森 李先锋 《Journal of Central South University》 SCIE EI CAS CSCD 2015年第7期2638-2645,共8页
A least squares version of the recently proposed weighted twin support vector machine with local information(WLTSVM) for binary classification is formulated. This formulation leads to an extremely simple and fast algo... A least squares version of the recently proposed weighted twin support vector machine with local information(WLTSVM) for binary classification is formulated. This formulation leads to an extremely simple and fast algorithm, called least squares weighted twin support vector machine with local information(LSWLTSVM), for generating binary classifiers based on two non-parallel hyperplanes. Two modified primal problems of WLTSVM are attempted to solve, instead of two dual problems usually solved. The solution of the two modified problems reduces to solving just two systems of linear equations as opposed to solving two quadratic programming problems along with two systems of linear equations in WLTSVM. Moreover, two extra modifications were proposed in LSWLTSVM to improve the generalization capability. One is that a hot kernel function, not the simple-minded definition in WLTSVM, is used to define the weight matrix of adjacency graph, which ensures that the underlying similarity information between any pair of data points in the same class can be fully reflected. The other is that the weight for each point in the contrary class is considered in constructing equality constraints, which makes LSWLTSVM less sensitive to noise points than WLTSVM. Experimental results indicate that LSWLTSVM has comparable classification accuracy to that of WLTSVM but with remarkably less computational time. 展开更多
关键词 least squares similarity information hot kernel function noise points
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