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基于MFCC的OFDM信号子载波调制方式识别方法 被引量:4
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作者 张海川 雷迎科 《弹箭与制导学报》 CSCD 北大核心 2016年第6期166-170,共5页
针对多径信道下传统的OFDM信号子载波调制方式识别方法存在识别率不高,判决门限不易确定,子载波调制方式识别不全面等问题,提出一种基于MFCC的OFDM信号子载波调制方式识别方法。利用语音模型下的识别算法提取OFDM信号的MFCC特征参数,计... 针对多径信道下传统的OFDM信号子载波调制方式识别方法存在识别率不高,判决门限不易确定,子载波调制方式识别不全面等问题,提出一种基于MFCC的OFDM信号子载波调制方式识别方法。利用语音模型下的识别算法提取OFDM信号的MFCC特征参数,计算出各阶MFCC特征参数的平均标准偏差和平均变化率,并将两类参数的组合作为OFDM信号子载波调制方式分类特征量对子载波调制方式进行识别。仿真实验结果表明,该方法能够有效实现多径信道下OFDM信号子载波多种调制方式的识别,且识别性能优于传统方法。 展开更多
关键词 OFDM 分类特征量 MFCC 递归降阶
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Early-stage Internet traffic identification based on packet payload size
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作者 吴同 韩臻 +1 位作者 王伟 彭立志 《Journal of Southeast University(English Edition)》 EI CAS 2014年第3期289-295,共7页
In order to classify the Intemet traffic of different Internet applications more quickly, two open Internet traffic traces, Auckland I1 and UNIBS traffic traces, are employed as study objects. Eight earliest packets w... In order to classify the Intemet traffic of different Internet applications more quickly, two open Internet traffic traces, Auckland I1 and UNIBS traffic traces, are employed as study objects. Eight earliest packets with non-zero flow payload sizes are selected and their payload sizes are used as the early-stage flow features. Such features can be easily and rapidly extracted at the early flow stage, which makes them outstanding. The behavior patterns of different Intemet applications are analyzed by visualizing the early-stage packet size values. Analysis results show that most Internet applications can reflect their own early packet size behavior patterns. Early packet sizes are assumed to carry enough information for effective traffic identification. Three classical machine learning classifiers, classifier, naive Bayesian trees, i. e., the naive Bayesian and the radial basis function neural networks, are used to validate the effectiveness of the proposed assumption. The experimental results show that the early stage packet sizes can be used as features for traffic identification. 展开更多
关键词 pattern recognition network measurement traffic classification traffic feature
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Clustering method based on data division and partition 被引量:1
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作者 卢志茂 刘晨 +2 位作者 S.Massinanke 张春祥 王蕾 《Journal of Central South University》 SCIE EI CAS 2014年第1期213-222,共10页
Many classical clustering algorithms do good jobs on their prerequisite but do not scale well when being applied to deal with very large data sets(VLDS).In this work,a novel division and partition clustering method(DP... Many classical clustering algorithms do good jobs on their prerequisite but do not scale well when being applied to deal with very large data sets(VLDS).In this work,a novel division and partition clustering method(DP) was proposed to solve the problem.DP cut the source data set into data blocks,and extracted the eigenvector for each data block to form the local feature set.The local feature set was used in the second round of the characteristics polymerization process for the source data to find the global eigenvector.Ultimately according to the global eigenvector,the data set was assigned by criterion of minimum distance.The experimental results show that it is more robust than the conventional clusterings.Characteristics of not sensitive to data dimensions,distribution and number of nature clustering make it have a wide range of applications in clustering VLDS. 展开更多
关键词 CLUSTERING DIVISION PARTITION very large data sets (VLDS)
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Role of Kasai procedure in surgery of hilar bile duct strictures 被引量:9
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作者 Jin-Bo Gao Li-Shan Bai Zhi-Jian Hu Jun-Wei Wu Xin-Qun Chai 《World Journal of Gastroenterology》 SCIE CAS CSCD 2011年第37期4231-4234,共4页
AIM:To assess the application of the Kasai procedure in the surgical management of hilar bile duct strictures.METHODS:Ten consecutive patients between 2005 and 2011 with hilar bile duct strictures who underwent the Ka... AIM:To assess the application of the Kasai procedure in the surgical management of hilar bile duct strictures.METHODS:Ten consecutive patients between 2005 and 2011 with hilar bile duct strictures who underwent the Kasai procedure were retrospectively analyzed.Kasai portoenterostomy with the placement of biliary stents was performed in all patients.Clinical characteristics,postoperative complications,and long-term outcomes were analyzed.All patients were followed up for 2-60 mo postoperatively.RESULTS:Patients were classified according to the Bismuth classification of biliary strictures.There were two Bismuth Ⅲ and eight Bismuth Ⅳ lesions.Six lesions were benign and four were malignant.Of the benign lesions,three were due to post-cholecystectomy injury,one to trauma,one to inflammation,and one to inflammatory pseudotumor.Of the malignant lesions,four were due to hilar cholangiocarcinoma.All patients underwent Kasai portoenterostomy with the placement of biliary stents.There were no perioperative deaths.One patient experienced anastomotic leak and was managed conservatively.No other complications occurred perioperatively.During the follow-up period,all patients reported a good quality of life.CONCLUSION:The Kasai procedure combined with biliary stents may be appropriate for patients with hilar biliary stricture that cannot be managed by standard surgical methods. 展开更多
关键词 Kasai procedure Hilar bile duct STRICTURE SURGERY
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Classification of underwater still objects based on multi-field features and SVM 被引量:4
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作者 TIAN Jie XUE Shan-hua HUANG Hai-ning ZHANG Chun-hua 《Journal of Marine Science and Application》 2007年第1期36-40,共5页
A Support Vector Machine is used as a classifier to the automatic detection and recognition of underwater still objects. Discrimination between the objects can be transferred into different projection spaces by the pr... A Support Vector Machine is used as a classifier to the automatic detection and recognition of underwater still objects. Discrimination between the objects can be transferred into different projection spaces by the process of multi-field feature extraction. The multi-field feature vector includes time-domain, spectral, time-frequency distribution and bi-spectral features. Underwater target recognition can be considered as a problem of small sample recognition. SVM algorithm is appropriate to this kind of problems because of its outstanding generalizability. The SVM is contrasted with a Gaussian classifier and a k-nearest classifier in some experiments using real data of lake or sea trial. The experimental results indicate that SVM is better than the others two. 展开更多
关键词 underwater still objects CLASSIFICATION feature support vector machine (SVM)
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A robust feature extraction approach based on an auditory model for classification of speech and expressiveness 被引量:5
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作者 孙颖 V.Werner 张雪英 《Journal of Central South University》 SCIE EI CAS 2012年第2期504-510,共7页
Based on an auditory model, the zero-crossings with maximal Teager energy operator (ZCMT) feature extraction approach was described, and then applied to speech and emotion recognition. Three kinds of experiments were ... Based on an auditory model, the zero-crossings with maximal Teager energy operator (ZCMT) feature extraction approach was described, and then applied to speech and emotion recognition. Three kinds of experiments were carried out. The first kind consists of isolated word recognition experiments in neutral (non-emotional) speech. The results show that the ZCMT approach effectively improves the recognition accuracy by 3.47% in average compared with the Teager energy operator (TEO). Thus, ZCMT feature can be considered as a noise-robust feature for speech recognition. The second kind consists of mono-lingual emotion recognition experiments by using the Taiyuan University of Technology (TYUT) and the Berlin databases. As the average recognition rate of ZCMT approach is 82.19%, the results indicate that the ZCMT features can characterize speech emotions in an effective way. The third kind consists of cross-lingual experiments with three languages. As the accuracy of ZCMT approach only reduced by 1.45%, the results indicate that the ZCMT features can characterize emotions in a language independent way. 展开更多
关键词 speech recognition emotion recognition zero-crossings Teager energy operator speech database
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Fault depth estimation using support vector classifier and features selection
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作者 Mohammad Ehsan Hekmatian Vahid E. Ardestani +2 位作者 Mohammad Ali Riahi Ayyub Memar Koucheh Bagh Jalal Amini 《Applied Geophysics》 SCIE CSCD 2013年第1期88-96,119,共10页
Depth estimation of subsurface faults is one of the problems in gravity interpretation. We tried using the support vector classifier (SVC) method in the estimation. Using forward and nonlinear inverse techniques, de... Depth estimation of subsurface faults is one of the problems in gravity interpretation. We tried using the support vector classifier (SVC) method in the estimation. Using forward and nonlinear inverse techniques, detecting the depth of subsurface faults with related error is possible but it is necessary to have an initial guess for the depth and this initial guess usually comes from non-gravity data. We introduce SVC in this paper as one of the tools for estimating the depth of subsurface faults using gravity data. We can suppose that each subsurface fault depth is a class and that SVC is a classification algorithm. To better use the SVC algorithm, we select proper depth estimation features using a proper features selection (FS) algorithm. In this research, we produce a training set consisting of synthetic gravity profiles created by subsurface faults at different depths to train the SVC code to estimate the depth of real subsurface faults. Then we test our trained SVC code by a testing set consisting of other synthetic gravity profiles created by subsurface faults at different depths. We also tested our trained SVC code using real data. 展开更多
关键词 depth estimation subsurface fault support vector classifier FEATURE featuresselection
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A new feature selection method for handling redundant information in text classification 被引量:3
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作者 You-wei WANG Li-zhou FENG 《Frontiers of Information Technology & Electronic Engineering》 SCIE EI CSCD 2018年第2期221-234,共14页
Feature selection is an important approach to dimensionality reduction in the field of text classification. Because of the difficulty in handling the problem that the selected features always contain redundant informa... Feature selection is an important approach to dimensionality reduction in the field of text classification. Because of the difficulty in handling the problem that the selected features always contain redundant information, we propose a new simple feature selection method, which can effectively filter the redundant features. First, to calculate the relationship between two words, the definitions of word frequency based relevance and correlative redundancy are introduced. Furthermore, an optimal feature selection(OFS) method is chosen to obtain a feature subset FS1. Finally, to improve the execution speed, the redundant features in FS1 are filtered by combining a predetermined threshold, and the filtered features are memorized in the linked lists. Experiments are carried out on three datasets(Web KB, 20-Newsgroups, and Reuters-21578) where in support vector machines and na?ve Bayes are used. The results show that the classification accuracy of the proposed method is generally higher than that of typical traditional methods(information gain, improved Gini index, and improved comprehensively measured feature selection) and the OFS methods. Moreover, the proposed method runs faster than typical mutual information-based methods(improved and normalized mutual information-based feature selections, and multilabel feature selection based on maximum dependency and minimum redundancy) while simultaneously ensuring classification accuracy. Statistical results validate the effectiveness of the proposed method in handling redundant information in text classification. 展开更多
关键词 Feature selection Dimensionality reduction Text classification Redundant features Support vector machine Naive Bayes Mutual information
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Discriminant Models for Uncertainty Characterization in Area Class Change Categorization
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作者 Jingxiong Zhang Jiong You 《Geo-Spatial Information Science》 2011年第4期255-261,共7页
Discriminant space defining area classes is an important conceptual construct for uncertainty characterization in area-class maps.Discriminant models were promoted as they can enhance consistency in area-class mapping... Discriminant space defining area classes is an important conceptual construct for uncertainty characterization in area-class maps.Discriminant models were promoted as they can enhance consistency in area-class mapping and replicability in error modeling.As area classes are rarely completely separable in empirically realized discriminant space,where class inseparabil-ity becomes more complicated for change categorization,we seek to quantify uncertainty in area classes(and change classes)due to measurement errors and semantic discrepancy separately and hence assess their relative margins objectively.Experiments using real datasets were carried out,and a Bayesian method was used to obtain change maps.We found that there are large differences be-tween uncertainty statistics referring to data classes and information classes.Therefore,uncertainty characterization in change categorization should be based on discriminant modeling of measurement errors and semantic mismatch analysis,enabling quanti-fication of uncertainty due to partially random measurement errors,and systematic categorical discrepancies,respectively. 展开更多
关键词 UNCERTAINTY information classes data classes discriminant models conditional simulation land cover change
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