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Research and implementation of continuous wave radar ranging method based on tri-frequency

High technology Letters 2014年第2期20卷 111-116页

作者：袁其平 Fan Lijieare Tong Zhengrong Yang Xiufeng Cao Ye School of Electronics Information Engineering Tianjin University of Technology Key Laboratory of Film Electronics and Communication Devices and Key Laboratory of Intelligent Computing and Novel Software Technolgy School of Computer and Communication EngineeringTianjin University of Technology

In the traditional dual-frequency radar ranging system,there is a contradiction between ranging precision and ranging *** a tri-frequency ranging method is *** principle of the tri-frequency ranging is that the distance can be achieved by transmitting the three frequencies and then measuring the three phase difference and calculating the number of ***'s more,range ambiguity will not occur within a certain *** experimental results show that,if the waveband is 0.47 ~0.57 m,the measurement of unambiguous distance of 25 ~500m can be *** the wavelength tolerance error is less than ± 10^(-7) and the phase error is less than ± 10^(-3),the precision of the distance ranging can reach 10^(-6).

关键词： tri-frequency ranging SNR phase error wavelength error

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The improvement of HDFS authentication model based on token push mechanism 18

The improvement of HDFS authentication model based on token ...

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2018 International Conference on Big Data and computing, ICBDC 2018

作者： Wang, Guiyuan Ning, Hongyun Tianjin Key Laboratory of Intelligence Computing and New Software Technology Tianjin China

ISBN: (纸本)9781450364263

In order to solve the problems of single point overload, repeated authentication, replay attack and time synchronization in the security authentication of HDFS, an improved Kerberos protocol based on token push mechanism is proposed. First of all, aiming at the problem of single point overload and repeated authentication, a three-stage access mechanism based on Agent is introduced. The KDC is responsible for the visitor's first login to HDFS for the first time. In the second phase, the visitor accesses the DataNode again through HDFS, the agent generates cross-node tokens and pushes to all involved DataNodes. In the third phase, visitor directly accesses DataNode. DataNode uses cross-node tokens to authenticate visitors. This mechanism divides users' login into three phases, which lightens the load of KDC authentication server. Secondly, reduce the number of NameNode and KDC authentication through the token push mechanism. Finally, in order to enhance the security of the authentication process and prevent replay attacks, a new parameter T-nonce is introduced in the authentication of Client, NameNode and DataNodes. This parameter is obtained by hash processing the timestamp and IP address. The timestamp and IP address are combined in the parameter generation function to ensure its real-time and uniqueness so that unauthorized users cannot implement replay attacks. This paper analyzes the security and efficiency of a single-time authentication both in the original model and the improved one, and the results show that the improved protocol can effectively improve the security and efficiency of HDFS authentication. © 2018 Association for computing Machinery.

关键词： Authentication

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Microblog text clustering based on BK-means algorithm 8

Microblog text clustering based on BK-means algorithm

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2018 8th International Workshop on Computer Science and Engineering, WCSE 2018

作者： Li, Qianru Mo, Xiuliang Wang, Chundong Tianjin Intelligent Computing and Software New Technology Key Laboratory School of Computer Science and Engineering Tianjin university of technology Tianjin300384 China

ISBN: (纸本)9789811178610

In recent years, the increasing popularity of social media such as WeChat and Weibo has facilitated the communication among people. However, due to the characteristics like large scale, fast propagation, low quality and diverse modalities of social short texts, the short text clustering faces the challenge of sparse features, high dimension and noise interference. The traditional clustering method based on vector space model is not good for short text data processing. With the improvement of K-means algorithm, this paper proposes a short-text clustering algorithm named BK-means which alleviates the effect of data sparseness. Firstly, we preprocess the wordset by means of word segmentation, stop-of-word and other operations, then extract the biterm using the BTM to model the document, and get the document-topic, the topic-word distribution matrix. Finally, we use the proposed BK-means algorithm to cluster short texts of documents represented by vectors. Experiments on the short text data of Sina Weibo have proved that the short text clustering algorithm based on BK-means is superior to the traditional one, and both the F-measure and the purity are improved. © Proceedings of 2018 the 8th International Workshop on Computer Science and Engineering, WCSE 2018. All rights reserved.

关键词： K-means clustering

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A novel policy iteration algorithm for solving the optimal consensus control problem of a discrete-time multiagent system with unknown dynamics

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Science China(Information Sciences) 2023年第8期66卷 269-270页

作者： Wenkai XU Li WANG Shiwen SUN Chengyi XIA Zengqiang CHEN Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin University of Technology The Engineering Research Center of Learning-Based Intelligent System Ministry of Education College of Artificial Intelligence Nankai University

At present, an increasing number of researchers have noticed the importance of optimal consensus control(OCC) of multiagent systems(MASs) because of their rich practical applications in various areas [1–4]. To accomp...

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keywords extraction method based on two-way feature fusion 2

Keywords extraction method based on two-way feature fusion

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2nd International Conference on Machine Learning and intelligent Systems Engineering, MLISE 2022

作者： Li, Yongrui Ning, Hongyun Tianjin University Of Technology Tianjin Key Laboratory Of Intelligence Computing & New Software Technology Tianjin China

ISBN: (数字)9781665492461

ISBN: (纸本)9781665492461

In order to improve the accuracy of keyword extraction, an improved method was proposed to solve the problem of missing keywords in traditional TF-IDF keyword extraction algorithm. In this method, two routes are used to extract the features of the text, one is responsible for storing the dependency syntactic relations in the preprocessing of the text, and the other is responsible for calculating the tF-IDF value, part of speech and position of the words, and constructing the comprehensive scoring weighting formula of the candidate keywords. Finally, the feature information of these two paths is fused in the stage of combinatorial optimization and sorted to form formal extracted keywords. Experimental results show that compared with the traditional TF-IDF algorithm and Textrank algorithm, the F value of the proposed method in Sina news data set is improved by 14.4% and 16.3% respectively, which effectively verifies that the proposed method can significantly improve the keyword extraction performance. © 2022 IEEE.

关键词： Syntactics

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TF-IDF keyword extraction method combining context and semantic classification 2020

TF-IDF keyword extraction method combining context and seman...

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3rd International Conference on Data Science and Information technology, DSIT 2020

作者： Wang, Xinyun Ning, Hongyun Tianjin Key Laboratory of Intelligence Computing and New Software Technology Tianjin University of Technology Tianjin China

ISBN: (纸本)9781450376044

keyword extraction plays the same role as the cornerstone in the field of natural language processing. Text classification, information retrieval, abstract generation and text clustering are all based on keyword extraction. This article takes the research of keyword extraction model as the subject. First, it analyzes the principle and limitations of the traditional keyword extraction model TF-IDF when extracting keywords. Secondly, it focuses on the problem of ignoring context and word polysemy in the keyword extraction model. To improve, introduce the concept of context vector, construct a chain-extensible structure for polysemous words, and propose a new keyword extraction method of TF-IDF that combines context and semantic classification. The specific research contents are as follows: TF-IDF extracts keywords based on multiple texts. The target keywords are words that appear frequently in the current text, but appear in other articles that are significantly lower in frequency than the current article. This method takes into account the characteristic content of the article and makes the article distinct. However, this method ignores the influence of the context of the article and the problem of word polysemy. Obviously, the expression of the thought of the article will not only be affected by the context of the words, but also by the semantics of the words. In order to solve the above problems, this paper proposes a TF-IDF keyword extraction method that combines context and semantic classification. First, the text constructs a linked list expandable structure to solve the problem of polysemy;then, the influence of context is introduced. Before inserting a new word into the extended structure, the semantic similarity is calculated through the context vector. Finally, iterate over the entire expandable structure, sum the word frequency with semantic similarity under each semantic, and then sort the results to obtain TopN words as the text keywords of the correspond

关键词： Semantics

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Text keyword Extraction Method Combined with Dependent Syntax Analysis 22

Text Keyword Extraction Method Combined with Dependent Synta...

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2nd International Conference on Control and intelligent Robotics, ICCIR 2022

作者： Li, Yongrui Ning, Hongyun Tianjin Key Laboratory of Intelligence Computing & New Software Technology Tianjin University of Technology Tianjin China

ISBN: (纸本)9781450397179

In order to improve the accuracy of key word extraction, an improved TF-IDF method was proposed to solve the problems that traditional TF-IDF keyword extraction algorithm could not recognize new words and polysemous words. This method first TF - IDF values, part of speech of words and position characteristics is calculated respectively, build candidate keywords weighted formula comprehensive score, then stored pretreatment before interdependence syntactic relations from the text, using the established syntactic relations to score higher candidate keywords for combinatorial optimization or reducing weight, the last order form formal extracting keywords. Experimental results show that compared with the traditional TF-IDF method, the multi-feature method based on dependency parsing improves the F value of the dataset containing new words by 10.4%, reaching 60.6%, and the F value of the dataset containing polysemous words by 17.9%, reaching 66.5%, which effectively verifies that the proposed method can improve the accuracy of keyword extraction. © 2022 ACM.

关键词： Syntactics

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Chinese keyword extraction method based on context and word classification

Chinese keyword extraction method based on context and word ...

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2020 International Conference on Computer Information and Big Data Applications, CIBDA 2020

作者： Wang, Xinyun Ning, Hongyun Tianjin Key Laboratory of Intelligence Computing &new Software Technology Tianjin University of Technology Tianjin China

ISBN: (纸本)9781728198378

The traditional keyword extraction algorithm TextRank increases the weight of the central word through the correlation between the words and the central word in the co-occurrence window, but the number of words in the co-occurrence window is limited, which will cause the poor expression of the semantic information of the word, and then affect accuracy of the keyword extraction results. In order to solve the limitation of word information in traditional keyword extraction methods and the problem of ignoring polysemy, this paper proposes a Chinese keyword extraction method based on context and semantic classification. © 2020 IEEE.

关键词： Semantics

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Multi-feature keyword extraction method based on TF-IDF and Chinese grammar analysis

Multi-feature keyword extraction method based on TF-IDF and ...

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2021 International Conference on Machine Learning and intelligent Systems Engineering, MLISE 2021

作者： Li, Yongrui Ning, Hongyun Tianjin Key Laboratory of Intelligence Computing New Software Technology Tianjin University of Technology Tianjin China

ISBN: (纸本)9781665417365

In order to solve the problem of over-dependence on word frequency in traditional TF-IDF algorithm, a multi-feature keyword extraction method combined with grammar analysis is proposed. By integrating the features of grammatical information, part of speech and word position, the method constructs a grading and weighting process for candidate keywords that takes into account various influencing factors comprehensively, and makes use of the grammatical information in the sentence to perform keyword sorting optimization operation to form the formally extracted keywords. The experimental results show that the proposed method is better than the traditional method, and can improve the problem of over-dependence on word frequency and extract satisfactory keywords. © 2021 IEEE.

关键词： Extraction

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Research on character vector generation method based on words and context 2019

Research on character vector generation method based on word...

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2019 International Conference on Artificial Intelligence and Computer Science, AICS 2019

作者： Wang, Yiying Ning, Hongyun Tianjin Key Laboratory of Intelligence Computing and New Software Technology Tianjin University of Technology Tianjin China

ISBN: (纸本)9781450371506

In order to solve the problem of character information limitation and the neglect of polysemy, a character vector generation method based on words and context is proposed. First of all, introduce the concept of context vector. Considering the fact that the same word has different meanings in different contexts, some even have the opposite meanings, therefore, to correctly understand the meaning of Chinese words, we must fully consider the context in which it is located. The sentence in which the character or word is located is expressed in the form of a vector called context vector. Secondly, construct the polysemous storage of words. Considering the importance of context and the polysemy of words, this paper expands the expression of the original word vector, and adds a context linked list for each word vector. The multiple semantics of words are stored in multiple context vectors in the linked list, so as to solve the polysemy problem of words. Finally, a character vector generation method based on words and context is proposed. The target character vector is calculated by using the word vectors of all words containing the target character and its context similarity. Meanwhile, attention mechanism is introduced. When calculating, the current character context and the word context are compared, according to the character and word context similarity to distribute weights, so that the words with more similar context make greater contribution to the generation of the character vector. This method not only add the word information to the character, but also distinguishes different semantics according to the context of the character, and makes all words participate in the calculation of the character vector according to different weights. The experiment uses the character vector evaluation method based on external tasks, taking the news title similarity task as an example, comparing the performance of the traditional CBOW method and the new words and context based chara

关键词： Semantics

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