检索结果-内蒙古大学图书馆

Unmanned Aerial Vehicle Path Planning Based on Improved Intelligent Water Drop Algorithm

学校读者我要写书评

暂无评论

Unmanned Aerial Vehicle Path Planning Based on Improved Inte...

International Conference on Instrumentation, Measurement, Computer, Communication and Control (IMCCC)

作者： Xixia Sun Su Pan Chao Cai Yanfang Chen Jie Chen Key Laboratory of Broadband Wireless Communication and Sensor Network Technology Nanjing University of Posts and Telecommunications Ministry of Education Nanjing China State Key Laboratory for Multispectral Information Processing Technologies School of Automation Huazhong University of Science and Technology Wuhan China Tianjin Zhong Wei Aerospace Data System Technology Co.Ltd Tianjin China

ISBN: (数字)9781538682463

ISBN: (纸本)9781538682470

The unmanned aerial vehicle (UAV) has been a research focus in recent years and the path planner is a key element of the UAV autonomous control module. In this paper, a novel UAV path planning method based on intelligent water drop algorithm is proposed. Firstly, the mathematical model of the UAV path planning problem is presented. Secondly, an improved intelligent water drop algorithm is proposed to solve the UAV path planning model. In the proposed algorithm, a novel path construction mechanism is proposed and an adaptive global soli update mechanism is developed. The proposed algorithm can overcome some shortages of the original ITD algorithm, such as slow search speed, easily falling into local minima, and so on. Experimental results demonstrate that the proposed planner outperforms the original ITD planner in a statistical manner.

关键词： Path planning Heuristic algorithms Optimization Unmanned aerial vehicles Mathematical model Soil Classification algorithms

Cryptanalysis of novel extended multivariate public key cryptosystem with invertible cycle

学校读者我要写书评

暂无评论

International Journal of network Security 2018年第3期20卷 509-514页

作者： Lu, Gang Xue, Linyuan Nie, Xuyun Qin, Zhiguang School of Information and Software Engineering University of Electronic Science and Technology of China Section 2 North Jianshe Road Chengdu610054 China State Key Laboratory of Information Security Institute of Information Engineering China Network and Data Security Key Laboratory of Sichuan Province China

In 2016, Qiao et al. proposed a novel extended multivariate public key cryptosystem (EMC) to enhance the security of multivariate public key cryptosystem. They applied it on Matsumoto-Imai (MI) encryption scheme and claimed that the enhanced MI scheme can be secure against Linearization Equation (LE) attack. Through analysis, we found that the enhanced MI scheme satisfied Quadratization Equations (QE). After finding all the quadratization equations, we can recover the plaintext corresponding to a valid ciphertext of the enhanced MI scheme. © International Journal of network Security.

关键词： Public key cryptography

Hierarchical taxonomy-aware and attentional graph capsule RCNNs for large-scale multi-label text classification

学校读者我要写书评

暂无评论

arXiv 2019年

作者： Peng, Hao Li, Jianxin Gong, Qiran Wang, Senzhang He, Lifang Li, Bo Wang, Lihong Yu, Philip S. Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100083 State Key Laboratory of Software Development Environment Beihang University Beijing100083 China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing211106 China Department of Biostatistics and Epidemiology University of Pennsylvania PhiladelphiaPA19104 United States National Computer Network Emergency Response Technical Team Coordination Center of China Beijing100029 China Department of Computer Science University of Illinois at Chicago ChicagoIL60607 United States

CNNs, RNNs, GCNs, and CapsNets have shown significant insights in representation learning and are widely used in various text mining tasks such as large-scale multi-label text classification. However, most existing deep models for multi-label text classification consider either the non-consecutive and long-distance semantics or the sequential semantics, but how to consider them both coherently is less studied. In addition, most existing methods treat output labels as independent medoids, but ignore the hierarchical relations among them, leading to useful semantic information loss. In this paper, we propose a novel hierarchical taxonomy-aware and attentional graph capsule recurrent CNNs framework for large-scale multi-label text classification. Specifically, we first propose to model each document as a word order preserved graph-of-words and normalize it as a corresponding words-matrix representation which preserves both the non-consecutive, long-distance and local sequential semantics. Then the words-matrix is input to the proposed attentional graph capsule recurrent CNNs for more effectively learning the semantic features. To leverage the hierarchical relations among the class labels, we propose a hierarchical taxonomy embedding method to learn their representations, and define a novel weighted margin loss by incorporating the label representation similarity. Extensive evaluations on three datasets show that our model significantly improves the performance of large-scale multi-label text classification by comparing with state-of-the-art approaches. Copyright © 2019, The Authors. All rights reserved.

关键词： Embeddings

Fractional spectral graph wavelets and their applications

学校读者我要写书评

暂无评论

arXiv 2019年

作者： Wu, Jiasong Wu, Fuzhi Yang, Qihan Kong, Youyong Liu, Xilin Zhang, Yan Senhadji, Lotfi Shu, Huazhong LIST Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education Nanjing210096 China International Joint Research Laboratory of Information Display and Visualization Southeast University Ministry of Education Nanjing210096 China INSERM U 1099 Université de Rennes 1 LTSI Rennes35000 France Centre de Recherche en Information Biomédicale Sino-francais Nanjing210096 China College of Data Science Taiyuan University of Technology Taiyuan030024 China

One of the key challenges in the area of signal processing on graphs is to design transforms and dictionaries methods to identify and exploit structure in signals on weighted graphs. In this paper, we first generalize graph Fourier transform (GFT) to graph fractional Fourier transform (GFRFT), which is then used to define a novel transform named spectral graph fractional wavelet transform (SGFRWT), which is a generalized and extended version of spectral graph wavelet transform (SGWT). A fast algorithm for SGFRWT is also derived and implemented based on Fourier series approximation. The potential applications of SGFRWT are also presented. Copyright © 2019, The Authors. All rights reserved.

关键词： Signal processing

Spherical paragraph model

学校读者我要写书评

暂无评论

arXiv 2017年

作者： Zhang, Ruqing Guo, Jiafeng Lan, Yanyan Xu, Jun Cheng, Xueqi CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China

Representing texts as fixed-length vectors is central to many language processing tasks. Most traditional methods build text representations based on the simple Bag-of-Words (BoW) representation, which loses the rich semantic relations between words. Recent advances in natural language processing have shown that semantically meaningful representations of words can be efficiently acquired by distributed models, making it possible to build text representations based on a better foundation called the Bag-of-Word-Embedding (BoWE) representation. However, existing text representation methods using BoWE often lack sound probabilistic foundations or cannot well capture the semantic relatedness encoded in word vectors. To address these problems, we introduce the Spherical Paragraph Model (SPM), a probabilistic generative model based on BoWE, for text representation. SPM has good probabilistic interpretability and can fully leverage the rich semantics of words, the word co-occurrence information as well as the corpus-wide information to help the representation learning of texts. Experimental results on topical classification and sentiment analysis demonstrate that SPM can achieve new state-of-the-art performances on several benchmark datasets. Copyright © 2017, The Authors. All rights reserved.

关键词： Sentiment analysis

MatchZoo: A toolkit for deep text matching

学校读者我要写书评

暂无评论

arXiv 2017年

作者： Fan, Yixing Pang, Liang Hou, JianPeng Guo, Jiafeng Lan, Yanyan Cheng, Xueqi Cas Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China

In recent years, deep neural models have been widely adopted for text matching tasks, such as question answering and information retrieval, showing improved performance as compared with previous methods. In this paper, we introduce the MatchZoo toolkit that aims to facilitate the designing, comparing and sharing of deep text matching models. Specifically, the toolkit provides a unified data preparation module for different text matching problems, a flexible layer-based model construction process, and a variety of training objectives and evaluation metrics. In addition, the toolkit has implemented two schools of representative deep text matching models, namely representation-focused models and interaction-focused models. Finally, users can easily modify existing models, create and share their own models for text matching in MatchZoo. Copyright © 2017, The Authors. All rights reserved.

关键词： Deep learning

A deep investigation of deep IR models

学校读者我要写书评

暂无评论

arXiv 2017年

作者： Pang, Liang Lan, Yanyan Guo, Jiafeng Xu, Jun Cheng, Xueqi CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China

The effective of information retrieval (IR) systems have become more important than ever. Deep IR models have gained increasing attention for its ability to automatically learning features from raw text;thus, many deep IR models have been proposed recently. However, the learning process of these deep IR models resemble a black box. Therefore, it is necessary to identify the difference between automatically learned features by deep IR models and hand-crafted features used in traditional learning to rank approaches. Furthermore, it is valuable to investigate the differences between these deep IR models. This paper aims to conduct a deep investigation on deep IR models. Specifically, we conduct an extensive empirical study on two different datasets, including Robust and LETOR4.0. We first compared the automatically learned features and handcrafted features on the respects of query term coverage, document length, embeddings and robustness. It reveals a number of disadvantages compared with hand-crafted features. Therefore, we establish guidelines for improving existing deep IR models. Furthermore, we compare two different categories of deep IR models, i.e. representation-focused models and interaction-focused models. It is shown that two types of deep IR models focus on different categories of words, including topic-related words and query-related words. Copyright © 2017, The Authors. All rights reserved.

关键词： Deep learning

Common latent space identification for heterogeneous co-transfer clustering

学校读者我要写书评

暂无评论

Neurocomputing 2017年 269卷 29-39页

作者： Yang, Liu Jing, Liping Liu, Bo Yu, Jian Beijing Key Lab of Traffic Data Analysis and Mining Beijing Jiaotong University Beijing China School of Computer Science and Technology Tianjin University Tianjin China College of Information Science and Technology Agricultural University of Hebei Baoding China

With the rapid development of collection techniques, it is easy to gather various data which come from different domains, such as images, videos, documents, and etc, how to group these heterogeneous data becomes a research issue. Traditional techniques handle these clustering tasks separately, that is one task for one domain, so that they ignore the interactions among domains. In this paper, we present a co-transfer clustering method to deal with these separate tasks together with the aid of co-occurrence data which contain some instances represented in different domains. The proposed method consists of two steps, one is to learn the subspace of different domains that uncovers the latent common topics and respects the intrinsic geometric structure, the next is to simultaneously cluster the instances in all domains via the symmetric nonnegative matrix factorization method. A series of experiments on real-world data sets have shown the performance of the proposed method is better than the state-of-the-art methods. © 2017 Elsevier B.V.

关键词： Co-transfer clustering Collective matrix factorization Heterogeneous feature spaces Symmetric nonnegative matrix factorization

Publisher Correction: A global overview of marine heatwaves in a changing climate

学校读者我要写书评

暂无评论

Communications Earth & Environment

Communications Earth & Environment 2024年第1期5卷 1页

作者： Antonietta Capotondi Tongtong Xu Dillon J. Amaya Regina R. Rodrigues Alex Sen Gupta Jessica A. Benthuysen Clara Deser Thomas L. Frölicher Natacha Le Grix Nicole S. Lovenduski Juliet Hermes Neil J. Holbrook Cristian Martinez-Villalobos Simona Masina Mathew Koll Roxy Amandine Schaeffer Robert W. Schlegel Kathryn E. Smith Chunzai Wang Cooperative Institute for Research in Environmental Sciences University of Colorado Boulder Boulder CO USA NOAA/Physical Sciences Laboratory Boulder CO USA Federal University of Santa Catarina Florianópolis Brazil Climate ChangeResearch Centre and CentreforMarine Science and Innovation University of New South Wales Sydney NSW Australia Australian Research Council Centre of Excellence for Climate Extremes University of New South Wales Sydney NSW Australia Australian Institute of Marine Science Crawley WA Australia Climate and Global Dynamics Laboratory National Center for Atmospheric Research Boulder CO USA Climate and Environmental Physics Physics Institute University of Bern Bern Switzerland Oeschger Centre for Climate Change Research University of Bern Bern Switzerland Department of Atmospheric and Oceanic Sciences and Institute of Arctic and Alpine Research University of Colorado Boulder Boulder CO USA South African Environmental Observation Network Cape Town South Africa Institute for Marine and Antarctic Studies University of Tasmania Hobart TAS Australia ARC Centre of Excellence for Climate Extremes University of Tasmania Hobart TAS Australia Faculty of Engineering and Science Universidad Adolfo Ibáñez Santiago Chile Data Observatory Foundation ANID Technology Center No. DO210001 Santiago Chile CMCC Foundation—Euro-Mediterranean Center on Climate Change Bologna Italy Centre for Climate Change Research Indian Institute of Tropical Meteorology Ministry of Earth Sciences Pune India School of Mathematics and Statistics UNSW Sydney NSW Australia Center for Marine Science and Innovation UNSW Sydney NSW Australia Laboratoire d’Océanographie de Villefranche Sorbonne University CNRS Villefranche-sur-mer France Marine Biological Association of the United Kingdom Plymouth UK State Key Laboratory of Tropical Oceanography South China Sea Institute of Oceanology Chinese Academy of Sciences Guangzhou China Global Ocean and Climate Research Center