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International Conference on Natural Language Processing and Knowledge engineering

作者： Liu, Xiaodong Ren, Fuji Yuan, Caixia Dept. of Information Sciences and Intelligent Systems Faculty of Engineering University of Tokushima Tokushima Japan Center for Intelligence Science and Technology/Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9781424468966

The technology of text category is widely used in natural language processing. As one of best text category algorithms, kNN is very popular used in many applications. Traditional kNN assumes that the distribution of training data is even, however, it is not the case for many situations. When we used kNN in our Topic Detection and Tracking (TDT) system, it did not perform well due to the bias of training data set. To overcome the obstacle caused by data bias, this paper proposes an approach which uses relative weight to adjust the weight of kNN (RWKNN). When evaluated on the data of TDT2 and TDT3 Chinese corpus, RWKNN proves to be robust on unbalanced data and yields better performance than the traditional kNN. ©2010 IEEE.

关键词： Natural language processing systems

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Plant Leaf Recognition Network Based on Feature Learning and Metric Learning 16th

Plant Leaf Recognition Network Based on Feature Learning and...

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16th International Conference on Intelligent Computing, ICIC 2020

作者： Yang, Hongwei Wu, Di Yuan, Changan Qin, Xiao Wu, Hongjie Zhao, Xingming Zhao, Zhongqiu Institute of Machine Learning and Systems Biology School of Electronics and Information Engineering Tongji University Shanghai China Guangxi Academy of Science Nanning530025 China School of Computer and Information Engineering Nanning Normal University Nanning530299 China School of Computer Science and Technology Soochow University Suzhou215006 China School of Electronic and Information Engineering Suzhou University of Science and Technology Suzhou215009 China Fudan University Shanghai200433 China Key Laboratory of Computational Neuro-Science and Brain-Inspired Intelligence Ministry of Education Shanghai China College of Computer Science and Information Engineering Hefei University of Technology Hefei230009 China

ISBN: (纸本)9783030607982

Plant image recognition is an important thing for protecting plants, protecting the environment, and protecting nature. Recently, most models in the field of plant leaf recognition make classification after extracting global features. In this paper, we propose a plant leaf recognition model based on metric learning. Metric learning calculates the similarity of the extracted feature vectors to obtain the distance between different sample features, so as to determine whether similar pictures belong to the same category, and then achieve the classification effect. In this study, feature triplet are used for metric learning, and the loss function we used is triplet-loss. © 2020, Springer Nature Switzerland AG.

关键词： Image recognition

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Experimental Testing of the Phased Array Antenna Beamsteering for the V2X Applications 25

Experimental Testing of the Phased Array Antenna Beamsteerin...

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25th IEEE International Conference of Young Professionals in Electron Devices and Materials, EDM 2024

作者： Saifullin, Talgat E. Grakhova, Elizaveta P. Ishmiyarov, Arsen A. Katerinkina, Ekaterina N. Letavin, Denis A. Kutluyarov, Ruslan V. Research Lab. "Sensor Systems Based on the Integrated Photonics Devices" Ufa University of Science and Technology Ufa Russia Telecommunication systems Dept. Ufa University of Science and Technology Ufa Russia Institute of Radio Electronics Information Technologies dept Ural Federal University Yekaterinburg Russia

ISBN: (纸本)9798350389234

The paper showcases experimental testing of a phased array antenna for connecting and tracking vehicles in Vehicle-to-Everything technology applications. The study involved measuring the performance of a microstrip half-octagon shaped emitter and a 14-element linear antenna array with a main lobe width of no more than 8 degrees. Both the emitter and antenna array were designed and manufactured using WL-CT350 laminate material. The antenna system operates within the frequency range of 5614 to 5943 MHz. To control the main lobe position of the antenna array, a beamforming circuit was developed, which consisted of a splitter and a phase control unit utilizing planar phase shifters. These phase shifters were controlled by Arduino Mega 2560 microcontrollers, which implemented machine-learning algorithms to determine the optimal service sector based on the location of the vehicle. Experimental tests were conducted in an anechoic chamber to demonstrate the antenna's ability to steer a focused radio beam across ten different sectors within a scanning range of 90 degrees. © 2024 IEEE.

关键词： Antenna lobes

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SLAM algorithm based on bounding box and deep continuity in dynamic scene

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International Journal of Wireless and Mobile Computing 2021年第4期21卷 349-364页

作者： Fang, Baofu Han, Xiumeng Wang, Zaijun Yuan, Xiaohui School of Computer Science and Information Engineering Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Anhui Hefei230009 China Key Laboratory of Flight Techniques and Flight Safety Civil Aviation Flight University of China Sichuan Guanghan618307 China Department of Computer Science and Engineering University of North Texas DentonTX311277 United States

To solve the problems of low positioning accuracy and poor robustness caused by the inability of Simultaneous Localisation and Mapping (SLAM) algorithm to deal with dynamic targets in dynamic scenes, a SLAM algorithm based on bounding box and depth continuity in dynamic scenes is proposed. Firstly, the pixel random search filling process is carried out in combination with the depth and the bounding box to obtain the pixel-level segmentation result of the prior dynamic target. Then, the dynamic features are screened to eliminate the influence of dynamic targets. According to the screening results, relative static features which can be used for posture optimisation are selected. Posture optimisation is carried out by combining the static features to obtain the optimised camera posture. All experimental results show that the proposed algorithm significantly improves the positioning accuracy and real-time performance of SLAM algorithm in complex dynamic scenes. Copyright © 2021 Inderscience Enterprises Ltd.

关键词： Mapping

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Adaptive reduced-rank beam-forming method using joint iterative optimization 5

Adaptive reduced-rank beam-forming method using joint iterat...

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5th IEEE Asia-Pacific Conference on Synthetic Aperture Radar, APSAR 2015

作者： He, Shun Yang, Zhiwei Liao, Guisheng National Lab of Radar Signal Processing Xidian University Xi'an China Communication and Information Engineering Collage Xi'An University of Science and Technology Xi'an China

ISBN: (纸本)9781467372961

The proposed method realizes adaptive reduced-dimension processing based on joint optimization of both reduced-dimension matrix and beam-forming weight vector. Meanwhile, the spatial spectrum reconstruction technology is used to update the covariance matrix of array data online. The simulation results show that the proposed method is robust to the dimension of the reduced-dimension matrix and significantly improves the output SINR of adaptive beam-former under the condition of few samples. © 2015 IEEE.

关键词： Mean square error

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Penalized partial least square discriminant analysis with l₁-norm for multi-label data

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Pattern Recognition 2015年第5期48卷 1724-1733页

作者： Liu, Huawen Ma, Zongjie Zhang, Shichao Wu, Xindong Department of Computer Science Zhejiang Normal University China NCMIS Academy of Mathematics and Systems Science CAS China Department of Computer Science Zhejiang Gongshang University China School of Computer Science and Information Engineering Hefei University of Technology China Department of Computer Science University of Vermont United States

Multi-label data are prevalent in real world. Due to its great potential applications, multi-label learning has now been receiving more and more attention from many fields. However, how to effectively exploit the correlations of variables and labels, and tackle the high-dimensional problems of data are two major challenging issues for multi-label learning. In this paper we make an attempt to cope with these two problems by proposing an effective multi-label learning algorithm. Specifically, we make use of the technique of partial least square discriminant analysis to identify a common latent space between the variable space and the label space of multi-label data. Moreover, considering the label space of the multi-label data is sparse, a l₁-norm penalty is further performed to constrain the Y-loadings of the optimization problem of partial least squares, making them sparse. The merit of our method is that it can capture the correlations and perform dimension reduction at the same time. The experimental results conducted on eleven public data sets show that our method is promising and superior to the state-of-the-art multi-label classifiers in most cases. © 2014 Elsevier Ltd. All rights reserved.

关键词： Dimension reduction Discriminant analysis Multi-label learning Partial least squares Sparse learning

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Analysis of user influence in Sina Microblog

Analysis of user influence in Sina Microblog

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2016 International Conference on Automotive engineering,Mechanical and Electrical engineering(AEMEE 2016)

作者： Jun Wang Zewen Cao Peiteng Shi Wensen Liu Science and Technology on Information Systems Engineering Laboratory National University of Defense Technology

This paper analyses the influence of users in Sina Microblog based on interaction of users in microblogging service. With the data from Open Weiboscope Data Access website, we focus on "active users" in original data and construct a probability transition matrix of information diffusion. Then we utilise the measurement of effective distance to define the users' influence, finding the top influence users in Sina Microblog. We find that the top users' effective distance to others are shorter than others. Usually, they also make more interactions(retweets) in microblogging service. Furthermore, we construct a net graph of users by selecting the minimum effective distance of users as their connection in graph, finding that top users are usually located at the centre of network. All the results demonstrate the measurement of effective distance as a novel perspective and effective tool, which can be well applied to the investigation of the social network of Sina Microblog.

关键词： influence of user Sina Microblog effective distance information diffusion

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Robust GP in robot learning 4th

Robust GP in robot learning

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International Conference on Evolutionary Computation - 4th International Conference on Parallel Problem Solving from Nature, PPSN 1996

作者： Hondo, Naohiro Iba, Hitoshi Kakazu, Yukinori Complex Systems Engineering Division of Systems and Information Engineering Hokkaido University N-13 W-8 Sapporo060 Japan Machine Inference Section Electrotechnical Lab 1-1-4 Umezono Tukuba Science CityIbaraki305 Japan

ISBN: (纸本)354061723X

This paper presents a new approach to Genetic Programming (i.e. GP). Our goal is to realize robustness by means of the automatic discovery of functions. In traditional GP, techniques have been proposed which attempt to discover certain subroutines for the sake of improved efficiency. So far, however, the robustness of GP has not yet been discussed in terms of knowledge acquisition. We propose an approach for robustness named COAST, which has a library for storing certain subroutines for reuse. We make use of the Wall Following Problem to illustrate the efficiency of this method. © 1996, Springer-Verlag. All rights reserved.

关键词： Genetic algorithms

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Generating the n-tuples of natural numbers by enzymatic numerical p system 3rd

Generating the n-tuples of natural numbers by enzymatic nume...

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3rd International Conference on Data science, Medicine, and Bioinformatics, IDMB 2019

作者： Zhang, Zhiqiang Kong, Yuan Chen, Zhihua Niu, Yunyun Ma, Mingyuan School of Electronics Engineering and Computer Science Peking University Beijing100871 China College of Mathematics and Systems Science Shandong University of Science and Technology Qingdao266590 China Institute of Computing Science and Technology Guangzhou University GuangzhouGuangdong510006 China School of Information Engineering China University of Geosciences in Beijing Beijing100083 China

ISBN: (纸本)9789811587597

Numerical P systems (NP systems) are a class of computing models inspired by both the cell structure and economics. Enzymatic numerical P systems (ENP systems) are a variant of NP systems, which were successfully applied in autonomous robot control. In this work, we design an algorithm using enzymatic numerical P systems to generate all the n-tuples of natural numbers in a well specified order. Specifically, we improve previously known results on generating all the n-tuples of natural numbers. Based on this method, we prove that a numerical P system with one membrane, and the production function used with polynomials of degree 1 with at most 2 variables can reach universality, which optimizes the previous known results. The results also give a positive answer to a problem formulated in [Fundamenta Informaticae, 2006, 73(1): 213-227]. © Springer Nature Singapore Pte Ltd 2020.

关键词： Numerical methods

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Multi-channel CNN to classify Nepali COVID-19 related tweets using hybrid features

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Journal of Ambient Intelligence and Humanized Computing 2024年第3期15卷 2047-2056页

作者： Sitaula, Chiranjibi Shahi, Tej Bahadur Department of Electrical and Computer Systems Engineering Monash University Wellington Rd ClaytonVIC3800 Australia Earth Observation and AI Research Group Department of Infrastructure Engineering The University of Melbourne ParkvilleVIC3010 Australia School of Engineering and Technology Central Queensland University Rockhampton Australia Central Department of Computer Science and Information Technology Tribhuvan University TU Rd Kathmandu Kirtipur44618 Nepal

Because of the current COVID-19 pandemic’s increasing fears among people, it has triggered several health complications such as depression and anxiety. Such complications have not only affected developed countries but also developing countries such as Nepal. These complications can be understood from peoples’ tweets and comments posted online after their felicitous analysis and sentiment classification. Nevertheless, owing to the limited number of tokens and words in each tweet, it is crucial to capture multiple pieces of information associated with them for better understanding. In this study, we, first, represent each tweet by combining both syntactic and semantic information, called hybrid features. The syntactic information is generated from the bag-of-words method, whereas the semantic information is generated from the combination of the fastText-based (ft) and domain-specific (ds) methods. Second, we design a novel multi-channel CNN (MCNN) model, which ensembles the multiple CNNs, to capture multi-scale information for better classification. Last, we evaluate the efficacy of both the proposed feature extraction method and the MCNN model classifying tweets into three sentiment classes (positive, neutral, and negative) on the NepCOV19Tweets dataset, which is the only public COVID-19 tweets dataset in the Nepali language. The evaluation results show that the proposed hybrid features outperform individual feature extraction methods with the highest classification accuracy of 69.7% and the MCNN model outperforms the existing methods with the highest classification accuracy of 71.3% during classification. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023.

关键词： COVID-19

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