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The Adjacency Codes of the First Yellow Graphs

Journal of Systems Science & Complexity 2023年第4期36卷 1757-1768页

作者： SHI Minjia LI Shitao KIM Jon-Lark SOLE Patrick Key Laboratory of Intelligent Computing and Signal Processing Ministry of EducationSchool of Mathematical SciencesAnhui UniversityHefei 230601China State Key Laboratory of Information Security Institute of Information EngineeringChinese Academy of SciencesBeijing 100093China School of Mathematical Sciences Anhui UniversityHefei 230026China Department of Math Sogang UniversitySeoulSouth Korea Aix Marseille Univ CNRSCentrale MarseilleI2MMarseilleFrance

The authors study the binary codes spanned by the adjacency matrices of the strongly regular graphs(SRGs)on at most two hundred vertices whose existence is *** authors show that in length less than one hundred they cannot be cyclic,except for the exceptions of the SRGs of parameters(85,42,20,21)and(96,60,38,36).In particular,the adjacency code of a(85,42,20,21)is the zero-sum *** the range[100,200]the authors find 29 SRGs that could possibly have a cyclic adjacency code.

关键词： Cyclic codes strongly regular graphs adjacency codes self-orthogonal codes

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Monotonicity and nonmonotonicity in L3-valued propositional logic

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Frontiers of Computer Science 2022年第4期16卷 33-43页

作者： Wei Li Yuefei Sui State Key Laboratory of Software Development Environment Beihang UniversityBeijing100083China Key Laboratory of Intelligent Information Processing Institute of Computing TechnologyChinese Academy of SciencesBeijing100190China School of Computer Science and Technology University of Chinese Academy of SciencesBeijing100049China

A sequent is a pair (Γ, Δ), which is true under an assignment if either some formula in Γ is false, or some formula in Δ is true. In L_(3)-valued propositional logic, a multisequent is a triple Δ∣Θ∣Γ, which is true under an assignment if either some formula in Δ has truth-value t, or some formula in Θ has truth-value m, or some formula in Γ has truth-value f. There is a sound, complete and monotonic Gentzen deduction system G for sequents. Dually, there is a sound, complete and nonmonotonic Gentzen deduction system G′ for co-sequents Δ: Θ: Γ. By taking different quantifiers some or every, there are 8 kinds of definitions of validity of multisequent Δ∣Θ∣Γ and 8 kinds of definitions of validity of co-multisequent Δ: Θ: Γ, and correspondingly there are 8 sound and complete Gentzen deduction systems for sequents and 8 sound and complete Gentzen deduction systems for co-sequents. Correspondingly their monotonicity is discussed.

关键词： sequent mulisequent gentzen deduction system monotonicity nonmonotonicity

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Global practical stabilization of the double integrator system with an imperfect sensor and subject to a bounded disturbance

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control Theory and Technology 2024年第3期22卷 468-478页

作者： Zhiyuan Zhu Yuanlong Li Zongli Lin Department of Automation Shanghai Jiao Tong Universityand Key Laboratory of System Control and Information Processing of Ministry of EducationShanghai 200240China Charles L.Brown Department of Electrical and Computer Engineering University of VirginiaP.0.Box 400743CharlottesvilleVA 22904-4743USA

This paper is concerned with global practical stabilization of the double integrator system with an imperfect sensor and subject to an additive bounded output *** imperfect sensor nonlinearity possesses the nonlinear characteristics of saturation and dead *** of the presence of output dead zone and the additive disturbance,the states cannot be expected to driven into an arbitrarily small neighborhood of the *** solve the global practical stabilization problem,we proposes a low gain-based linear dynamic output feedback law,under which the first state enters and remains in a bounded set whose size is depended on the bound of disturbance and the range of dead zone and the second state enters and remains in a pre-specified arbitrarily small set,both in finite *** results illustrate the effectiveness of our proposed control method.

关键词： Output saturation Disturbance rejection Output dead zone Low gain feedback-Linear output feedback Practical stabilization

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Inspection Area Allocation for Forest Surveillance Based on Different Starting Points of Multi-UAVs 7th

Inspection Area Allocation for Forest Surveillance Based on ...

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7th Chinese Conference on Swarm Intelligence and Cooperative control, CCSICC 2023

作者： Zhou, Zhengjun Mu, Lingxia Ning, Kaikai Zhang, Youmin Xue, Xianghong Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an University of Technology Xi’an710048 China Department of Mechanical Industrial and Aerospace Engineering Concordia University MontrealQCH3G 1M8 Canada

ISBN: (纸本)9789819733392

An inspection allocation algorithm based on different starting points and endurance capabilities of unmanned aerial vehicles (UAVs) is proposed for the multi-UAVs forest inspection process. Firstly, the area map to be inspected is rasterized, and the distances between the starting points of each UAV and individual grid cells are computed, which will be used for area decomposition. Subsequently, an evaluation function is employed to allocate individual grid cells reasonably to each UAV. Then, the boundary lines of the allocated sub-regions are generated, by which the raster map is converted into an inspection map with continuous boundaries. This ultimately yields the sub-regions for inspection by each UAV. Simulation results demonstrate that this method effectively allocates the forest inspection area to the various UAVs in a rational manner. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Inspection

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An EEG Source Imaging-based Feature Extraction Method for Motor Imagery Classification

An EEG Source Imaging-based Feature Extraction Method for Mo...

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2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022

作者： Li, Junhan Zheng, Nengheng Shenzhen University Guangdong Key Laboratory of Intelligent Information Processing College of Electronics and Information Engineering Shenzhen China Pengcheng Laboratory Shenzhen China

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

This paper presents a new feature extraction method for Electroencephalogram (EEG)-based motor imagery (MI) classification. Current researches mostly classify different MIs by detecting the event-related desynchronization (ERD) phenomenon from the EEG signals. Due to the poor spatial resolution of the MI-EEG signals, the cortical area (source) activating the MI cannot be located accurately with the EEG (sensor) signal, which might degrade the classification accuracy. This study adopts the EEG source imaging (ESI) technique to estimate the cortical area where source ERD happens from the EEG signal. An improved ESI method based on the linearly constrained minimum variance (LCMV) algorithm, in which an average LCMV filter and an average baseline covariance are constructed for the ESI, is proposed to locate the activated cortical area from the noisy EEG signals. The source ERD features are then extracted. Analytical results show that, for subjects with obvious average source ERD phenomenon, their activated cortical area in a single-trial MI can be well located. MI classification results also support the feasibility of the proposed method for MI-EEG signal processing. © 2022 IEEE.

关键词： Electroencephalography

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Blur region detection and segmentation of partial blur image

Blur region detection and segmentation of partial blur image

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2021 International Conference on Signal processing and Communication Technology, SPCT 2021

作者： Wang, Kui-Kui Hou, Yi-Feng Lv, Mei-Ni Gong, Ping Yang, Qiu-Hui Gan, Hui Guangxi Key Laboratory of Image Processing and Intelligent Information System Wuzhou University Wuzhou543000 China

ISBN: (数字)9781510653344

ISBN: (纸本)9781510653337

In order to address the detection and segmentation of partial blur for natural images,a no-reference and training-free algorithm was proposed. Firstly, the test image was re-blurred by a Gaussian low-pass filter. Secondly, the pixel-wise singular value decomposition within N×N neighborhoods for both the test image and the re-blurred image were conducted to obtain the ratio of the end singular value to all singular value. According to the ratio,the blurriness distribution function was structured and the blurriness distribution map was drawed. Finally, combined with the K-means clustering algorithm and the erosion and dilation operations, the test image could be segmented into blur region and clear region. Experiment results show that the proposed approach can effectively detect and segment the partial blur image and behave well at precision, recall and F-score. © 2022 SPIE.

关键词： Distribution functions

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Kalman Filter Adaptive Speed Tracking control for High-speed Trains with Noise Interference

Kalman Filter Adaptive Speed Tracking Control for High-speed...

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IEEE International Conference of intelligent Robotic and control Engineering (IRCE)

作者： Chunli Zhang Yuwei Xie Lei Yan Fucai Qian College of Automation and Information Engineering Xi’an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an China

ISBN: (数字)9798350352399

ISBN: (纸本)9798350352405

In this paper, high-speed train is regarded as a single prime point, a new tracking control method combining adaptive control and Kalman filtering is proposed. This paper solves the noise interference problem in the tracking control process of high-speed train. The method first linearises the original system, then uses Kalman filtering to do model estimation, and the controller is designed for the predictive model through adaptive control. Finally, MATLAB is used to simulate and verify the effectiveness of this method. This proposed method successfully solves the problems of nonlinearity, noise interference and unknown parameters in system. The error caused by resistance in the high-speed train tracking control process can be dealt with effectively.

关键词： Resistance Filtering Noise Process control Interference Predictive models Mathematical models Kalman filters Adaptive control MATLAB

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ABCD-HN: An Artificial Network Benchmark for Community Detection on Heterogeneous Networks 18th

ABCD-HN: An Artificial Network Benchmark for Community Dete...

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18th CCF Conference on Computer Supported Cooperative Work and Social Computing, ChineseCSCW 2023

作者： Liu, Junjie Guo, Kun Wu, Ling Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350108 China College of Computer and Data Science/College of Software Fuzhou University Fuzhou350108 China Key Laboratory of Spatial Data Mining and Information Sharing Ministry of Education Fuzhou350108 China

ISBN: (纸本)9789819996360

Community detection is essential for identifying cohesive groups in complex networks. Artificial benchmarks are critical for evaluating community detection algorithms, offering controlled environments with known community structures. However, existing benchmarks mainly focus on homogeneous networks and overlook the unique characteristics of heterogeneous networks. This paper proposes a novel artificial benchmark, called ABCD-HN (Artificial Network Benchmark for Community Detection on Heterogeneous Networks), for community detection in heterogeneous networks. This benchmark enables the generation of artificial heterogeneous networks with controllable community quantity, node quantity, and community complexity. Additionally, an evaluation framework for artificial heterogeneous networks is proposed to assess their effectiveness. Experimental results demonstrate the effectiveness and usability of ABCD-HN as a benchmark for artificial heterogeneous networks. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Heterogeneous networks

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Faster and More Robust Low-Resource Nearest Neighbor Machine Translation 12th

Faster and More Robust Low-Resource Nearest Neighbor Machin...

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12th National CCF Conference on Natural Language processing and Chinese Computing, NLPCC 2023

作者： Sun, Shuo Hou, Hongxu Yang, Zongheng Wang, Yisong College of Computer Science Inner Mongolia University National and Local Joint Engineering Research Center of Intelligent Information Hohhot China Processing Technology for Mongolian Inner Mongolia Key Laboratory of Mongolian Information Processing Technology Hohhot China

ISBN: (纸本)9783031446955

Transformer-based neural machine translation (NMT) models have achieved performance close to human-level on some languages, but still suffer from poor interpretability and scalability of the models. Many advanced studies enhance the model’s translation ability by building external memory modules and utilizing retrieval operations, however, it suffers from poor robustness and low decoding efficiency while improving the model performance, especially for low-resource translation tasks. In this paper, we propose a confidence-based gating mechanism to optimize the decoding efficiency by building a sub-network to determine the confidence of the model’s own translation capability and then decide whether the current translation needs to be retrieved from the memory module. By reducing the number of retrievals to improve the model’s translation speed without degrading the translation quality as much as possible. In addition, we use a nonparametric dynamic Monte Carlo-based algorithm to fuse retrieval probabilities and model predictions to improve the generalization and robustness of the model. Extensive experiments on different datasets demonstrate the effectiveness of our method. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Neural machine translation

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Improving viewpoint-independent object-centric representations through active viewpoint selection 24

Improving viewpoint-independent object-centric representatio...

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Proceedings of the 38th International Conference on Neural information processing Systems

作者： Yinxuan Huang Chengmin Gao Bin Li Xiangyang Xue Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University

ISBN: (纸本)9798331314385

Given the complexities inherent in visual scenes, such as object occlusion, a comprehensive understanding often requires observation from multiple viewpoints. Existing multi-viewpoint object-centric learning methods typically employ random or sequential viewpoint selection strategies. While applicable across various scenes, these strategies may not always be ideal, as certain scenes could benefit more from specific viewpoints. To address this limitation, we propose a novel active viewpoint selection strategy. This strategy predicts images from unknown viewpoints based on information from observation images for each scene. It then compares the object-centric representations extracted from both viewpoints and selects the unknown viewpoint with the largest disparity, indicating the greatest gain in information, as the next observation viewpoint. Through experiments on various datasets, we demonstrate the effectiveness of our active viewpoint selection strategy, significantly enhancing segmentation and reconstruction performance compared to random viewpoint selection. Moreover, our method can accurately predict images from unknown viewpoints.

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