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IEEE Transactions on Artificial Intelligence 2023年第2期4卷 268-281页

作者： Tian, Ye Pan, Jingwen Yang, Shangshang Zhang, Xingyi He, Shuping Jin, Yaochu Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Institutes of Physical Science and Information Technology Hefei230601 China Hefei Comprehensive National Science Center Institute of Artificial Intelligence Hefei230088 China Anhui University School of Computer Science and Technology Hefei230601 China Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Artificial Intelligence Hefei230601 China Anhui University Anhui Engineering Laboratory of Human-Robot Integration System and Intelligent Equipment School of Electrical Engineering and Automation Hefei230601 China Bielefeld University Faculty of Technology Bielefeld33619 Germany

The sparse adversarial attack has attracted increasing attention due to the merit of a low attack cost via changing a small number of pixels. However, the generated adversarial examples are easily detected in vision since the perturbation to each pixel is relatively large. To achieve imperceptible and sparse adversarial attacks, this article formulates a bi-objective constrained optimization problem, simultaneously minimizing the 0 and 2 distances to the original image, and proposes a dual-population-based constrained evolutionary algorithm to solve it. The proposed method solves the optimization problem by evolving two populations, where one population is responsible for finding feasible solutions (i.e., successful attacks) and the other one is to minimize both the 0 and 2 distances. Moreover, a population initialization strategy and two genetic operators are customized to accelerate the convergence speed. Experimental results indicate that the proposed method can achieve high success rates with low attack costs, and strikes a better balance between the 0 and 2 distances than state-of-the-art methods. © 2020 IEEE.

关键词： Evolutionary algorithms

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Secured and Cooperative Publish/Subscribe Scheme in Autonomous Vehicular Networks

arXiv

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arXiv 2023年

作者： Wang, Yuntao Su, Zhou Xu, Qichao Luan, Tom H. Lu, Rongxing School of Cyber Science and Engineering Xi’an Jiaotong University Xi’an China School of Mechatronic Engineering and Automation Shanghai University Shanghai China Faculty of Computer Science University of New Brunswick Fredericton Canada

In order to save computing power yet enhance safety, there is a strong intention for autonomous vehicles (AVs) in future to drive collaboratively by sharing sensory data and computing results among neighbors. However, the intense collaborative computing and data transmissions among unknown others will inevitably introduce severe security concerns. Aiming at addressing security concerns in future AVs, in this paper, we develop SPAD, a secured framework to forbid free-riders and promote trustworthy data dissemination in collaborative autonomous driving. Specifically, we first introduce a publish/subscribe framework for inter-vehicle data transmissions. To defend against free-riding attacks, we formulate the interactions between publisher AVs and subscriber AVs as a vehicular publish/subscribe game, and incentivize AVs to deliver high-quality data by analyzing the Stackelberg equilibrium of the game. We also design a reputation evaluation mechanism in the game to identify malicious AVs in disseminating fake information. Furthermore, for lack of sufficient knowledge on parameters of the network model and user cost model in dynamic game scenarios, a two-tier reinforcement learning based algorithm with hotbooting is developed to obtain the optimal strategies of subscriber AVs and publisher AVs with free-rider prevention. Extensive simulations are conducted, and the results validate that our SPAD can effectively prevent free-riders and enhance the dependability of disseminated contents, compared with conventional schemes. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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Design of A Third-order Inline Waveguide Bandpass Filter With Cross Coupling and Spurious Mode Suppression

Design of A Third-order Inline Waveguide Bandpass Filter Wit...

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IEEE International Wireless Symposium (IWS)

作者： Wangtao Ye Zai-Cheng Guo Lei Zhu Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing School of Electrical and Automation Engineering Nanjing Normal University Nanjing Department of Electrical and Computer Engineering Faculty of Science and Technology University of Macau China

ISBN: (数字)9798350389999

ISBN: (纸本)9798350390001

A third-order inline waveguide bandpass filter (BPF) with cross coupling and spurious mode suppression is designed in this paper. Based on the conventional cascaded-coupled filter, the cross coupling is realized by reducing the height of the second resonating cavity and introducing a stacked cavity for cross coupling, which generates a transmission zero (TZ) on the right side of passband. In order to eliminate the spurious modes caused by the cross-coupled cavity, a short-circuited post is introduced to make TE101 and TE102 mode of the cavity introduced for cross coupling self-suppressed. Therefore, the proposed inline waveguide BPF is with one TZ on the right side of passband while preserving almost the same bandwidth of stopband when compared to the conventional cascaded-coupled filter. The design process is illustrated in detail and the measured results verify the feasibility of the proposed inline waveguide BPF.

关键词： Band-pass filters Couplings Wireless communication Bandwidth Filtering theory Passband

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Multi-objective performance prediction based on a self-constructed Al-Si alloy dataset

Multi-objective performance prediction based on a self-const...

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Information Systems and Computing Technology (ISCTech), International Conference on

作者： Dan Hu Yingli Liu Tao Shen Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming China Yunnan Key Laboratory of Computer Technologies Application Kunming University of Science and Technology Kunming China

The combination of data-driven approaches and machine learning techniques has changed the design approach for new materials, propelling materials science research into the realm of the fourth paradigm. Researchers in the field of materials science have increasingly focused on utilizing machine learning methods to uncover the underlying nonlinear relationships within material data. However, there is currently limited research in the field of aluminum-silicon alloys regarding the prediction of alloy performance based on the composition-process-structure relationship. Additionally, there is a lack of data sharing in the aluminum-silicon alloy domain. Therefore, this study proposes a methodology. Firstly, an aluminum-silicon alloy dataset is constructed by mining relevant information from materials literature. Then, high-dimensional features in the dataset are subjected to feature selection techniques for dimensionality reduction. Subsequently, a multilayer perceptron model is applied to investigate the relationship between input features such as alloy composition, preparation process, and microstructural parameters, and output features such as yield strength, ultimate tensile strength, and elongation, enabling performance prediction.

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A Novel Approach with Fusion Edge Prediction Strategy for Table Structure Recognition

A Novel Approach with Fusion Edge Prediction Strategy for Ta...

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Information Systems and Computing Technology (ISCTech), International Conference on

作者： Yingli Liu Guangtao Zhang Tao Shen Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming China Yunnan Key Laboratory of Computer Technologies Application Kunming University of Science and Technology Kunming China

Table, as an information carrier with high information density and direct display form, are widely used in scientific literature and social production documents. Accurately identifying tables from various documents will significantly promote the intelligent application of documents. The table’s structural information is crucial for comprehending how the text content in the table relates to one another, but teaching a computer to fully understand a table’s structure is still a very difficult undertaking. In this research work, we provided a unique edge-based method to recognize table structures. Specifically, we define the inter-cell relationships as an edge prediction problem and leverage graph convolutional networks to learn representations of the cells. These learned representations are then utilized to directly predict the connections between the cells. The experimental results conducted on three publicly available datasets for table structure recognition provide compelling evidence supporting the feasibility and usefulness of our presented methodology.

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Identification of personalized driver genes for individuals using graph convolution network

Identification of personalized driver genes for individuals ...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Peng, Wei Yu, Piaofang Dai, Wei Fu, Xiaodong Liu, Li Pan, Yi Kunming University of Science and Technology Faculty of Information Engineering and Automation Kunming China Computer Technology Application Key Lab of Yunnan Province Kunming University of Science and Technology Kunming China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China

ISBN: (纸本)9781665468190

The correct identification of the driver genes that lead to cancer development is essential for understanding the mechanisms of cancer and developing drugs to treat it. Currently, most computational methods for identifying cancer driver genes are based on a cohort of patients. However, due to the heterogeneity of cancers, patients diagnosed with the same cancers may have different genomic characteristics and present varied clinical symptoms. It requires devising effective methods to identify personalized cancer driver genes in an individual. This work developed a novel method to predict personalized cancer driver genes of a single sample based on graph convolution networks, namely pDriverGCN. pDriverGCN constructed a mutant gene-sample heterogeneous network according to the known driver genes of samples. Then it employed two separate graph convolution network models to learn feature representations for genes and samples by gathering the features of themselves and their neighbors. Finally, pDriverGCN used the feature representations to reconstruct the association matrix between genes and samples through a linear correlation coefficient decoder. We apply our model to identify personalized driver genes of samples on the TCGA datasets. The experimental results show that our model outperforms state-of-the-art methods being evaluated at both population and individual levels. © 2022 IEEE.

关键词： Genes

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Distributed formation control of end-effector of mixed planar fully- and under-actuated manipulators

arXiv

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arXiv 2023年

作者： Peng, Zhiyu Jayawardhana, Bayu Xin, Xin School of Automation Southeast University Nanjing210096 China Engineering and Technology Institute Groningen Faculty of Science and Engineering University of Groningen Groningen9747 AG Netherlands Faculty of Computer Science and Systems Engineering Okayama Prefectural University Okayama719-1197 Japan

This paper addresses the problem of end-effector formation control for a mixed group of two-link manipulators moving in a horizontal plane that comprises of fully-actuated manipulators and underactuated manipulators with only the second joint being actuated (referred to as the passive-active (PA) manipulators). The problem is solved by extending the distributed end-effector formation controller for the fully-actuated manipulator to the PA manipulator moving in a horizontal plane by using its integrability. This paper presents stability analysis of the closed-loop systems under a given necessary condition, and we prove that the manipulators’ end-effector converge to the desired formation shape. The proposed method is validated by simulations. Copyright © 2023, The Authors. All rights reserved.

关键词： End effectors

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Adaptive outer synchronization between two delayed oscillator networks with cross couplings

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science China(Information sciences) 2020年第10期63卷 267-269页

作者： Jianbao ZHANG Ancai ZHANG Jinde CAO Jianlong QIU Fuad E.ALSAADI School of Automation Southeast University School of Information Science and Engineering Linyi University Department of Electrical and Computer Engineering Faculty of EngineeringKing Abdulaziz University

Dear editor,Chaos synchronization is a typical kind of collective behaviors induced by external control or mutual couplings between chaotic nodes [1].During the past decades,increasing interest has been devoted to study the interplay between network topology and chaos synchronization [1,2].Many types of synchronization phenomena have been proposed such as complete synchronization,cluster synchronization [3],and outer synchroni-

关键词： Adaptive control Complex networks Lyapunov method Outer synchronization Cross coupling

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SIF-STGDAN: A Social Interaction Force Spatial-Temporal Graph Dynamic Attention Network for Decision-Making of Connected and Autonomous Vehicles

SIF-STGDAN: A Social Interaction Force Spatial-Temporal Grap...

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IEEE Symposium on Intelligent Vehicle

作者： Qi Liu Yujie Tang Xueyuan Li Fan Yang Xin Gao Zirui Li School of Mechanical Engineering Beijing Institute of Technology Beijing China Faculty of Computer Science Dalhousie University Halifax Canada Chair of Traffic Process Automation "Friedrich List" Faculty of Transport and Traffic Sciences TU Dresden Germany

ISBN: (数字)9798350348811

ISBN: (纸本)9798350348828

The collaborative decision-making technology of connected and autonomous vehicles (CAVs) is critical in today’s autonomous driving. Recently, graph reinforcement learning (GRL)-based methods have demonstrated exemplary performance in solving decision-making problems by implementing graphic technologies. However, current GRL-based research faces the challenge of modeling the interaction completely and extracting driving features efficiently. To address these issues, this paper proposes a social interaction force (SIF) spatial-temporal graph dynamic attention network (SIF-STGDAN) to solve the decision-making of CAVs. First, a SIF model is established to better represent the mutual effect between vehicles; an on-ramp merging scenario is then constructed and modeled by graph representation. Then, the SIF-STGDAN is proposed by combining the temporal convolutional network (TCN) and graph dynamic attention network to extract the graphic features of the on-ramp scenario efficiently, and the double deep q-learning (DDQN) algorithm is utilized to generate the optimized driving strategies for CAVs. Finally, experiments are conducted, and results show that our proposed SIF-STGDAN outperforms the baselines in terms of safety, efficiency, and model stability.

关键词： Graphics Heuristic algorithms Decision making Merging Force Feature extraction Stability analysis

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Human-Aware Navigation in Crowded Environments Using Adaptive Proxemic Area and Group Detection

Human-Aware Navigation in Crowded Environments Using Adaptiv...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Carlos Medina-Sánchez Simon Janzon Matteo Zella Jesús Capitán Pedro J. Marrón Faculty of Business Administration and Economics Institute for Computer Science and Business Information Systems (ICB) University of Duisburg-Essen Essen Germany Faculty of Electrical Engineering and Computer Science University of Applied Sciences Niederrhein Krefeld Germany Department of System Engineering and Automation University of Seville Seville Spain

Navigation is an essential task for social robots. However, certain rules must be followed to allow them to move without causing distraction or discomfort to people. Considering that the context surrounding robots and persons affects the expected behavior, this work defines a social area around a person that adapts to the real situation. In addition, the social context of a person is extended to identify groups of people, which the robot should take into account while navigating. With this understanding of the surrounding of the robot together with the ability to predict the trajectory of individuals as well as groups, the proposed solution not only effectively addresses collision avoidance while promoting socially acceptable behavior but also outperforms the majority of recent works in terms of accuracy. Furthermore, a dedicated policy is introduced to react to social navigation conflicts. The evaluation performed in a simulated environment shows that the computation of our proposed solution is at least 8 times faster than the best state-of-the-art approach while preserving comparable social conduct. Also, the results of realistic experiments performed using Gazebo and a real robot are reported.

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