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24th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Xu, Xiaofeng Zhang, Weisha Huang, Yuanyuan Lu, Hongyu Lu, Jiazhong School of Cybersecurity Xin Gu Industrial College Chengdu University of Information Technology Chengdu610225 China Advanced Cryptography and System Security Key Laboratory of Sichuan Province Chengdu610225 China SUGON Industrial Control and Security Center Chengdu610225 China School of Foreign Languages University of Electronic Science and Technology of China Chengdu611731 China

ISBN: (纸本)9789819615247

The detection of fake news is not confined to a single domain, and due to domain transfer, the reliance on detection methods specific to a single domain becomes inadequate when dealing with multi-domain issues. When a multi-domain detection occurs, labels from one domain focus exclusively on the news characteristics of that particular domain, ignoring the possibility that part of the news may include functions from more than one domain. To address these challenges, we propose a multi-domain fake news detection model based on fuzzy rules, incorporating a fuzzy mechanism and an optimization module for fuzzy association rules. In this model, we first introduce a fuzzy mechanism that applies fuzzy processing to domains through neural networks, constructing fuzzy domain labels for each news domain. Subsequently, we incorporate fuzzy association rules to extract a broader range of sample features, thereby enhancing detection effectiveness. Experiments conducted on a fake news dataset demonstrate that our model achieves commendable detection performance with a relatively low training cost, reaching an F1 score of over 97.13% for sample detection. This model successfully overcomes the limitations of single-domain detection in fake news identification and improves the precision of feature extraction through fuzzy rules, showing superior performance in handling multi-domain fake news detection. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： fake news detection fuzzy rules multi-domain social media

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Sparsity-enhanced Convolutional Neural Network for IoT Cyberattacks Detection 17

Sparsity-enhanced Convolutional Neural Network for IoT Cyber...

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17th International Conference on Computational Intelligence and security, CIS 2021

作者： Kang, Yingyun Feng, Yongxin Chen, Yongqun Wang, Wei School of Information Science and Engineering Shenyang Ligong University Shenyang China Science and Technology on Communication Information Security Control Laboratory Jiaxing China

ISBN: (纸本)9781665494892

The Internet of Things (IoT) is a collection of interconnected devices that can be linked without the need for human interaction. IoT will have a significant impact on our lives in terms of economics, commerce, and society. IoT network nodes are typically resource-constrained, which makes them attractive targets for cyberattacks. However, due to the unique properties of IoT nodes, current solutions are insufficient to cover the complete security spectrum of IoT networks. A more intelligent IoT network security solution is required. Artificial intelligence is one of the most suited computing paradigms for providing embedded intelligence in IoT and is being used to address various security issues. To detect IoT cyber threats, we introduce a sparsity-enhanced convolutional neural network in this paper. Recently published IoT datasets are used for experiments. The proposed neural network is effective by experimental results and analysis. © 2021 IEEE.

关键词： Internet of things

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STARS Assisted Semi-Grant-Free NOMA communications

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IEEE Transactions on Wireless communications 2025年第6期24卷 4904-4919页

作者： Xie, Jin Yue, Xinwei Zhao, Jingjing Zou, Yixuan Liu, Yuanwei Liu, Rongke Ding, Zhiguo Beijing Information Science and Technology University Key Laboratory of Information and Communication Systems Ministry of Information Industry Key Laboratory of Modern Measurement and Control Technology Ministry of Education Beijing100101 China Beihang University School of Electronic and Information Engineering Beijing100191 China Queen Mary University of London School of Electronic Engineering and Computer Science LondonE1 4NS United Kingdom The University of Hong Kong Department of Electrical and Electronic Engineering Hong Kong Kyung Hee University Department of Electronic Engineering Gyeonggi-do Yongin-si17104 Korea Republic of Khalifa University Department of Computer and Information Engineering Abu Dhabi United Arab Emirates

This paper investigates the performance of simultaneously transmitting and reflecting surface (STARS) assisted semi-grant-free non-orthogonal multiple access network with randomly distributed users. By deploying STARS, the transmit signals of grant-based user (GBU) and grant-free users (GFUs) can be exquisitely adjusted to reduce interference. We propose a maximum channel scheduling (MCS) protocol that allows a GFU to access GBU’s channel with the assistance of STARS. In particular, the impacts of perfect/imperfect successive interference cancellation (pSIC/ipSIC) on MCS protocol are taken into account. To characterize the performance of STARS aided MCS (STARS-MCS) network, we derive the expressions of outage probability for GBU and GFU with pSIC/ipSIC. By applying convolution theorem and Laplace transform, the asymptotic expressions of outage probability and diversity orders for GBU and GFU are attained. We further design a STARS-based power control (SPC) strategy to eliminate the outage probability error floor and improve the outage performance. Numerical results show that: 1) The performance of STARS-MCS outperforms the existing benchmarks in terms of outage probability and system throughput;2) The SPC strategy can effectively improve the performance of the STARS-MCS network and eliminate the outage probability error floor at high signal-to-noise ratios;and 3) By adjusting reflection and transmission coefficients of STARS, the outage performance of GBU and GFU can be greatly enhanced. © 2002-2012 IEEE.

关键词： Laplace transforms

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Data-Driven Active Power Dispatch for Wind Farms 13

Data-Driven Active Power Dispatch for Wind Farms

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13th IEEE Data Driven control and Learning Systems Conference, DDCLS 2024

作者： Jiao, Xuguo Zhang, Daoyuan Wang, Xin Ma, Zhaoxing Zhang, Zhenyong Liu, Wenfeng School of Information and Control Engineering Qingdao University of Technology Qingdao266520 China College of Control Science and Engineering Zhejiang University State Key Laboratory of Industrial Control Technology Hangzhou310027 China Ministry of Education Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Jinan250014 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan250014 China College of Computer Science and Technology Guizhou University State Key Laboratory of Public Big Data Guiyang550025 China School of Civil Engineering Qingdao University of Technology Qingdao266520 China

ISBN: (纸本)9798350361674

Active power dispatch of wind farms plays an important role in power grid scheduling. In this paper, a data-driven active power dispatch strategy for wind farms is proposed, which uses the key point of minimizing the standard deviation of wind turbines' output power series to reduce their fluctuations. First, to build the optimization problem, the historical active power output data of each wind turbine is collected from the Supervisory control And Data Acquisition (SCADA) system. Then, the objective function is developed based on the idea of minimizing the standard deviation of the output power time series. Meanwhile, considering the constraints of wind turbines' output power and active power balance of the grid, the feasible domain of the cost function is constructed. Further, to solve the proposed nonlinear optimization problem with equality and inequality constraints, the trust region interior point method (TRIPM) is employed to explore optimal solution within the feasible domain. Finally, actual data collected from wind farms is used to test effectiveness of the designed approach and experimental results show that our method can achieve better output power smoothing performance than conventional Proportional Dispatch (PD) method. © 2024 IEEE.

关键词： Cost functions

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Simultaneous Localization and Recognition of Subwavelength Non-Cooperative Entities Based on SISO Time Reversal and Neural Networks

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IEEE Sensors Journal 2024年

作者： Wang, Yinchen Duan, Yu Ye, Yuqi Wang, Ren Li, Biao Jiang, Bin Liu, Xin Wang, Bing-Zhong University of Electronic Science and Technology of China Institute of Applied Physics Chengdu611731 China No. 36 Research Institute of CETC Communication Information Security Control Laboratory Jiaxing314033 China

The simultaneous localization and recognition of subwavelength non-cooperative entities within complex multi-scattering environments using a simplified system continues to pose a substantial challenge. In this paper, we address this challenge by synergistically integrating time reversal time-frequency phase prints (TRTFPPs) and neural networks. Initially, a time reversal (TR) single-input single-output (SISO) framework is employed to generate TRTFPPs. Subsequently, neural networks are employed to comprehend the TRTFPPs. Specifically, a cascaded neural network structure is embraced, encompassing both a recognition neural network and distinct neural networks for localizing different entities. Using the devised method, we have achieved the simultaneous subwavelength recognition and localization through numerical simulations for metallic entities and experimental testing for metallic and non-metallic entities. The proposed methodology holds applicability across various electromagnetic systems, including but not limited to detection, imaging, human-computer interaction, and the Internet of Things (IoT). © 2001-2012 IEEE.

关键词： Multiple scattering

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Multi-view infrared small target recognition based on YOLOv5

Multi-view infrared small target recognition based on YOLOv5

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2022 Chinese Automation Congress, CAC 2022

作者： Gong, Hua Liu, Shasha Guan, Song Yu, Kuaikuai Liu, Fang College of Science Shengyang Ligong University Shenyang110159 China Science and Technology on Electro-Optical Information Security Control Laboratory Tianjing300308 China

ISBN: (纸本)9781665465335

In order to improve the recognition accuracy of small infrared targets in complex environment, a SyluInfrared dataset is built by taking multi-view infrared images with the Frock TiX600 infrared thermal imager. Seven image enhancement algorithms including image brightening, contrast adjustment, image blurring, image flipping and image scaling are designed to improve the image quality of small infrared targets. The YOLOv5 deep learning model is used to identify small infrared targets with multiple views to improve the recognition accuracy of small infrared targets. The experimental results show that the accuracy of multi-view infrared small target recognition model for YOLOv5 deep network is 97.7%, which is 6.6% higher than that of single-view infrared small target recognition model based on YOLOv5. The multi-view target recognition strategy significantly improves the infrared small target recognition performance. © 2022 IEEE.

关键词： Image enhancement

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Dunhuang murals image inpainting method based on fusion classification pre-training model

Dunhuang murals image inpainting method based on fusion clas...

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2024 International Conference on Culture-Oriented science and technology, CoST 2024

作者： Ren, Hui Zhang, Chengya Sun, Ke Ye, Chenyu Communication University of China School of Information and Communication Engineering Beijing100024 China Ministry of Culture and Tourism Key Laboratory of Audiovisual Technology and Intelligent Control System Beijing100024 China Beijing Key Laboratory of Modern Performing Arts Technology Beijing100024 China University of Pennsylvania School of Engineering and Applied Science PA19104 United States

ISBN: (纸本)9798350380347

Murals are an important part of China's cultural heritage. Because of their age, Dunhuang murals have suffered from discoloration, fading and damage. With the development of computer image restoration technology, digital image processing provides strong support for mural restoration. Although there are many research results in the field of ancient mural images, there is no pre-trained model based on ancient mural images available for migration. There is a method for Dunhuang murals image restoration based on Generative Adversarial Network (GAN) with improved ResNet50 structure as an encoder in the generator. This research result proposes a generative adversarial network model with a parallel double convolutional extraction of deep features for the generator, combined with a ternary heterogeneous joint discriminator. On this basis, this paper proposes a binary classification task to distinguish ancient mural images from modern natural style images, so that the model pre-learns features such as color, texture, and structure of mural images, which are inputted into the restoration network as a priori information, in order to achieve the effect of reducing the probability of incomplete restoration of the model, and better generalization ability. The experimental results show that the method proposed in this paper has an overall improvement in the evaluation indexes such as Peak Signal to Noise Ratio(PSNR) and Structural Similarities(SSIM) when compared with other methods, and the restored images are more in line with human subjective vision, which proves the validity of the method. © 2024 IEEE.

关键词： Generative adversarial networks

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Unicast Beamforming in the Presence of a Smart Reactive Jammer: A Stackelberg Game Approach 24

Unicast Beamforming in the Presence of a Smart Reactive Jamm...

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24th IEEE International Workshop on Signal Processing Advances in Wireless communications, SPAWC 2023

作者： Shi, Bai Shao, Huaizhong Lin, Jingran Zhai, Zhikai University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu China Laboratory of Electromagnetic Space Cognition and Intelligent Control Beijing100083 China Peng Cheng Laboratory Shenzhen518055 China

ISBN: (纸本)9781665496261

The malicious jamming attacks will disrupt legitimate public wireless communication systems, which has become a critical issue that must be considered in modern communication system design. This paper considers an anti-jamming unicast transmit beamforming problem with a smart reactive jammer. We model this problem as a Stackelberg game and formulate a bilevel optimization problem, in which the base station maximizes the minimum signal-to-interference-jamming-noise ratio (SIJNR) of legitimate users and the jammer minimizes the maximum SIJNR. This problem is quite challenging due to its hierarchical structure and nonconvexity. A penalty method is developed to solve this problem, which transforms it into a mathematical program with equilibrium constraint (MPEC) problem after implementing a tight semi-definite relaxation (SDR) method. The numerical results demonstrate its superior performance. © 2023 IEEE.

关键词： Beamforming

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Interval Type-2 Fuzzy Hierarchical Adaptive Cruise Following-control for Intelligent Vehicles

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IEEE/CAA Journal of Automatica Sinica 2022年第9期9卷 1658-1672页

作者： Hong Mo Yinghui Meng Fei-Yue Wang Dongrui Wu the College of Electric and Information Engineering Changsha University of Science and TechnologyChangsha 410114China the State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190 Qingdao Academy of Intelligent Industries Qingda 266109 the Center of China Economic and Social Security University of Chinese Academy of SciencesBeijing 100190China the Ministry of Education Key Laboratory on Image Information Processing and Intelligent Control the School of Artificial Intelligence and AutomationHuazhong University of Science and TechnologyWuhan 430074China

Intelligent vehicles can effectively improve traffic congestion and road traffic *** cruise followingcontrol(ACFC)is a vital part of intelligent *** this paper,a new hierarchical vehicle-following control strategy is presented by synthesizing the variable time headway model,type-2 fuzzy control,feedforward+fuzzy proportion integration(PI)feedback(F+FPIF)control,and inverse longitudinal dynamics model of ***,a traditional variable time headway model is improved considering the acceleration of the lead ***,an interval type-2 fuzzy logic controller(IT2 FLC)is designed for the upper structure of the ACFC system to simulate the driver's operating *** reduce the nonlinear influence and improve the tracking accuracy for the desired acceleration,the control strategy of F+FPIF is given for the lower control ***,the lower control method proposed in this paper is compared with the fuzzy PI control and the traditional method(no lower controller for tracking desired acceleration)***,the proportion integration differentiation(PID),linear quadratic regulator(LQR),subsection function control(SFC)and type-1 fuzzy logic control(T1 FLC)are respectively compared with the IT2 FLC in control performance under different ***,the simulation results show the effectiveness of IT2 FLC for the upper structure and F+FPIF control for the lower structure.

关键词： Adaptive cruise following-control(ACFC) feedforward+fuzzy proportion integration(PI)feedback(F+FPIF) type-2 fuzzy control variable time headway

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Infrared Target Detection Based on the Fusion of Mask R-CNN and Image Enhancement Network

Infrared Target Detection Based on the Fusion of Mask R-CNN ...

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2022 Chinese Automation Congress, CAC 2022

作者： Liu, Fang Guan, Song Yu, Kuaikuai Gong, Hua Shengyang Ligong University College of Science Shenyang110159 China Science and Technology on Electro-Optical Information Security Control Laboratory Tianjing300308 China

ISBN: (纸本)9781665465335

In this paper, we focus on the problem of the infrared image detection. Aiming at the problem of the low contrast of the infrared images, an adaptive gamma transformation algorithm (AGT) is established to intelligently improve the contrast of the infrared images. Aiming at the problem of the weak edge information of the infrared images, an improved Sobel operator edge enhancement algorithm (ISOEE) is designed to enrich the edge features of the infrared images. In order to solve the problem that the traditional infrared image detection algorithm relies on expert experience, the fusion infrared images detection model AIE-Mask R-CNN based on Mask R-CNN, AGT algorithm and ISOEE algorithm is proposed. This fusion model can realize accurate and intelligent detection for the infrared images. The experimental results show that the fusion model AIE-Mask R-CNN improves the detection accuracy of the infrared images. © 2022 IEEE.

关键词： Edge detection

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