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  • Open Access

    ARTICLE

    Perception Enhanced Deep Deterministic Policy Gradient for Autonomous Driving in Complex Scenarios

    Lyuchao Liao1,2, Hankun Xiao2,*, Pengqi Xing2, Zhenhua Gan1,2, Youpeng He2, Jiajun Wang2

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 557-576, 2024, DOI:10.32604/cmes.2024.047452

    Abstract Autonomous driving has witnessed rapid advancement; however, ensuring safe and efficient driving in intricate scenarios remains a critical challenge. In particular, traffic roundabouts bring a set of challenges to autonomous driving due to the unpredictable entry and exit of vehicles, susceptibility to traffic flow bottlenecks, and imperfect data in perceiving environmental information, rendering them a vital issue in the practical application of autonomous driving. To address the traffic challenges, this work focused on complex roundabouts with multi-lane and proposed a Perception Enhanced Deep Deterministic Policy Gradient (PE-DDPG) for Autonomous Driving in the Roundabouts. Specifically, the… More >

  • Open Access

    ARTICLE

    An Industrial Intrusion Detection Method Based on Hybrid Convolutional Neural Networks with Improved TCN

    Zhihua Liu, Shengquan Liu*, Jian Zhang

    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 411-433, 2024, DOI:10.32604/cmc.2023.046237

    Abstract Network intrusion detection systems (NIDS) based on deep learning have continued to make significant advances. However, the following challenges remain: on the one hand, simply applying only Temporal Convolutional Networks (TCNs) can lead to models that ignore the impact of network traffic features at different scales on the detection performance. On the other hand, some intrusion detection methods consider multi-scale information of traffic data, but considering only forward network traffic information can lead to deficiencies in capturing multi-scale temporal features. To address both of these issues, we propose a hybrid Convolutional Neural Network that supports… More >

  • Open Access

    ARTICLE

    Improved Blending Attention Mechanism in Visual Question Answering

    Siyu Lu1, Yueming Ding1, Zhengtong Yin2, Mingzhe Liu3,*, Xuan Liu4, Wenfeng Zheng1,*, Lirong Yin5

    Computer Systems Science and Engineering, Vol.47, No.1, pp. 1149-1161, 2023, DOI:10.32604/csse.2023.038598

    Abstract Visual question answering (VQA) has attracted more and more attention in computer vision and natural language processing. Scholars are committed to studying how to better integrate image features and text features to achieve better results in VQA tasks. Analysis of all features may cause information redundancy and heavy computational burden. Attention mechanism is a wise way to solve this problem. However, using single attention mechanism may cause incomplete concern of features. This paper improves the attention mechanism method and proposes a hybrid attention mechanism that combines the spatial attention mechanism method and the channel attention More >

  • Open Access

    ARTICLE

    Image Inpainting Detection Based on High-Pass Filter Attention Network

    Can Xiao1,2, Feng Li1,2,*, Dengyong Zhang1,2, Pu Huang1,2, Xiangling Ding3, Victor S. Sheng4

    Computer Systems Science and Engineering, Vol.43, No.3, pp. 1145-1154, 2022, DOI:10.32604/csse.2022.027249

    Abstract Image inpainting based on deep learning has been greatly improved. The original purpose of image inpainting was to repair some broken photos, such as inpainting artifacts. However, it may also be used for malicious operations, such as destroying evidence. Therefore, detection and localization of image inpainting operations are essential. Recent research shows that high-pass filtering full convolutional network (HPFCN) is applied to image inpainting detection and achieves good results. However, those methods did not consider the spatial location and channel information of the feature map. To solve these shortcomings, we introduce the squeezed excitation blocks More >

  • Open Access

    ARTICLE

    Mixed Attention Densely Residual Network for Single Image Super-Resolution

    Jingjun Zhou1,2, Jing Liu3, Jingbing Li1,2,*, Mengxing Huang1,2, Jieren Cheng4, Yen-Wei Chen5, Yingying Xu3,6, Saqib Ali Nawaz1

    Computer Systems Science and Engineering, Vol.39, No.1, pp. 133-146, 2021, DOI:10.32604/csse.2021.016633

    Abstract Recent applications of convolutional neural networks (CNNs) in single image super-resolution (SISR) have achieved unprecedented performance. However, existing CNN-based SISR network structure design consider mostly only channel or spatial information, and cannot make full use of both channel and spatial information to improve SISR performance further. The present work addresses this problem by proposing a mixed attention densely residual network architecture that can make full and simultaneous use of both channel and spatial information. Specifically, we propose a residual in dense network structure composed of dense connections between multiple dense residual groups to form a More >

  • Open Access

    ARTICLE

    SSD Real-Time Illegal Parking Detection Based on Contextual Information Transmission

    Huanrong Tang1, Aoming Peng1, Dongming Zhang2, Tianming Liu3, Jianquan Ouyang1, *

    CMC-Computers, Materials & Continua, Vol.62, No.1, pp. 293-307, 2020, DOI:10.32604/cmc.2020.06427

    Abstract With the improvement of the national economic level, the number of vehicles is still increasing year by year. According to the statistics of National Bureau of Statics, the number is approximately up to 327 million in China by the end of 2018, which makes urban traffic pressure continues to rise so that the negative impact of urban traffic order is growing. Illegal parking-the common problem in the field of transportation security is urgent to be solved and traditional methods to address it are mainly based on ground loop and manual supervision, which may miss detection… More >

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