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12th International Conference on computer Engineering and Networks, CENet 2022

作者： Ou, Yangze Sun, Yaqi Yu, Xiaozhong Yun, Liuqing College of Computer Science and Technology Hengyang Normal University Hunan Hengyang421008 China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hunan Hengyang421008 China

ISBN: (纸本)9789811969003

With more and more developed technology, unmanned driving technology has gradually entered people’s vision. Many cars are now equipped with self-parking technology, which allows the vehicle to enter the garage through a pre-set path without manual operation. At the same time, the traffic light identification mentioned in this paper can also be used for driverless cars, and can also be combined with manual driving to reduce the probability of traffic accidents caused by running red lights. This design is based on Transformer and Resnet convolutional neural network, using feature extraction technology. First of all, the data set used in this algorithm is self-made COCO data set. Then, the corresponding training is carried out to obtain the corresponding model of traffic signal light recognition. Finally, the trained model is used to predict the relevant traffic signal light. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Convolution

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Multi-phase field simulation of competitive grain growth for directional solidification

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Chinese Physics B 2022年第6期31卷 683-694页

作者： Chang-Sheng Zhu Zi-Hao Gao Peng Lei Li Feng Bo-Rui Zhao College of Computer and Communication Lanzhou University of TechnologyLanzhou 730050China State Key Laboratory of Gansu Advanced Processing and Recycling of Non-Ferrous Metal Lanzhou University of TechnologyLanzhou 730050China Network&Information Center Lanzhou University of TechnologyLanzhou 730050China

The multi-phase field model of grain competitive growth during directional solidification of alloy is *** multi-phase field models for thin interface layer thickness conditions,the grain boundary evolution and grain elimination during the competitive growth of SCN-0.24-wt%camphor model alloy bi-crystals are *** effects of different crystal orientations and pulling velocities on grain boundary microstructure evolution are quantitatively *** obtained results are shown *** the competitive growth of convergent bi-crystals,when favorably oriented dendrites are in the same direction as the heat flow and the pulling speed is too large,the orientation angle of the bi-crystal from small to large size is the normal elimination phenomenon of the favorably oriented dendrite,blocking the unfavorably oriented dendrite,and the grain boundary is along the growth direction of the favorably oriented *** the pulling speed becomes small,the grain boundary shows the anomalous elimination phenomenon of the unfavorably oriented dendrite,eliminating the favorably oriented *** the process of competitive growth of divergent bi-crystal,when the growth direction of favorably oriented dendrites is the same as the heat flow direction and the orientation angle of unfavorably oriented grains is small,the frequency of new spindles of favorably oriented grains is significantly higher than that of unfavorably oriented grains,and as the orientation angle of unfavorably oriented dendrites becomes larger,the unfavorably oriented grains are more likely to have stable secondary dendritic arms,which in turn develop new primary dendritic arms to occupy the liquid phase grain boundary space,but the grain boundary direction is still parallel to favorably oriented *** addition,the tertiary dendritic arms on the developed secondary dendritic arms may also be blocked by the surrounding lateral branches from further developing into nascent main

关键词： multi-phase field model directional solidification grain competition growth grain boundary orientation

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Intra-Guided Dual-Energy Material Decomposition Method for Dual-Source CBCT Systems 46

Intra-Guided Dual-Energy Material Decomposition Method for D...

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46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Zhong, Xinyun Lyu, Tianling Wu, Zhan Gao, Wei Xi, Yan Zhao, Wei Zhu, Wentao Chen, Yang Zhejiang Lab Hangzhou China First-Imaging Tech. Shanghai China Beihang University Beijing China Southeast University School of Computer Science and Engineering Laboratory of Image Science and Technology Nanjing China Southeast University Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Nanjing China

ISBN: (纸本)9798350371499

Dual-energy CBCT imaging plays a crucial role in advanced imaging applications due to its ability to quantify material components. Although there are multiple established systems for dual-energy imaging, they often come with high deployment costs. This study investigates the feasibility of dual-energy material decomposition on a less expensive dual-source CBCT system. Due to the specific geometry of dual-source CBCT, cone-beam (CB) artifacts seriously degrade the material decomposition quality when the direct inversion method is adopted. In this paper, a novel dual-source intra-guided material decomposition framework is proposed to suppress CB artifacts in the material maps. Firstly, a dual-source two-pass algorithm is developed for artifact pre-correction during image reconstruction. Then, the structure of small cone-angle regions from one X-ray source is utilized to guide cone beam artifact removal in large cone-angle regions from another source. Finally, direct matrix inverse material decomposition is applied to the filtered image. Extensive experiments have been conducted on the authentic human phantom and demonstrate that the proposed framework achieves excellent performance in cone beam artifact reduction compared to other methods, and significantly enhances the quality of the material maps. © 2024 IEEE.

关键词： Image reconstruction

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An array of two periodic leaky-wave antennas with sum and difference beam scanning for application in target detection and tracking

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Frontiers of information technology & Electronic Engineering 2023年第4期24卷 567-581页

作者： Mianfeng HUANG Juhua LIU School of Electronics and Information Technology Sun Yat-sen UniversityGuangzhou 510006China Guangdong Provincial Key Laboratory of Optoelectronic Information Processing Chips and Systems Sun Yat-sen UniversityGuangzhou 510006Chin

An array of two substrate-integrated waveguide(SIW) periodic leaky-wave antennas(LWAs) with sum and difference beam scanning is proposed for application in target detection and tracking. The array is composed of two periodic LWAs with different periods, in which each LWA generates a narrow beam through the n=-1 space harmonic. Due to the two different periods for the two LWAs, two beams with two different directions can be realized, which can be combined into a sum beam when the array is fed in phase or into a difference beam when the array is fed 180?out of phase. The array integrated with 180?hybrid is designed, fabricated, and *** results show that the sum beam can reach a gain up to 15.9 dBi and scan from-33.4?to 20.8?. In the scanning range, the direction of the null in the difference beam is consistent with the direction of the sum beam,with the lowest null depth of-40.8 dB. With the excellent performance, the antenna provides an alternative solution with low complexity and low cost for target detection and tracking.

关键词： Antenna Leaky-wave antenna(LWA) Substrate-integrated waveguide(SIW) Sum and difference beam Target detection and tracking

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Variational Graph Autoencoders Method Based on Attentional Mechanisms for Overlapping Community Detection 12th

Variational Graph Autoencoders Method Based on Attentional M...

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12th International Conference on computer Engineering and Networks, CENet 2022

作者： Wen, Kunhui Lin, Mugang Zhu, Xuanying Zhao, Huihuang College of Computer Science and Technology Hengyang Normal University Hunan Hengyang421008 China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hunan Hengyang421008 China

ISBN: (纸本)9789811969003

Detecting overlapping communities of an attribute network is an important and difficult issue in network science. Traditional methods for overlapping community detection generally considered only its topological structure and neglected node attribute so that the detected communities could be inaccurate. Graph neural networks (GNN) have recently gained attention because of their learning representation ability for node attributes and the topology of a graph and demonstrating superior performance in natural language processing, data mining and pattern recognition, etc. However, GNN methods are still less for overlapping community detection. In this paper, we present an overlapping community detection method based on GNN for attribute networks. It applies a variational graph autoencoder based on attentional mechanisms to learn the representation of nodes in the graph and enhances the representation learning capability of GNN through semi-supervised learning. Various real-world networks of publicly available datasets are used to verify the effectiveness of the proposed approach. The experimental results show that the proposed method outperforms many other methods. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Graph neural networks

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Image Quality Assessment Method Based on Cross-Modal 20th

Image Quality Assessment Method Based on Cross-Modal

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20th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2023

作者： Long, Teng Chen, Li Wang, Yubo School of Computer Science and Technology Wuhan University of Science and Technology Hubei Wuhan430065 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology Hubei Wuhan430065 China

ISBN: (纸本)9789819970247

Deep learning methods have achieved remarkable results in the direction of image quality assessment tasks. However, most of the related studies focus only on image unimodal, ignoring the potential advantages that come with the development of cross-modal techniques. Cross-modal models have implied a wealth of information, which provides new research directions and possibilities in the field of image quality assessment. In this paper, the feasibility of cross-modal models in image quality assessment is first explored for the image quality binary classification task. Subsequently, the optimization prompting method is combined with the tuning of the image encoder in the cross-modal model so that the cross-modal model can be used for image quality assessment scoring. To verify the feasibility of the cross-modal model on the image quality assessment task, an empirical analysis was conducted on the binary image quality dataset PQD, and it was found that the F1 score improved by 18% over the baseline model. Further, we propose an adaptive cross-modal image quality assessment method AC-IQA. On the image quality scoring dataset, compared with the previous optimal methods, AC-IQA improves the PLCC and SROCC metrics on the TID2013 dataset by 5.5% and 9.5%, respectively, and on the KADID dataset by 6.2% and 5.2%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Image quality

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Traffic Sign Recognition System Based on YOLOv5 12th

Traffic Sign Recognition System Based on YOLOv5

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12th International Conference on computer Engineering and Networks, CENet 2022

作者： Zhang, Lin Sun, Yaqi Chen, Wenli Liang, Xiaoman College of Computer Science and Technology Hengyang Normal University Hunan Hengyang421008 China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hunan Hengyang421008 China

ISBN: (纸本)9789811969003

Traffic sign recognition is an important part of intelligent driving and an important content of intelligent transportation system. However, the current traffic sign detection algorithm has some problems, such as high network complexity, low recognition efficiency, poor recognition accuracy, and difficult edge deployment. Aiming at the above problems, this paper proposes to use YOLOv5 single-stage target detection algorithm to identify and detect traffic signs. The input terminal of YOLOv5 enhances and improves adaptive image scaling through Mosaic data, thus reducing information redundancy, reducing computation and improving target detection speed. The CSP structure of Backbone solves the problem of large amount of calculation in reasoning design from the Angle of network structure design. By adopting FPN + CPN structure, the fusion ability of network features is strengthened. Through hardware and software integration design, YOLOv5s model can be deployed to edge equipment, thus forming a set of traffic sign target detection system with fast inference speed, strong anti-interference ability and high identification accuracy. The results show that the overall identification accuracy of traffic signs is up to 98% by adjusting the parameters and improving the algorithm. Through testing 676 images, the recognition accuracy is 96.15% and the average detection speed is 19 frames/s, which meets the requirements of real-time traffic sign detection. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Traffic signs

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Deep Learning Image Steganalysis Method Fused with CBAM 12th

Deep Learning Image Steganalysis Method Fused with CBAM

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12th International Conference on computer Engineering and Networks, CENet 2022

作者： Chen, Huasuo Jiao, Ge College of Computer Science and Technology Hengyang Normal University Hunan Hengyang421002 China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hunan Hengyang421002 China

ISBN: (纸本)9789811969003

Steganography is a critical technical tool for preventing the disclosure of sensitive information. The detection performance of picture steganography algorithms based on deep learning has to be enhanced in tandem with the ongoing improvement of adaptive steganography algorithm performance. In this paper, a new model SRNet-CBAM based on SRNet fusion channel attention module and spatial attention module was presented to address the challenge of adaptive steganography analysis and the difficulty of model focused analysis for picture favorable regions. In three different embedding rates of WOW, S-UNIWARD, and HUGO algorithms, the experimental results reveal that the SRNet-CBAM model increases the accuracy of the SRNet model by 1.36% on average. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Steganography

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Hybrid-integrated chalcogenide photonics

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Light(Advanced Manufacturing) 2023年第4期4卷 167-182页

作者： Bin Zhang Di Xia Xin Zhao Lei Wan Zhaohui Li Guangdong Provincial Key Laboratory of Optoelectronic Information Processing Chips and Systems School of Electrical and Information TechnologySun Yat-sen UniversityGuangzhou 510275China Key Laboratory of Optoelectronic Materials and Technologies Sun Yat-sen UniversityGuangzhou 510275China Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai) Zhuhai519000China Department of Electronic Engineering College of Information Science and

High-quality photonic materials are critical for promoting integrated photonic devices with broad bandwidths,high efficiencies,and flexibilities for high-volume chip-scale ***,we designed a home-developed chalcogenide glass(ChG)-Ge_(25)Sb_(10)S_(65)(GeSbS)for optical information processing chips and systems,which featured an ultrabroad transmission window,a high Kerr nonlinearity and photoelastic coefficient,and compatibility with the photonic hybrid integration technology of silicon ***-integrated GeSbS microresonators and microresonator arrays with high quality factors and lithographically controlled fine structures were fabricated using a modified nanofabrication ***,considering the high Kerr nonlinearity and photoelastic effect of ChGs,we realised a novel ChG hybrid integrated chip,inspired by recent advances in integrated soliton microcombs and acousto-optic(AO)modulators.

关键词： Chalcogenide glasses Photonic integrated chips Soliton microcombs Acousto-optic interactions

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An improved algorithm for frequent sequence pattern mining based on PrefixSpan-ComplexPrefixSpan 23

An improved algorithm for frequent sequence pattern mining b...

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Proceedings of the 4th International Conference on Artificial Intelligence and computer Engineering

作者： Shiyong Ning Shihui Zhang Heilongjiang Provincial Key Laboratory of Electronic Commerce and Information Processing China School of Computer and Information Engineering Harbin University of Commerce China

ISBN: (纸本)9798400708831

In order to solve the problem that the sequence pattern mining algorithm requires a large computation time, an improved PrefixSpan algorithm is proposed. Because PrefixSpan is an algorithm that does not need to produce candidate sequences, and it has a lower temporal complexity and spatial complexity compared to other sequence pattern mining algorithms. However, the traditional PrefixSpan algorithm is only applicable to mining frequent patterns in single sequences. This paper introduces a multi-sequence frequent pattern mining framework, applied to the medical data set synthesized by Synthea generator. The results show that the algorithm has certain feasibility and has certain theoretical and practical significance for the study of multi-sequence frequent pattern mining.

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