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Adaptive Image Steganographic Analysis System Based on Deep Convolutional Neural Network 11th

Adaptive Image Steganographic Analysis System Based on Deep ...

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11th International Conference on computer Engineering and Networks, CENet2021

作者： Jiao, Ge College of Computer Science and Technology Hengyang Normal University HengyangHunan421002 China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application HengyangHunan421002 China

ISBN: (纸本)9789811665530

Adaptive steganography emplaces the message into the hard-to-detect noise area or the complex texture area of the image, so the steganography analysis method based on artificial design features needs to design a very complex feature extraction algorithm to detect the steganography image. In view of the advantages of deep learning steganography in automatic extraction of image features and high detection accuracy, an image steganography analysis algorithm is designed by using deep convolutional neural network, and an image steganography analysis system based on deep learning is developed. The system realizes the functions of neural network structure analysis, image steganography and verification, steganography analysis and so on. The system simplifies the operation process of algorithm comparison, reduces the complexity of algorithm performance evaluation, and verifies the feasibility of the proposed algorithm. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Feature extraction

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Traffic Sign Recognition System Based on YOLOv5 12th

Traffic Sign Recognition System Based on YOLOv5

引用

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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Research on Model Predictive Control-Based Feedback Strategies for Autonomous Driving

Research on Model Predictive Control-Based Feedback Strategi...

引用

IEEE Advanced Information Management,Communicates,Electronic and Automation Control Conference (IMCEC)

作者： Ting Li Xianxia Xu Xuejuan Liu Jie Liu School of Computer and Information Engineering Guangxi Vocational Normal University Nanning China Key Laboratory of Application Technology of Intelligent Connected Vehicle Nanning China

ISBN: (数字)9798350316537

ISBN: (纸本)9798350316544

To address the issues of control stability and safety in autonomous vehicles, this study focuses on research related to Model Predictive Control (MPC) algorithms and algorithm optimization. By leveraging MPC control algorithms and system dynamics techniques, we have achieved stable autonomous driving. Simulation experiments based on Model Predictive Control have been conducted to validate the feasibility and effectiveness of MPC algorithms for feedback control in autonomous driving. Two distinct simulation scenarios have been designed to verify the MPC algorithm’s performance under different road conditions. The results indicate that the MPC algorithm effectively enhances the control stability and safety of autonomous driving vehicles.

关键词： System dynamics Heuristic algorithms Roads Predictive models Prediction algorithms Stability analysis Safety

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Research on Environmental Perception Based on YOLOv8 and LiDAR Data Fusion

Research on Environmental Perception Based on YOLOv8 and LiD...

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Information technology,Big Data and Artificial Intelligence (ICIBA), International Conference on IEEE International Conference on

作者： ZhenHong Luo HuiLing Li Ting Li Jie Liu School of Computer and Information Engineering Guangxi Vocational Normal University Nanning China Key Laboratory of Application Technology of Intelligent Connected Vehicle Nanning China

ISBN: (数字)9798350363609

ISBN: (纸本)9798350363616

To enhance the sensing capability of driverless cars in complex dynamic scenarios, a fusion method of radar and depth camera is emphasized and a technical framework for integrating multi-source sensors is proposed. A YOLOv8 neural network detection model combined with a self-attention network structure is first used to achieve correct recognition and depth estimation of vehicles and pedestrians, then further distance data of dynamic environment is acquired by M10P LiDAR, and finally information from D435i camera and radar information are fused by matrix projection. In the experiments, the conventional YOLOv8 model was compared to the YOLOv8 model enhanced with self-attention, the camera and LIDAR measurements were evaluated against the fusion results. The experimental results demonstrate that the new model significantly improves the confidence and stability of target detection, while the fused distance measurements are more accurate than those from individual sensors.

关键词： Laser radar Pedestrians Radar detection Object detection Sensor fusion Cameras Data models Stability analysis Vehicle dynamics Intelligent sensors

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Heavy Rainfall Prediction Model Using Sample Entropy Derived from GNSS-PWV and PSO-SVM 14th

Heavy Rainfall Prediction Model Using Sample Entropy Derived...

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14th China Satellite Navigation Conference, CSNC 2024

作者： Wu, Fanming Li, Dengao Zhao, Jinhua Feng, Ran Shi, Danyang Zhang, Xinfang Zhao, Jumin College of Data Science Taiyuan University of Technology Taiyuan030024 China College of Information and Computer Taiyuan University of Technology Taiyuan030024 China Key Laboratory of Big Data Fusion Analysis and Application of Shanxi Province Taiyuan030024 China Intelligent Perception Engineering Technology Center of Shanxi Taiyuan030024 China Shanxi Province Engineering Technology Research Center of Spatial Information Network Taiyuan030024 China

ISBN: (纸本)9789819969272

There is a growing interest to use Global Navigation Satellite System (GNSS) inversed PWV for heavy rainfall prediction. When heavy rainfall occurs, it requires the atmosphere to contain sufficient water vapour and undergo strong upward motion. However, existing models using GNSS-PWV for heavy rainfall prediction have not accounted for the effect of the complex motion of water vapour. In this paper, an hourly heavy rainfall prediction model using sample entropy derived from GNSS-PWV and PSO-SVM is proposed. The sample entropy of GNSS-PWV is used to measure the complexity of the water vapour movement process before heavy rainfall occurs. Meanwhile, combining GNSS-PWV time domain features and co-located meteorological data, the occurrence of hourly heavy rainfall events is predicted by support vector machine optimized with particle swarm. To verify the validity and feasibility of the proposed algorithm, Hong Kong's HKSC station in Sham Shui Po is used for the model to train and test. The result shows that probability of detection, false alarm rate and critical success index of the proposed algorithm in this paper have significantly improved over other heavy rainfall prediction models. © 2024, Aerospace Information Research Institute.

关键词： Support vector machines

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Research on Zenith Tropospheric Delay Model Based on TCN Improving HGPT2 Model 14th

Research on Zenith Tropospheric Delay Model Based on TCN Imp...

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14th China Satellite Navigation Conference, CSNC 2024

作者： Li, Dengao Shi, Danyang Zhao, Jumin Wu, Fanming Yan, Liangquan Feng, Ran Zhang, Xinfang Zhao, Jinhua College of Data Science Taiyuan University of Technology Taiyuan030024 China College of Information and Computer Taiyuan University of Technology Taiyuan030024 China Key Laboratory of Big Data Fusion Analysis and Application of Shanxi Province Taiyuan030024 China Intelligent Perception Engineering Technology Center of Shanxi Taiyuan030024 China Shanxi Province Engineering Technology Research Center of Spatial Information Network Taiyuan030024 China

ISBN: (纸本)9789819969272

The zenith tropospheric delay (ZTD) obtained by global navigation satellite system (GNSS) atmospheric sounding is a pivotal data source for water vapor monitoring. Meteorological changes in Antarctica play an important role in analysis of the global climate, but factors such as complex climatic conditions can limit the collection of meteorological data needed for ZTD retrieval. Therefore, it is necessary to establish high-precision ZTD models that do not rely on measured meteorological data. The existing global pressure and temperature 3 (GPT3) model has limited ability to capture complex weather variations and cannot obtain high-precision GPT3_ZTD. To address the above issues, this research proposes a high-precision ZTD model through using temporal convolutional network (TCN) for improving the hourly global pressure and temperature 2 (HGPT2) model. The HGTP2 model based on Fourier analysis and the time-segmentation concept can consider the linear trend of climate variations, simultaneously the TCN is introduced to simulate the long-term temporal dependence of HGPT2_ZTD, so as to achieve the goal of obtaining high-precision ZTD without relying on measured meteorological data. The experimental results show that the precision of TCN_ZTD obtained from the proposed model is higher than GPT3_ZTD and HGPT2_ZTD when using the GNSS_ZTD observations obtained from 35 stations in Antarctica as a reference, and the improvement of the model is obvious. © 2024, Aerospace Information Research Institute.

关键词： Fourier analysis

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Prediction of Ionospheric TEC Based on BLS-LSTM-GRU Hybrid Model 14th

Prediction of Ionospheric TEC Based on BLS-LSTM-GRU Hybrid M...

引用

14th China Satellite Navigation Conference, CSNC 2024

作者： Li, Dengao Zhang, Xinfang Zhao, Jumin Wu, Fanming Feng, Ran Zhao, Jinhua Shi, Danyang College of Data Science Taiyuan University of Technology Taiyuan030024 China College of Information and Computer Taiyuan University of Technology Taiyuan030024 China Key Laboratory of Big Data Fusion Analysis and Application of Shanxi Province Taiyuan030024 China Intelligent Perception Engineering Technology Center of Shanxi Taiyuan030024 China Shanxi Province Engineering Technology Research Center of Spatial Information Network Taiyuan030024 China

ISBN: (纸本)9789819969272

In satellite communications, global navigation satellite systems(GNSS) and other important space activities, the value of total electron content (TEC) in the ionosphere directly affects the size of ionospheric delay. Achieving accurate prediction of ionospheric total electron content through modeling can effectively improve the reliability and accuracy of GNSS positioning. Considering the high prediction accuracy of long short-term memory neural network (LSTM) and the fast training speed of gated recurrent unit network (GRU), but it is easy to fall into the local optimum, while the width learning system(BLS) can effectively avoid this drawback. Therefore, this paper proposes a model for predicting the total electron content of the ionosphere, which consists of a broad learning system, an LSTM network and a GRU networks. Six parameters of TEC, geomagnetic index Dst, Kp, Ap, solar activity index F10.7, and hour (HD) are selected as input features from the observed data to predict the variation of the total ionospheric electron content. The results generate prediction maps of the total ionospheric electron content and are compared with empirical models and conventional neural network models at different times and solar activity conditions. The experimental results show that the root mean square error (RMSE) of the prediction results of this BLS-LSTM-GRU model is decreased by 23.07%, 17.48% and 9.46% compared with the IRI-2016, NeQuick and CNN-LSTM prediction models, respectively, with higher prediction accuracy. © 2024, Aerospace Information Research Institute.

关键词： Long short-term memory

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Passive Positioning and Filtering technology Using Relative Height Measurement Information 3rd

Passive Positioning and Filtering Technology Using Relative ...

引用

3rd International Conference on Artificial Intelligence, 2022

作者： Cao, Lai-Tian Chen, Kai-Wei Sun, Peng Qiang, Xiao-Min Zhang, Yi-Ming Zhang, Li-Jia Beijing Aerospace Automatic Control Institute Beijing100854 China National Key Laboratory of Science and Technology on Aerospace Intelligent Control Beijing100854 China Beijing Institute of Computer Technology and Application Beijing100854 China

ISBN: (纸本)9789811694226

Under the condition of obtaining the passive measurement angle information and the relative height information of the positioning system and the target, using the least squares algorithm to locate the radiation source target, and then using the Kalman filter to filter and predict the position data after the least square estimation, so as to make the best estimation of the motion state of the radiation source target. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Kalman filters

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A Multi-Armed Bandits Learning-Based Approach to Service Caching in Edge Computing Environment 30th

A Multi-Armed Bandits Learning-Based Approach to Service Ca...

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30th International Conference on Web Services, ICWS 2023

作者： Li, Jinpeng Zhao, Jiale Chen, Peng Xia, Yunni Li, Fan Li, Yin Zeng, Feng Liu, Hui School of Computer Chongqing University Chongqing400030 China School of Computer and Software Engineering Xihua University Chengdu610039 China Key Laboratory of Fundamental Synthetic Vision Graphics and Image Science for National Defense Sichuan University Chengdu610065 China Guangzhou Institute of Software Application Technology Guangzhou510990 China Limited Shenzhen518129 China School of Computer Science and Technology Beijing Institute of Technology Beijing100083 China

ISBN: (纸本)9783031448355

Mobile edge computing (MEC) is a newly emerging concept that provides significant local computing power and reduces end-to-end latency. In MEC environments, caching frequently accessed services on edge servers effectively reduces latency and improves system responsiveness. An ongoing research topic in such a cachable MEC context is to design novel algorithms for yielding high-quality caching decision that guarantee high user-perceived quality-of-service (QoS) and high system responsiveness of delivery of cached content with the difference of caching capacities of edge servers and diversified content popularity appropriately addressed. In this article, we propose a multi-armed bandits learning-based method busing a Thompson sampling for generating caching decisions. We introduce a genetic multi-armed bandits algorithm (GMAB), which synthesizes the genetic algorithm (GA) and multi-armed bandits (MAB), for optimizing caching effectiveness with timing and space constraints. The experiment results show that GMAB outperforms traditional methods in terms of multiple aspects. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Genetic algorithms

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Spectral-Spatial Fusion Hypergraph Convolutional Network for Multispectral Point Cloud Classification 11

Spectral-Spatial Fusion Hypergraph Convolutional Network for...

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11th International Conference on Information Systems and Computing technology, ISCTech 2023

作者： Huang, Jiangbo Wang, Qingwang Zhang, Zifeng Liu, Sivang Wang, Zhifeng Shen, Tao Kunming University of Science and Technology Yunnan Key Laboratory of Computer Technologies Application Kunming China Kunming University of Science and Technology School of Information Engineering and Automation Kunming China Real Estate Resource Management Center General Logistics Department of Pla Beijing China Beijing Anlu International Technology Co Beijing China

ISBN: (纸本)9798350342406

Graph Neural Networks (GNNs) based on simple graph can directly model 3D multispectral point clouds, thus avoiding the problem of 3D spatial information loss when converting 3D point clouds to 2D feature images. However, considering the dense distribution of objects in remote sensing scenes, GNNs only consider the association between pairs of vertices, which is insufficient for exploring high-order correlations and long-term dependence correlations. Hypergraph neural network (HGNN) is a more generalized framework that overcomes the limitations of traditional simple graph. In this paper, we propose a spectral-spatial fusion hypergraph convolutional network (S2FHGCN) for 3D multispectral point cloud classification. S2FHGCN first generates two hypergraphs from multidimensional features including spectral feature and spatial feature, respectively. The hypergraph structure can better handle complex high-order correlations for representation learning. Then the two hypergraphs are fused to obtain global representation information. Finally, the multidimensional features and the fused hypergraph are input to the hypergraph convolution network. The experimental results on real multispectral point clouds demonstrate the effectiveness of our proposed S2FHGCN for multispectral point cloud classification. © 2023 IEEE.

关键词： Remote sensing

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