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Enhancing aquaculture water quality forecasting using novel adaptive multi-channel spatial-temporal graph convolutional network

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International Journal of Agricultural and Biological Engineering 2025年第1期18卷 279-291页

作者： Tianqi Xiang Xiangyun Guo Junjie Chi Juan Gao Luwei Zhang College of Computer Science Beijing Information Science&Technology UniversityBeijing 102206China College of Management Science and Engineering Beijing Information Science&Technology UniversityBeijing 102206China College of Engineering China Agricultural UniversityBeijing 100083China

In recent years,aquaculture has developed rapidly,especially in coastal and open ocean *** practice,water quality prediction is of critical ***,traditional water quality prediction models face limitations in handling complex spatiotemporal *** address this challenge,a prediction model was proposed for water quality,namely an adaptive multi-channel temporal graph convolutional network(AMTGCN).The AMTGCN integrates adaptive graph construction,multi-channel spatiotemporal graph convolutional network,and fusion layers,and can comprehensively capture the spatial relationships and spatiotemporal patterns in aquaculture water quality *** aquaculture water quality data and the metrics MAE,RMSE,MAPE,and R^(2) were collected to validate the *** results show that the AMTGCN presents an average improvement of 34.01%,34.59%,36.05%,and 17.71%compared to LSTM,respectively;an average improvement of 64.84%,56.78%,64.82%,and 153.16%compared to the STGCN,respectively;an average improvement of 55.25%,48.67%,57.01%,and 209.00%compared to GCN-LSTM,respectively;and an average improvement of 7.05%,5.66%,7.42%,and 2.47%compared to TCN,*** indicates that the AMTGCN,integrating the innovative structure of adaptive graph construction and multi-channel spatiotemporal graph convolutional network,could provide an efficient solution for water quality prediction in aquaculture.

关键词： water quality prediction aquaculture spatial-temporal graph convolutional network multi-channel adaptive graph construction

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spatial-temporal graph convolutional network Boosted Flow-Frame Prediction For Video Anomaly Detection 48

Spatial-Temporal Graph Convolutional Network Boosted Flow-Fr...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Cheng, Kai Zeng, Xinhua Liu, Yang Zhao, Mengyang Pang, Chengxin Hu, Xing Fudan University Academy for Engineering & Technology Shanghai China Shanghai University of Electric Power School of Eie Shanghai China University of Shanghai for Science and Technology School of Oece Shanghai China

ISBN: (纸本)9781728163277

Video Anomaly Detection (VAD) is a critical technology for intelligent surveillance systems and remains a challenging task in the signal processing community. An intuitive idea for VAD is to use a two-stream network to learn appearance and motion normality, respectively. However, existing approaches usually design a network architecture for the appearance stream with effort, then apply a similar architecture to the motion stream, ignoring the unique appearance and motion characteristics. In this paper, we propose STGCN-FFP, an unsupervised spatial-temporal graph convolutional networks (STGCN) boosted Flow-Frame Prediction model. Specifically, we first design an STGCN-based memory module to extract and memorize normal patterns for optical flow, which is more suitable for learning motion normality. Then, we use a memory-augmented auto-encoder to model normal appearance patterns. Finally, the latent representation of two streams is fused to predict future frames, boosting the model to learn spatial-temporal normality. To our knowledge, STGCN-FFP is the first work applying STGCN to uniquely model the motion normality. Our method performs comparably to the state-of-the-art methods on three benchmarks. © 2023 IEEE.

关键词： deep auto-encoder memory network spatial-temporal graph convolutional network unsupervised learning Video anomaly detection

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Detecting and Mitigating DDoS Attacks in SDN Using spatial-temporal graph convolutional network

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IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING 2022年第6期19卷 3855-3872页

作者： Cao, Yongyi Jiang, Hao Deng, Yuchuan Wu, Jing Zhou, Pan Luo, Wei Wuhan Univ Elect Informat Sch Wuhan 430072 Hubei Peoples R China Huazhong Univ Sci & Technol Hubei Engn Res Ctr Big Data Secur Sch Cyber Sci & Engn Wuhan 430074 Hubei Peoples R China China Ship Dev & Design Ctr Wuhan 430064 Hubei Peoples R China

With the development of data plane programmable Software-Defined networking (SDN), Distributed Denial of Service (DDoS) attacks on the data plane increasingly become fatal. Currently, traditional attack detection methods are mainly used to detect whether a DDoS attack occurs and it is difficult to find the path that the attack flow traverses the network, which makes it difficult to accurately mitigate DDoS attacks. In this article, we propose a detection method based on spatial-temporal graph convolutional network (ST-GCN) over the data plane programmable SDN, which maps the network into a graph. It senses the state of switches through In-band network Telemetry (INT) with sampling, inputs the network state into the spatial-temporal graph convolutional network detection model, and finally finds out the switches through which DDoS attack flows pass. Based on this, we propose a defense method combined with an enhanced whitelist and a precise dropping strategy, which can effectively mitigate DDoS attacks and minimize the impact on legitimate network traffic. The evaluation results show that our detection method can accurately detect the path that the DDoS attack flows pass through, and can effectively mitigate the DDoS attack. Compared to classic methods, our method improves the detection accuracy by nearly 10%. At the same time, the southbound interface load and CPU overhead brought by our detection and defense process are much lower than the classic methods.

关键词： Denial-of-service attack Computer crime Whitelists Telemetry Feature extraction Distributed databases Delays DDoS data plane programmable SDN in-band network telemetry spatial-temporal graph convolutional network

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Dense spatial-temporal graph convolutional network Based on Lightweight OpenPose for Detecting Falls

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Computers, Materials & Continua 2023年第10期77卷 47-61页

作者： Xiaorui Zhang Qijian Xie Wei Sun Yongjun Ren Mithun Mukherjee School of Computer Science Nanjing University of Information Science&TechnologyNanjing210044China Wuxi Research Institute Nanjing University of Information Science&TechnologyWuxi214100China Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET) Nanjing University of Information Science&TechnologyNanjing210044China School of Automation Nanjing University of Information Science&TechnologyNanjing210044China School of Artificial Intelligence Nanjing University of Information Science&TechnologyNanjing210044China

Fall behavior is closely related to high mortality in the elderly,so fall detection becomes an important and urgent research ***,the existing fall detection methods are difficult to be applied in daily life due to a large amount of calculation and poor detection *** solve the above problems,this paper proposes a dense spatial-temporal graph convolutional network based on lightweight *** OpenPose uses MobileNet as a feature extraction network,and the prediction layer uses bottleneck-asymmetric structure,thus reducing the amount of the *** bottleneck-asymmetrical structure compresses the number of input channels of feature maps by 1×1 convolution and replaces the 7×7 convolution structure with the asymmetric structure of 1×7 convolution,7×1 convolution,and 7×7 convolution in *** spatial-temporal graph convolutional network divides the multi-layer convolution into dense blocks,and the convolutional layers in each dense block are connected,thus improving the feature transitivity,enhancing the network’s ability to extract features,thus improving the detection *** representative datasets,Multiple Cameras Fall dataset(MCF),and Nanyang Technological University Red Green Blue+Depth Action Recognition dataset(NTU RGB+D),are selected for our experiments,among which NTU RGB+D has two evaluation *** results show that the proposed model is superior to the current fall detection *** accuracy of this network on the MCF dataset is 96.3%,and the accuracies on the two evaluation benchmarks of the NTU RGB+D dataset are 85.6%and 93.5%,respectively.

关键词： Fall detection lightweight OpenPose spatial-temporal graph convolutional network dense blocks

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Auto-STGCN: Autonomous spatial-temporal graph convolutional network Search

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ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA 2023年第5期17卷 1-21页

作者： Wang, Chunnan Zhang, Kaixin Wang, Hongzhi Chen, Bozhou Harbin Inst Technol Harbin 150001 Heilongjiang Peoples R China

In recent years, many spatial-temporal graph convolutional network (STGCN) models are proposed to deal with the spatial-temporal network data forecasting problem. These STGCN models have their own advantages, i.e., each of them puts forward many effective operations and achieves good prediction results in the real applications. If users can effectively utilize and combine these excellent operations integrating the advantages of existing models, then they may obtain more effective STGCN models thus create greater value using existing work. However, they fail to do so due to the lack of domain knowledge, and there is lack of automated system to help users to achieve this goal. In this article, we fill this gap and propose Auto-STGCN algorithm, which makes use of existing models to automatically explore high-performance STGCN model for specific scenarios. Specifically, we design Unified-STGCN framework, which summarizes the operations of existing architectures, and use parameters to control the usage and characteristic attributes of each operation, so as to realize the parameterized representation of the STGCN architecture and the reorganization and fusion of advantages. Then, we present Auto-STGCN, an optimization method based on reinforcement learning, to quickly search the parameter search space provided by Unified-STGCN, and generate optimal STGCNmodels automatically. Extensive experiments on real-world benchmark datasets show that our Auto-STGCN can find STGCN models superior to existing STGCN models used for search space construction, which demonstrates the effectiveness of our proposed method.

关键词： Automated machine learning spatial-temporal graph convolutional network neural architecture search

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Transient Voltage Prediction Method Based on spatial-temporal graph convolutional network 9

Transient Voltage Prediction Method Based on Spatial-Tempora...

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9th International Forum on Electrical Engineering and Automation (IFEEA)

作者： Yang, Xintong Dong, Yu Wang, Jing Wang, Changjiang State Grid Jilin Elect Power Co Ltd Econ & Technol Res Inst Changchun Peoples R China Northeast Elect Power Univ Dept Elect Engn Jilin Jilin Peoples R China

ISBN: (纸本)9781665464215

Research on data-driven transient voltage forecasting method is of great significance to transient voltage stability evaluation, but the existing transient voltage forecasting methods can't effectively incorporate the temporal and spatial characteristics of transient voltage into the model. To solve this problem, a transient voltage forecasting method based on the spatial-temporal graph convolutional network model is proposed. Firstly, the transient voltage prediction is modeled as a sample space mapping problem. It trains the neural network model by data- driven method and establishes the mapping from the spatial structure and time series data of power grid in transient process to the predicted value of transient voltage. Then, the model can quickly and accurately predict the transient voltage of the system by extracting the spatial structure characteristics and time series characteristics of the power grid before, during and after the fault in the transient process. Finally, the test results based on New England 39-bus system verify the feasibility, effectiveness and superiority of the proposed method.

关键词： electric power system transient voltage prediction data driven temporal and spatial characteristics spatial-temporal graph convolutional network

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Fall Detection Method for Infrared Videos Based on spatial-temporal graph convolutional network

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SENSORS 2024年第14期24卷 4647页

作者： Yang, Junkai He, Yuqing Zhu, Jingxuan Lv, Zitao Jin, Weiqi Beijing Inst Technol Sch Opt & Photon MOE Key Lab Optoelect Imaging Technol & Syst Beijing 100081 Peoples R China

The timely detection of falls and alerting medical aid is critical for health monitoring in elderly individuals living alone. This paper mainly focuses on issues such as poor adaptability, privacy infringement, and low recognition accuracy associated with traditional visual sensor-based fall detection. We propose an infrared video-based fall detection method utilizing spatial-temporal graph convolutional networks (ST-GCNs) to address these challenges. Our method used fine-tuned AlphaPose to extract 2D human skeleton sequences from infrared videos. Subsequently, the skeleton data was represented in Cartesian and polar coordinates and processed through a two-stream ST-GCN to recognize fall behaviors promptly. To enhance the network's recognition capability for fall actions, we improved the adjacency matrix of graph convolutional units and introduced multi-scale temporal graph convolution units. To facilitate practical deployment, we optimized time window and network depth of the ST-GCN, striking a balance between model accuracy and speed. The experimental results on a proprietary infrared human action recognition dataset demonstrated that our proposed algorithm accurately identifies fall behaviors with the highest accuracy of 96%. Moreover, our algorithm performed robustly, identifying falls in both near-infrared and thermal-infrared videos.

关键词： fall detection infrared video skeleton extraction spatial-temporal graph convolutional network

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spatial-temporal Tensor graph convolutional network for Traffic Speed Prediction

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2023年第1期24卷 92-103页

作者： Xu, Xuran Zhang, Tong Xu, Chunyan Cui, Zhen Yang, Jian Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing 210094 Peoples R China

Accurate traffic speed prediction is crucial for the guidance and management of urban traffic, which at the same time requires a model with a satisfactory computational burden and memory space in applications. In this paper, we propose a factorized spatial-temporal Tensor graph convolutional network for traffic speed prediction. Traffic networks are modeled and unified into a graph tensor that integrates spatial and temporal information simultaneously. We extend graph convolution into tensor space and propose a tensor graph convolution network to extract more discriminating features from spatial-temporal graph data. We further introduce Tucker decomposition and derive a factorized tensor convolution to reduce the computational burden, which performs separate filtering in small-scale space, time, and feature modes. Besides, we can benefit from noise suppression of traffic data when discarding those trivial components in the process of tensor decomposition. Extensive experiments on the three real-world datasets demonstrate that our method is more effective than traditional prediction methods, and achieves state-of-the-art performance.

关键词： Tensors Convolution Predictive models Hidden Markov models Data models Computational modeling Feature extraction Traffic speed prediction tensor decomposition spatial-temporal graph convolutional network higher-order principal components analysis

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Aspect-level Sentiment Analysis Based on Heterogeneous spatial-temporal graph convolutional networks 24

Aspect-level Sentiment Analysis Based on Heterogeneous Spati...

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3rd International Conference on Cyber Security, Artificial Intelligence and Digital Economy (CSAIDE)

作者： Jin, Mengqing Wang, Xun Xu, Changlin Jiangsu Univ Sci Technol Sch Comp Zhenjiang 212003 Jiangsu Peoples R China

ISBN: (纸本)9798400718212

As a subtask of fine-grained sentiment analysis, aspect-level sentiment analysis aims to predict the sentiment polarity of aspect words. In recent years, more and more research achievements had been made in this field, but most of the existing methods basically only considered the unified modeling of a paragraph, and the lack of correlation between sentences. The recognition and analysis of emotion not only depended on the content of the sentence itself, but also depended on the emotional fluctuations of the sentence over time. To solve this problem, this paper proposed an aspect-level sentiment analysis model based on Heterogeneous spatial -temporal graph convolutional networks (H-STGCN). The model first utilized Bi-LSTM and syntactic dependency tree to model the context, and then constructs each clause as a spatial- temporal graph convolutional network. The attention mechanism calculated the propagation weights, enhancing the feature expression of target aspect words in both time and space dimensions. Experimental results show that the model performs better than the control model on public datasets.

关键词： Aspect-level sentiment analysis spatial-temporal graph convolutional network Attention mechanism Syntactic dependency

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Multi-View spatialtemporal graph convolutional network for Traffic Prediction

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2024年第8期25卷 9572-9586页

作者： Wei, Shuqing Feng, Siyuan Yang, Hai Hong Kong Univ Sci & Technol Div Emerging Interdisciplinary Area Hong Kong Peoples R China Hong Kong Polytech Univ Dept Logist & Maritime Studies Hong Kong Peoples R China Hong Kong Univ Sci & Technol Dept Civil & Environm Engn Hong Kong Peoples R China Hong Kong Univ Sci & Technol Guangzhou Intelligent Transportat Thrust Guangzhou 511453 Peoples R China

Multi-step traffic speed prediction is a challenging issue due to the multiple spatial-temporal dependencies among roads. Some spatial dependencies, especially those formed by different traffic modes, are not fully exploited, and how to simultaneously consider spatial and temporal dependencies and effectively integrate them within a single prediction framework needs further exploration. To tackle the above issues, we propose a multi-view spatial-temporal graph convolutional framework MVSTG, which adequately exploits the multi-view spatial-temporal dependencies and their interactions to improve the accuracy of traffic prediction. Multi-view temporal learning captures the multiple temporal trends by temporal convolution from multi-granularity historical data, and multi-view spatial learning handles the multiple spatial correlations by graph convolution from multiple graphs. In addition, view-wise attention-based fusion is proposed to adaptively identify the importance of each upstream view, fuse the multi-view information, and generate integrated results for downstream views. The experiments on two real-world urban traffic datasets demonstrate that the multi-view data and the proposed model framework enhance performance on the accuracy of speed prediction, especially in mid-term and long-term prediction.

关键词： Roads Correlation Public transportation convolutional neural networks Predictive models Data models Computational modeling Attention mechanism multi-view learning spatial-temporal graph convolutional network traffic prediction

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