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Multimodal Joint Prediction of Traffic Spatial-Temporal Data with Graph Sparse Attention Mechanism and Bidirectional Temporal Convolutional Network

SSRN

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SSRN 2024年

作者： Zhang, Dongran Yan, Jiangnan Polat, Kemal Alhudhaif, Adi Li, Jun Guangdong Provincial Key Laboratory of Intelligent Transportation System School of Intelligent Systems Engineering Sun Yat-sen University Shenzhen518107 China Department of Electrical and Electronics Engineering Faculty of Engineering Bolu Abant Izzet Baysal University Bolu Turkey Department of Computer Science College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University Al-Kharj11942 Saudi Arabia

Traffic flow prediction plays a crucial role in the management and operation of urban transportation systems. While extensive research has been conducted on predictions for individual transportation modes, there is relatively limited research on joint prediction across different transportation modes. Furthermore, existing multimodal traffic joint modeling methods often lack flexibility in spatial-temporal feature extraction. To address these issues, we propose a method called Graph Sparse Attention Mechanism with Bidirectional Temporal Convolutional Network (GSABT) for multimodal traffic spatial-temporal joint prediction. First, we use a multimodal graph multiplied by self-attention weights to capture spatial local features, and then employ the Top-U sparse attention mechanism to obtain spa-tial global features. Second, we utilize a bidirectional temporal convolutional network to enhance the temporal feature correlation between the output and input data, and extract inter-modal and intra-modal temporal features through the share-unique module. Finally, we have designed a multimodal joint prediction framework that can be flexibly extended to both spatial and temporal dimensions. Extensive experiments conducted on three real datasets indicate that the proposed model consistently achieves state-of-the-art predictive performance. © 2024, The Authors. All rights reserved.

关键词： Forecasting

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API Usage Pattern Search Based on Model Checking 7th

API Usage Pattern Search Based on Model Checking

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7th International Symposium on Dependable Software engineering: Theories, Tools, and Applications, SETTA 2021

作者： Ding, Xue-er Niu, Jun Wang, Jia Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo315211 China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai201804 China

ISBN: (纸本)9783030912642

Reusing existing class libraries can improve the productivity of software development. API usage patterns are useful resources for programmers in reusing class libraries. Existing approaches often exploit API graphs to model semantic relations between API elements of class libraries, and traversal graphs to capture API usage patterns. However, those approaches cannot describe the number of parameters and the number of overload methods, and may not search for various API usage patterns. In this paper, we propose an automatic and complete approach to searching for API usage patterns by model checking technique. We introduce a novel kind of API transition system model to completely describe the relations between all API elements of existing class libraries. We obtain API usage constraints from queries, namely the numbers and types of input and output objects and class types transformed based on APIs replacement model, and give the logical characterizations of the constraints by suitable CTL* (computation tree logic) formulas. Then, we can obtain suitable API usage patterns by model checking the logical formulas in related API transition system models based on model checker NuSMV. The experiments indicate that, in contrast to existing approaches, our approach can automatically and effectively search for various API usage patterns. © 2021, Springer Nature Switzerland AG.

关键词： Model checking

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Gradient and non-gradient-based first order reliability method for robust and accurate structural reliability analysis

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Procedia Structural Integrity 2023年 48卷 348-355页

作者： Behrooz Keshtegar Mohamed El Amine Ben Seghier José A.F.O. Correia Department of Civil Engineering Faculty of Engineering University of Zabol P.B. *** Zabol Iran Laboratory for Computational Mechanics Institute for Computational Science and Artificial Intelligence Van Lang University Ho Chi Minh City Vietnam Faculty of Mechanical - Electrical and Computer Engineering School of Technology Van Lang University Ho Chi Minh City Vietnam INEGI Faculty of Engineering University of Porto Rua Dr. Roberto Frias 4200-465 Porto Portugal

The first order reliability method (FORM) may provide unstable and inaccurate results for gray/ highly nonlinear performance functions. In this study, FORM approaches based on gradient and non-gradient methods are compared for both accuracy and robustness of results in nonlinear/complex reliability problems. Two gradient based sensitivity vectors using steepest descent search direction as (HL-RF) and directional stability transformation method (DSTM) are compared to two non-gradient approaches of harmony search optimization (HS) and particle swarm optimization (PSO). A refined probabilistic model using FORM is used for non-gradient optimization methods. The accuracy and robustness of FORM-based gradient formulations and refined FORM-based non-gradient approaches are compared on nonlinear/complex performance functions. The comparative results demonstrate that the PSO-based refined FORM provides accurate and robust results compared to FORM using HL-RF and DSTM. In addition to that, PSO shows superior performances compared to HS and gradient-FORM in complex engineering structures applications.

关键词： Type your keywords here separated by semicolons

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Multimodal Joint Prediction of Traffic Spatial-Temporal Data with Graph Sparse Attention Mechanism and Bidirectional Temporal Convolutional Network

arXiv

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arXiv 2024年

Traffic flow prediction plays a crucial role in the management and operation of urban transportation systems. While extensive research has been conducted on predictions for individual transportation modes, there is relatively limited research on joint prediction across different transportation modes. Furthermore, existing multimodal traffic joint modeling methods often lack flexibility in spatial-temporal feature extraction. To address these issues, we propose a method called Graph Sparse Attention Mechanism with Bidirectional Temporal Convolutional Network (GSABT) for multimodal traffic spatial-temporal joint prediction. First, we use a multimodal graph multiplied by self-attention weights to capture spatial local features, and then employ the Top-U sparse attention mechanism to obtain spatial global features. Second, we utilize a bidirectional temporal convolutional network to enhance the temporal feature correlation between the output and input data, and extract inter-modal and intra-modal temporal features through the share-unique module. Finally, we have designed a multimodal joint prediction framework that can be flexibly extended to both spatial and temporal dimensions. Extensive experiments conducted on three real datasets indicate that the proposed model consistently achieves state-of-the-art predictive performance. Copyright © 2024, The Authors. All rights reserved.

关键词： Urban transportation

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A Stochastic Gradient Approach for Communication Efficient Confederated Learning

A Stochastic Gradient Approach for Communication Efficient C...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Bin Wang Jun Fang Hongbin Li Yonina C. Eldar Yangtze Delta Region Institute (Huzhou) University of Electronic Science and Technology of China (UESTC) Huzhou China National Key Laboratory of Wireless Communications UESTC China Department of Electrical and Computer Engineering Stevens Institute of Technology USA Faculty of Mathematics and Computer Science Weizmann Institute of Science Israel

In this work, we consider a multi-server federated learning (FL) framework, referred to as Confederated Learning (CFL), in order to accommodate a larger number of users. To reduce the communication overhead of the CFL system, we propose a linearly convergent stochastic gradient method. The proposed algorithm incorporates a conditionally-triggered user selection (CTUS) mechanism as the central component. Simulation results show that it achieves advantageous communication efficiency over GT-SAGA.

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Single Block Encoder-Decoder Transformer Model for Multi-Step Traffic Flow Forecasting

Single Block Encoder-Decoder Transformer Model for Multi-Ste...

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IEEE Symposium on computers & Informatics (ISCI)

作者： Mas Omar Fitri Yakub Mohamad Sofian Abu Talip Mohd Nazmin Maslan Vijay Kumar Sinha Muljono Muljono WEE Laboratory Malaysia-Japan International Institute of Technology Universiti Teknologi Malaysia Kuala Lumpur Malaysia Department of Electronic System Engineering Malaysia-Japan International Institute of Technology Universiti Teknologi Malaysia Kuala Lumpur Malaysia Department of Electrical Engineering Faculty of Engineering University of Malaya Kuala Lumpur Malaysia Faculty of Industrial and Manufacturing Technology and Engineering Universiti Teknikal Malaysia Melaka Malaysia Department of Computer Science & Engineering School of Engineering & Technology Chitkara University Himachal Pradesh India Faculty of Computer Science Universitas Dian Nuswantoro Semarang Indonesia

ISBN: (数字)9798350353853

ISBN: (纸本)9798350353860

Accurate traffic flow forecasting is crucial for managing and planning urban transportation systems. Despite the widespread use of sequence modelling models like Long Short-Term Memory (LSTM) for this purpose, the potential of Transformer models remains underexplored. This is particularly true for the simplest form of a single block encoder-decoder Transformer model, which can be finely tuned through optimised hyperparameters. This paper examines the performance of a singular horizon-step forecasting method for multi-step traffic flow forecasting using a proposed Single Block Encoder-Decoder Transformer model optimised with a Grid Search algorithm. Results demonstrate that this model can enhance forecasting accuracy compared to the state-of-the-art LSTM model typically used for multi-step forecasting. The model effectively captures long-range temporal dependencies within a single road traffic flow dataset. It was tested on hourly traffic flow data to forecast the next 24 hours for the I5-North freeway in California, sourced from the Caltrans Performance Measurement System. The optimal configuration included an embedding dimension of 32, a feed-forward dimension of 128, and 8 attention heads. Results show a significant improvement, with a 4.7% reduction in Root Mean Squared Error compared to an LSTM model with two hidden layers of 100 neurons each, showcasing the potential of Single Block Encoder-Decoder Transformer models for real-world traffic prediction applications.

关键词： Accuracy Neurons Transportation Predictive models Traffic control Transformers Road traffic

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A Multi-Layer Software Architecture for Aerial Cognitive Multi-Robot Systems in Power Line Inspection Tasks

A Multi-Layer Software Architecture for Aerial Cognitive Mul...

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2021 International Conference on Unmanned Aircraft Systems, ICUAS 2021

作者： Silano, Giuseppe Bednar, Jan Nascimento, Tiago Capitan, Jesus Saska, Martin Ollero, Anibal Czech Technical University Faculty of Electrical Engineering Prague Czech Republic Universidade Federal da Paraiba Department of Computer Systems Brazil Grvc Robotics Laboratory University of Seville Spain

ISBN: (纸本)9780738131153

This paper presents a multi-layer software architecture to perform cooperative missions with a fleet of quad-rotors providing support in electrical power line inspection operations. The proposed software framework guarantees the compliance with safety requirements between drones and human workers while ensuring that the mission is carried out successfully. Besides, cognitive capabilities are integrated in the multi-vehicle system in order to reply to unforeseen events and external disturbances. The feasibility and effectiveness of the proposed architecture are demonstrated by means of realistic simulations. © 2021 IEEE.

关键词： Inspection

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A Novel F-SVM based on PSO for Gait Phase Recognition in Lower Limb Exoskeleton 18

A Novel F-SVM based on PSO for Gait Phase Recognition in Low...

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18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023

作者： Lv, Pingping Zhang, Chi Yi, Feng Yuan, Ting Li, Shupei Zhang, Meitong Ningbo University Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Faculty of Electrical Engineering and Computer Science Ningbo315201 China Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Zhejiang Key Laboratory of Robotics and Intelligent Manufacturing Equipment Technology Ningbo315201 China Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences College of Materials Science and Opto-Electronic Technology University of Chinese Academy of Sciences Ningbo315201 China Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo315201 China

ISBN: (纸本)9798350312201

To meet the requirements of lower limb exoskeleton robots for lightweight equipment and accurate gait phase detection under different speeds, the kinematics and dynamics information are investigated. In this paper, we propose a novel radius-margin-based supporting vector machine (SVM) model with particle swarm optimization algorithm (PSO) in feature space called PSO-F-SVM for gait phase detection. The proposed method considers both the maximization of margin and the minimization of radius and uses PSO to optimize the parameter in F-SVM, which can enhance the performance of the SVM classifier. Plantar pressure insoles with flexible and elastic sensors are designed for gait feature measurement. We conduct comparative experiments between the proposed method and other variants of SVM under four speeds. The experimental result indicates that the proposed method improves the accuracy by 2.68% compared with the standard SVM. Moreover, more than 98% accuracy can be obtained at four speeds, which demonstrates that the proposed method outperforms the other variants of SVM. © 2023 IEEE.

关键词： Support vector machines

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Structural reliability analysis using Conjugate FORM-based Support Vector Regression

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Procedia Structural Integrity 2023年 48卷 356-362页

作者： Mohamed El Amine Ben Seghier Behrooz Keshtegar José A.F.O. Correia Laboratory for Computational Mechanics Institute for Computational Science and Artificial Intelligence Van Lang University Ho Chi Minh City Vietnam Faculty of Mechanical - Electrical and Computer Engineering School of Technology Van Lang University Ho Chi Minh City Vietnam Department of Civil Engineering Faculty of Engineering University of Zabol P.B. *** Zabol Iran INEGI Faculty of Engineering University of Porto Rua Dr. Roberto Frias 4200-465 Porto Portugal

Appropriate reliability methods for estimating the failure probability using most efficient and robust properties are crucial in structural reliability analyses of engineering structures. In this paper, an enhanced first-order reliability method (FORM) using conjugate search direction was coupled with artificial intelligence technique called support vector regression (SVR) named as SVR-CFORM for structural reliability analyses of complex limit state functions. The conjugate FORM (CFORM) is formulated using adaptive formulas to search the most probable point to improve the robustness of iterative FORM formula, whereas the SVR is used to predict the performance functions to improve the efficiency of the proposed reliability method. Several FORM formulas such as HL-RF, directional stability transformation method, and finite step length are used to compare the performance of the proposed SVR-CFORM in terms of both efficiency and robustness using different numerical/structural reliability examples. In comparison to other studied FORM formulas, the proposed SVR-CFORM method improves the iterative FORM formulation more efficiently and with more stable results.

关键词： Structural reliability First-order reliability method Support vector regression Conjugate FORM Failure probability

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Long-Term Time Series Anomaly Detection with Transformer and Variational Autoencoder for Water Quality

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SSRN 2024年

作者： Lin, Yongze Qiao, Junfei Bi, Jing Yuan, Haitao Zhou, MengChu Beijing Laboratory of Smart Environmental Protection Beijing University of Technology Beijing100124 China Faculty of Information Technology Beijing University of Technology Beijing100124 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Department of Electrical and Computer Engineering New Jersey Institute of Technology NewarkNJ07102 United States

In many fields, time series anomaly detection is gaining more and more attention, \textit{e.g.}, air pollution, geological hazards, and network traffic detection. Time series anomaly detection in water quality aims to identify anomalous samples. However, there are several challenges in anomaly detection of water quality time series: 1) Difficulty in obtaining local and global temporal dependencies of time series;2) Difficulty in learning a representation map from time series that enables effective discrimination of anomalies;and 3) Complex nonlinear relationship in water quality time series, making it difficult to detect long-term anomalies. To solve these problems, this work proposes a time series anomaly detection model called Transformer with Variational Autoencoder (TransVAE). First, this work integrates local and global temporal position embedding to improve temporally dependent representation. Second, It adopts a Variational Autoencoder (VAE) to fuse signals and reconstruct their expected distribution. Third, we use a Transformer to process nonlinear relationships in the time series and use residual mechanisms to improve long-term series anomaly detection accuracy. This work adopts two real-world datasets to evaluate the proposed TransVAE, and experiments demonstrate that TransVAE outperforms several state-of-the-art methods in terms of time series anomaly detection accuracy. © 2024, The Authors. All rights reserved.

关键词： Time series

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