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

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SSRN

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

Elevator Fault Diagnosis Based on AO-VMD-BF and Multi Model Fusion

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Science Technology and engineering 2024年第35期24卷 15023-15030页

作者： Qiu, Chao-Jie Zhang, Lin-Xuan Li, Ming-Hong Zhang, Pan-Pan Zheng, Xing School of Electrical Engineering Xinjiang University Urumqi830017 China National Computer Integrated Manufacturing System Engineering Technology Research Center Tsinghua University Beijing100084 China

In order to accurately achieve elevator fault diagnosis, an elevator fault diagnosis based on AO-VMD-BF and multi model fusion was proposed. Firstly, the variational mode decomposition(VMD) optimized by the aquila optimizer(AO) algorithm was used to decompose the signal into multiple modal components, and the pearson correlation coefficient was used to remove false components. To address the problem of noise in the remaining signal, the butterworth filter(BF) was used for secondary denoising, the denoised vibration signal could be obtained by reconstructing the filtered modal sub quantum sequence. Then extract time-domain, frequency-domain, and entropy features to form a multi domain feature vector set. Finally, a Stacking ensemble learning elevator fault diagnosis model was established based on convolutional neural network(CNN), random forest(RF), support vector machine(SVM), and adaptive boosting(AdaBoost) models, with extreme gradient boosting(XGBoost) as the meta classifier. The analysis of experimental results shows that the proposed method can effectively extract fault features from elevator car vibration signals, and accurately and effectively diagnose elevator faults. © 2024 Beijing Kexue Jishu yu Gongcheng Zazhishe. All rights reserved.

关键词： Variational mode decomposition

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

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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

Development of Water Pipelines Energy Harvesting system 1

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Development of Water Pipelines Energy Harvesting System

1st International Recent Trends in Technology, engineering and Computing Conference, IRTTEC 2020

作者： Liew, H.F. Rosemizi, Abd R Romli, M I Fahmi Nuraidah, M.T. Perlis02100 Malaysia School of Computer and Communication Engineering Universiti Malaysia Perlis Pauh Putra Campus Perlis Arau02600 Malaysia School of Electrical System Engineering Universiti Malaysia Perlis Pauh Putra Campus Perlis Arau02600 Malaysia

Previously, the threats of negative effects caused from burning process of the fossil fuels is somewhat alarming which it may lead to acid rain, increasing greenhouse emissions and global warming. Based on the highlighted issue, this study aims to develop a system where the input power came from water pipes energy harvesting system. Water pipes harvesting system will be designed using polyvinyl chloride pipes and water turbines, to transform kinetic energy of a water flow into electrical energy and the energy performance will be monitored in a web-server using Arduino and ESP8266 Wi-Fi module as its processing centre. This research studies the feasibility of harvesting some energy from pipeline vibrations, and it is also observed that harvestable energy varies with harvesting location across a pipeline network as well as the pipe flow rate in the water-air and kinetic energy in the water pressure fluctuation. The water pipelines energy harvesting systems are modelled and then analyzed by using simulated data and real data using Arduino. Turbine generator are mounted on the pipeline surface at different locations to simultaneously collect vibrations-induced energy data from water pumping. The water flow rate energy harvester system and the water pressure fluctuation introduced in this research may be able to generate up to 6.8 mW's power. The output produced from the energy harvesting system is expected to be enough to light up a 3.8 V direct current light emitting diode bulb. © Published under licence by IOP Publishing Ltd.

关键词： Kinetics

Hotel Information system Management Audit Using COBIT 2019 - APO12

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Hotel Information System Management Audit Using COBIT 2019 -...

Innovation in Technology (INOCON), IEEE International Conference for

作者： Joy Nashar Utamajaya Suhono Harso Supangat Ford Lumban Gaol Benny Ranti BINUS Graduate Program School of Information System BINUS University Jakarta Indonesia School of Electrical Engineering and Informatic Bandung Institute of Technology Bandung Indonesia Computer Science Department BINUS Graduate Program Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department Faculty of Computer Science University of Indonesia Depok Indonesia

In general, public or private organizations or companies have used information-based technology as a support to improve business performance to be more effective and efficient in order to achieve a company’s business goals. Given the large contribution of information technology in the application of hotel applications as a supporting information system, it is a vital system that must avoid risks that can hinder and cause harm to the hotel management business processes. Therefore, it is necessary to carry out risk management using the COBIT 5 framework to manage possible risks that may occur based on the APO12 (Manage Risk) domain. From the evaluation results of the data obtained through observation and interviews as well as the calculation of the results of the questionnaire based on 6 APO12 subdomains, the results of the risk management level capability assessment in hotel applications are still at level 3 or have reached the level of established process with the target to be achieved at level 4 resulting in a gap of 1 level. Based on the results obtained, it is necessary to propose recommendations that can be used by hotels in improving the application of information technology risk management so that in the future it can achieve the expected target level so that the APO12 level of capability can increase and become more optimal.

关键词： Technological innovation Companies Risk management Information technology Interviews Information systems

Decomposable Sparse Tensor on Tensor Regression

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arXiv 2022年

作者： Mao, Haiyi Dou, Jason Xiaotian Department of System and Computational Biology School of Medicine University of Pittsburgh United States Department of Electrical and Computer Engineering University of Pittsburgh United States

Most regularized tensor regression research focuses on tensors predictors with scalars responses or vectors predictors to tensors responses. We consider the sparse low rank tensor on tensor regression where predictors X and responses Y are both high-dimensional tensors. By demonstrating that the general inner product [Raskutti et al., 2019] or the contracted product [Lock, 2018] on a unit rank tensor can be decomposed into standard inner products and outer products, the problem can be simply transformed into a tensor to scalar regression followed by a tensor decomposition. So we propose a fast solution based on stagewise search composed by contraction part and generation part which are optimized alternatively. We successfully demonstrate that our method can outperform current methods in terms of accuracy, predictors selection by effectively incorporating the structural information. © 2022, CC BY.

关键词： Tensors

Electrification of Transportation: Policy Framework, Technical Aspects and Challenges in Pakistan - A Case Study

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e-Prime - Advances in electrical engineering, Electronics and Energy 2024年 10卷

作者： Masood, Arsalan Hassan, Syed Zulqadar Kamal, Tariq Khan, Salman Rizvi, Syed Asad Abbas Salman, Salman Department of Electrical Engineering Faculty of Engineering and Architecture University of Sialkot Pakistan Department of Computer Science Faculty of Information Technology and Computer Science University of Central Pubjab Lahore Pakistan School of Technology and Innovations Electrical Engineering University of Vaasa Vaasa Finland Department of Electrical Power Engineering and Mechatronics Tallinn University of Technology Estonia State Key Lab. for Alternate Electrical Power System with Renewable Energy Sources (North China Electric Power University) Beijing China Department of Electrical Engineering Balochistan University of Engineering and Technology Balochistan Khuzdar Pakistan

Pakistan faces severe air pollution and high dependency on imported fossil fuels for transportation, urgently needing solutions. Electrification of transportation could address these issues, but Pakistan lacks a clear roadmap for adopting electric vehicles. This paper examines the electrification of transportation in Pakistan, focusing on policies, technical aspects, and standards. It looks at best practices from developed countries and discusses incentives offered by the Pakistani government to boost electric vehicle adoption. The study also explores challenges and opportunities in adopting electric vehicles within existing power systems. A SWOT analysis is included, highlighting strengths, weaknesses, opportunities, and threats related to electric vehicle policies and market integration in Pakistan. By reviewing these factors, the paper provides insights into the obstacles and potential benefits of electric vehicle adoption. It also suggests ways for developing nations like Pakistan to encourage widespread use of electric vehicles, aiming to reduce fossil fuel dependency and meet environmental goals. © 2024

关键词： Electric transportation Electric vehicles Key challenges Policy SWOT analysis

GrabDAE: An Innovative Framework for Unsupervised Domain Adaptation Utilizing Grab-Mask and Denoise Auto-Encoder

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arXiv 2024年

作者： Chen, Junzhou Wen, Xuan Zhang, Ronghui Ren, Bingtao Wu, Di Xu, Zhigang Wang, Danwei The Guangdong Provincial Key Laboratory of Intelligent Transport System School of Intelligent Systems Engineering Sun Yat-sen University Guangzhou510275 China The School of Transportation Science and Engineering Beihang University State Key Lab of Intelligent Transportation System Beijing100191 China The School of Computer Science and Engineering Sun Yat-sen University Guangzhou510006 China The Guangdong Key Laboratory of Big Data Analysis and Processing Guangdong510006 China The School of Information Engineering Chang’an University Shaanxi Xi’an710064 China The School of Electrical and Electronic Engineering Nanyang Technological University Singapore639798 Singapore

Unsupervised Domain Adaptation (UDA) aims to adapt a model trained on a labeled source domain to an unlabeled target domain by addressing the domain shift. Existing Unsupervised Domain Adaptation (UDA) methods often fall short in fully leveraging contextual information from the target domain, leading to suboptimal decision boundary separation during source and target domain alignment. To address this, we introduce GrabDAE, an innovative UDA framework designed to tackle domain shift in visual classification tasks. GrabDAE incorporates two key innovations: the Grab-Mask module, which blurs background information in target domain images, enabling the model to focus on essential, domain-relevant features through contrastive learning;and the Denoising Auto-Encoder (DAE), which enhances feature alignment by reconstructing features and filtering noise, ensuring a more robust adaptation to the target domain. These components empower GrabDAE to effectively handle unlabeled target domain data, significantly improving both classification accuracy and robustness. Extensive experiments on benchmark datasets, including VisDA-2017, OfficeHome, and Office31, demonstrate that GrabDAE consistently surpasses state-of-the-art UDA methods, setting new performance benchmarks. By tackling UDA’s critical challenges with its novel feature masking and denoising approach, GrabDAE offers both significant theoretical and practical advancements in domain adaptation. © 2024, CC BY-NC-SA.

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Digital Over-the-Air Federated Learning in Multi-Antenna systems

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arXiv 2023年

作者： Wang, Sihua Chen, Mingzhe Shen, Cong Yin, Changchuan Brinton, Christopher G. The Beijing Laboratory of Advanced Information Network The Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing100876 China The Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral GablesFL33146 United States The Charles L. Brown Department of Electrical and Computer Engineering University of Virginia CharlottesvilleVA United States The School of Electrical and Computer Engineering Purdue University West LafayetteIN United States

In this paper, the performance optimization of federated learning (FL), when deployed over a realistic wireless multiple-input multiple-output (MIMO) communication system with digital modulation and over-the-air computation (AirComp) is studied. In particular, a MIMO system is considered in which edge devices transmit their local FL models (trained using their locally collected data) to a parameter server (PS) using beamforming to maximize the number of devices scheduled for transmission. The PS, acting as a central controller, generates a global FL model using the received local FL models and broadcasts it back to all devices. Due to the limited bandwidth in a wireless network, AirComp is adopted to enable efficient wireless data aggregation. However, fading of wireless channels can produce aggregate distortions in an AirComp-based FL scheme. To tackle this challenge, we propose a modified federated averaging (FedAvg) algorithm that combines digital modulation with AirComp to mitigate wireless fading while ensuring the communication efficiency. This is achieved by a joint transmit and receive beamforming design, which is formulated as an optimization problem to dynamically adjust the beamforming matrices based on current FL model parameters so as to minimize the transmitting error and ensure the FL performance. To achieve this goal, we first analytically characterize how the beamforming matrices affect the performance of the FedAvg in different iterations. Based on this relationship, an artificial neural network (ANN) is used to estimate the local FL models of all devices and adjust the beamforming matrices at the PS for future model transmission. The algorithmic advantages and improved performance of the proposed methodologies are demonstrated through extensive numerical experiments. Copyright © 2023, The Authors. All rights reserved.

关键词： Beamforming