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arXiv 2024年

作者： Ismaeel, Ayad Ghany Krishnadas, J. Manishankar, S. Natarajan, Yuvaraj Mahmood, Sarmad Nozad Alani, Sameer Shather, Akram H. Computer Technology Engineering College of Engineering Technology Al-Kitab University Kirkuk36001 Iraq Department of Computer Science and Engineering Sahrdaya College of Engineering and Technology Thrissur Kodakara680684 India Department of Computer Science and Engineering Sri Shakthi Institute of Engineering and Technology Coimbatore641062 India Electronic and Control Engineering Techniques Technical Engineering College Northern Technical University Kirkuk36001 Iraq Computer Center University of Anbar Ramadi55431 Iraq Department of Computer Engineering Technology Al-Kitab University Altun Kopru Kirkuk36001 Iraq

This paper examines the use of deep recurrent neural networks to classify traffic patterns in smart cities. We propose a novel approach to traffic pattern classification based on deep recurrent neural networks, which can effectively capture traffic patterns' dynamic and sequential features. The proposed model combines convolutional and recurrent layers to extract features from traffic pattern data and a SoftMax layer to classify traffic patterns. Experimental results show that the proposed model outperforms existing methods regarding accuracy, precision, recall, and F1 score. Furthermore, we provide an in-depth analysis of the results and discuss the implications of the proposed model for smart cities. The results show that the proposed model can accurately classify traffic patterns in smart cities with a precision of as high as 95%. The proposed model is evaluated on a real-world traffic pattern dataset and compared with existing classification methods. © 2024, CC BY-NC-ND.

关键词： Classification (of information)

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Energy Efficient Improving Routing Model for UAVs Assisted Vehicular Adhoc Networks

Energy Efficient Improving Routing Model for UAVs Assisted V...

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2023 International Conference in Advances in Power, Signal, and Information Technology, APSIT 2023

作者： Alsalamy, Ali Abedi, Firas Abbas, Fatima Hashim Noori, Mohammed S. Alkhafaji, Mohamed Ayad Alkhayyat, Ahmed Alani, Sameer Guneser, Muhammet Tahir Mahmood, Sarmad Nozad College of Information Technology Imam Ja'afar Al-Sadiq University Al-Muthanna66002 Iraq College of Medical and Health Technologies Al-Zahraa University for Women Karbala Iraq Al-Mustaqbal University College Medical Laboratories Techniques Department Hillah Babil51001 Iraq Northern Technical University Department of Computer Engineering Technology Mosul41000 Iraq College of Engineering Technology National University of Science and Technology Dhi Qar64001 Iraq College of Technical Engineering The Islamic University Najaf Iraq Research Center University of Mashreq Baghdad Iraq Collage of Engeneering Karabuk University Department of Electrical Electronic Karabuk Turkey College of Engineering Technology Al-Kitab University Karkuk Iraq

ISBN: (纸本)9798350339369

Vehicular Adhoc Networks (VANETs) have widespread applications in intelligent transportation systems, serving diverse purposes. These networks are characterized by their dynamic nature, which unfortunately leads to communication instability, causing increased energy consumption, delays, and routing overhead. To overcome obstacles at ground level, Unmanned Aerial Vehicles (UAVs) are introduced, enabling data transmission through an aerial medium, free from ground-level obstructions. However, for effective communication in UAV-assisted VANETs, a reliable routing protocol is essential. This paper proposes an improved routing model for UAV-assisted VANETs, called IUAVA-RP, which employs parameter-based routing and optimal path selection. The optimal path selection is performed using a decision-making process, resulting in highly effective and optimal routing for efficient data transmission. The proposed IUAVA-RP protocol is simulated using NS2 and SUMO, and its performance analysis includes parameters such as: energy efficiency, packet delivery ratio, end-to-end delay, and routing overhead. Comparative analysis is conducted with two existing protocols, HGFA-RP and AOMDV-RP. The results demonstrate that the proposed IUAVA-RP protocol achieves higher energy efficiency and packet delivery ratio, as well as lower end-to-end delay and routing overhead compared to the earlier protocols. © 2023 IEEE.

关键词： Antennas

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Retraction Note: Resnet-based deep learning multilayer fault detection model-based fault diagnosis

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Multimedia Tools and Applications 2024年第38期83卷 86323-86324页

作者： Jaber, Mustafa Musa Ali, Mohammed Hasan Abd, Sura Khalil Jassim, Mustafa Mohammed Alkhayyat, Ahmed Majid, Mohammed Sh. Alkhuwaylidee, Ahmed Rashid Alyousif, Shahad Informatics Institute for Postgraduate Studies Iraqi Commission for Computers and Informatics Baghdad Iraq Department of Computer Science Al-Turath University College Baghdad Iraq Computer Techniques Engineering Department Faculty of Information Technology Imam Ja’afar Al-Sadiq University Najaf Iraq Directorate of Research and development Ministry of Higher Education and Scientific Research Baghdad Iraq Department of Medical Instruments Engineering Techniques Al-Farahidi University Baghdad Iraq College of Technical Engineering The Islamic University Najaf Iraq Medical Instrumentation Techniques Engineering Department Al-Mustaqbal University College Babylon Iraq Computer Technical Engineering Mazaya University College Thi Qar Iraq Department of Electrical and Electronic Engineering College of Engineering Gulf University Sanad Kingdom of Bahrain Research Centre University of Mashreq Baghdad Iraq

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Image Segmentation and Recognition of Oracle Bone Topographies Based on Deep Learning

Image Segmentation and Recognition of Oracle Bone Topographi...

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Image Processing and Computer Applications (ICIPCA), IEEE International Conference on

作者： Zhengen Lv Yihui Liu Yunjie Cai Qi Liu College of Mechanical and Control Engineering Guilin University of Technology Guilin China School of Mechanical and Electronic Engineering Jingdezhen Ceramic University Jingdezhen China College of Mechanical and Electrical Engineering Guangxi Agricultural Engineering Vocational Technical College Chongzuo China School of Energy and Electrical Engineering Qinghai University Xining China

ISBN: (数字)9798350360240

ISBN: (纸本)9798350384161

Image segmentation and recognition of oracle bone topography is a common challenge for in-depth study of oracle bones. The key lies in how to accurately and efficiently process and recognize the original oracle bone topography images and solve the practical problems in the research based on the image digitization results. The research of this topic helps to promote the research of oracle bone holography and the construction of digitization project. Image segmentation of oracle bone tablet is to use digital image processing and computer vision technology to extract independent text regions with distinctive image features and non-overlapping each other in the complex background of the original topographic image. The study of automatic segmentation and recognition of oracle bone characters on original topos has important social significance. In this paper, the image preprocessing model based on the channel attention mechanism is firstly utilized to initially judge and identify the interference elements present in the original oracle bone topography image. Meanwhile, a joint filtering noise reduction model is established to effectively remove the location-like noise and artificial texture in the image. Then, a fast oracle bone image segmentation model based on neural network is established, and each single character region in the image is successfully segmented out by building and training the model through deep learning. In this paper, 76 common oracle scripts and their variant scripts are trained and recognized in combination with the oracle recognition enhancement model, and a one-to-many link between Chinese characters and oracle images is established. The accuracy and efficiency of oracle bone recognition are further improved by segmenting and classifying the test set images for recognition.

关键词： Deep learning Image segmentation Image recognition Accuracy Computational modeling Noise reduction Neural networks Surfaces Bones Character recognition

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A Robust control Approach for Virtually Coupled Train Set with Parameter Uncertainty Under External Perturbations 1

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2nd International Conference on Information control, Electrical engineering and Rail Transit, ICEERT 2022

作者： Zheng, Yi Wang, Yihui Gao, Peichang Zhu, Songwei Li, Shukai Yao, Xiuming State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing China Department of Railway Communication and Signalling Baotou Railway Vocational and Technical College Baotou China School of Electronic and Information Engineering Beijing Jiaotong University Beijing China

ISBN: (数字)9789819964314

ISBN: (纸本)9789819964307

Virtual coupling has been widely studied in recent years as the next-generation signalling system, which separates trains by relative braking distance to increase railway line capacity. This paper investigates the robust H∞ control problem for virtually coupled train set (VCTS) to reduce the influence of disturbances on train operations. By capturing the dynamic evolution of VCTS, an error state-space model is established considering uncertain parameters, input constraints, and disturbances. Based on the robust H∞ optimal control theory, the H∞ state feedback control law is obtained, where the robust state feedback control gain is solved by linear matrix inequalities (LMIs) based on the condition for the existence of the robust H∞ state feedback control law. Finally, the model accuracy and control performance are verified via numerical simulations, which illustrate errors are controlled within a small range and converge to zero quickly, further the robustness of VCTS is guaranteed. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Robust control

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Direction of Arrival Estimation Method under Sensor Failure Scenarios via covariance matrix completion 7

Direction of Arrival Estimation Method under Sensor Failure ...

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7th International Conference on Signal and Image Processing, ICSIP 2022

作者： Sun, Bing Wu, Chen-Xi Zhu, Wen-Li Yuanwang Survey Vessel China Satellite Maritime Tracking and Control Department Jiangyin China National University of Defense Technology College of Electronic Engineering Hefei China China xi'An Satellite Control Center Technical Department Xi'an China

ISBN: (数字)9781665495639

ISBN: (纸本)9781665495639

The performance of direction of arrival (DOA) estimation degrades in the case of sensor failures. To solve this problem, a virtual array DOA estimation method based on covariance matrix completion is proposed. First, the covariance matrix is extended to a high-dimensional covariance matrix to construct a virtual array. Then, the matrix completion problem is constructed, and the extended covariance matrix is completed by the OptSpace method. Finally, using the obtained target matrix, DOA estimation is performed based on the root-MUSIC method. Theoretical and simulation results show that the proposed method can effectively restore the estimation accuracy in the sensor failure scenario. © 2022 IEEE.

关键词： Direction of arrival

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Identification of vulnerable node groups in wind power grids based on K-order structure entropy 5

Identification of vulnerable node groups in wind power grids...

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5th International Conference on Mechanical, Electric, and Industrial engineering, MEIE 2022

作者： Li, Lijuan Li, Yue Liu, Zhiqiang Zeng, Yiwei Ding, Gangwei Zhang, Qingsong College of Automation and Electronic Information Xiangtan University Xiangtan411105 China Hunan Engineering Research Center of Multi-energy Cooperative Control Technology Xiangtan411105 China HuaiHua Vocational and Technical College Huaihua418000 China

Power outage accidents are caused by chain failures caused by the failure of vulnerable components affecting a certain range of components. Identifying the spread impact range of vulnerable nodes can effectively improve the system's vulnerability. Large-scale wind power grids connection increases the complexity of the power systems and brings new challenges to the identification and simulation of vulnerable nodes. In this paper, a method for identification of vulnerable node groups in wind power grids is proposed. The method identifies the central node of the vulnerable node groups in wind power grids based on interval electrical number, and determines the influence range of the central node through the minimum K-order structure entropy, so as to obtain the vulnerable node groups in wind power grids. An IEEE-39 bus system with wind power is used to verify the rationality of the proposed method, which provides a reference for the prevention and control of interlocking faults of power grids with wind power. © Published under licence by IOP Publishing Ltd.

关键词： Wind power

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Renewable Energy Generation Prediction using Structure Embedded based Gated Recurrent Unit

Renewable Energy Generation Prediction using Structure Embed...

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Software, Systems and Information Technology (SSITCON), International Conference on

作者： E C Vidya Sagar Mudunuri K Barathi S. Madhubalan D. R Anita Sofia Liz Ramy Riad Hussein Department of Electrical and Electronics Engineering Sagi Rama Krishnam Raju Engineering College Bhimavaram India Department of Electrical and Electronics Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India Department of Electrical and Electronic Engineering Sona College of Technology Salem India Department of Computer Science and Engineering New Prince Shri Bhavani College of Engineering and Technology Chennai India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq

ISBN: (数字)9798350352931

ISBN: (纸本)9798350352948

The renewable energy provides several environmental and economic benefits that compared to other energy resources such as nuclear and fuel-based energy. The data acquired and utilized for predicting renewable energy generation that includes strong-volatility, significant randomness, and intermittent behavior. However, the variation in data obtained from various energy generation resources lead to significant challenge and results in inaccurate prediction results. To overcome this problem, a Structure Embedded based Gated Recurrent Unit (SE-GRU) method is proposed for predicting the renewable energy generation and consumption accurately. The proposed SE-GRU model solves the issue of variation in data due to factor like climate conditions by capturing long term dependencies and immediate fluctuations that enhanced the prediction results. Initially, the data obtained from renewable resources are preprocessed by using techniques like normalization and filling missing values. These preprocessed data are forwarded to proposed prediction model SE-GRU to predict the energy generation and consumption accurately. The experimental results of proposed SE-GRU method achieved Mean Squared Error (MSE) of 0.19 which is less than existing prediction approaches such as Echo State-Network-Convolutional Neural Network (ESNCNN).

关键词： Renewable energy sources Noise Neural networks Predictive models Logic gates Software systems Data models Filling Information technology Meteorology

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Mobility and Resource Allocation with Intelligent Clustering in UAVs Assisted VANETs

Mobility and Resource Allocation with Intelligent Clustering...

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2023 International Conference in Advances in Power, Signal, and Information Technology, APSIT 2023

作者： Mahmood, Sarmad Nozad Al-Dolaimy, F. Alkhayyat, Ahmed Alani, Sameer Alkhafaji, Mohamed Ayad Abbas, Fatima Hashim Guneser, Muhammet Tahir Alsalamy, Ali Seker, Cihat College of Engineering Technology Al-Kitab University kirkuk Iraq College of Medical and Health Technologies Al-Zahraa University for Women Karbala Iraq College of Technical Engineering The Islamic University Najaf Iraq University of Mashreq Research Center Baghdad Iraq College of Engineering Technology National University of Science and Technology Dhi Qar64001 Iraq Al-Mustaqbal University College Medical Laboratories Techniques Department Hillah Babil51001 Iraq Collage of Engeneering Karabuk University Department of Electrical Electronic Karabuk Turkey College of Information Technology Imam Ja'afar Al-Sadiq University Al-Muthanna66002 Iraq

ISBN: (纸本)9798350339369

In the past decade, there has been significant development and utilization of Unmanned Aerial Vehicles (UAVs) in Vehicular Ad Hoc Networks (VANETs) across various applications. The integration of UAVs in VANETs has provided vehicles with enhanced performance by enabling communication through an aerial medium, thereby bypassing ground-level obstacles. Efficient management of mobility and network resources becomes crucial when dealing with a large number of highly dynamic vehicles. To address this, the proposed approach in this paper is Mobility and Resource Allocation with Intelligent Clustering in UAVs-assisted VANETs (MRAIC-UAVs). The key components of the proposed approach include the network model, mobility model, clustering strategy, and UAVs Cluster Head (CH) selection process. The selection of CHs is based on the evaluation of parameters such as residual energy, UAVs mobility, UAVs degree difference, distance, and UAVs stability. This approach significantly improves network energy efficiency and packet delivery ratio. The simulation is conducted using OMNET++ with the SUMO mobility generator, and a comparison is made with earlier models such as PRO-UAVs and RJEDC-UAVs. The performance analysis considers parameters such as packet delivery ratio, end-to-end delay, energy efficiency, and energy consumption. The simulation results demonstrate that the proposed MRAIC-UAVs approach achieves higher energy efficiency and packet delivery ratio while exhibiting lower end-to-end delay and energy consumption compared to earlier approaches. © 2023 IEEE.

关键词： Vehicular ad hoc networks

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Domain Adversarial Interaction Network for Cross-Domain Fault Diagnosis 4th

Domain Adversarial Interaction Network for Cross-Domain Fau...

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4th International Conference on Machine Learning for Cyber Security, ML4CS 2022

作者： Lu, Weikai Chen, Jian Zheng, Hao Fan, Haoyi Wei, Eng Yee Cao, Xinrong Zhang, Deyang School of Electronic Electrical Engineering and Physics Fujian University of Technology Fuzhou350118 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou350121 China School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou450001 China School of Computer Sciences Universiti Sains Malaysia Penang11800 Malaysia Henan Provincial Institute of Scientific and Technical Information Zhengzhou450003 China

ISBN: (纸本)9783031200984

Intelligent fault diagnosis has been widely used in the industry and plays a crucial role in the health management of machinery. In recent years, unsupervised domain adaptation (UDA) has been applied to fault diagnosis, showing excellent performance under variable working conditions. However, most existing UDA-based methods do not consider the temporal relations in the fault signal, resulting in sub-optimal performance. In this paper, we proposed a domain adversarial interaction network (DAIN) to solve this problem. By downsampling sub-sequences of fault signals and interacting with their features, DAIN can obtain feature representations containing the temporal relations. In addition, domain adversarial learning and maximum mean discrepancy (MMD) are applied to DAIN to align the domain discrepancy and distribution discrepancy of source and target domains. We conducted extensive experiments on the public Paderborn University (PU) dataset, and the results demonstrate that the proposed method can achieve higher cross-domain fault diagnosis accuracy than the existing methods. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Failure analysis

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