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Hybrid ConvLSTM with Attention for Precise Driver Behavior Classification 6

Hybrid ConvLSTM with Attention for Precise Driver Behavior C...

引用

6th IEEE International Conference on Artificial Intelligence and Knowledge engineering, AIKE 2023

作者： Mustafa, Ghulam Cho, Seong-Je Hwang, Youngsup Dept. of Computer Engineering Sun Moon University Asan-si Korea Republic of Dept. of Software Science Dankook University Yongin Korea Republic of

ISBN: (纸本)9798350331288

Driving behavior classification plays an important role in many real-world applications, including traffic accident prevention, driver safety, usage-based insurance, and optimizing ridesharing services. In this research, we introduce a novel approach called Hybrid ConvLSTM with Attention for Driver Behavior Classification (HCLA-DBC), designed to achieve precise and reliable driver behavior classification. Our main focus is to distinguish three fundamental driving behavior classes: normal, drowsy, and aggressive, which play a vital role in supporting advanced driver-assistance systems (ADAS). To evaluate the performance of our proposed models, we conducted experiments using the publicly available UAH-DriveSet dataset. The evaluation comprised six distinct models, encompassing two machine learning models (SVM and Gradient Boosting) and four deep learning models (CNN, MLP, LSTM, and HCLADBC). Our HCLA-DBC model outperformed all other models and previous results on the same data. On the test data, it achieved an accuracy of 94.12%, precision of 94.24%, recall score of 94.12%, F1-score of 94.12%, and a macro-average ROC AUC of 98.83. These exceptional outcomes underscore the efficacy of our hybridized approach and its potential to significantly enhance driver behavior classification. The HCLA-DBC model not only promises new possibilities for driver behavior analysis but also paves the way for elevated ADAS capabilities and safer driving experiences. © 2023 IEEE.

关键词： Support vector machines

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Single-Phase Unidirectional Multilevel Rectifiers Based on Cascaded Cells

Single-Phase Unidirectional Multilevel Rectifiers Based on C...

引用

2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Monteiro, Amanda Pereira Jacobina, Cursino Brandão Da Costa Bahia, Filipe Antônio De Sousa, Reuben Palmer Rezende Cardoso, Jean Torelli Pinto, Alan Robson Andrade Federal University of Ceará Mechanical Engineering Dept. Fortaleza Brazil Federal University of Campina Grande Electrical Engineering Dept. Campina Grande Brazil Federal University of Bahia Electrical and Computer Engineering Dept. Salvador Brazil Federal University of Pernambuco Electronics and Systems Dept. Recife Brazil

ISBN: (纸本)9798350376067

This paper proposes and investigates two new single-phase unidirectional multilevel rectifiers based on cascaded cells. Compared to conventional topology, as the number of cascaded cells increases, proposed ones employ, proportionally, a reduced controlled switch count and driver circuits, which may reduce control complexity and increase reliability. The dc-link voltage ratios are calculated for asymmetrical operation to provide the maximum number of voltage levels generated in the ac side of the rectifiers, ensuring a high power quality. Besides, the proposed configurations can synthesize a higher number of voltage levels by employing the same controlled switch count as the conventional one so that it is possible to provide a higher levels-per-switch ratio. Simulation and experimental results are presented to validate the performance and feasibility of the proposed topologies. © 2024 IEEE.

关键词： Topology

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5th International Conference on Communications, Information System and computer engineering, CISCE 2023

作者： Jin, Jiayuan Jiang, Xiantao Song, Tian Richard Yu, Fei. Weili Liu, Jin Shanghai Maritime University Dept. Information Engineering Shanghai China Tokushima University Dept. Electrical and Electronics Engineering Tokushima Japan Carleton University Dept. Systems and Computer Engineering Ottawa Canada

ISBN: (纸本)9798350326796

A new Quadtree with nested multi-type tree(QTMT) partitioning method is adopted in the latest video coding standard Versatile Video Coding(VVC) to achieve effective encoding. Compared with Quadtree(QT) in HEVC, QTMT can provide enhanced encoding performance. However, this improved coding performance is obtained at the cost of additional computational load from recursive and nested searches for the best CU structure. A context similarity-enabled CU partitioning algorithm is proposed to achieve a balance between coding efficiency and compression quality in VVC. According to the difference between frames, the method of frame difference algorithm is used to calculate the possibility that the current block can follow the same subblock division mode of the previous frame, so as to skip the unnecessary division selection process. The experimental results show that the proposed method can save the encoding time up to 18.22% with about 0.91% BD-rate degradation on average compared with VVC software reference VTM-9.3 © 2023 IEEE.

关键词： Encoding (symbols)

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Evaluation of Incremental Conductance MPPT Algorithm under Varying Conditions 21

Evaluation of Incremental Conductance MPPT Algorithm under V...

引用

21st International Multi-Conference on systems, Signals and Devices, SSD 2024

作者： Nezam, Ahmad M. Mahmood, Musaria K. Karim, Sulaiman M. Addawe, Abdirahim Ankara Yildirim Beyazit University Dept. of Energy Systems Engineering Ankara Turkey Jannat Iraq College Dept. of Medical Instr. Tech. Eng. Ramadi Iraq Al-Hadba'a University College Dept. of Computer Technology Engineering Mosul Iraq Karabük Üniversitesi Dept. of Computer Engineering Karabük Turkey

ISBN: (纸本)9798350374131

Photovoltaic-based renewable energy production is constantly developing and becoming an increasing part of mod-ern energy markets. The power efficiency of a PV system is one of the most challenging problems, especially in changing climate conditions. Tracking the Maximum Power Point (MPP) to achieve optimal power production is one of the most important requirements of any PV-based system. This article explores the functionality of the Incremental Conductance (INC) algorithm as a highly effective approach for Maximum Power Point Tracking (MPPT). Simulations are conducted to investigate the effectiveness of INC across diverse operational case studies, considering both Standard Test Conditions (STC) and Partial Shading Conditions (PSC). In this investigation, key factors including system power efficiency, settling time, and steady-state power oscillation are taken into account. Simulation reveals rapid tracking and reduced oscillation under STC, meanwhile, it occasionally demonstrates inadequacy in accurately tracking the MPP under PSC in the case of multi-panel systems. © 2024 IEEE.

关键词： Solar panels

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Wildfire Spread Prediction Using Attention Mechanisms in U-NET

Wildfire Spread Prediction Using Attention Mechanisms in U-N...

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2023 International Conference on Electrical, computer, Communications and Mechatronics engineering, ICECCME 2023

作者： Shah, Kamen Pantoja, Maria California Polytechnic State University Dept. Computer Science and Software Engineering San Luis Obispo United States

ISBN: (纸本)9798350322972

Global warming has increased large-scale natural disasters at an alarmingly greater frequency. These natural disasters affect humans and the ecosystems that all species rely upon for food and shelter. A significant byproduct of global warming is the emergence of mega-fires, which are wildfires that burn more than 100,000 acres. Mega fires have exhibited the capacity to destroy entire ecosystems and urban areas. Researchers forecast a significant increase in wildfires during the next decade. This is a fundamentally growing problem that mandates modern solutions. Currently, simulation models are employed to predict wildfire propagation and aid firefighters in suppressing them. However, these methods are computationally expensive and often inaccurate. A recent advance in this area is the usage of neural networks (NN), which are systematically more accurate and computationally efficient. This paper focuses on predicting wildfire spread using NN with an attention mechanism to improve spatial recognition and overall performance in neural networks for wildfire spread prediction. We train different models on 12 distinct observational variables derived from the Google Earth Engine catalog. Evaluation is conducted with accuracy, Dice coefficient score, ROC-AUC, and F1-score. Results show that when augmenting segmentation models with attention mechanisms, the attention component improves feature suppression and recognition, improving overall performance. Furthermore, we use ensemble modeling to reduce bias and variation, leading to more consistent and accurate predictions. The architecture presented in this research achieved a ROC-AUC score of 86.2% and an accuracy of 82.1% when inferencing on wildfire propagation at 30-minute intervals. © 2023 IEEE.

关键词： Forecasting

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Energy Efficient joint user association and power allocation using Parameterized Deep DQN 9

Energy Efficient joint user association and power allocation...

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9th International Conference on computer and Communication engineering, ICCCE 2023

作者： Mughees, Amna Tahir, Mohammad Sheikh, Muhammad Aman Amphawan, Angela Yap, Kian Meng Habaebi, Mohamed Hadi Islam, Md. Rafiqul Sunway University Dept. of Computing and Information Systems Petaling Jaya Malaysia University of Turku Dept. of Computing Turku Finland Cardiff Metropolitan University Dept. of Electronics and Computer Systems Engineering Cardiff United Kingdom International Islamic University Dept. of Electrical and Computer Engineering Kuala Lumpur Malaysia

ISBN: (纸本)9798350325218

Using small cells to create an ultra-dense network for 5G and beyond is a promising strategy to improve network coverage, data demands and reduce latency. Despite using small cells, these dense wireless networks result in performance degradation and increased energy consumption. Energy consumption is a crucial parameter for sustainable future wireless networks. In order to improve quality of service (QoS) and Energy Efficiency (EE), efficient resource allocation strategies are required. This paper investigates a Parameterized Double Deep Q-Network (PDDQN) based framework for joint user association and power allocation to improve EE and throughput. Apart from other conventional machine learning approaches, considering single state space of the joint optimization problem, our proposed framework considers both discrete and continuous state spaces. Our proposed PDDQN technique also solves the generalization problem that occurs due to similar states. The simulation results indicate that the proposed work significantly improves energy EE and throughput in large-scale learning problems. © 2023 IEEE.

关键词： Energy efficiency

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USRP-Based Single Anchor Positioning: AoA with 5G Uplink Signals, and UWB Ranging 11

USRP-Based Single Anchor Positioning: AoA with 5G Uplink Sig...

引用

11th Workshop on Satellite Navigation Technology, NAVITEC 2024

作者： Spanos, Thodoris Fabra, Fran Lopez-Salcedo, Jose A. Seco-Granados, Gonzalo Kanistras, Nikos Lapin, Ivan Paliouras, Vassilis University of Patras Dept. of Electrical and Computer Engineering Greece Loctio P.C Greece Dept. of Telecommunication and Systems Eng Spain Radio Navigation Systems and Techniques Section European Space Agency Netherlands

ISBN: (纸本)9798350356854

This paper presents a novel testbed designed for 5th-Generation (5G) positioning using Universal software Radio Peripherals (USRPs). The testbed integrates multiple units: an Operation Unit for test management, a User Unit equipped with an Ettus E312 USRP, and a Station Unit featuring an Ettus N310 USRP equipped with a three-element Uniform Linear Array for Angle of Arrival estimation. Alongside ultra wide-band ranging, the testbed estimates the user's position relative to the base station. Signal processing algorithms are executed in a dedicated processing unit. Key challenges addressed include phase misalignment between RX channel pairs due to different Local Oscillators in the Ettus N310, necessitating real-time calibration for precise signal alignment. High sampling rates (up to 61.44 MSps) result in large IQ sample files, managed efficiently using a snapshot technique to optimize storage without compromising testbed positioning capabilities. The testbed synchronizes angular measurements with ranging estimates allowing consistent performance evaluation for real-life cases of dynamic users (e.g. pedestrian). Experimental results demonstrate the testbed's effectiveness in achieving accurate pedestrian user localization. © 2024 IEEE.

关键词： Testbeds

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Enhancing Cybersecurity Through Advanced Fraud and Anomaly Detection Techniques: A Systematic Review

Enhancing Cybersecurity Through Advanced Fraud and Anomaly D...

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2024 International Conference on Science, engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024

作者： Olowe, Oluwambo Tolulope Adebiyi, Ayodele A. Marion, Adebiyi O. Tobi, Olowe Mofiyinfoluwa Olaniyan, Deborah Olaniyan, Julius Emmanuel, Aderemi Akindeji, Kayode Landmark University Dept of Computer Science Omuaran Nigeria University of Pretoria Dept. of Industrial & Systems Engineering Pretoria South Africa Durban University of Technology Dept. of Power Electric Engineering Durban South Africa

ISBN: (纸本)9798350358155

Fraud and anomaly detection in cybersecurity have become critical areas of research, prompting a comprehensive review of recent developments and model effectiveness. This study investigates prevalent trends and challenges in fraud and anomaly detection, focusing on data science approaches within e-commerce and smart cities. Through the analysis, key factors influencing detection efficacy, such as deep learning models and hybrid approaches, are examined. Findings highlight the multidimensional nature of cyber threats across various domains and underscore the importance of proactive security measures, including robust authentication, network fortification, and employee training. This paper contributes to enhancing awareness and understanding of evolving cyber threats while offering actionable insights for bolstering detection capabilities and mitigating risks. © 2024 IEEE.

关键词： Cyber attacks

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Predictive Analysis of Jordan Road Accidents Severity Using Machine Learning

Predictive Analysis of Jordan Road Accidents Severity Using ...

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2023 International Symposium on Networks, computers and Communications, ISNCC 2023

作者： Mustafa, Dheya Sawaged, Layan Khabour, Safaa M. The Hashemite University Faculty of Engineering Dept. of Computer Engineering Zarqa13133 Jordan Yarmouk University Faculty of Information Technology and Computer Sciences Dept. of Information Systems Irbid21163 Jordan

ISBN: (纸本)9798350335590

Jordan has a high and growing level of traffic accidents reaching 160,600) accidents in 2021, (11,241) of them had human injuries, (589) deaths, and (320) million JOD losses. Road traffic accidents are currently the 8th leading cause of death worldwide, accounting for about 1.35 million fatalities annually. Understanding the primary causes of these accidents and the circumstances in which they occur is essential if governments throughout the world are to put policies in place to limit the number of fatalities brought on by traffic accidents. This project has two objectives: (i) to learn more about Jordan's present road accident situation by performing an exploratory study on several road accident datasets, and (ii) to investigate the effectiveness of various machine-learning techniques in predicting the severity of road accidents in Jordan. The dataset entities were collected, collated, explored, and prepared for use in the model. To assess their predictive performance, five different classification algorithms were trained and tested. The findings of the present study demonstrate that the best algorithm was Logistic Regression, which had an accuracy of 98.1%. © 2023 IEEE.

关键词： Machine learning

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Ensuring Electronic Health Record (EHR) Privacy using Zero Knowledge Proofs (ZKP) and Secure Encryption Schemes on Blockchain 5

Ensuring Electronic Health Record (EHR) Privacy using Zero K...

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5th International Conference on Advancements in Computing, ICAC 2023

作者： Ranaweera, T.A.V.Y. Hewage, H.N.H. Hapuhinna, H.K.D.W.M.C.B. Preethilal, K.L.K.T. Senarathne, Amila Ruggahakotuwa, Laneesha Sri Lanka Institute of Information Technology Dept. Computer Systems Engineering Malabe Sri Lanka

ISBN: (纸本)9798350358131

In the haste of EHR digitization, the protection of patient data becomes paramount, casting a spotlight on small healthcare clinics grappling with the threats of an ever more interconnected healthcare sector. That is due to the existing centralized system and traditional password-based authentications which cause a single point of failure. Besides this patient consent for EHR sharing is narrowed to a one-time consent. To overcome these problems, this study suggests a robust solution that makes data sharing utilizing blockchain while achieving privacy using web-based Zero Knowledge Proofs (ZKP) authentication and Homomorphic Encryption (HE). The research also considers securely outsourcing EHRs to research organizations to improve data exchange for research purposes. Along with patient-controlled encryption, consent control, and time-limited data access, it also explores dynamic consent management while complying with healthcare standards. Lastly, this study stream-lines secure data sharing with insurance providers while ensuring patient privacy and defending against insurance fraud. © 2023 IEEE.

关键词： Blockchain

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