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19th IEEE International Conference on Automation Science and engineering, CASE 2023

作者： Proia, Silvia Cavone, Graziana Carli, Raffaele Dotoli, Mariagrazia Polytechnic of Bari Department of Electrical and Information Engineering Italy University Roma Tre Department of Civil Computer and Aeronautics Technologies Engineering Italy

ISBN: (纸本)9798350320695

The inspection of railway systems with traditional wayside detectors allows mainly the detection of wheels and axle bearings defects and can be time-demanding, unsafe, and heavily dependent on humans. To overcome these issues and then optimize and automate the rail and track diagnosis process, drones can be an excellent solution thanks to their onboard state-of-the-art cameras and sensors. Thus, with the aim of rapidly collecting highly accurate data, an innovative hybrid movable railway diagnostic architecture, consisting of a diagnostic train and a fleet of drones, is defined in this work. From the control point of view, the main interest is in optimally managing the crucial phase of drones returning to and landing on the moving train when the railway inspection mission is completed. To control the fleet of drones, a combination of consensus algorithm in the leader-following mode and linear quadratic regulator (LQR) is implemented for the flight formation phase and the landing phase on the moving base platform (i.e., the diagnostic train), respectively. The landing phase is performed both as vertical or oblique descent through a go to goal and as oblique descent along a predefined path. The obtained results of the railway diagnostic architecture simulations are presented and discussed in detail. In particular, they show that the vertical and oblique descent performed as go to goal are certainly faster than the oblique descent along a predefined path. © 2023 IEEE.

关键词： Drones

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Battery Monitoring System and Control Using LoRa Technology for Battery Behavior and Analysis 4

Battery Monitoring System and Control Using LoRa Technology ...

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4th International Conference on Innovative Research in Applied Science, engineering and Technology, IRASET 2024

作者： Isa, Awang Abdul Hadi Iqbal, Mohammad Tariq Dani, Muhammad Norfauzi Jiann, Tan Soon Laila, Dina Shona Sulthan, Sheik Muhammed Universiti Teknologi Brunei Electrical And Electronics Engineering Bandar Seri Begawan Brunei Memorial University Of Newfoundland Electrical And Computer Engineering St. John'sNL Canada Universiti Teknologi Brunei Civil Engineering Bandar Seri Begawan Brunei Luleå University Of Technology Electrical And Space Engineering Luleå Sweden

ISBN: (纸本)9798350309508

Batteries are intricate electrochemical devices that demonstrate stochastic and nonlinear behavior contingent upon environmental and operational conditions;therefore, battery monitoring is an essential component throughout their application. This paper introduces a web-based battery monitoring and control system that utilizes Long Range (LoRa) communication technology, an element of Internet of Things (IoT), utilizing the ESP32 microcontroller. The system provides comprehensive online data in real-time through the integration of a multitude of sensors. The proposed system aims to overcome the constraints of current communication technology by capitalizing on the advantages of LoRa, a technology known for its efficient long-range and low-energy transmission, which renders it highly suitable for applications involving real-time monitoring. Furthermore, a control operation allows users to manage critical battery functions, including charging and discharging. The study performed a thorough experimental assessment of the proposed system across various operations, and the outcomes effectively corresponded with the primary objective and goals of the research. The system under consideration successfully facilitates remote monitoring and user control in real-time, provides long-term data visualization through data logging, and enables battery condition evaluation. The implementation of data logging was intended to optimize the utilization of future battery evaluation metrics, including State-of-Charge (SOC), State-of-Health (SOH), and Remaining Useful Life (RUL). Consequently, the developed system is well-suited for numerous applications that demand efficient energy storage solutions, including Electric Vehicles (EVs) and renewable energy. © 2024 IEEE.

关键词： Internet of things

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Proposal of an AI based approach for Urban Traffic Prediction from Mobility Data

Proposal of an AI based approach for Urban Traffic Predictio...

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Berlotti, Mariaelena Di Grande, Sarah Cavalieri, Salvatore Torrisi, Vincenza Inturri, Giuseppe University of Catania Department of Electrical Electronic and Computer Engineering Catania Italy University of Catania Department of Civil Engineering and Architecture Catania Italy

ISBN: (纸本)9798350324457

The rapid urbanization of our world has led to an ever-increasing urban population, resulting in significant challenges in managing transportation systems. Traffic congestion, with its associated environmental pollution, safety hazards, and prolonged travel times, continues to plague urban areas. Despite numerous efforts to mitigate these issues, the problem persists and hampers urban development. This paper addresses the pressing need for effective traffic flow forecasting of the city of Catania as a critical element in traffic management. Catania's rapid urbanization has created a complex transportation network as the city's population has expanded into suburbs and neighbouring areas, causing congestion, pollution, and increased individual motorization due to the high demand for mobility. This has led to severe traffic congestion in the central district, necessitating an efficient traffic management strategy, particularly through the utilization of traffic flow forecasting. A central challenge in traffic flow prediction is the cost and practicality of sensors deployment on every road. To address this issue, the authors propose a novel two-level machine learning approach. The first level employs an unsupervised clustering model to extract patterns from big data generated by sensors, while the second level employs supervised machine learning models for traffic flow forecasting within each cluster. Importantly, this approach enables predictions for roads lacking sensor data by utilizing a really small subset of these new data from alternative sources and assigning roads to appropriate clusters. © 2023 IEEE.

关键词： big data clustering forecasting machine learning traffic flow

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Impact of navigation apps on congestion and spread dynamics on a transportation network

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Data Science for Transportation 2024年第2期6卷 1-13页

作者： Bagabaldo, Alben Rome Gan, Qianxin Bayen, Alexandre M. González, Marta C. Department of Civil and Environmental Engineering University of California Berkeley 94720 CA United States Department of Electrical Engineering and Computer Sciences University of California Berkeley 94720 CA United States Department of City and Regional Planning University of California Berkeley 94720 CA United States Department of Electrical and Computer Engineering Cornell University New York 10044 NY United States School of Civil Environmental and Geological Engineering Mapua University Manila 1002 Philippines

In recent years, the widespread adoption of navigation apps by motorists has raised questions about their impact on local traffic patterns. Users increasingly rely on these apps to find better, real-time routes to minimize travel time. This study uses microscopic traffic simulations to examine the connection between navigation app use and traffic congestion. The research incorporates both static and dynamic routing to model user behavior. Dynamic routing represents motorists who actively adjust their routes based on app guidance during trips, while static routing models users who stick to known fastest paths. Key traffic metrics, including flow, density, speed, travel time, delay time, and queue lengths, are assessed to evaluate the outcomes. Additionally, we explore congestion propagation at various levels of navigation app adoption. To understand congestion dynamics, we apply a susceptible–infected–recovered (SIR) model, commonly used in disease spread studies. Our findings reveal that traffic system performance improves when 30–60% of users follow dynamic routing. The SIR model supports these findings, highlighting the most efficient congestion propagation-to-dissipation ratio when 40% of users adopt dynamic routing, as indicated by the lowest basic reproductive number. This research provides valuable insights into the intricate relationship between navigation apps and traffic congestion, with implications for transportation planning and management. © The Author(s) 2024.

关键词： Congestion dynamics SIR modeling Traffic congestion Transportation network

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An Extreme Value Theory Approach for Understanding Queue Length Dynamics in Adaptive Corridors 27

An Extreme Value Theory Approach for Understanding Queue Len...

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27th IEEE International Conference on Intelligent Transportation Systems, ITSC 2024

作者： Mustavee, Shakib Kachroo, Pushkin Agarwal, Shaurya University of Central Florida Department of Civil Environmental and Construction Engineering OrlandoFL United States University of Nevada Department of Electrical and Computer Engineering Las Vegas United States

ISBN: (纸本)9798331505929

This paper introduces a novel approach employing extreme value theory to analyze queue lengths within a corridor controlled by adaptive controllers. We consider the maximum queue lengths of a signalized corridor consisting of nine intersections every two minutes, roughly equivalent to the cycle length. Our research shows that maximum queue lengths at all the intersections follow the extreme value distributions. To the best knowledge of the authors, this is the first attempt to characterize queue length time series using extreme value analysis. These findings are significant as they offer a mechanism to assess the extremity of queue lengths, thereby aiding in evaluating the effectiveness of the adaptive signal controllers and corridor management. Given that extreme queue lengths often precipitate spillover effects, this insight can be instrumental in preempting such scenarios. © 2024 IEEE.

关键词： Queueing theory

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Hybrid Method for Monitoring Sundarbans Mangrove Forest Using Satellite Images 2

Hybrid Method for Monitoring Sundarbans Mangrove Forest Usin...

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2nd International Conference on Information and Communication Technology, ICICT 2024

作者： Al Naim, M.D. Abdullah Sarar, Shihab Hasan, Mahdi Tasin, Md Maruf Hossain Farrok, Omar Department of Civil Engineering Ahsanullah University of Science and Technology Bangladesh Department of Computer Science and Engineering Ahsanullah University of Science and Technology Bangladesh Department of Electrical and Electronic Engineering Ahsanullah University of Science and Technology Bangladesh

ISBN: (纸本)9798331508227

A hybrid method for monitoring Sundarbans mangrove forest using satellite images with machine learning techniques is presented in this paper. The Random Forest algorithm, data scaling, cloud, and shadow masking are used to enhance the precision of land cover classification over the Sundarbans. The study utilizes Landsat 8 imagery to classify land into deep forest, light forest, barren land, and water bodies. Significant fluctuations in land cover types over the analyzed period, with notable variations in forest density, barren land, and water bodies are identified. Normalized Difference Vegetation Index and Normalized Difference Water Index are two popular methods for observing vegetation health and water bodies in remote sensing. But it fails to classify different objects. This paper focuses on solving this issue using machine learning technique by combining the two methods along with the other band’s reflectance as a feature to improve the classification and show a great f1 and recall. This study uses a hybrid model to monitor land cover changes in the Sundarbans mangrove forest from 2013 to 2022, achieving an overall accuracy of 0.98. Significant findings include a deep forest cover increase from 52.82% to 55.735%, fluctuations in barren land from 2.04% to a peak of 8.38% in 2021, and a decrease in water bodies from 32.18% to 28.82%, indicating possible changes in river dynamics and hydrological conditions. ©2024 IEEE.

关键词： Landsat

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Exploration and Detection of Cyber-Attacks on MPC-Based Vehicular Platoons 27

Exploration and Detection of Cyber-Attacks on MPC-Based Vehi...

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27th IEEE International Conference on Intelligent Transportation Systems, ITSC 2024

作者： Choudhury, Tashfique Duan, Chengwei Zhang, Hanyu Du, Lili Ray, Sandip University of Florida Department of Electrical and Computer Engineering GainesvilleFL32611 United States University of Florida Department of Civil & Coastal Engineering GainesvilleFL32611 United States

ISBN: (纸本)9798331505929

We develop an extensible platform for exploring cyber-attacks on connected and autonomous vehicle (CAV) platoon controllers. A unique feature of our platform is the ability to address a large spectrum of cyber threats, including new attacks designed after deployment. This platform facilitates systematic exploration of cyber-attacks, helping to uncover real-time vulnerabilities across different designs of platoon controllers. Additionally, we introduce a novel, dual-stage detection mechanism that employs a time-series machine learning (ML) model for the real-time detection of attacks on the perception channels of a Model Predictive Control (MPC)-based CAV platoon. We present extensive experimental results to showcase the effectiveness and feasibility of our approach. © 2024 IEEE.

关键词： Cyber attacks

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From Making Spaces to Making Change: The Future of Making in ECE 53

From Making Spaces to Making Change: The Future of Making in...

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53rd IEEE ASEE Frontiers in Education International Conference, FIE 2023

作者： Jordan, Shawn Cheville, Alan Smith, Casey The Polytechnic School Ira A. Fulton Schools of Engineering Arizona State University MesaAZ United States Bucknell University Department of Electrical & Computer Engineering LewisburgPA United States University of Illinois Urbana-Champaign Department of Electrical & Computer Engineering UrbanaIL United States

ISBN: (纸本)9798350336429

In this paper, we discuss the results and key findings of a daylong 'Making and Makerspaces in electrical and computer engineering' (ECE) workshop hosted in conjunction with the ECE Department Heads Association Annual Conference in 2022. This workshop brought together thought leaders in making and makerspaces from across ECE, engineering education, and the broader maker community to discuss the current state of (change)making in ECE, making with purpose, and anti-racist engineering spaces in addition to live maker activities at lunch and an unconference to discuss community, partnerships, and ECE in mechanical engineering makerspaces. Broadly, a key finding from the conference was the need to shift the conversation from talking about making spaces (which tend to focus more on the equipment and facilities management) to ecosystems of making change, including culture, identity, community partnerships, shifting from labs to makerspaces, and shifting from spaces to makerspaces. Another key finding was an ecosystem view of possible touchpoints for making in an engineering program, including making in pre-college, making for recruitment, making in undergraduate design classes, making in extracurricular settings, making for fun, and making at home - all leading toward growing a thriving, diverse engineering workforce of the future. The themes that emerged from the workshop discussions were summarized and presented to department heads about how makerspaces connect to broader efforts in the ECE community such as how to become more equitable and inclusive, how to rebrand programs to attract new students, and promote best practices in educating 21st century learners. © 2023 IEEE.

关键词： DEI design ECE makerspace making

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A Prototype Preceding Vehicle Identification System Development and Field Evaluation 10

A Prototype Preceding Vehicle Identification System Developm...

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10th International Conference on Vehicle Technology and Intelligent Transport Systems, VEHITS 2024

作者： Mu, Zeyu Tu, Guancheng Shi, Austin Yang, Kun Sun, Yixin Shen, Cong Park, B. Brian Department of Systems & Information Engineering University of Virginia CharlottesvilleVA United States Department of Computer Science University of Virginia CharlottesvilleVA United States Department of Electrical & Computer Engineering University of Virginia CharlottesvilleVA United States Departments of Computer Science and Electrical & Computer Engineering University of Virginia CharlottesvilleVA United States Departments of Civil & Environmental Engineering and Systems & Information Engineering University of Virginia CharlottesvilleVA United States

ISBN: (纸本)9789897587030

Preceding vehicle identification is crucial for establishing cooperative platooning. This paper presents the development of a prototype preceding vehicle identification system (PVIS) and its field evaluation for the assessment of commercial viability. We designed and assembled a prototype consisting of a processing unit (Jetson Nano board), a communication device (Wi-Fi dongle), a GPS unit, and a distance measurement sensor (Terabee sensor). The Jetson Nano integrates the SparkFun GPS-RTK-SMA unit, the Terabee time-of-flight sensor, and the Wi-Fi dongle. The PVIS prototype in the ego vehicle measures the distance to its preceding vehicle and receives the GPS data from potential preceding vehicles with the PVIS prototypes. With these, the PVIS in the ego vehicle determines the connectivity of the preceding vehicle. The field evaluation results showed that the prototype PVIS works as designed, and each successful identification takes about 5.3 seconds. However, it was found that the Terabee (time of flight) sensor, at times, did not properly measure distances, likely due to an angle issue caused by the roadway surface and vibration of the vehicle. We discussed how to overcome the challenges identified and enhance the prototype for successful commercialization. Copyright © 2024 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Vehicle to vehicle communications

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Monotonic Behavior of Improved Soft Sediment under Direct Simple Shear Loading

Monotonic Behavior of Improved Soft Sediment under Direct Si...

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Geotechnical Frontiers 2025: Soil Characterization and Improvement

作者： Talebi, Mahdi Oathes, Tyler J. Miskewitz, Robert Arnold, Kaleb M. Pharande, Eva Dept. of Civil and Environmental Engineering Rutgers Univ. PiscatawayNJ United States Dept. of Electrical and Computer Engineering Rutgers Univ. PiscatawayNJ United States

ISBN: (数字)9780784486016

Dredging operations produce large volumes of sediment annually;a significant portion of which needs to be improved due to its high water content, poor workability, and low strengths. Assessing improvement efficacy and evaluating improved sediment behavior is commonly limited to unconfined compression strength tests;rarely are any other strength tests such as direct simple shear or triaxial performed. This leads to a lack of understanding of the deformation response of improved sediment under confining pressures that would be encountered in the field, limiting the potential beneficial uses of the material. This paper presents an experimental study on the behavior of cement-improved sediment from Wallabout Channel under direct simple shear loading. The stress–strain response and stress paths were evaluated for specimens subjected to different confining pressures and curing times. Also, unconfined compression tests were conducted to compare the DSS tests with techniques widely employed in practice. Results illustrated that both the curing time and vertical confining stress were key factors for the strength and deformation characteristics of the improved sediment. Moreover, it was shown that the impact of the strength gains due to cementation and the pozzolanic reaction is related to the depth of the improvement. The implications of these results on practice and future research needs are discussed. © ASCE.

关键词： Compressive strength

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