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intelligent Traffic Management System: An Advanced Solution for Helmet Compliance, Traffic Signal Violation Detection, Number Plate Identification, Cell Phone Usage Tracking, and Proximity-Based Police Station Alerts 2

Intelligent Traffic Management System: An Advanced Solution ...

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2nd international conference on Computer, Communication and Control, IC4 2024

作者： Kayalvizhi, S. Jayanth, J. Karthikeyan, V. Nithish Kumar, R. Computer Science and Engineering Chennai89 India

ISBN: (纸本)9798350387933

intelligent Traffic Management System (ITMS) is a sophisticated solution designed to address a variety of urban traffic management challenges. This comprehensive system employs cutting-edge technologies to ensure helmet compliance, detect traffic signal violations, identify license plates, track cell phone usage, and provide police stations with proximity-based alerts. The system can accurately identify helmetless individuals by implementing computer vision algorithms. ITMS uses computer vision and machine learning techniques to detect traffic signal violations in addition to monitoring helmet compliance. The system can identify and record instances of red light running by analyzing live video feeds from traffic cameras, allowing law enforcement to issue appropriate penalties. Furthermore, ITMS includes a number plate identification feature that extracts license plate information using optical character recognition. This enables efficient and automated vehicle movement monitoring, allowing for effective traffic surveillance and the identification of vehicles involved in criminal activities. Another important feature of ITMS is its ability to monitor cell phone usage while driving. The system can detect drivers using cell phones and generate alerts to deter distracted driving by leveraging mobile network data and smart devices. Finally, ITMS sends proximity-based alerts to police stations, allowing for faster response times and more efficient deployment of law enforcement personnel in the event of an emergency or traffic incident. In summary, ITMS is a sophisticated and all-encompassing solution that combines advanced technologies to ensure helmet compliance, detect traffic signal violations, identify license plates, track cell phone usage, and provide proximity-based police station alerts. ITMS aims to improve road safety and streamline traffic management by combining these features into a single system for a better commuting experience. © 2024 IEEE.

关键词： Motor transportation

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Transfer learning-based Driver Distraction Detection 2

Transfer Learning-based Driver Distraction Detection

引用

2nd international conference on Sustainable Computing and data Communication Systems, ICSCDS 2023

作者： Goel, Lipika Chennamaneni, Sukruthi Golla, Anjali Puchhakayala, Sravya Rudraram, Adithi Gokaraju Rangaraju Institute of Engineering and Technology Department of CSE Telangana India

ISBN: (纸本)9781665491990

Due to driver's preoccupation, there have been more accidents on the roads recently all around the world. One of the key explanations is attributed to human behavior. Accidents have been linked mostly to drivers' distraction. This sudden road crush frequently causes injuries, property loss, or even fatalities. The automated interpretation of the driver's behavior is one of the trickiest subjects in the field of intelligent transportation systems (ITSs). This study looks on the human action recognition aspect of distracted driving posture recognition. There have been several reports of inattentive driving causing auto accidents. The goal is to use cutting-edge transfer learning techniques like MobileNetV2 and DenseNet to identify distracted driving activities more effectively. As driving is a complex task, it requires complete attention of the driver. Distractions of the driver can be due to talking phone calls, texting, talking to another passenger, drinking, operating radio. To identify distractions while driving, it is crucial to observe and evaluate the driver's behavior. Images of the driver serve as the main source of data and that comprises of face, arms, and hands of the person inside the car. To decrease vehicle accidents and enhance transportation security, it is extremely desirable and has received a significant amount of study to develop a system that can detect distracted driving. In this study, transfer learning models like DenseNet and MobileNetV2 are used to classify and detect driver distraction. The best performance was produced by MobileNetV2, which has an accuracy of 93.8. © 2023 IEEE.

关键词： Accidents

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Prediction of Maintenance Equipment Failures Using automated Machine learning 22nd

Prediction of Maintenance Equipment Failures Using Automated...

引用

22nd international conference on intelligent data engineering and automated learning

作者： Ferreira, Luis Pilastri, Andre Sousa, Vitor Romano, Filipe Cortez, Paulo CCG ZGDV Inst EPMQ IT Engn Matur & Qual Lab Guimaraes Portugal Univ Minho ALGORITMI Ctr Dept Informat Syst Guimaraes Portugal Valuekeep Braga Portugal

ISBN: (纸本)9783030916077;9783030916084

Predictive maintenance is a key area that is benefiting from the Industry 4.0 advent. Recently, there have been several attempts to use Machine learning (ML) in order to optimize the maintenance of equipments and their repairs, with most of these approaches assuming an expert-based ML modeling. In this paper, we explore an automated Machine learning (AutoML) approach to address a predictive maintenance task related to a Portuguese software company. Using recently collected data from one of the company clients, we firstly performed a benchmark comparison study that included four open-source modern AutoML technologies to predict the number of days until the next failure of an equipment and also determine if the equipments will fail in a fixed amount of days. Overall, the results were very close among all AutoML tools, with AutoGluon obtaining the best results for all ML tasks. Then, the best AutoML predictive results were compared with a manual ML modeling approach that used the same dataset. The results achieved by the AutoML approach outperformed the manual method, thus demonstrating the quality of the automated modeling for the predictive maintenance domain.

关键词： automated machine learning Predictive maintenance Supervised learning

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Prediction of Driving Departure of Mining Autonomous Transport Vehicles Based on GRU Network 22

Prediction of Driving Departure of Mining Autonomous Transpo...

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22nd IEEE international conference on Industrial Informatics, IndIN 2024

作者： Li, Lecong Yu, Guizhen Li, Han Xia, Qi Zhang, Xiangyu Cai, Han School of Transportation Science and Engineering Ministry of Industry and Information Technology Beihang University Beijing China State Key Lab of Intelligent Transportation System Ministry of Industry and Information Technology Beihang University Beijing China The Key Laboratory of Autonomous Transportation Technology for Special Vehicles Ministry of Industry and Information Technology Beihang University Beijing China

ISBN: (纸本)9798331527471

Open-pit mining areas have special geological structures, complex road networks, multi-rotation sections and poor road conditions, which bring many challenges to the operation of mining autonomous transport vehicles. Due to the large size and high control difficulty of mining autonomous transport vehicles, poor control effect and inaccurate steering mechanism implementation are prone to occur during the driving process, which leads to the vehicle departure from the reference path. To ensure the safety of autonomous operation in intelligent mine, this paper proposes a method for predicting driving departure of mining autonomous vehicles based on Gated Recurrent Unit (GRU). Firstly, the vehicle's historical trajectory data is obtained via on-board sensors, followed by data cleaning and normalization processes. Key features are extracted, and a vehicle driving scene recognition module based on a GRU-based network is designed using deep learning. Subsequently, a GA-seq2seqGRU trajectory prediction module is constructed utilizing the scene recognition results, and the Genetic Algorithms (GA) is employed to tune the network hyper-parameters. Based on the predicted trajectories, the overall departure risk is calculated using two departure judgment methods based on cross-lane time and predicted lateral deviation, which are mapped to the departure level. Simulation experiments demonstrate that the accuracy of the driving scene recognition model in the proposed method in this paper reaches 0.9721, which is better than the 0.9585 of the Long Short Memory Neural Network (LSTM) model, and the trajectory prediction model based on the results of the driving scene recognition has a smaller RMSE value than the LSTM, GRU, and GA-seq2seqLSTM models when the prediction time domains are 1s, 2s, 3s, 4s, and 5s, and the departure detection module The average time consumed is 0.142ms. © 2024 IEEE.

关键词： Mine transportation

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Detecting Communities in Feature-Rich Networks with a K-Means Method 22nd

Detecting Communities in Feature-Rich Networks with a K-Mean...

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22nd international conference on intelligent data engineering and automated learning

作者： Shalileh, Soroosh Mirkin, Boris HSE Univ Dept Data Anal & Artificial Intelligence Pokrovsky Blvd 11 Moscow Russia HSE Univ Lab Methods Big Data Anal LAMBDA Pokrovsky Blvd 11 Moscow Russia Birkbeck Univ London Dept Comp Sci & Informat Syst Malet St London WC1E 7HX England

ISBN: (纸本)9783030916077;9783030916084

The main result of this paper is an extension of the K-means algorithm to the issue of community detection in feature-rich networks. This is based on a data-recovery criterion additively combining conventional least-squares criteria for approximation of the network link data and the feature data at network nodes. The dimension of the space at which the method operates is the sum of the number of nodes and the number of features, which may be high indeed. To tackle the so-called curse of dimensionality, we replace the innate Euclidean distance with cosine distance. We experimentally validate our proposed methods and demonstrate their efficiency by comparing them to most popular approaches using both synthetic data and real-world data.

关键词： Node-attributed network Feature-rich network Cluster analysis Community detection data recovery K-means clustering

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SOC estimation of lithium-ion batteries based on transfer learning under low temperature conditions 22

SOC estimation of lithium-ion batteries based on transfer le...

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22nd IEEE international conference on Industrial Informatics, IndIN 2024

作者： Yang, Xinglong Jiang, Tianzhu Xu, Ming Li, Shun Ji, Changjian Liu, Xueying Ma, Bin FAW Bestune Automotive Co. Ltd Changchun China School of Artificial Intelligence Changchun China College of Communication Engineering Jilin University Changchun China

ISBN: (纸本)9798331527471

Accurate estimation of the SOC of lithium-ion batteries is important for range prediction, battery health monitoring and optimization of driving strategies in pure electric vehicles. However, due to the dynamic complexity and uncertainty of the actual operating conditions of vehicles, it is difficult to develop an accurate SOC estimation algorithm that is applicable to different operating conditions. In this paper, we propose a battery SOC estimation method that combines CNN-LSTM network and Kalman filter (KF) and apply a migration learning method to accelerate the model training process. Firstly, the CNN-LSTM network is used to learn the nonlinear relationship between SOC and the measured values, then KF is applied to refine the output of the LSTM network, and finally the migration learning method is used to migrate the model parameters from one operating condition to other operating conditions. By using the data from three different temperatures under CLTC conditions for training and validation, the results show a high prediction accuracy at different temperatures for both the base model and the migration learning model, where the root mean square error is less than 3%. In addition, the model training part and model prediction part of the method can be deployed in the cloud and the vehicle to realize vehicle-cloud cooperative estimation, respectively, and can be extended to multi-state cooperative estimation. © 2024 IEEE.

关键词： Mean square error

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Accelerating Scientific Simulations with Bi-Fidelity Weighted Transfer learning 22

Accelerating Scientific Simulations with Bi-Fidelity Weighte...

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22nd IEEE international conference on Machine learning and Applications, ICMLA 2023

作者： Borowiec, Katarzyna Lu, Dan Chandan, Vikas Chatterjee, Samrat Ramuhalli, Pradeep Tipireddy, Ramakrishna Halappanavar, Mahantesh Liu, Frank Nuclear Energy and Fuel Cycle Division Oak Ridge National Laboratory Oak Ridge United States Computational Sciences and Engineering Division Oak Ridge National Laboratory Oak Ridge United States Energy and Environment Directorate Pacific Northwest National Laboratory Richland United States Physical and Computational Sciences Directorate Pacific Northwest National Laboratory Richland United States Computer Science and Mathematics Division Oak Ridge National Laboratory Oak Ridge United States

ISBN: (纸本)9798350345346

High-fidelity modeling is an essential design tool for many engineering applications. However, for complex systems, computational cost can be a limiting factor. Analyzing parameter sensitivity, uncertainty quantification, and design optimization require many model evaluations. Surrogate models are often used to develop the relationship between model parameters and quantities of interest. However, in the case of complex systems, surrogate models require several degrees of freedom and, thus, a large number of data points to determine the correct dependencies. For many applications, this may be prohibitively expensive. The reduction of computational requirements can be achieved by leveraging low-fidelity models. Low-fidelity models represent the system at a coarser resolution with the advantage of computational efficiency. Therefore, a bi-fidelity modeling paradigm, which augments the accuracy of a low-fidelity model in a computationally efficient manner by invoking limited runs of a high-fidelity model, can be leveraged to sufficiently balance the accuracy and computational requirements. In this work, a bi-fidelity weighted transfer learning method using neural networks was applied to a computational fluid dynamics heat transfer modeling problem. The transfer learning advantage was investigated as a function of hyperparameters. Our main finding is that the use of a bi-fidelity modeling paradigm achieves accuracy close to that of a high-fidelity Gaussian process model while significantly reducing computational cost. The bi-fidelity model achieves comparable performance with 90 high-fidelity samples-that is, 60% less than the samples needed to achieve similar accuracy without the use of bi-fidelity modeling © 2023 IEEE.

关键词： bi-fidelity modeling surrogate modeling transfer learning

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automated Theory Substitution Toward Proof-Driven Software Development

Automated Theory Substitution Toward Proof-Driven Software D...

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international conference on Software engineering Research, Management and Applications (SERA)

作者： John Scebold Eric Bond Emily Gray Jared Ziegler Two Six Technologies Arlington VA USA University of Michigan Ann Arbor MI USA

ISBN: (数字)9798350391343

ISBN: (纸本)9798350391350

Formal verification of large, real-world software systems has shown increasing success over the years (seL4, CompCert, s2n) but has failed to attain wide-spread adoption. This is largely due to the esoteric expertise required to write and maintain formal proofs. Experience with interactive theorem provers (ITPs) is not nearly as ubiquitous among software developers as, say, integrating unit tests into continuous inte-gration/continuous deployment (CI/CD) pipelines. To alleviate this lack of experience and democratize proving, we present automated THeorY SubsTitution (AMETHYST), an artificially intelligent proof assistant. AMETHYST is an effort to improve the state of proof repair automation using machine learning. Specifically, we apply a language model and other machine learning techniques to a dataset of hand-written proofs and repairs for the Isabelle/HOL ITP. We demonstrate that AMETHYST can complete truncated proofs which represent once-correct proofs that broke due to code changes.

关键词： Training Codes Automation Pipelines Neural networks Machine learning Maintenance engineering

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A Robust Self-generating Training ANFIS Algorithm for Time Series and Non-time Series Intended for Non-linear Optimization 22nd

A Robust Self-generating Training ANFIS Algorithm for Time S...

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22nd international conference on Hybrid intelligent Systems, HIS 2022 and the 18th international conference on Information Assurance and Security, IAS 2022

作者： Raj, A. Stanley Henrietta, H. Mary Loyola College Tamil Nadu Chennai600034 India Saveetha Engineering College Tamil Nadu Chennai602105 India

ISBN: (纸本)9783031274084

This paper provides an alternative to the conventional method of solving a complex problem using the artificial novel process. Algorithm testing is used to measure economic order (EOQ) quantities and groundwater depletion. This work incorporates both neurofuzzy and adaptive neurofuzzy to acquire the appropriate time series and non-time series tests. Effective asset management should be based on incorporating various decision variables like demand, setup costs, and order costs. The proposed self-training database algorithm announces an effective EOQ prediction model and water table level data graphically. Further, the data sets for both crisp and fuzzy models are examined and analyzed using the algorithm. The evaluation of the test results is suitable for use with any non-linear problem. This function proves that this algorithm works well for both time series and non-time series details. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Time series

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AI-Driven Assessment of Safety Risk at Road Intersections Using Drone Videos 22

AI-Driven Assessment of Safety Risk at Road Intersections Us...

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22nd IEEE international conference on Industrial Informatics, IndIN 2024

作者： Zhang, Shile Wang, Yan Yan, Yongjun Department of Civil and Environmental Engineering The Hong Kong Polytechnic University Hong Kong School of Mechanical Engineering Nanjing University of Science and Technology Nanjing China

ISBN: (纸本)9798331527471

Urban intersections pose significant safety challenges due to the convergence of diverse traffic flows from various directions, resulting in a disturbingly high rate of annual road fatalities. Specifically, the presence of permissive left-turn signals introduces potential conflicts between vehicles making left turns and those proceeding straight from opposite directions. To address this issue, this article introduces a dynamic safety assessment model tailored for urban intersections. Leveraging drone videos, high-resolution vehicle trajectory data is extracted, enabling the conflicts identification of rear-end and angular. Through an ensemble learning approach utilizing artificial intelligence (AI) techniques, conflict frequency is estimated at a granular five-minute interval. Notably, the Random Forest model exhibits superior performance, yielding a mean squared error (MSE) of 5.35. Further analysis employing SHAP (SHapley Additive exPlanations) highlights the critical role of variables such as vehicle deceleration rate and speed. The insights garnered from this study can inform the development of proactive traffic management systems, facilitating real-time assessment of intersection safety conditions and the implementation of measures to mitigate potential risks. © 2024 IEEE.

关键词： Drones

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