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检索条件"任意字段=25th International Conference on Intelligent Data Engineering and Automated Learning"

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Analysis of pharmacy drug consumption following extreme weather conditions 25

Analysis of pharmacy drug consumption following extreme weat...

引用

25th KES international conference on Knowledge-Based and intelligent Information & engineering Systems (KES)

作者： Spichkova, Maria Simic, Milan RMIT Univ Sch Comp Technol Melbourne Vic Australia RMIT Univ Sch Engn Melbourne Vic Australia

this paper presents the results of the project conducted within the Research embedded in teaching initiative in collaboration with Nostradata. the main objective of the research was to analyse if the peak values of such weather conditions, like rainfall and wind speed, can impact the drug purchase transactions within shorts time periods (one, three, and five days) of time, after the peak values of the weather parameters have appeared. the basis for the automated analysis were the data collected over more than six years, restricted to the Melbourne region in Australia. (C) 2021 the Authors. Published by Elsevier B.V. this is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of KES international.

关键词： Software engineering project-based learning data analytics

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Edge-assisted Gossiping learning: Leveraging V2V Communications between Connected Vehicles 25

Edge-assisted Gossiping Learning: Leveraging V2V Communicati...

引用

IEEE 25th international conference on intelligent Transportation Systems (ITSC)

作者： Di Giacomo, Giuseppe Harri, Jerome Chiasserini, Carla Fabiana EURECOM Commun Syst F-06904 Sophia Antipolis France Politecn Torino CARS Polito I-10129 Turin Italy

ISBN: (数字)9781665468800

ISBN: (纸本)9781665468800

intelligent vehicles are quickly becoming mobile, powerful computers, able to collect, exchange, and process sensed data. they are therefore expected not just to consume ITS services, but also to actively contribute to the implementation of relevant ITS applications. With an increasing role of machine learning (ML) approaches, vehicles are called to put into use their computing capabilities and sensed data for the training of ML models. this can be enacted through distributed learning approaches, which however may lead to significant communication overhead or to learners converging to different models. In this work, we envision a new distributed learning scheme, named EAGLE, that, with the assistance of the network edge, aims at exploiting the vehicles' data and computing capabilities, while enabling an efficient learning process. To this end, EAGLE combines the advantages of two existing schemes, namely, federated learning and gossiping learning, yielding a distributed paradigm that ensures both scalability and model consistency. Our results, obtained using two different real-world data sets, show that EAGLE can improve learning accuracy by 20%, while reducing the communication overhead by 45%.

关键词： Connected vehicles Distributed machine learning Deep learning

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Iterative Label Improvement: Robust Training by Confidence Based Filtering and dataset Partitioning 25

Iterative Label Improvement: Robust Training by Confidence B...

引用

25th international conference on Pattern Recognition (ICPR)

作者： Haase-Schuetz, Christian Stal, Rainer Hertlein, Heinz Sick, Bernhard Robert Bosch GmbH Automated Driving Engn Cognit Syst Abstatt Germany Univ Kassel Intelligent Embedded Syst Grp Kassel Germany

ISBN: (纸本)9781728188089

State-of-the-art, high capacity deep neural networks not only require large amounts of labelled training data, they are also highly susceptible to labelling errors in this data, typically resulting in large efforts and costs and therefore limiting the applicability of deep learning. To alleviate this issue, we propose a novel meta training and labelling scheme that is able to use inexpensive unlabelled data by taking advantage of the generalization power of deep neural networks. We show experimentally that by solely relying on one network architecture and our proposed scheme of combining self-training with pseudo-labels, both label quality and resulting model accuracy, can be improved significantly. Our method achieves state-of-the-art results, while being architecture agnostic and therefore broadly applicable. Compared to other methods dealing with erroneous labels, our approach does neither require another network to be trained, nor does it necessarily need an additional, highly accurate reference label set. Instead of removing samples from a labelled set, our technique uses additional sensor data without the need for manual labelling. Furthermore, our approach can be used for semi-supervised learning.

关键词： Deep neural networks

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Vision-Based Traffic Sign Detection and Localization in Tokyo Metropolitan Area 25

Vision-Based Traffic Sign Detection and Localization in Toky...

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IEEE 25th international conference on intelligent Transportation Systems (ITSC)

作者： Yang, Zhehui Maeda, Hiroya Zhao, Chenbo Sato, Go Sekimoto, Yoshihide Univ Tokyo Dept Civil Engn Tokyo Japan Univ Tokyo Inst Ind Sci Tokyo Japan Univ Tokyo Ctr Spatial Informat Sci Tokyo Japan

ISBN: (数字)9781665468800

ISBN: (纸本)9781665468800

Being a world-class metropolis, Tokyo has a significantly complex transportation system. A system to automatic detect and localize traffic signs is needed to be developed for the digital management of transportation systems and automated mapping platforms. Owing to the rapid development of computer vision algorithms in recent years, more accurate and efficient ways to achieve these tasks with minimal labor costs have been proposed. In this study, we propose a vision-based pipeline to detect, classify, and locate traffic signs in different road scenarios in Tokyo metropolitan area. Detection was performed using YOLOv4. Classification was performed by comparing the VGG19, ResNet101, and Swin-B networks. Because collecting driving data with ground truth distance data is a challenging task for supervised learning algorithms, we originally tested a self-supervised deep estimation network to perform localization. To make our study more plausible, we conducted a driving experiment and created a custom dataset to test our concept. the results showed that the proposed system is capable of detecting and locating traffic signs, with a 7.5% error rate cap up to 20 m ahead, from a single image of the Tokyo road area.

关键词： Location awareness Training Roads Urban areas Pipelines Supervised learning Estimation

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An Online Traffic Simulation Modeling Framework for Real-Time Road Traffic Control and Management 25

An Online Traffic Simulation Modeling Framework for Real-Tim...

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IEEE 25th international conference on intelligent Transportation Systems (ITSC)

作者： Qin, Yuanqi Jin, Junchen Hua, Wen Dai, Xingyuan Wang, Xiao Zhejiang Lab Hangzhou 310030 Peoples R China Zhejiang Supcon Informat Technol Co Ltd Hangzhou 310030 Peoples R China ECARX Technol Co Ltd Shanghai Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100049 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

ISBN: (数字)9781665468800

ISBN: (纸本)9781665468800

Online traffic simulation feeds from online information to simulate vehicle movement in real-time, which has recently seen substantial advancement in road traffic control and management. It has been a challenging problem due to three aspects: 1) the diversity of traffic patterns caused by heterogeneous layouts of urban intersections;2) the complexity of spatiotemporal correlations;3) the requirement of adjusting traffic model parameters in a real-time system. To cater to these challenges, this paper proposes an online traffic simulation modeling framework via a meta-learner. In particular, simulation models with various intersection layouts are automatically generated using an open-source simulation tool, SUMO, according to static traffic geometry attributes. through a meta-learning technique, the proposed modeling framework enables an automated learning process for estimating model settings capable of adapting traffic model parameters according to dynamic traffic information in real-time. Such a process is featured with various traffic scenarios and different spatiotemporal correlations. through computational experiments, we demonstrate that the meta-learning-based framework is able to self-adapt its effectiveness according to real-time traffic data.

关键词： Adaptation models Correlation Computational modeling Layout Real-time systems data models Spatiotemporal phenomena

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Head Pose Estimation Using a Chest-Mounted Camera and Its Evaluation Based on CG Images 13

Head Pose Estimation Using a Chest-Mounted Camera and Its Ev...

引用

Joint 13th international conference on Soft Computing and intelligent Systems and 25th international Symposium on Advanced intelligent Systems, SCIS and ISIS 2024

作者： Yamada, Taisei Yamazoe, Hirotake Graduate School of Engineering University of Hyogo Himeji Japan

ISBN: (纸本)9798350373332

In everyday life, we naturally turn our heads or focus our gaze on objects of interest. By capturing a person's head pose or gaze direction, it is possible to record their actions and identify the objects they are focusing on. this study proposes a head pose estimation to infer and document the objects of interest based on head movements. We utilize a camera mounted on the user's chest and develop a deep-learning model to estimate head pose from the captured images. the training data was generated using a CG model, and the model's performance was validated. © 2024 IEEE.

关键词： Deep learning

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On the Potential of Randomization-based Neural Networks for Driving Behavior Classification 25

On the Potential of Randomization-based Neural Networks for ...

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IEEE 25th international conference on intelligent Transportation Systems (ITSC)

作者： Del Ser, Javier Manibardo, Eric L. Lana, Ibai

ISBN: (数字)9781665468800

ISBN: (纸本)9781665468800

Naturalistic Driving has recently garnered the attention from the community working on Deep learning models, producing a plethora of modeling proposals on account of reaching an increasingly better predictive performance. Little attention has been paid to the computational implications of adopting such models, particularly when used for inferring the behavior of the driver from naturalistic driving data. this work enters this uncharted research area by probing the balance between complexity and performance of randomization-based neural networks for driving behavior classification. To this end, results from an extensive experimental benchmark comparing these networks to diverse Deep learning and ensemble learning models are discussed, unveiling a significantly better-balanced trade-off between performance and complexity of randomization-based neural networks, and suggesting more concern with the efficiency of models in prospective studies.

关键词： Deep learning Computational modeling Neural networks Predictive models data models Behavioral sciences Complexity theory

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Predicting Parameters for Modeling Traffic Participants 25

Predicting Parameters for Modeling Traffic Participants

引用

IEEE 25th international conference on intelligent Transportation Systems (ITSC)

作者： Moradipari, Ahmadreza Bae, Sangjae Alizadeh, Mahnoosh Pari, Ehsan Moradi Isele, David Honda Res Inst USA Inc San Jose CA 95134 USA Univ Calif Santa Barbara Santa Barbara CA 93106 USA

ISBN: (数字)9781665468800

ISBN: (纸本)9781665468800

Accurately modeling the behavior of traffic participants is essential for safely and efficiently navigating an autonomous vehicle through heavy traffic. We propose a method, based on the intelligent driver model, that allows us to accurately model individual driver behaviors from only a small number of frames using easily observable features. On average, this method makes prediction errors that have less than 1 meter difference from an oracle with full-information when analyzed over a 10-second horizon of highway driving. We then validate the efficiency of our method through extensive analysis against a competitive data-driven method such as Reinforcement learning that may be of independent interest.

关键词： Road transportation Meters Navigation Reinforcement learning Predictive models data models Behavioral sciences

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Outlier-robust Inverse Reinforcement learning and Reward-based Detection of Anomalous Driving Behaviors 25

Outlier-robust Inverse Reinforcement Learning and Reward-bas...

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IEEE 25th international conference on intelligent Transportation Systems (ITSC)

作者： Li, Dan Shehab, Mohamad Louai Liu, Zexiang Arechiga, Nikos DeCastro, Jonathan Ozay, Necmiye Univ Michigan EECS Ann Arbor MI 48109 USA Univ Michigan Robot Inst Ann Arbor MI 48109 USA TRI Colorado Springs CO USA

ISBN: (数字)9781665468800

ISBN: (纸本)9781665468800

this paper considers an outlier detection problem for a collection of vehicles or agents. these agents are represented by Markov decision processes and the trajectory data are assumed available. the work aims to learn the intentions or reward functions of agents, and infer the anomalous agents whose intentions differ from the majority. To achieve this, we propose a joint inverse reinforcement learning framework, which enables learning of a common reward function that captures the behavior of the majority as well as individual rewards for normal and abnormal agents. An example on the detection and analysis of driving behaviors is provided, demonstrating the effectiveness of the proposed framework.

关键词： Reinforcement learning Markov processes Behavioral sciences Trajectory Indexes Task analysis Anomaly detection

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Combination Optimization between Traffic Signal and Connected and automated Vehicle's Trajectory at the Isolated Intersection 25

Combination Optimization between Traffic Signal and Connecte...

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IEEE 25th international conference on intelligent Transportation Systems (ITSC)

作者： Hu, Haiyang Li, Kunyao Chu, Duanfeng Lu, Liping Wuhan Univ Technol ChongQing Res Inst Wuhan 430070 Peoples R China Wuhan Univ Technol Intelligent Transportat Syst Res Ctr Wuhan 430070 Peoples R China China Merchants Testing Vehicle Technol Res Inst Chongqing Peoples R China Chongqing Engn Res Ctr Res & Testing Automated Dr Chongqing Peoples R China Wuhan Univ Technol Sch Comp & Artificial Intelligence Wuhan 430070 Peoples R China

ISBN: (数字)9781665468800

ISBN: (纸本)9781665468800

Faced with increasing vehicle population,.signalized intersection plays an increasingly important manager role and must accept this great challenge. this paper proposes a combination optimization method considering traffic signal and vehicle trajectory at the isolated intersection. In this combination method, optimal signal cycle sequence and phase time are obtained based on these hypothetical trajectories. then, vehicles' hypothetical trajectories are optimized in each phase according to the proposed new trajectory method and satisfactory time headway and desired velocity are obtained in this process. Finally, optimal signal phase time need to be optimized once more according to the new trajectories. Simulation tests are conducted in SUMO to compare proposed combination optimization method with ASC method (i.e. actual signal control method). Test results data of proposed combination method is positive in each evaluation criteria.

关键词： Trajectory planning Sociology Optimization methods Transportation Trajectory Safety Statistics

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