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A data-Driven Integrated Framework for Virtual Testing of Autonomous Vehicles in Mixed Traffic Scenarios 8th

A Data-Driven Integrated Framework for Virtual Testing of Au...

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8th international conference on intelligent Transport Systems, INTSYS 2024

作者： Caroleo, Brunella Sadeghi, Javad Botta, Cristiana Nikneshan, Shadi Arnone, Maurizio Fondazione LINKS Via P.C. Boggio 61 Turin10138 Italy

ISBN: (纸本)9783031863691

the increasing presence of autonomous vehicles (AVs) in urban environments introduces both opportunities and challenges, particularly regarding their interactions with traditional vehicles and other road users. this paper presents a comprehensive framework designed to assess the integration of AVs in mixed traffic scenarios. the framework is built upon real-world data collected from AV trials conducted in Turin, Italy. By leveraging traffic microsimulation along with machine learning techniques, the study proposes a framework aimed at assessing ex-ante the impacts on traffic of AVs introduction, thus constituting a relevant tool of virtual testing of CCAM (Cooperative, Connected, and automated Mobility) trials before the physical introduction of autonomous vehicles on public roads. the integration of High-Performance Computing (HPC) ensures the efficiency of these simulations, enabling real-time analysis and testing. the proposed framework not only provides decision-makers with a tool for virtual testing of AV deployment, but also offers actionable insights into traffic management strategies. the study’s findings contribute to a deeper understanding of the role AVs can play in future urban mobility systems, particularly as cities prepare for the broader adoption of CCAM technologies. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering 2025.

关键词： Decision making

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data-Driven Modelling and Predictive Control for the Geometry and Mechanical Properties of Freeform Bending Processes 11

Data-Driven Modelling and Predictive Control for the Geometr...

引用

11th Vienna international conference on Mathematical Modelling, MAthMOD 2025

作者： Ismail, Ahmed Böhm, Viktor Stebner, Sophie Kong, Linghao Volk, Wolfram Münstermann, Sebastian Lohmann, Boris Automatic Control School of Engineering and Design Technical University of Munich Boltzmannstr. 15 Garching85748 Germany Metal Forming and Casting School of Engineering and Design Technical University of Munich Walther-Meißner-Str. 4 Garching85748 Germany Integrityy of Materials and Structures RWTH Aachen Unviersity Intzestr. 1 Aachen52072 Germany

Freeform bending is a technique used to bend different tube profiles into complex structures. In the realm of Aritificial Intelligence and Machine learning, data-driven approaches in modeling and in optimization based control contribute to the job being executed, by increasing product quality and reducing energy consumption and material waste. In previous works, soft-sensors as well as different factors affecting the geometry and the residual stresses have been investigated and been utilized in a preliminary closed-loop control structure. Later on, this control structure has been replaced with a control strategy called Residual Strategy Algorithm, that could, based on previous deviations in the geometry, proactively react in order to reduce their effects. In this paper, the previously developed Residual Strategy Algorithm is extended to include both, the geometry as well as the residual stresses of the tube being bent. Also the modelling of both will be discussed altogether. the validation of the algorithm is done using simulation results. Copyright © 2025 the Authors.

关键词： Predictive control systems

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A Freight Forecasting Method for Multimodal Transport Networks Using a High-Fidelity Integrated Modeling Approach 11th

A Freight Forecasting Method for Multimodal Transport Networ...

引用

11th international conference on Traffic and Transportation Studies, ICTTS 2024

作者： Wang, Zongbao Zhong, Ming Li, Linfeng Safdar, Muhammad Hunt, John Douglas Intelligent Transportation Systems Research Center Wuhan University of Technology Wuhan430063 China Department of Civil and Environmental Engineering University of Waterloo WaterlooN2L3G1 Canada Department of Civil Engineering University of Calgary CalgaryT2N1N4 Canada

ISBN: (纸本)9789819796434

Forecasting interregional freight demand for large regions has been a challenging task due to intricate interactions between freight demand and multimodal transportation networks. With this, this paper endeavors to develop a high-fidelity integrated modeling approach to forecast freight demand within a large region. the proposed framework combines spatial economic modeling with a multimodal transportation supernetwork modeling approach to systematically model the quantitative relationship between freight demand, socioeconomic activities, and multimodal transportation networks. First, a "high-fidelity" spatial economic module is developed based on input-output tables, land use, and population/employment data. Parameters of the spatial economic model are calibrated using observed trip length distribution and traffic count data. then a "high-resolution" multimodal transport supernetwork, consisting of a detailed highway, railway and waterway networks, is developed to assign freight demand to the multimodal transportation supernetwork. Finally, a case study is conducted using the proposed method for the Yangtze River Economic Belt, which demonstrates the utility of the proposed approach in systematically modeling and forecasting the freight demand, as well as in analyzing the impact of related economic, land-use, and transport policies in a high-fidelity fashion. the proposed approach supports improved decision-making in planning and operating regional multimodal transportation systems and related systems, such as economy, land-use and possibly environment. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Multimodal transportation

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Performance Evaluation of Modern Object Detection Models for automated Fruit Recognition in Smart Agriculture

Performance Evaluation of Modern Object Detection Models for...

引用

Mechatronics and Robotics engineering (ICMRE), international conference on

作者： Sarmad Ahmad Saad Bin Imran Usman Asad Ayesha Zeb Department of Mechatronics Engineering College of E&ME National University of Sciences and Technology Islamabad Pakistan

ISBN: (数字)9798331509293

ISBN: (纸本)9798331509309

An accurate and reliable image-based fruit detection system is essential for advancing agricultural tasks such as yield mapping and robotic harvesting. this paper benchmarks five state-of-the-art object detection frameworks on a merged dataset of 10 common fruit classes. the challenge of scarcity of high-quality fruit dataset detection of multiple classes of fruit is addressed. data merging and augmentation techniques are used to increase data samples and capture variability. Multiple architectures are used to explore computational trade-offs of varying deep learning networks. Model accuracy and inference speed are evaluated for considerations of future deployment in an agricultural environment. this study offers guidance on model selection for fruit detection based on accuracy and latency requirements. Future research should evaluate latencies on resource-constrained devices and should prioritize improving accuracy while reducing overall complexity and inference speed for fruit detection tasks.

关键词： Deep learning Smart agriculture Performance evaluation Accuracy Computational modeling Object detection Computer architecture Transformers Real-time systems Reliability

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Prediction of Compaction Parameters Based on the Atterberg Limit by Using a Machine learning Approach 11th

Prediction of Compaction Parameters Based on the Atterberg L...

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11th EAI international conference on Advancement of Science and Technology, ICAST 2023

作者： Kassa, Semachew Molla Wubineh, Betelhem Zewdu Geremew, Africa Mulumar Azmatch, Tezera Firew Kumar, Nandyala Darga Faculty of Civil and Water Resource Engineering Bahir Dar University Bahir Dar Ethiopia College of Engineering and Technology Wachemo University Hossaena Ethiopia Faculty of Information and Communication Technology Wroclaw University of Science and Technology Wroclaw Poland Faculty of Civil and Environmental Engineering Addis Ababa Institute of Technology Addis Ababa University Addis Ababa Ethiopia College of Engineering Manthani Jawaharlal Nehru Technological University Hyderabad India

ISBN: (纸本)9783031641503

the compacting parameters are one of the basic elements used to determine the California bearing ratio value, which is used to determine the subgrade strength of infrastructures. the maximum dry density (MDD) and the optimum moisture content (OMC) are the compaction parameters that are determined from the Atterberg limit tests. For this relationship, around 252 soil samples are used to model the relationship between the compaction parameters and the Atterberg limits on the liquid limit (LL), the plastic limit (PL), and the plasticity index. To do this experiment, the random forest (RF), support vector machine (SVM), and decision tree algorithms are used on 80% of the training data and 20% of the testing data. In this model, different performance measurement techniques were used. the mean absolute error, coefficient of determination, mean squared error, and root mean square errors are used to evaluate the performance of the regression model. According to the performance measure, in OMC and MDD, the value of R2 of the random forest algorithm is 0.55 and 0.54, respectively. this relationship is better than any other model, which is done using support vector machines and decision tree algorithms in the case of the coefficient of determination. the most important characteristics are the plasticity index and the liquid limit in maximum dry density and the optimum moisture content, respectively. the paper’s findings may be useful and applicable in many fields of civil engineering. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Random forests

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intelligent IoT System with AI and ML Integration for Smart Home Automation

Intelligent IoT System with AI and ML Integration for Smart ...

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Trends in Material Science and Inventive Materials (ICTMIM), international conference on

作者： P. Vishnu Kumar Allam Prathyusha Nowly HimaBindu Palukuru Manaswini Department of Internet of Things G. Pullaiah College of Engineering and Technology Kurnool Andhra Pradesh India

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

the integration of Artificial Intelligence (AI), Machine learning (ML), and the Internet of things (IoT) is transforming smart home automation by enhancing security, energy efficiency, and user convenience. this paper presents an AI-powered IoT system that employs real-time monitoring and adaptive automation to optimize household operations. the system utilizes ultrasonic sensors for water level monitoring, motion sensors for automated lighting and fan control, and DHT11 sensors for climate regulation. AI-driven facial recognition ensures secure access control, while predictive energy management reduces electricity consumption by dynamically adjusting appliance usage. the system effectively detects hazards such as gas leaks and fires in real time, triggering immediate alerts and safety measures. With seamless device coordination and automated responses, this framework enhances home security, energy efficiency, and overall automation reliability. Despite challenges in data security and system integration, the findings highlight that AI-ML-driven IoT automation is a viable solution for creating self-sustaining and intelligent smart homes.

关键词： Automation Face recognition Smart homes Machine learning Real-time systems Energy efficiency Sensor systems Hazards Internet of things intelligent sensors

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Analysis and Prediction of Overseas Cargo on the China-Laos Railway 11th

Analysis and Prediction of Overseas Cargo on the China-Laos ...

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11th international conference on Traffic and Transportation Studies, ICTTS 2024

作者： Hu, Hengchaung Zhang, Yaqin Li, Wang School of Traffic and Transportation Beijing Jiaotong University Beijing100044 China Institute of Computing Technology China Academy of Railway Sciences CorporationLimited Beijing100081 China Beijing Engineering Research Center of Urban Traffic Information Intelligent Sensing andService Technologies Beijing100044 China Key Laboratory of Active Safety Assurance and Risk Prevention in the Railway Industry Beijing Jiaotong University Beijing100044 China

ISBN: (纸本)9789819796397

this paper makes an in-depth analysis of the characteristics of overseas cargo on the China-Laos Railway and predicts the future freight trend based on historical data, so as to provide decision-making support for enhancing the influence of the China-Laos Railway freight market and promoting the quality and increment of freight transportation. Firstly, the current situation of cargo transportation were analyzed. then, by using the K-means cluster analysis method, the cluster analysis of the freight volume of major stations outside the China-Laos Railway was carried out, and the characteristics of freight categories between stations were discussed. Finally, based on the method of time series analysis, the total amount of freight and the total amount of major goods are predicted, so as to reveal the development trend of China-Laos railway freight transportation and provide decision-making support for relevant departments and enterprises to formulate industrial development strategies. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： characteristics of the goods China-Laos Railway cluster analysis time series analysis

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VisioSLR: A Vision data-Driven Framework for Sign Language Video Recognition and Performance Evaluation on Fine-Tuned YOLO Models 16

VisioSLR: A Vision Data-Driven Framework for Sign Language V...

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16th international conference on Ambient Systems, Networks and Technologies Networks, ANT 2025 / 8th international conference on Emerging data and Industry 4.0, EDI40 2025

作者： Ismail, Leila Shahin, Nada Tesfaye, Henok Hennebelle, Alain Intelligent Distributed Computing and Systems (INDUCE) Lab Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University United Arab Emirates

With the emergence of AI for good, there has been an increasing interest in building computer vision data-driven deep learning inclusive AI solutions. Sign language Recognition (SLR) has gained attention recently. It is an essential component of a sign-to-text translation system to support the deaf and hard-of-hearing population. this paper presents a computer VISIOn data-driven deep learning framework for Sign Language video Recognition (VisoSLR). VisioSLR provides a precise measurement of translating signs for developing an end-to-end computational translation system. Considering the scarcity of sign language datasets, which hinders the development of an accurate recognition model, we evaluate the performance of our framework by fine-tuning the very well-known YOLO models, which are built from a signs-unrelated collection of images and videos, using a small-sized sign language dataset. Gathering a sign language dataset for signs training would involve an enormous amount of time to collect and annotate videos in different environmental setups and multiple signers, in addition to the training time of a model. Numerical evaluations of VisioSLR show that our framework recognizes signs with a mean average precision of 97.4%, 97.1%, and 95.5% and 11, 12, and 12 milliseconds of recognition time on YOLOv8m, YOLOv9m, and YOLOv11m, respectively. © 2025 Elsevier B.V.. All rights reserved.

关键词： Artificial intelligence Computer vision Deep learning Fine-tuning Machine translation Neural machine translation Sign language Sign language recognition Transfer learning YOLO

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High-Speed Train Delay Propagation and Prediction Based on Markov Chains and Ensemble learning 11th

High-Speed Train Delay Propagation and Prediction Based on M...

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11th international conference on Traffic and Transportation Studies, ICTTS 2024

作者： Lu, Xinqi Shen, Pengju Zhou, Gang Song, Liying Chen, Yingxu School of Transportation Beijing Jiaotong University Beijing100044 China China Railway United International Multimodal Transport Zhengzhou Co. Ltd. Henan Zhengzhou450000 China

ISBN: (纸本)9789819796397

this paper addresses the issue of delays in high-speed rail trains, applying the Markov chain model to analyze the lateral propagation mechanism of delays and based on this, constructs an integrated CNN-LSTM-Attention model. the model incorporates the Maximum Information Coefficient-based MIC-BP feature selection algorithm to optimize the input feature set, effectively enhancing the model’s performance in practical applications. In the case study using Guangzhou-Shenzhen high-speed rail data, the model achieved a prediction accuracy of 97.71% within an allowable error margin of one minute, demonstrating its effectiveness in handling complex datasets, providing a basis for the adjustment of high-speed railway transportation organizations. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Markov processes

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MDSE-SLSTM: A Mobility-Driven Based Deep learning Framework for Passenger Flow Distribution Forecasting in Multimodal Transportation Hub 11th

MDSE-SLSTM: A Mobility-Driven Based Deep Learning Framework ...

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11th international conference on Traffic and Transportation Studies, ICTTS 2024

作者： Dai, Zhicheng Li, Dewei School of Traffic and Transportation Beijing Jiaotong University Beijing100044 China Frontiers Science Center for Smart High-Speed Railway System Beijing Jiaotong University Beijing100044 China

ISBN: (纸本)9789819796434

To address the challenges of accurately reconstructing passenger travel chains and predicting the spatiotemporal distribution of passenger flow in transportation hubs, this paper proposes a deep learning architecture based on mobility-driven spatial embedding and extended long short-term memory networks (MDSE-sLSTM). Initially, we conducted route choice behavior experiments in a virtual reality (VR) hub scenario to reconstruct passenger mobility chains. then, using Skip-gram, we analyzed the semantic correlations between nodes representing functional areas to achieve a spatially embedded representation of the hub area, obtaining spatial correlation features. In the temporal dimension, an extended bidirectional long short-term memory network with multi-head attention mechanism was employed to jointly learn the time-varying patterns of passenger flow data. Validation on the passenger flow distribution dataset of Tianjin West Station shows that the MDSE-sLSTM model outperforms traditional prediction models. Ablation experiments further confirm that the method of extracting spatial features based on mobility-driven approaches significantly improves passenger flow prediction compared to extracting spatial features based on the actual physical network. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Multimodal transportation

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