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Machine learning-Based Prediction of Effective Prestress Values Under Anchorage for Post-tensioned Small Box Girders 11th

Machine Learning-Based Prediction of Effective Prestress Val...

引用

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

作者： Lv, Pengfei Li, Yuan School of Highway Chang’an University Xian710064 China

ISBN: (纸本)9789819796434

Effective prestress under the anchorage refers to the prestress force retained by the prestress tendons under the anchorage opening of the working anchorage after the prestress tendons are tensioned and anchored. In order to improve the accuracy of the selection of the effective prestress under anchorage value of post-tensioned method small box girder, based on the measured data of effective prestress under anchorage of small box girder of a highway in Guangdong, four machine learning models, namely, multiple linear regression (MLR), adaptive boosting regression (AdaBoost), random forest (RF) and extreme gradient boosting (XGBoost), are used to comprehensively analyze the factors affecting the value. the results show that the mean square error (MSE) of the extreme gradient boosting model is 0.11, the mean absolute error (MAE) is 0.08, the Standard Deviation of the Residuals (SDR) is 0.33, and the goodness of fit (R-Squared) is 0.99. It means that the model predicts accurately, so it can be applied to the problem of accurately fetching the value of the effective prestress force under the anchorages of post-tensioned small box girders under certain conditions. In the XGBoost model, the degree of contribution of each factor to the model value is evaluated by introducing the Gini impurity (Gini) value, and the results show that the thickness of end web is the most important factor affecting the value of effective prestress under anchorages, accounting for 21.65%. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Mean square error

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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...

引用

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

A Freight Forecasting Method for Multimodal Transport Networ...

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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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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 ...

引用

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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Development of a learning-Based Control System for Robot-Assisted Grinding of Rubber Roller

Development of a Learning-Based Control System for Robot-Ass...

引用

international conference on Autonomous Robots and Agents, ICARA

作者： Fabian Reiling Maximilian Bause Stefan Gröger Christian Henke Steven Koppert Ansgar Trächtler Scientific Automation Fraunhofer IEM Paderborn Germany Intelligent Automotive Industry Capgemini Invent Hamburg Germany Control Engineering and Mechatronics Heinz Nixdorf Institute Paderborn Germany

ISBN: (数字)9798331509231

ISBN: (纸本)9798331509248

Grinding rubber rollers to a specific geometry presents significant challenges due to the material's non-linear behavior. Currently, this process has not been successfully automated, primarily because the control concepts implemented thus far do not achieve the desired accuracy for economical operation. Consequently, these rollers are manually ground to the required geometry on a grinding wheel. Previous work has led to the development of a system that automates the grinding of rollers using a robot. the objective of this paper is to demonstrate, through modeling, how a learning-based control system can be developed for this process to reduce production time and facilitate an economical operation. To this end, we propose two control concepts: one that directly predicts the required control variable using a learning-based controller, and another that employs an AI-corrected control mechanism, which utilizes the control variable provided by a proportional controller to correct the error between the control variable and the control difference. Finally, we conduct a comparative simulation of the performance of these concepts against a conventional controller.

关键词： Geometry Hands Manufacturing processes Process control Wheels Production Control systems Parametric statistics Rubber Mobile robots

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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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IoT-Driven intelligent Waste Management System with Disinfectant Spray

IoT-Driven Intelligent Waste Management System with Disinfec...

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

作者： N. S. Swapna Basutkar Kalyan Rao Kummari Netra Paradesi Abhinav Department of Internet of Things G. Pullaiah College of Engineering and Technology Kurnool India

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

the rapid increase in urban waste generation demands innovative solutions to improve public health and operational efficiency. this paper presents an IoT-driven intelligent waste management system that integrates real-time monitoring, dynamic route optimization, and automated disinfectant mechanisms. Smart bins equipped with ultrasonic, gas, rain, and infrared sensors transmit data via ESP8266 Wi-Fi/GSM modules to a cloud platform (thingSpeak), enabling anomaly detection and route optimization using a modified Travelling Salesman Problem (TSP) algorithm. A solenoid-based disinfectant system reduces bacterial load by 98% (verified through lab testing [5]), while the TSP algorithm reduces collection time by 40% and fuel consumption by 30% compared to traditional methods [4]. A three-level dashboard (admin, collector, user) provides actionable insights through a mobile app. Deployed in a 1 km 2 testbed with 10 bins, the system demonstrates scalability and cost-effectiveness. Future work includes integrating machine learning for predictive analytics and automated waste segregation. this solution aligns with UN Sustainable Development Goals (SDGs) 11 and 13 [1], promoting cleaner cities and climate action.

关键词： Waste management Rain Machine learning algorithms Urban areas Real-time systems Mobile applications Predictive analytics Optimization Gas detectors intelligent sensors

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Research on Factor Support Vector Multi-classification Algorithm Based on Factor Space theory 6th

Research on Factor Support Vector Multi-classification Algor...

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6th IFIP TC 12 international conference on Intelligence Science, ICIS 2024

作者： Zhang, Kaijie Zeng, Fanhui Li, Jiaxin College of Intelligent Engineering and Mathematics Liaoning Technical University Fuxin123000 China College of Science Liaoning Technical University Fuxin123000 China

ISBN: (纸本)9783031712524

In order to solve the accuracy and complexity of multiple classification algorithms, on the basis of factor space theory, the divisibility measurement condition and the construction condition of binary tree are defined. On the basis of balanced binary tree, combining the relationship between class spacing and sample circle radius, a factor support vector multiple classification algorithm(M-FSV) is proposed by using recursion idea and the principle of "easy classification first". Experiments are done with one-to-one support vector machine and balanced binary tree support vector machine algorithm, and experimental comparison is made. the results of 8 data sets in UCI database show that the training time of M-FSV is less than that of SVM, and the algorithm accuracy is higher than that of SVM. the research results expand the theory and application of factor space, and provide a new idea and simple method for classification problems in machine learning. © IFIP international Federation for Information Processing 2025.

关键词： Vectors

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