检索结果-内蒙古大学图书馆

Muon identification in a compact single-layered water Cherenkov detector and gamma/hadron discrimination using Machine Learning techniques

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Conceição, R. González, Borja Serrano Guillén, A. Pimenta, M. Tomé, B. LIP Av. Prof. Gama Pinto 2 LisbonP-1649-003 Portugal Universidade de Lisboa Av. Rovisco Pais 1 Lisbon1049-001 Portugal Computer Architecture and Technology Department University of Granada Granada Spain

The muon tagging is an essential tool to distinguish between gamma and hadron-induced showers in wide field-of-view gamma-ray observatories. In this work, it is shown that an efficient muon tagging (and counting) can be achieved using a water Cherenkov detector with a reduced water volume and 4 PMTs, provided that the PMT signal spatial and time patterns are interpreted by an analysis based on Machine Learning (ML). The developed analysis has been tested for different shower and array configurations. The output of the ML analysis, the probability of having a muon in the WCD station, has been used to notably discriminate between gamma and hadron induced showers with S/√B ∼ 4 for shower with energies E0 ∼ 1 TeV. Finally, for proton-induced showers, an estimator of the number of muons was built by means of the sum of the probabilities of having a muon in the stations. Resolutions about 20% and a negligible bias are obtained for vertical showers with Nµ > 10. Copyright © 2021, The Authors. All rights reserved.

关键词： Observatories

Traffic4cast at NeurIPS 2022 – Predict Dynamics along Graph Edges from Sparse Node Data: Whole City Traffic and ETA from Stationary Vehicle Detectors

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Neun, Moritz Eichenberger, Christian Martin, Henry Spanring, Markus Siripurapu, Rahul Springer, Daniel Deng, Leyan Wu, Chenwang Lian, Defu Zhou, Min Lumiste, Martin Ilie, Andrei Wu, Xinhua Lyu, Cheng Lu, Qing-Long Mahajan, Vishal Lu, Yichao Li, Jiezhang Li, Junjun Gong, Yue-Jiao Grötschla, Florian Mathys, Joël Wei, Ye Haitao, He Fang, Hui Malm, Kevin Tang, Fei Kopp, Michael Kreil, David Hochreiter, Sepp Vienna Austria Institute of Cartography and Geoinformation ETH Zurich Switzerland School of Data Science University of Science and Technology of China China Huawei Noah’s Ark Lab Bolt Technology Tallinn Estonia University of Bucharest Bucharest Romania Department of Civil and Environmental Engineering Northeastern University BostonMA United States Transportation Systems Engineering Technical University of Munich Germany Layer 6 AI Toronto Canada School of Coumpute Science and Engineering South China University of Technology Guangzhou China ETH Zurich Switzerland Department of Computer Science Loughborough University Loughborough United Kingdom School of Architecture Building and Civil Engineering Loughborough University Loughborough United Kingdom HERE Technologies ChicagoIL United States Kaiko Zurich Switzerland Machine Learning Institute Johannes Kepler University Linz Austria

The global trends of urbanization and increased personal mobility force us to rethink the way we live and use urban space. The Traffic4cast competition series tackles this problem in a data-driven way, advancing the latest methods in machine learning for modeling complex spatial systems over time. In this edition, our dynamic road graph data combine information from road maps, 1012 probe data points, and stationary vehicle detectors in three cities over the span of two years. While stationary vehicle detectors are the most accurate way to capture traffic volume, they are only available in few locations. Traffic4cast 2022 explores models that have the ability to generalize loosely related temporal vertex data on just a few nodes to predict dynamic future traffic states on the edges of the entire road graph. In the core challenge, participants are invited to predict the likelihoods of three congestion classes derived from the speed levels in the GPS data for the entire road graph in three cities 15 min into the future. We only provide vehicle count data from spatially sparse stationary vehicle detectors in these three cities as model input for this task. The data are aggregated in 15 min time bins for one hour prior to the prediction time. For the extended challenge, participants are tasked to predict the average travel times on super-segments 15 min into the future – super-segments are longer sequences of road segments in the graph. The competition results provide an important advance in the prediction of complex city-wide traffic states just from publicly available sparse vehicle data and without the need for large amounts of real-time floating vehicle data. © 2023, CC BY-NC-SA.

关键词： Travel time

Thermodynamic Analysis of a Jet Engine Using a Liquid Propellant

学校读者我要写书评

暂无评论

Research Square

Research Square 2022年

作者： Alliche, Mounir Rebhi, Redha Inc, Mustafa Kidar, Ali Menni, Younes Ameur, Houari Lorenzini, Giulio Alsharif, Sameer Althobaiti, Ahmed Aly, Ayman A. Department of Mechanical Engineering Faculty of Technology University Yahia Fares of Medea Medea26000 Algeria Department of Computer Engineering Biruni University Istanbul Turkey Department of Mathematics Science Faculty Firat University Elazig23119 Turkey Department of Medical Research China Medical University Hospital China Medical University Taichung40402 Taiwan Faculty of Exact Sciences and Informatics Ziane Achour University of Djelfa Algeria Faculty of Technology Ziane Achour University of Djelfa Algeria Unit of Research on Materials and Renewable Energies Department of Physics Faculty of Sciences Abou Bekr Belkaid University P.O. Box 119 Tlemcen13000 Algeria Department of Technology University Centre of Naama - Salhi Ahmed P.O. Box 66 Naama 45000 Algeria Department of Engineering and Architecture University of Parma Parco Area Delle Scienze 181/A Parma43124 Italy Department of Computer Engineering College of Computers and Information Technology Taif University P.O. Box 11099 Taif21944 Saudi Arabia Department of Electrical Engineering College of Engineering Taif University Taif21944 Saudi Arabia Department of Mechanical Engineering College of Engineering Taif University Taif21944 Saudi Arabia

Combustion-chamber is a critical component of the propulsion engine, which is widely used in the space industry and aeronautics. The goal of this article is to perform a numerical-analysis on the combustion process using a liquid-type propellant. The steps that must be followed until total combustion is achieved are emphasized. It concerns the fuel feeding phase, its injection and the combustion operation. The amount of combustion products and the energy generated are evaluated. It has been shown that the liquid propellant may present an efficient alternative fuel than the kerosene. In addition, the temperature of combustion does not exceed a certain limit to avoid structural problems in the chamber. The parametric survey allowed determining the range of the most influence factors, including the pressure, mixture richness, velocity and flow rates of injection for the fuel and oxidizer. The number and type of injectors revealed a considerable influence on the velocity and flow rates of injection. To maximize thrust force and system propulsion, a careful selection of chamber material and ignition methods is required. A thorough inspection on the issues of walls cooling showed the necessary survey of maximum temperatures that may be reached during the combustion. Finally, an investigation of the thermal exchange through the walls will be very interesting. © 2022, CC BY.

关键词： Surveys

Corrigendum to "Electrical coupling regulated by GABAergic nucleo-olivary afferent fibres facilitates cerebellar sensory-motor adaptation" [Neural Netw. 155 (2022) 422-438]

学校读者我要写书评

暂无评论

Neural networks : the official journal of the International Neural Network Society 2023年 165卷 676页

作者： Niceto R Luque Francisco Naveros Ignacio Abadía Eduardo Ros Angelo Arleo Sorbonne Université INSERM CNRS Institut de la Vision 17 rue Moreau F-75012 Paris France Department of Computer Architecture and Technology CITIC-University of Granada 2 Calle Periodista Rafael Gómez Montero ES 18014 Granada Spain. Electronic address: nluque@ugr.es. Department of Computer Architecture and Technology CITIC-University of Granada 2 Calle Periodista Rafael Gómez Montero ES 18014 Granada Spain Department of Neuroscience Baylor College of Medicine Houston TX USA. Department of Computer Architecture and Technology CITIC-University of Granada 2 Calle Periodista Rafael Gómez Montero ES 18014 Granada Spain. Sorbonne Université INSERM CNRS Institut de la Vision 17 rue Moreau F-75012 Paris France.

Adaptive Window Size Based Deep Neural Network for Driving Maneuver Prediction

学校读者我要写书评

暂无评论

Adaptive Window Size Based Deep Neural Network for Driving M...

第32届中国控制与决策会议

作者： Jun Gao Honghui Zhu Yi Lu Murphey School of Electromechanical and Architecture Engineering Jianghan University School of Logistics Engineering Wuhan University of Technology Department of Electrical and Computer Engineering University of Michigan-Dearborn

Driving maneuver prediction is one of the most challenging tasks in Advanced Driver Assistance System（ADAS）, it can provide an early notification for ADAS to predict dangerous circumstances and take appropriate actions. However, it is difficult to well modeling the driving maneuver process due to the complexity and uncertainty of traffic status. To address this issue, we propose a novel model, denoted as DMPM, which uses deep learning method for Driving Maneuver Prediction（DMP） from multi-modal data, i.e. front view videos and vehicle signals. Firstly, with Adaptive Window Size Selector（AWSS）, DMPM is able to dynamically identify the optimal sliding window size for the input data. Secondly, the Global Context Video Network（GCV Network） is proposed combing with Root-ResNet+Weighted Channel Dropout（WCD） architecture to extract the features from multi-modal data efficiently. Specially, including the Global Context（GC） block, GCV Network has an ability of modeling long-range dependency. Finally, a Long Short-Term Memory（LSTM） network that captures temporal dependencies is leveraged for driving maneuver prediction. The experimental results show that the DMPM is capable of learning the latent features of driving maneuver and achieving significantly better performance than other popular models on a real-world driving data set.

关键词： Driving Maneuver Prediction Deep Learning Adaptive Window Size Selector GCV Network

The facechannel: A fast & furious deep neural network for facial expression recognition

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Barros, Pablo Churamani, Nikhil Sciutti, Alessandra Cognitive Architecture for Collaborative Technologies Unit Istituto Italiano di Tecnologia Genova Italy Department of Computer Science and Technology University of Cambridge United Kingdom

Current state-of-the-art models for automatic Facial Expression Recognition (FER) are based on very deep neural networks that are effective but rather expensive to train. Given the dynamic conditions of FER, this characteristic hinders such models of been used as a general affect recognition. In this paper, we address this problem by formalizing the FaceChannel, a light-weight neural network that has much fewer parameters than common deep neural networks. We introduce an inhibitory layer that helps to shape the learning of facial features in the last layer of the network and thus improving performance while reducing the number of trainable parameters. To evaluate our model, we perform a series of experiments on different benchmark datasets and demonstrate how the FaceChannel achieves a comparable, if not better, performance to the current state-of-the-art in FER. Our experiments include cross-dataset analysis, to estimate how our model behaves on different affective recognition conditions. We conclude our paper with an analysis of how FaceChannel learns and adapt the learned facial features towards the different datasets. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

Exocentric to egocentric image generation via parallel generative adversarial network

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Liu, Gaowen Tang, Hao Latapie, Hugo Yan, Yan Department of Computer Science Texas State University United States DISI University of Trento Italy Chief Technology & Architecture Office Cisco United States

Cross-view image generation has been recently proposed to generate images of one view from another dramatically different view. In this paper, we investigate exocentric (third-person) view to egocentric (first-person) view image generation. This is a challenging task since egocentric view sometimes is remarkably different from exocentric view. Thus, transforming the appearances across the two views is a nontrivial task. To this end, we propose a novel Parallel Generative Adversarial Network (P-GAN) with a novel cross-cycle loss to learn the shared information for generating egocentric images from exocentric view. We also incorporate a novel contextual feature loss in the learning procedure to capture the contextual information in images. Extensive experiments on the Exo-Ego datasets [1] show that our model outperforms the state-of-the-art approaches. Copyright © 2020, The Authors. All rights reserved.

关键词： Generative adversarial networks

The FaceChannel: A light-weight deep neural network for Facial Expression Recognition

学校读者我要写书评

暂无评论

arXiv 2020年

Current state-of-the-art models for automatic Facial Expression Recognition (FER) are based on very deep neural networks that are difficult to train. This makes it challenging to adapt these models to changing conditions, a requirement from FER models given the subjective nature of affect perception and understanding. In this paper, we address this problem by formalising the FaceChannel, a light-weight neural network that has much fewer parameters than common deep neural networks. We perform a series of experiments on different benchmark datasets to demonstrate how the FaceChannel achieves a comparable, if not better, performance, as compared to the current state-of-the-art in FER. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

Collaborating with a Robot Biases Human Spatial Attention

学校读者我要写书评

暂无评论

iScience 2025年

作者： Giulia Scorza Azzarà Joshua Zonca Francesco Rea Joo-Hyun Song Alessandra Sciutti Robotics Brain and Cognitive Sciences Unit Italian Institute of Technology 16152 Genoa GE Italy Department of Computer Science Bioengineering Robotics and Systems Engineering University of Genoa 16145 Genoa GE Italy Cognitive Architecture for Collaborative Technologies (CONTACT) Unit Italian Institute of Technology 16152 Genoa GE Italy Department of Cognitive & Psychological Sciences Brown University 02912 Providence RI USA

Advancements in collaborative technologies have increased the accessibility of Human-Robot Interaction (HRI), yet a gap remains in understanding how HRI affects human systems. This work investigates how HRI influences human visual processing, specifically through the near-hand effect, a phenomenon where the proximity of one’s hand enhances spatial attention. Research has shown that this attentional priority can extend to another person’s hand during collaborative tasks, integrating it into an individual’s body schema. A critical question arises: Can a robot’s anthropomorphic hand similarly bias human attentional priority? Our findings revealed that collaborative HRI facilitates target detection near the robot’s hand, an effect absent before the interaction. Additionally, we examined social and kinematic metrics that enhance this attentional shift by fostering joint body schema formation. These results highlight that HRI can shape human visual processing and body schema integration, offering insight into the interplay between our perceptual and cognitive systems and robotic collaborators.

关键词： Collaborative Human-Robot Interaction Visual Processing Human Spatial Attention Near-Hand Effect Joint Body Schema