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Jointly Learning V2X Communication and Platoon Control with Deep Reinforcement Learning

Jointly Learning V2X Communication and Platoon Control with ...

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ieee International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

作者： Tong Liu Lei Lei Zhiming Liu Kan Zheng Key Lab of Universal Wireless Communications Beijing University of Posts and Telecommunications Beijing China School of Engineering University of Guelph Guelph Canada College of Electrical Engineering and Computer Sciences Ningbo University Ningbo China

In autonomous vehicle platooning, Vehicle-to-Everything (V2X) communications are leveraged in cooperative adaptive cruise control (CACC) to improve control performance. Since exchanging information at all times incurs significant communication overhead in vehicular networks, it is important to determine when V2X communication is necessary. To solve this problem, we propose a Deep Reinforcement Learning (DRL)-based algorithm named Attention-DDPG, which learns platoon control with Deep Deterministic Policy Gradient (DDPG), and learns when to communicate with an attention network. Specifically, each preceding vehicle is equipped with a deep neural network (DNN), which takes as input its local state and platoon control action and determines whether to transmit its acceleration or not to the following vehicle at each time step. the attention network of a preceding vehicle is trained using the feedback from the following vehicle on the value of V2X information in the form of an advantage function. In order to evaluate Attention-DDPG, simulations are performed using real driving data, and performance is compared with those of two baselines that communicate and do not communicate at all times, respectively. the results demonstrate that Attention-DDPG strikes a competitive tradeoff between control performance and communication overhead while ensuring platoon string stability.

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Resource Allocation Optimization with Cluster Based Auction in Wireless Virtual Reality Network

Resource Allocation Optimization with Cluster Based Auction ...

引用

Australasian Telecommunication networks and Applications conference, ATNAC

作者： Xinyu Wan Feiran You Abbas Jamalipour School of Electrical and Computer Engineering The University of Sydney Sydney NSW Australia

ISBN: (数字)9798350379112

ISBN: (纸本)9798350379129

Virtual reality (VR) has shown great potential in various fields to provide novel experiences and enhance industrial working efficiencies. the characteristics of high resolution for VR streaming lead to the high requirement of bandwidth during VR streaming, while the proactive streaming in wireless VR communication provides users with an immersive experience under strict latency constraints through viewport prediction. As a large amount of user data is required to train a strong-applicable prediction model and these data are collected in a distributed manner, the federated learning (FL) framework can be utilized to gather local training weights into global weights without occupying limited bandwidth for data transmission. Considering users may have varying individual requirements while participating in global FL training, an auction-based user clustering algorithm is proposed in this paper to group users based on their preferences for user utility. the simulated annealing (SA) scheme is utilized to select the number of users in each cluster for FL participation and a cluster-based resource allocation approach is presented to allocate resources to users based on characteristics of clusters. By considering the utility of both the small base station (SBS) and users, the proposed approach can provide users with enhanced utility and SBS with sustainable bandwidth usage.

关键词： Wireless communication Training Solid modeling Spectral efficiency Distributed databases Bandwidth Simulated annealing Predictive models Resource management Optimization

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A Stable Spatial Pooling Algorithm for Hierarchical Temporal Memory Network

A Stable Spatial Pooling Algorithm for Hierarchical Temporal...

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International conference on Tools for Artificial Intelligence (ICTAI)

作者： Dejiao Niu Xudong Wu Tao Cai Lei Li Jie Jiang Yuhan Chen The School of Computer Science and Communication Engineering Jiangsu University Zhenjiang China

Hierarchical Temporal Memory (HTM) is an emerging neural network technology which is inspired by the structure and the working principles of human neocortex. HTM uses a set of active columns to represent the input and inhibits frequently activated columns by a boosting mechanism. However, the boosting mechanism may cause an instable representation and thus affects the HTM performance. To address this issue, we propose a stable spatial pooling algorithm for HTM, where the column loadability is introduced, and a new column activation strategy based on the loadability is presented. We implement the proposed algorithm and carry out various experiments on both the synthetic and real-world sequential datasets. the results show that our HTM can generate more stable internal representation for the input data. the prediction accuracy of the proposed HTM outperforms that of the conventional HTM and the popular LSTM networks.

关键词： Neural networks Organizations Learning (artificial intelligence) Prediction algorithms Boosting Task analysis

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RITNet: A Rotation Invariant Transformer based Network for Point Cloud Registration

RITNet: A Rotation Invariant Transformer based Network for P...

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International conference on Tools for Artificial Intelligence (ICTAI)

作者： Min Yang Yaochen Li Su Wang Shaohan Yang Hujun Liu School of Software Engineering Xi'an Jiaotong University China Department of Computer Science Xi'an Jiaotong-liverpool University China School of Computer Science and Technology Xi'an Jiaotong University China

Conventional point cloud registration methods usually employ an encoder-decoder architecture, where mid-level features are locally aggregated to extract geometric information. However, the over-reliance on local features may raise the boundary points cannot be adequately matched for two point clouds. To address this issue, we argue that the boundary features can be further enhanced by the rotation information, and propose a rotation invariant representation to replace common 3D Cartesian coordinates as the network inputs that enhances generalization to arbitrary orientations. Based on this technique, we propose rotation invariant Transformer for point cloud registration, which utilizes insensitivity to arrangement and quantity of data in the Transformer module to capture global structural knowledge within local parts for overall comprehension of each point clouds. Extensive quantitative and qualitative experimental on ModelNet40 evaluations show the effectiveness of the proposed method.

关键词： Point cloud compression three-dimensional displays Fuses Transformers Feature extraction Data mining Artificial intelligence

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A new multiscale texture surface defect detection method based on convolutional neural network

A new multiscale texture surface defect detection method bas...

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International conference on Tools for Artificial Intelligence (ICTAI)

作者： Kaixiang Li Min Dong Dezhen Li School of Information Engineering Zhengzhou University Zhengzhou China Henan Institute of Advanced Technology Zhengzhou University Zhengzhou China

Traditional computer defect detection methods usually focus on the handcrafted features, but these methods have many limitations. In this paper, an approach of texture surface defect detection based on convolution neural network (CNN) and wavelet analysis is proposed. the approach combines wavelet analysis with patches extraction, which can detect and locate many kinds of defects in complex texture background, especially tiny defects in large-scale images. It is evaluated on DAGM 2007 dataset and Micro surface defect database, the results demonstrate that it has a high accuracy in defect detection with only a small amount of training data.

关键词： Wavelet transforms Surface waves Databases Convolution Neural networks Training data Wavelet analysis

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Is it a bug or a feature? Identifying software bugs using graph attention networks

Is it a bug or a feature? Identifying software bugs using gr...

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International conference on Tools for Artificial Intelligence (ICTAI)

作者： Nikos Kanakaris Ilias Siachos Nikos Karacapilidis Industrial Management and Information Systems Lab MEAD University of Patras Patras Greece

this paper proposes a novel approach for identifying software bugs by building on a meaningful combination of word embeddings, graph-based text representations and graph attention networks. Existing approaches aim to advance each of the above components individually, without considering an integrative approach. As a result, they ignore information that is related to either the structure of a given text or an individual word of the text. Instead, our approach seamlessly incorporates both semantic and structural characteristics into a graph, which are then fed to a graph attention network in order to classify Github issues as bugs or features. Our experimental results demonstrate a significant improvement in terms of accuracy, precision and recall of the proposed approach compared to a list of classical and graph-based machine learning models. the dataset for the experiments reported in this paper has been retrieved from the *** platform and concerns Github issues with short-text attributes.

关键词： Measurement computer bugs Semantics Buildings Machine learning Software Data models

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Recognition of local Bird Species of Yunnan Using Transfer Learning Based on ResNet

Recognition of Local Bird Species of Yunnan Using Transfer L...

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International conference on Vision, Image and Signal Processing (ICVISP)

作者： Wei Yang College of Big Data BaoShan University BaoShan China

ISBN: (数字)9798331541460

ISBN: (纸本)9798331541477

this study created the GMBID dataset for locally protected bird species in Yunnan and explored the application of transfer learning with different ResNet architectures (ResNet18, ResNet34, ResNet50) on this dataset. By comparing the performance of these models in terms of accuracy, precision, recall, and F1 score, the study found that ResNet34 performed the best, achieving a good balance between high accuracy, model complexity, and computational efficiency. Although ResNet50 has a deeper architecture, its performance improvement over ResNet34 was limited, and it incurred higher computational costs. ResNet18, while training faster, had slightly lower overall performance compared to ResNet34. this study highlights the practical significance of applying transfer learning in data-scarce regions, particularly in the fields of bird conservation and ecological monitoring. Future work could focus on expanding the dataset, experimenting with more advanced model architectures, and optimizing transfer learning strategies to further enhance model performance, providing technical support for wildlife conservation.

关键词： Training Accuracy Biological system modeling Computational modeling Transfer learning computer architecture Birds Computational efficiency Monitoring Residual neural networks

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Block-term Dropout For Robust Adversarial Defense

Block-term Dropout For Robust Adversarial Defense

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International conference on Tools for Artificial Intelligence (ICTAI)

作者： Chen Ouyang Wei Yang Rong Hu School of Computer Science and Technology University of Science and Technology of China Hefei China

Deep neural networks (DNNs) have lately shown tremendous performance in various applications. However, along-side their superiority in these tasks, recent studies have demon-strated that DNNs are easily fooled by adversarial attacks. To guard against adversarial examples, we provide a new solution to hardening DNNs through Block-term Dropout (BT-Dropout), an adversarial defense technique that leverages a latent high-order factorization of the network. Specifically, we impose low-rank block-term tensor structure on the weights of fully-connected layer to obtain compact networks, and then apply BT-Dropout in the latent subspace without pruning the weights directly. Meanwhile, for activation tensor fed into fully-connected layer, Tucker Dropout which can be viewed as a special case of BT-Dropout is introduced to preserve multilinear structure of activations. Furthermore, we show that BT-Dropout implicitly regularizes the tensor decomposition. Comprehensive experiments have demonstrated the effectiveness of our proposed method to improve the adversarial robustness for the models on standard image classification benchmarks.

关键词： Tensors Neural networks Stochastic processes Benchmark testing Network architecture Robustness Task analysis

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Modelling and Comparative Analysis of Low-Voltage Distribution networks at Varying Levels of Approximation

Modelling and Comparative Analysis of Low-Voltage Distributi...

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Australasian Universities Power Engineering conference, AUPEC

作者： Jiawei Zhang Gregor Verbič Rahmat Heidari Frederik Geth School of Electrical and Computer Engineering The University of Sydney Sydney Australia CSIRO Newcastle Australia GridQube Brisbane Australia

ISBN: (数字)9798350377941

ISBN: (纸本)9798350377958

this paper focuses on the modelling of low-voltage (LV) distribution networks at various levels of approximation. Based on an Australian representative LV network topology provided with only approximate sequence impedances, we construct a comprehensive unbalanced three-phase four-wire LV network model with Australian standard conductors. In addition, we adopt three widely applied approximations: (i) neutral Kronreduced model, (ii) sequence impedance model; and (iii) singlephase equivalent model. Real-life load data are utilised, and power flow analyses are carried out using the constructed Australian LV distribution network with 61 customers and three approximate models. Results show discrepancies between the original four-wire model and the approximated alternatives, in terms of bus voltage magnitudes, network losses, as well as transformer capacity utilisation levels. It is observed that simpler models exhibit greater inaccuracies. In addition, it is noted that the nontrivial neutral voltage negatively impacts the voltage performance, highlighting the importance of considering the neutral voltage shift. through comprehensive analyses, this paper provides insight into the fidelity of various approximation levels in representing the original four-wire LV network model, as well as the risks to anticipate in the adopted assumptions. It underscores the importance of employing a detailed model that includes neutral voltage considerations, providing researchers with a clear understanding of how their modelling choices may impact the outcomes for LV distribution network studies.

关键词： Analytical models Low voltage Computational modeling Voltage Distribution networks Transformers Conductors Impedance Load modeling Context modeling

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Multitask Learning Neural networks for Pandemic Prediction with Public Stance Enhancement

Multitask Learning Neural Networks for Pandemic Prediction w...

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International conference on Tools for Artificial Intelligence (ICTAI)

作者： Zhong Zhang Feng Xie Xuechen Zhao Bin Zhou Jian Chen Lei Tian School of Computer National University of Defence Technology Changsha China Key Lab. of Software Engineering for Complex Systems National University of Defence Technology Changsha China

State and local governments have imposed health policies to contain the spread of COVID-19 since it had a serious impact on human daily life. However, the public stance on these measures may be time-varying. It is likely to escalate the infection in the area where the public is negative or resistant. To take advantage of the correlation between public stance on health policies and the COVID-19 statistics, we propose a novel framework, Multitask Learning Neural networks for Pandemic Prediction with Public Stance Enhancement (MP3), which is composed of three modules: (1) Stance awareness module to make stance detection on health policies from users' tweets in social media and convert them into a stance time series. (2) Temporal feature extraction module that applies Convolution Neural Network and Recurrent Neural Network to extract and fuse local patterns and long-term correlations from COVID-19 statistics. Moreover, a Stance Latency-aware Attention is proposed to capture dynamic social effects and fuse them with temporal features. (3) Multi-task prediction module to adopt Graph Convolution Network to model the spread of pandemic and employ multi-task learning to simultaneously predict COVID-19 statistics and the trend of public stance on health policies. the proposed framework outperforms state-of-the-art baselines on both confirmed cases and deaths prediction tasks.

关键词： COVID-19 Correlation Pandemics Social networking (online) Fuses Convolution Multitasking

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