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Universal Path Planning for Autonomous Vehicles Considering Real-Time and Obstacle-Avoidance Performance 24

Universal Path Planning for Autonomous Vehicles Considering ...

引用

24th COTA international conference of Transportation Professionals: Resilient, Intelligent, Connected, and Lowcarbon Multimodal Transportation, CICTP 2024

作者： Wang, Ying Zhao, Dan Li, Shurong Wei, Chong School of Traffic Management People’s Public Security Univ. of China Beijing China MOT Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport School of Traffic and Transportation Beijing Jiaotong Univ. Beijing China

ISBN: (数字)9780784485484

ISBN: (纸本)9780784485484

Effectively handling various scenarios with autonomous vehicles involves the problem of planning paths. Starting from the practical application, we propose a universal path planning method, which can respond to the dynamic traffic environment in a timely manner. the method achieves long-term driving behavioral objectives through the construction of a linear optimization framework that combines a reactive obstacle avoidance strategy and spatial discretization techniques. As for the obstacle avoidance strategy, we introduce the exogenous parameter "time" to realize the consideration of bistatic (static and dynamic) obstacles, while the spatial discretization of path solving is realized by determining the optimal end position through constructing a dynamic objective function. the feasibility and validity of the proposed planning method are verified by simulation experiments. © ASCE.

关键词： Autonomous vehicles

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Ripple Sparse Self-Attention for Monaural Speech Enhancement 48

Ripple Sparse Self-Attention for Monaural Speech Enhancement

引用

48th IEEE international conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Zhang, Qiquan Zhu, Hongxu Song, Qi Qian, Xinyuan Ni, Zhaoheng Li, Haizhou University of New South Wales School of Electrical Engineering and Telecommunications Australia National University of Singapore Department of Electrical and Computer Engineering Singapore Alibaba Group China Meta United States The Chinese University of HongKong Shenzhen Research Institute of Big Data School of Data Science Shenzhen China

ISBN: (纸本)9781728163277

the use of Transformer represents a recent success in speech enhancement. However, as its core component, self-attention suffers from quadratic complexity, which is computationally prohibited for long speech recordings. Moreover, it allows each time frame to attend to all time frames, neglecting the strong local correlations of speech signals. this study presents a simple yet effective sparse self-attention for speech enhancement, called ripple attention, which simultaneously performs fine- and coarse-grained modeling for local and global dependencies, respectively. Specifically, we employ local band attention to enable each frame to attend to its closest neighbor frames in a window at fine granularity, while employing dilated attention outside the window to model the global dependencies at a coarse granularity. We evaluate the efficacy of our ripple attention for speech enhancement on two commonly used training objectives. Extensive experimental results consistently confirm the superior performance of the ripple attention design over standard full self-attention, blockwise attention, and dual-path attention (Sep-Former) in terms of speech quality and intelligibility. © 2023 IEEE.

关键词： Speech enhancement

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A Scheme of Anti Gradient Leakage of Federated Learning Based on Blockchain 5th

A Scheme of Anti Gradient Leakage of Federated Learning Base...

引用

5th international conference on Security and Privacy in New Computing Environments, SPNCE 2022

作者： Zhang, Xin Liu, Yuanzhen Yang, Yanbo Zhang, Jiawei Li, Teng Li, Baoshan School of Information Engineering Inner Mongolia University of Science & Technology Bao Tou014010 China School of Cyber Engineering Xidian University Xi’an710071 China

ISBN: (纸本)9783031306228

Federated learning provides a new solution for data security and privacy protection in the process of machine learning. In distributed learning, interactive gradient rather than direct exchange of data avoids the direct acquisition of data by malicious participants. the latest research shows that the gradient will also leak the data privacy during user training, current solutions to gradient leakage problems are mainly divided into two categories: 1. Encrypt the parameters of the model updating process by using the cryptography scheme;2. Increase the interference noise for the gradient through thinning, gradient compression, adding noise and other schemes. However, the cryptographic scheme cannot be targeted at the aggregator, and the noise scheme has too much impact on the performance of the model. this paper uses the trust mechanism of blockchain to design an anti gradient leakage scheme for this problem: when the participants upload the gradient, the noise is added through the blockchain smart contract, and the noise value is randomly divided into multiple copies and stored in the blockchain. the central server obtains the noise sum of all participants through the smart contract, and aggregates the gradient to remove the noise. In the training process, all participants can only obtain the aggregation gradient and noise gradient, and cannot recover user data. At the same time, due to the automatic execution and tamper proof characteristics of blockchain smart contracts, malicious interference in the data exchange process is avoided. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Blockchain

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Learning Aided Intelligent Mechanism for Channel Estimation in 5G Wireless Networks 5

Learning Aided Intelligent Mechanism for Channel Estimation ...

引用

5th international conference on Emerging Smart Computing and Informatics, ESCI 2023

作者： Monga, Sakhshra Taneja, Ashu Saluja, Nitin Garg, Roopali Chitkara University Institute of Engineering and Technology Chitkara University Punjab India University Institute of Engineering and Technology Panjab University India

ISBN: (纸本)9781665475242

the attenuation of signals due to propagation environment is the major challenge of wireless communication which results in frequent call drops, reduced signal strength and low transmission rates. the propagation channel often degrades the signal quality at the receiver due to channel effects including fading, shadowing, path loss and other overhead. the propagation channel and its successful estimation is very important for ensuring communication reliability in next generation wireless systems. this paper presents an intelligent mechanism based on deep learning to estimate the wireless channel such that the system spectral efficiency is enhanced. the impact of signal distortion due to hardware effects is also considered. Further, the proposed scheme is compared with the conventional LMMSE channel estimation scheme. Also, to extract the data using the estimated channel, the performance of proposed scheme is evaluated using three receivers namely, RZF, MMSE and modified MMSE. It is observed that the proposed scheme outperforms the LMMSE scheme in terms of normalised mean square error (NMSE) by 14.43% and by 27.16% in the absence of distortion. In the end, the performance comparison with the system with known perfect channel state information (CSI) is also performed. © 2023 IEEE.

关键词： Spectrum efficiency

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Solving Combinatorial Optimization Problem Over Graph through QUBO Transformation and Deep Reinforcement Learning 24

Solving Combinatorial Optimization Problem Over Graph Throug...

引用

24th IEEE international conference on data Mining, ICDM 2024

作者： Pu, Tianle Chen, Chao Zeng, Li Liu, Shixuan Sun, Rui Fan, Changjun College of Systems Engineering National University of Defense Technology Laboratory for Big Data and Decision Changsha China

ISBN: (纸本)9798331506681

Many complex problems encountered in both production and daily life can be conceptualized as combinatorial optimization problems (COPs). Many ad-hoc deep learning methods have been proposed to solve these problems, but there still lacks an effective unified framework. In this work, we take a step towards this goal by designing an unified end-to-end deep reinforcement learning framework named CONQUER. CONQUER first translates various COPs into the unified QUBO (Quadratic Unconstrained Binary Optimization) formalization, and afterwards trains a neural QUBO solver to search high-quality strategies, which are then checked and repaired to obtain the solutions for original COPs. the QUBO solver is a purely data-driven neural model, which does not rely on any expert inputs. We adopt the graph transformer network to represent the QUBO graph, so as to effectively capture the features of problem objective and constraints. the Deep Q-network is utilized to train the QUBO solver to find long-sighted solution strategies. Despite being trained solely on small synthetic graphs, CONQUER exhibits promising generality on much larger instances, and can be easily adapted to real-world datasets. Experimentally, we show that CONQUER achieves the state-of-the-art performances on four COPs over graphs, including minimum vertex cover, maximum independent set, maximum clique and maximum cut problem. © 2024 IEEE.

关键词： Deep reinforcement learning

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Algorithm Design and Software Implementation of Laser Doppler Vibration Signal Based on Overlapping Frames 5

Algorithm Design and Software Implementation of Laser Dopple...

引用

5th international conference on Computer Information Science and Application technology, CISAT 2022

作者： Li, Fu Zhang, Zhiping Hao, Lingling Wang, Ning Zhang, Chen Yang, Xiaofeng Shanghai Engineering Research Center for Ultra-Precision Motion Control and Measurement Academy for Engineering and Technology Fudan University Shanghai201210 China Shanghai YinGuan Semiconductor Technology Co. Shanghai200000 China Shanghai PaiPin Optoelectronics Technology Co. Shanghai200000 China

ISBN: (纸本)9781510660076

Vibration is a widespread phenomenon in nature, and vibration detection and analysis of objects can improve the safety of precision equipment. However, real-time performance in the existing laser Doppler interferometry data analysis and processing system remains an ongoing challenge. this paper implements a laser Doppler vibration software based on overlapping frames, according to the data processing method of real-time spectrum analyzer in RF signal measurement and parallel programming method. Under different overlap rates, this paper compares the difference in refresh rate caused by three data processing methods. Experimental results show that the higher the overlap rate, the better the real-time performance of the software. the system can meet the design index of refresh rate(13Hz), has good display fluency and real-time display, and has certain reference significance for related research. © 2022 SPIE.

关键词： Doppler effect

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PM2.5 concentration prediction based on CNN-GRU model fused with Luong attention 5

PM2.5 concentration prediction based on CNN-GRU model fused ...

引用

2023 5th international conference on Information Science, Electrical, and Automation engineering, ISEAE 2023

作者： Wang, Zhen Liu, Lizhi Hubei Key Laboratory of Intelligent Robot Wuhan Institute of Technology Wuhan430205 China School of Computer Science and Engineering Artificial Intelligence Wuhan Institute of Technology Wuhan430205 China

ISBN: (纸本)9781510667440

In order to improve the accuracy of PM2.5 concentration prediction, a CNN-GRU deep learning model based on fusion of Luong Attention is proposed. Firstly, the correlation between various air pollutants and meteorological factors and PM2.5 concentration is comprehensively analyzed, and the high correlation data is formed into a feature set. Secondly, the feature set is input into CNN for feature dimensioning, and then the output results of each time step are extracted through GRU. Finally, by introducing the Luong attention mechanism, the attention scores of the hidden states at each position of the output sequence are calculated, and the context vector is weighted to highlight the input step information that plays a key role in the prediction of PM2.5 concentration. the results show that using the CNN-GRU model with attention mechanism to predict the PM2.5 concentration in the next 24 hours, compared with the machine model and other deep learning models, RMSE and MAE have a certain decline, and have a higher generalization ability. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Deep learning

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Artificial Intelligence empowered content caching for energy optimization in vehicular networks 5

Artificial Intelligence empowered content caching for energy...

引用

5th international conference on Information Management and Machine Intelligence, ICIMMI 2023

作者： Gupta, Divya Bansal, Ankit Kaur, Jaspreet Wadehra, Sanchit Department of Computer Science and Engineering Chandigarh University India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798400709418

Indeed, the development of future autonomous and intelligent transportation systems depends heavily on vehicular networks. they allow vehicles to connect with one another, with roadside infrastructure, and with centralized control systems. RSUs can store regularly accessed data including map updates, traffic data, and software updates. data caching is one of the main responsibilities of Road side Units (RSUs) in Vehicular application as sending the same content repeatedly wastes a lot of system resources. Utilizing caching resources effectively can lower the link load imposed by content transmission, thus enhancing the quality of experience (QoE) for users. However, inefficient collaboration among network routers may lead to substantial energy wastage. To address the aforementioned problem and reduce the requested content energy consumption, this paper proposes artificial intelligence empowered collaborative content caching scheme utilizing the concept of both on path caching and off path caching. Performance analyses show that the proposed technique outperforms other current caching strategies. © 2023 ACM.

关键词： Energy utilization

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Federated Learning in Smart Agriculture: An Overview 15

Federated Learning in Smart Agriculture: An Overview

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15th international conference on Knowledge and Systems engineering, KSE 2023

作者： Le, Duy-Dong Dao, Minh-Son Tran, Anh-Khoa Nguyen, thai-Binh Le-thi, Hong-Gam Industrial University of Ho Chi Minh City University of Economics Ho Chi Minh City Ho Chi Minh Viet Nam Nict Big Data Integration Research Centers Tokyo Japan University of Economics Ho Chi Minh City Ueh Vinh Long Campus Ho Chi MinhChi Minh Viet Nam

ISBN: (纸本)9798350329742

Agriculture is critical to the socioeconomic growth of countries across the world. Agricultural methods in many countries have been affected by current scientific and technical breakthroughs. However, ASEAN nations, which rely largely on agriculture, have an urgent need for smart agricultural research. While detailed assessments have been undertaken throughout the world, a gap in the research of federated learning has been observed. this article intends to fill this need by collecting research on machine learning, deep learning, and federated learning techniques in agriculture from both industrialized countries and those with economic constraints similar to those of ASEAN. Analyses have revealed that the combination of federated learning with IoT devices is the best way to develop smart agriculture. © 2023 IEEE.

关键词： Agriculture

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Deep Learning-Based Hybrid Model For Severity Prediction of Leaf Smut Rice Infection 5

Deep Learning-Based Hybrid Model For Severity Prediction of ...

引用

5th international conference on Emerging Smart Computing and Informatics, ESCI 2023

作者： Tanwar, Vishesh Lamba, Shweta Sharma, Bhanu Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9781665475242

Conventional rice crop disease prediction models show some drawbacks, such as the expensive cost of acquiring the input data necessary to run the model, the absence of spatial information, or the shortage of high-quality datasets. these problems are discussed in this work, which also develops a yield prediction fusion model. Convolutional neural networks (CNN) and support vector machines make up the prediction model (SVM). In this work, Leaf smut infection of rice health is discussed. the infected plant's pictures are first collected through secondary sources. the deep learning method's best characteristic is the feature extraction and classification of the different levels of blight infection severity is done using CNN and SVM. Mild, Average, Severe, and Profound are the four severity projection levels used in the study. Kaggle etc. are the data repositories that were utilized, and the total size of the dataset was 272. the suggested approach produces four severity-level predictions with 98% accuracy. © 2023 IEEE.

关键词： Convolutional neural networks

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