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作者： Liu, Tong Tang, Lun Wang, Weili He, Xiaoqiang Chen, Qianbin Zeng, Xiaoping Jiang, Haitao Chongqing University of Posts and Telecommunications Key Laboratory of Mobile Communication Chongqing400065 China Chongqing Vocational Institute of Engineering Department of Big Data and Internet of Things Chongqing402260 China Chongqing University School of Microelectronics and Communications Chongqing400044 China

Applications in the Internet of Vehicles (IoV) are usually accompanied by ultralow network response latency requirement. A promising approach to meet this demand is combining the IoV with mobile edge computing and enabling edge devices to share their communication, computation, and caching (3C) resources via edge intelligent cooperation. However, the allocation of 3C resources supported by artificial intelligence (AI) demands a huge number of training data and strong computing ability which is impossible to achieve on resource-limited on board unit (OBU) or road side unit (RSU). In this article, we propose a digital twin (DT) supported edge intelligent cooperation scheme, which empowers the optimal 3C resource allocation and edge intelligent cooperation possible. We focus on the response delay minimization in order to meet the requirement of latency-sensitive applications in the IoV. Specifically, mathematical expressions of the network response time are formulated according to modeling the workflow of the edge server as an M/M/1/N/FCFS queuing process. Especially, we conduct a detailed analysis of the deviations in 3C resource between the physical world and the DT space, based on which we further discuss the impact of these deviations in offloading decision. Furthermore, a mathematical optimization model aiming at minimizing the latency is formulated. In view of its complexity, we apply a deep deterministic policy gradient algorithm to solve it by modeling the cooperation process between edge nodes as a Markov decision process. Finally, we carry out simulations to demonstrate that our algorithm outperforms the existing schemes in terms of network response latency. © 2014 IEEE.

关键词： Resource allocation

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Fault Estimation for Nonlinear Systems with Disturbance Based on Reinforcement Iterative Learning Strategy

Fault Estimation for Nonlinear Systems with Disturbance Base...

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data Driven Control and Learning Systems (DDCLS)

作者： Zhengquan Chen Ruirui Huang Zhiheng Zhang Yandong Hou School of Computer and Information Engineering Henan University Kaifeng China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China School of Artificial Intelligence Henan University Zhengzhou China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

This paper addresses the problem of low fault estimation accuracy in Lipschitz nonlinear systems with external disturbances, presenting an innovative strategy based on a Reinforcement Iterative Learning (RIL) framework. Initially, we conducted precise mathematical modeling, comprehensively considering the impact of Lipschitz non-linearity and external disturbances. Subsequently, an iterative learning fault observer was designed, and the optimization problem for its parameters was reformulated into a reinforcement learning task, clearly defining the states, actions, and reward functions. Employing the Advantage Actor-Critic (A2C) reinforcement learning method, we optimized the gain parameters of the iterative learning fault estimator to approximate non-linearity. Simultaneously, with the increase in iteration count, the neural network weights were continually optimized, achieving accurate fault estimation. Experimental results demonstrate a significant improvement in fault estimation using the proposed method.

关键词： Sufficient conditions Accuracy Neural networks Estimation Reinforcement learning Observers Iterative methods

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Research on Task Scheduling Method of Mobile Delivery Cloud Computing Based on HPSO Algorithm

Research on Task Scheduling Method of Mobile Delivery Cloud ...

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作者： Li, Jianjun Liu, Junjun Yang, Yu Su, Fangyuan School of Computer and Information Engineering Harbin University of Commerce Harbin150028 China Heilongjiang Cultural Big Data Theory Application Research Center Harbin150028 China Heilongjiang Key Laboratory of E-Commerce and Information Processing Harbin150028 China

Study of cloud computing task scheduling problem, in view of many existing in the process of logistics transportation route choice question, to consider in a certain amount of time constraints, road clearance restriction, road comfort, and driving cost constraints under the condition of how to carry out the task allocation, to achieve the shortest path as the optimization goal, application of particle swarm optimization (PSO) algorithm to extend it a task scheduling method based on hybrid particle swarm optimization (PSO) cloud is proposed. Experimental results show that hybrid particle swarm optimization plays a good role in cloud computing task scheduling, which can shorten the distance to a certain extent, improve the efficiency, improve the applicability and scalability of the algorithm, not trapped in local optimization, and achieve the optimal global effect. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Cloud computing

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Tobacco Curing State Recognition Based on a Multi-Sensor Feature Fusion Approach 10

Tobacco Curing State Recognition Based on a Multi-Sensor Fea...

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10th International Conference on Computer and Communications, ICCC 2024

作者： Zhang, Jili Cao, Yang Wei, Jianyu Yang, Jing He, Mingxiong Huang, Congguang Wu, Banghong Yu, Yabo Huang, Zhengbin Rao, Chenglin Li, Shiyong China Tobacco Guangxi Industrial Co. Ltd. Guangxi China Mechanical Engineering College Guizhou University Guizhou China Guizhou University State Key Laboratory of Public Big Data Guizhou China Hechi Branch of China Tobacco Guangxi Zhuang Autonomous Region Corporation Guangxi China

The accurate identification of tobacco curing status is a complex process involving comprehensive analysis of multiple types of feature data sources. The influence of each type of feature on the state recognition model varies with the change in the curing process, and the existing models cannot reasonably utilize these feature data, which makes it difficult to improve the model recognition accuracy. To address these challenges, this paper proposes a dynamic data fusion scheme based on clustering methods and uses the LightGBM algorithm and data fusion method to build a state recognition fusion model that integrates six state recognition sub-models, which can accurately identify the curing status. SRFM aims to integrate curing expertise and automatically adjust the data fusion strategy according to the changes in multi-source data to meet the needs of comprehensive data analysis in recognition. Through experimental comparison, SRFM outperforms the existing models, with an accuracy of 99.8% in identifying the curing status and a macro F1-Score of 99.9%. © 2024 IEEE.

关键词： curing process data fusion model integration state identification Tobacco curing

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Bidirectional Weighted Co-Association-Based Clustering Ensemble Algorithm for High-Speed Train Delay Prediction 29th

Bidirectional Weighted Co-Association-Based Clustering Ensem...

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29th International Conference on Computational and Experimental engineering and Sciences, ICCES 2023

作者： Zhao, Yi Bai, Muchen Ni, Panpan Jiang, Yunan Feng, Xuan Zhang, Xiaobo School of Design SouthWest Jiaotong University Chengdu611756 China School of Computing and Artificial Intelligence SouthWest Jiaotong University Chengdu611756 China Sichuan Institute of Computer Sciences Chengdu610042 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu611756 China Manufacturing Industry Chains Collaboration and Information Support Technology Key Laboratory of Sichuan Province Southwest Jiaotong University Chengdu611756 China

ISBN: (纸本)9783031425141

In recent years, high-speed trains are one of the popular research topics in the field of transportation and have attracted much attention from scholars at home and abroad in view of their high efficiency and safety features. High-speed trains are susceptible to unexpected situations of delayed arrival due to the disturbance of external factors including wind and temperature during the trip. The delay of high-speed trains has a vital impact on people's work efficiency and living standards. To solve the problem of intelligent forecast of high-speed train delay, we design and implement a clustering ensemble algorithm based on text data of bullet train operation environment monitoring. We firstly combine the concepts of cosine similarity, Jaccard similarity and information entropy, then fuse a bidirectional weighting formula, and further propose a novel Clustering Ensemble algorithm based on Bidirectional Weighted Co-association (CE-BWC). This approach consists of an inter-cluster weighting formula used to obtain the implicit generalized information by weighting each cluster between clusters, and a reliability weighting formula transferring the cluster information to the data item itself to measure the correlation degree of the data group. Experiments prove that our CE-BWC has better prediction performance for high-speed train delay and outperforms the state-of-the-art benchmark algorithms. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Efficiency

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Quantum-inspired Hash Function Based on Parity-dependent Quantum Walks with Memory

arXiv

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arXiv 2023年

作者： Zhou, Qing Tang, Xueming Lu, Songfeng Yang, Hao Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China

In this paper, we develop a generic controlled alternate quantum walk model (called CQWM-P) by combining parity-dependent quantum walks with distinct arbitrary memory lengths and then construct a quantum-inspired hash function (called QHFM-P) based on this model. Numerical simulation shows that QHFM-P has near-ideal statistical performance and is on a par with the state-of-the-art hash functions based on discrete quantum walks in terms of sensitivity of hash value to message, diffusion and confusion properties, uniform distribution property, and collision resistance property. Stability test illustrates that the statistical properties of the proposed hash function are robust with respect to the coin parameters, and theoretical analysis indicates that QHFM-P has the same computational complexity as that of its peers. © 2023, CC BY.

关键词： Hash functions

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Generalizable Thermal-based Depth Estimation via Pre-trained Visual Foundation Model

Generalizable Thermal-based Depth Estimation via Pre-trained...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Ruoyu Fan Wang Zhao Matthieu Lin Qi Wang Yong-Jin Liu Wenping Wang Department of Computer Science and Technology Tsinghua University China State Key Laboratory of Public Big Data Guizhou University Guiyang China Department of Computer Science and Engineering Texas A&M University USA

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Depth estimation is a crucial task in computer vision, applicable to various domains such as 3D reconstruction, robotics, and autonomous driving. In particular, thermal-based depth estimation has unique advantages, including night-time vision. However, the existing depth estimation method remains challenging in robust generalization due to limited data resources and spectral differences between thermal and RGB images. In this paper, we present a self-supervised approach to enhance thermal-based depth estimation by leveraging pre-trained visual models initially designed for RGB data. In detail, we design a novel two-stage training strategy, incorporating Low-rank Adapters and Convolutional Adapters, which not only significantly improves accuracy and robustness but also enables impressive zero-shot generalization capabilities. Our method outperforms existing thermal-based depth estimation models, opening new possibilities for cross-modal applications in computer vision and robotics research.

关键词： Training Adaptation models Visualization Computer vision Three-dimensional displays Computational modeling Estimation

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An Ultra-Wideband Cascaded Power Amplifier for Military Individual Radio

An Ultra-Wideband Cascaded Power Amplifier for Military Indi...

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IEEE International Conference on Electronic Information and Communication Technology (ICEICT)

作者： Fei Gao Jingchang Nan Hongji Fan Cong Wang Panpan Tang Zhaoqi Li Liaoning Key Laboratory of Radio Frequency and Big Data for Intelligent Applications Liaoning Technical University Huludao China Southeast University Nanjing China Department of Microwave Engineering Harbin Institute of Technology Harbin China

ISBN: (数字)9798350384437

ISBN: (纸本)9798350384444

In recent years, there has been a tremendous interest in military individual radio in wireless communication. To obtain sufficient operating distance and long standby time, the output power and efficiency of power amplifiers have become the research focus by some of the research teams. GaN has higher electron mobility and forbidden bandwidth than other materials. Nowadays, GaN has become the mainstream key technology for radio frequency (RF) power amplifiers in 5G communication systems. In this work, a 500MHz~2500MHz GaN-based HEMT wideband cascaded power amplifier was designed. The output power of the driver amplifier is 36.6dBm ± 0.5dB, the input return loss is < -5dB, the drain efficiency is > 20%, the DC power consumption is < 25W, the input power is 5dBm, 1dB compression point is 7.8dBm, 0 dBm signal input and 33dBm signal output. The input power of the output stage is 36dBm; the output power is 44.75dB, the efficiency is more than 30%, and the input return loss < -8dB, small signal gain >10 dB, input voltage standing wave ratio < -10dB, output voltage standing wave ratio < -3dB. The demonstrated high performance of the cascaded power amplifier validates its applications in military wireless communication.

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SMPC Task Decomposition: A Theory for Accelerating Secure Multi-party Computation Task

arXiv

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arXiv 2023年

作者： Feng, Yuanqing Bai, Tao Lu, Songfeng Tang, Xueming Wu, Junjun Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China

Today, we are in the era of big data, and data are becoming more and more important, especially private data. Secure Multi-party Computation (SMPC) technology enables parties to perform computing tasks without revealing original data. However, the underlying implementation of SMPC is too heavy, such as garbled circuit (GC) and oblivious transfer(OT). Every time a piece of data is added, the resources consumed by GC and OT will increase a lot. Therefore, it is unacceptable to process large-scale data in a single SMPC task. In this work, we propose a novel theory called SMPC Task Decomposition (SMPCTD), which can securely decompose a single SMPC task into multiple SMPC sub-tasks and multiple local tasks without leaking the original data. After decomposition, the computing time, memory and communication consumption drop sharply. We then decompose three machine learning (ML) SMPC tasks using our theory and implement them based on a hybrid protocol framework called ABY. Furthermore, we use incremental computation technique to expand the amount of data involved in these three SMPC tasks. The experimental results show that after decomposing these three SMPC tasks, the time, memory and communication consumption are not only greatly reduced, but also stabilized within a certain range. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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A wind field retrieval method based on the expected NRCS 4

A wind field retrieval method based on the expected NRCS

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4th International Conference on Geology, Mapping, and Remote Sensing, ICGMRS 2023

作者： Wang, Yan Geng, Xupu Li, Yan Shang, Shaoping He, Zhigang Xie, Yanshuang Wei, Guomei Liu, Dongsheng College of Ocean and Earth Sciences Xiamen University Xiamen361005 China State Key Laboratory of Marine Environmental Science Xiamen University Xiamen361005 China Engineering Research Center of Ocean Remote Sensing Big Data Fujian Province University Xiamen361102 China Dongshan Swire Marine Station Xiamen University Xiamen China Key Laboratory of Underwater Acoustic Communication and Marine Information Technology Minister of Education Xiamen University Xiamen China Piesat Information Technology Co. Ltd. Beijing China

ISBN: (纸本)9781510672741

China has developed two miniaturized synthetic aperture radar (SAR) satellites called HiSea-1 and Chaohu-1. These satellites, which were launched in December 2020 and February 2022, respectively, can obtain high-resolution sea surface wind fields in day and night conditions. In a recent study, the Cross-Calibrated Multi-Platform (CCMP) wind vector, SAR look direction, and SAR incidence angle were used as inputs into CMOD5.N to output an expected σ0. The wind field was then retrieved from a matrix determined by SAR-measured σ0 and the expected σ0. This method was applied to 90 HiSea-1 and Chaohu-1 SAR images. The retrieved wind speeds were compared with the ECMWF Reanalysis v5 (ERA5) reanalysis data. The root-mean-square error (RMSE) and mean bias error (MBE) were found to be 0.98 m/s and -0.02 m/s, respectively. © 2024 COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Synthetic aperture radar

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