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Fixed-time command filtered output feedback control for twin-roll inclined casting system with prescribed performance

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Mathematical Biosciences and engineering 2024年第2期21卷 2282-2301页

作者： Gao, Dongxiang Zhang, Yujun Wu, Libing Liu, Sihan School of Computer and Software Engineering University of Science and Technology Liaoning Liaoning CO Anshan114051 China School of Science University of Science and Technology Liaoning Liaoning CO Anshan114051 China

The article investigates the issue of fixed-time control with adaptive output feedback for a twin-roll inclined casting system (TRICS) with disturbance. First, by using the mean value theorem, the nonaffine functions are decoupled to simplify the system. Second, radial basis function neural networks (RBFNNs) are introduced to approximate an unknown term, and a nonlinear neural state observer is created to handle the effects of unmeasured states. Then, the backstepping design framework is combined with prescribed performance and command filtering techniques to demonstrate that the scheme proposed in this article guarantees system performance within a fixed-time. The control design parameters determine the upper bound of settling time, regardless of the initial state of the system. Meanwhile, it ensures that all signals in the closed-loop system (CLS) remain bounded, and it can also maintain the tracking error within a predefined range within a fixed time. Finally, simulation results assert the effectiveness of the method. © 2024 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License.

关键词： Closed loop systems

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Uvcgan-Dehaze: a dehazing method for unpaired images

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Soft Computing 2024年第20期28卷 12217-12226页

作者： Li, Canlin Zhang, Xiangfei Zhang, Wenjiao Su, Haowen Bi, Lihua School of Computer Science and Technology Zhengzhou University of Light Industry Zhengzhou China School of Software Engineering Zhengzhou University of Light Industry Zhengzhou China

To solve the problems of feature loss and color difference after image dehazing and poor dehazing effect in real hazy images, a method UVCGAN-Dehaze is proposed for unpaired image dehazing. In the proposed model, the generator learns the relationship between image features and low-frequency features at multiple scales by combining the U-Net with the transformer. We strengthen the connection between spatial and channel features by adding Convolutional Block Attention Module(CBAM) before the skip connection of each layer of U-Net of the generator and adaptively adjusting the weight allocation of the feature map, so that the image after dehazing can better retain the detailed information of the image. Simultaneously, in order to reduce the feature loss and color difference after image dehazing, we propose a loss function named Feature Laplacian Perception (FLP) loss, which jointly constrains the generator by fusing the perception loss and Laplacian loss, so that the generated image reduces the feature loss and color difference. The method not only generates dehazing images with high perceptual quality in terms of important regions and details, but also avoids content and color loss. Extensive experiments verify the effectiveness of the method on synthetic images and real hazy images, and the proposed method achieves better dehazing results when compared with the experimental results of previous typical methods. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Color

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Efficient Cache Retrieving Scheme in VANET via Online Reinforcement Learning

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IEEE Transactions on Vehicular Technology 2025年第6期74卷 9506-9519页

作者： Tan, Zhenhao Chen, Bin Li, Ruidong Yang, Lei Xu, Xiaohua University of Science and Technology of China School of Computer Science and Technology Hefei230026 China Kanazawa University Institute of Science and Engineering Ishikawa920-1192 Japan South China University of Technology School of Software Engineering Guangdong510640 China

The implementation of intelligent driving technology is inseparable from reliable wireless communication and efficient cache scheduling in Vehicle Ad-hoc Networks (VANETs). The purpose of cache scheduling in VANETs is to provide efficient and fast data services to vehicles. In response to difficult to handle wireless communication interference and high requirements for communication delay and energy consumption optimization in vehicle-road cooperation, we propose a new communication model of "Retrieving-Cast"to simplify the cache scheduling process oriented by data requirements. In V2I mode, consider the uplink of all roadside units(RSUs) retrieving data from vehicles, and prioritize data retrieving based on factors such as the number, density, and urgency of vehicles within the communication coverage range of RSUs. We hope that as many high-priority RSUs as possible to complete cached data retrieving as soon as possible under wireless communication interference. To overcome the limitations of traditional heuristic algorithms with low scheduling efficiency and deep learning methods requiring a large amount of training data, we propose a double-online reinforcement learning method, which efficiently solves the cache retrieval scheduling problem. Through experiments and analysis, our method achieved better scheduling results compared to traditional approximation algorithms and single greedy Deep Q-learning methods. The designed data transformation method also addresses the issue of requiring a large amount of training data, enabling rapid computation of scheduling requests in the network at an extremely low training cost, meeting the low-latency computation requirements in VANETs. © 1967-2012 IEEE.

关键词： Reinforcement learning

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Network Traffic Anomaly Detection Based on DGBi-SA Model

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engineering Letters 2025年第3期33卷 612-619页

作者： Wang, Xin Dai, Hong Huang, Zheng Han, Yue University of Science and Technology Liaoning Liaoning CO Anshan114051 China School of Computer Science and Software Engineering University of Science and Technology Liaoning Liaoning CO Anshan114051 China

Network traffic anomaly detection plays a crucial role in today's network security and performance management. In response to the challenges in current network traffic data processing, such as insufficient structuring and dynamics, the paper proposes a network traffic anomaly detection approach based on DGBi-SA model, which integrates Dynamic Graph Convolutional Networks (DyGCN), Bi-directional Long Short-Term Memory (Bi-LSTM), and the Self-Attention mechanism. To solve the problem of insufficient structuring, the model employs DyGCN to capture network traffic's dynamic graph structural characteristics proficiently. Simultaneously, combining Bi-LSTM and the Self-Attention mechanism enhances the model's ability to capture temporal and spatial dependencies. The main objective of the research in the paper is to improve the accuracy and detection efficiency of network traffic prediction, thereby achieving effective detection and timely response to abnormal behaviors in complex network environments. Through a sequence of experiments conducted on the CICIDS-2017 dataset, the paper verifies that the DGBi-SA model can effectively solve the above problems. The experimental results indicate that the model outperforms existing methods in key performance indicators such as AUC, precision, recall, and F1 score, thereby proving that the DGBi-SA model has important application value and scientific significance in network traffic anomaly detection. © 2025, International Association of Engineers. All rights reserved.

关键词： Long short-term memory

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Fed-BRMC: Byzantine-Robust Federated Learning via Dual-Model Contrastive Detection

Fed-BRMC: Byzantine-Robust Federated Learning via Dual-Model...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Gan, Xiaoyun Lu, Peng Gan, Shanyu Xian, Youqian Peng, Kaichen Liu, Peng Li, Dongcheng School of Computer Science and Engineering School of Software Guangxi Normal University Guilin China

ISBN: (纸本)9781665410205

Federated Learning (FL) is a distributed privacy-protecting machine learning paradigm that enables collaborative training among multiple parties without the need to share raw data. This mode of training renders FL particularly susceptible to attacks. A pivotal challenge within FL is the defense against Byzantine attacks, which can alter the model aggregation process and ultimately lead to the failure of Federated Learning convergence. Existing Byzantine defense mechanisms often result in diminished model accuracy in scenarios characterized by non-independent and identically distributed (non-IID) data. To alleviate this issue, we propose a novel defense approach named Fed-BRMC. This method combines a self-developed federated Dual-Model Contrastive Detection technique with Similarity Detection methods, leveraging historical update information from users to deeply mine the characteristics of model parameters, effectively distinguishing between Byzantine and honest users. Extensive experimentation has shown that Fed-BRMC significantly improves user identification accuracy in non-IID data scenarios compared to existing methods and notably enhances the global model accuracy of Byzantine-robust Federated Learning. © 2024 IEEE.

关键词： Adversarial machine learning

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CBBFT - A Byzantine Fault Tolerance Mechanism for Education Credit Banks 7

CBBFT - A Byzantine Fault Tolerance Mechanism for Education ...

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7th International Conference on computer Information science and Application Technology, CISAT 2024

作者： Li, Xin Zhou, Lianghaojie Chen, Ningtian Jiang, Jianyong Liu, Peng GuangXi Normal University School of Computer Science and Engineering School of Software Guilin China

ISBN: (纸本)9798350375107

In the education sector, an increasing amount of research is beginning to explore the application of blockchain technology to credit banks. This paper proposes a consortium blockchain consensus mechanism tailored for credit bank applications, aiming to address issues such as single point of failure, large message volumes, and slowest node limitations inherent in existing BFT-like consensus protocols. This includes algorithms for secret random selection of block proposer nodes based on reputation scores and block selection, as well as introducing a reputation model. Subsequently, a detailed analysis of the security of this consensus protocol is conducted. Finally, the paper evaluates the performance of the proposed consensus protocol through experimentation. © 2024 IEEE.

关键词： Blockchain

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A Novel Graph Structure Learning Based Semi-Supervised Framework for Anomaly Identification in Fluctuating IoT Environment

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computer Modeling in engineering & sciences 2024年第9期140卷 3001-3016页

作者： Weijian Song Xi Li Peng Chen Juan Chen Jianhua Ren Yunni Xia School of Computer and Software Engineering Xihua UniversityChengdu610039China West China Second University Hospital Sichuan UniversityChengdu610065China School of Computer Science Chongqing UniversityChongqing400044China

With the rapid development of Internet of Things(IoT)technology,IoT systems have been widely applied in health-care,transportation,home,and other ***,with the continuous expansion of the scale and increasing complexity of IoT systems,the stability and security issues of IoT systems have become increasingly ***,it is crucial to detect anomalies in the collected IoT time series from various ***,deep learning models have been leveraged for IoT anomaly ***,owing to the challenges associated with data labeling,most IoT anomaly detection methods resort to unsupervised learning ***,the absence of accurate abnormal information in unsupervised learning methods limits their *** address these problems,we propose AS-GCN-MTM,an adaptive structural Graph Convolutional Networks(GCN)-based framework using a mean-teacher mechanism(AS-GCN-MTM)for anomaly *** performs better than unsupervised methods using only a small amount of labeled *** Teachers is an effective semi-supervised learning method that utilizes unlabeled data for training to improve the generalization ability and performance of the ***,the dependencies between data are often unknown in time series *** solve this problem,we designed a graph structure adaptive learning layer based on neural networks,which can automatically learn the graph structure from time series *** not only better captures the relationships between nodes but also enhances the model’s performance by augmenting key *** have demonstrated that our method improves the baseline model with the highest F1 value by 10.4%,36.1%,and 5.6%,respectively,on three real datasets with a 10%data labeling rate.

关键词： IoT multivariate time series anomaly detection graph learning semi-supervised mean teachers

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Controllable chaos in the coupled waveguide-optomechanical system with phase shifter

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science China(Physics,Mechanics & Astronomy) 2025年第4期68卷 26-34页

作者： Deng-Wei Zhang Pei-Qi Qin Li-Li Zheng Zhi-Ming Zhan Xin-You Lü Department of Mathematics and Physics Luoyang Institute of Science and TechnologyLuoyang 471023China School of Computer and Information Engineering School of SoftwareLuoyang Institute of Science and TechnologyLuoyang 471023China School of Artificial Intelligence Jianghan UniversityWuhan 430056China School of Physics Huazhong University of Science and TechnologyWuhan 430074China

We theoretically investigate chaotic dynamics in an optomechanical system composed of a whispering-gallery-mode(WGM)microresonator and a *** find that tuning the optical phase using a phase shifter and modifying the coupling strength via a unidirectional waveguide(IWG)can induce chaotic *** underlying reason for this phenomenon is that adjusting the phase and coupling strength via the phase shifter and IWG bring the system close to an exceptional point(EP),where field localization dynamically enhances the optomechanical nonlinearity,leading to the generation of chaotic *** addition,due to the sensitivity of chaos to phase in the vicinity of the EP,we propose a theoretical scheme to measure the optical phase perturbations using *** work may offer an alternative approach to chaos generation with current experimental technology and provide theoretical guidance for optical signal processing and chaotic secure communication.

关键词： cavity optomechanics chaos exceptional point

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On UAV Serving Node Deployment for Temporary Coverage in Forest Environment:A Hierarchical Deep Reinforcement Learning Approach

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Chinese Journal of Electronics 2023年第4期32卷 760-772页

作者： WANG Li WU Xuewei WANG Yanhui XIAO Zhe LI Liang FEI Aiguo School of Computer Science (National Pilot Software Engineering School) Beijing University of Posts and Telecommunications School of Electronic Engineering Beijing University of Posts and Telecommunications

Unmanned aerial vehicles(UAVs) can be effectively used as serving stations in emergency communications because of their free movements, strong flexibility, and dynamic coverage. In this paper, we propose a coordinated multiple points based UAV deployment framework to improve system average ergodic rate, by using the fuzzy C-means algorithm to cluster the ground users and considering exclusive forest channel models for the two cases, i.e., associated with a broken base station or an available base station. In addition, we derive the upper bound of the average ergodic rate to reduce computational complexity. Since deep reinforcement learning(DRL) can deal with the complex forest environment while the large action and state space of UAVs leads to slow convergence, we use a ratio cut method to divide UAVs into groups and propose a hierarchical clustering DRL(HC-DRL) approach with quick convergence to optimize the UAV deployment. Simulation results show that the proposed framework can effectively reduce the complexity, and outperforms the counterparts in accelerating the convergence speed.

关键词： Deep learning Base stations Simulation Clustering algorithms Forestry Reinforcement learning Autonomous aerial vehicles

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Stroke Disease Prediction Based on Multi-Model Ensemble Learning 5

Stroke Disease Prediction Based on Multi-Model Ensemble Lear...

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5th International Seminar on Artificial Intelligence, Networking and Information Technology, AINIT 2024

作者： Cui, Xiaojian School of Computer Science and Engineering School of Software Guangxi Normal University Guilin China

ISBN: (纸本)9798350385557

Stroke is a kind of acute cerebrovascular disease, which is caused by the sudden rupture of blood vessels in the brain or the blockage of blood vessels that can not flow into the brain and cause brain tissue damage. Stroke has the characteristics of acute onset and rapid progress. Once stroke, it can lead to extremely serious consequences, and many people suffer from it. But because of the lack of effective treatment for stroke, prevention is the best response. So we use multi-model ensemble learning method to train a model. It can analyze biological signals and lifestyle habits to predict the probability of a person having a stroke. People can adjust their living habits in time according to the probability of stroke. In order to reduce the probability of stroke, protect their own life and health. According to our experiments, the accuracy of the trained model is high, which can help people to prevent stroke disease. © 2024 IEEE.

关键词： Learning systems

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