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A copula-based reliability model for phased mission systems with dependent components (vol 39, pg 1533, 2023)

QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL 2023年第7期39卷 3179-3179页

作者： Li, X. Zhan, K. Xiong, X. Vu, T. Q. Key Laboratory of Advanced Transducers and Intelligent Control System Ministry of Education Taiyuan University of Technology Taiyuan China School of Media Design College of Technology and Design University of Economics Ho Chi Minh City Vietnam

Recently, researches on the reliability analysis of phased mission systems (PMSs) are very popular. And most of the existing research on PMSs assumed that all the components are statistically independent on each other, which is not meet the engineering reality. The system structure changes from phase to phase in the PMSs, so the dependency among components may also change in different phases. Meanwhile, the well-known phase dependency is also existing in the system reliability modeling. To model the varying dependency among components and phase dependency at the same time, a copula-based method is proposed for the reliability analysis of PMS with correlated components. Firstly, the system reliability of multi-state PMS with dependent components is evaluated by different copula functions. Then, multiple indicators are used to select the optimal copula functions for different phases. And an engineering case is illustrated to show the proposed copula-based method.

关键词： copula dependent components modular method phased mission system

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An end-to-end instance segmentation method based on improved ConvNeXt V2

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International Journal of Wireless and Mobile Computing 2024年第4期26卷 384-392页

作者： Wang, Wenlu Sun, Ying Xu, Manman Bai, Dongxu Huang, Li Zou, Chunlong Chen, Baojia Tang, Dalai Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education Wuhan University of Science and Technology Hubei Wuhan China Research Centre for Biomimetic Robot and Intelligent Measurement and Control Wuhan University of Science and Technology Hubei Wuhan China Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering Wuhan University of Science and Technology Hubei Wuhan China College of Computer Science and Technology Wuhan University of Science and Technology Hubei Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology Hubei Wuhan China College of Mechanical Engineering Hubei University of Automotive Technology Hubei Shiyan China Hubei Key Laboratory of Hydroelectric Machinery Design and Maintenance China Three Gorges University Hubei Yichang China School of Computer Information Management Inner Mongolia University of Finance and Economics Hohhot China

In order to improve the efficiency of indoor mobile robots in locating and segmenting environmental instances, an instance segmentation method based on RTMDet is proposed. Firstly, the more powerful ConvNeXt V2 is used as the backbone network of the model, which improves the performance of the existing RTMDet model. Subsequently, NAS-FPN is used as a converged network to detect the network at any given time. Finally, AdamW is used as the model optimiser, which effectively solves the problem of excessive memory occupation when updating parameters and improves the performance of the model. The proposed method obtains the highest evaluation index in all cases. After training and testing on the Cityscapes data set, the recognition accuracy reaches 65.0%. © 2024 Inderscience Enterprises Ltd.

关键词： Statistical tests

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An Autonomous Navigation Method for Planetary Rover Based on Multi-modal Fusion and Multi-factor Graph Optimization

An Autonomous Navigation Method for Planetary Rover Based on...

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2023 International Symposium on Structural Dynamics of Aerospace, ISSDA 2023

作者： Zheng, Bo Cao, Tao Hu, Tao Qian, Zhouyuan Wang, Shisun Han, Fei Shanghai Aerospace Control Technology Institute Shanghai201109 China Shanghai Key Laboratory of Aerospace Intelligent Control Technology Shanghai201109 China NIO Co. Ltd. Shanghai201805 China

Planetary surface is a complex unstructured environment with strong light, strong shadows, weak textures and numerous obstacles. There is no GPS signal support and satellite networking support on the planetary surface. The limited computer and sensor capabilities of planetary rover have a significant gap from the ground autonomous driving configuration, which presents a great challenge to the autonomous navigation methods of planetary rover. Extreme lighting conditions will increase the camera mismatching rate, and poor texture will increase the lidar matching error, which will reduce the estimation accuracy. Moreover, camera and lidar produce great motion distortion in the rugged terrain and bumpy environment, which leads to a large increase in the cumulative error of odometer. Camera and lidar are not suitable as the main sensor of SLAM algorithm in unstructured environment because they are sensitive to the change of environment. Therefore, an autonomous navigation method for planetary rover based on multi-modal fusion and multi-factor graph optimization is presented in this paper. The IMU (Inertial Measurement Unit) odometer node is added to the lidar odometer and visual odometer thread, and the IMU is taken as the central node to build a multi-factor graph optimization model. A multi-factor graph optimization model and a strong adaptive constraint strategy with reasonable weights are constructed. The pose estimation of other sensors is used to constrain IMU bias, meanwhile historical rut tracking, obstacle contour matching and skyline feature matching are introduced. Finally, motion prediction is achieved in the IMU odometer node. Simulation results and field tests show that this method can effectively cope with the unstructured environment on planetary surface, which achieves adaptive and robust autonomous navigation of planetary rover. © Published under licence by IOP Publishing Ltd.

关键词： Optical radar

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The Internet of Things under Federated Learning:A Review of the Latest Advances and Applications

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Computers, Materials & Continua 2025年第1期82卷 1-39页

作者： Jinlong Wang Zhenyu Liu Xingtao Yang Min Li Zhihan Lyu School of Information and Control Engineering Qingdao University of TechnologyQingdao260043China Anhui Province Key Laboratory of Intelligent Building&Building Energy Saving Anhui Jianzhu UniversityHefei230000China Department of Game Design Uppsala UniversityUppsala75310Sweden

With the rapid development of artificial intelligence,the Internet of Things(IoT)can deploy various machine learning algorithms for network and application *** the IoT environment,many sensors and devices generatemassive data,but data security and privacy protection have become a serious *** learning(FL)can achieve many intelligent IoT applications by training models on local devices and allowing AI training on distributed IoT devices without data *** review aims to deeply explore the combination of FL and the IoT,and analyze the application of federated learning in the IoT from the aspects of security and privacy *** this paper,we first describe the potential advantages of FL and the challenges faced by current IoT systems in the fields of network burden and privacy ***,we focus on exploring and analyzing the advantages of the combination of FL on the Internet,including privacy security,attack detection,efficient communication of the IoT,and enhanced learning *** also list various application scenarios of FL on the ***,we propose several open research challenges and possible solutions.

关键词： Federated learning Internet of Things sensors machine learning privacy security

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Research on Named Entity Recognition for Oral History Text

Research on Named Entity Recognition for Oral History Text

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2024 International Conference on Culture-Oriented Science and technology, CoST 2024

作者： Hu, Chaoyu Bai, Shilei Zhang, Mengfei Communication University of China School of Information and Communication Engineering Beijing China Beijing Key Laboratory of Modern Entertainment Technology Beijing China Key Laboratory of Acoustic Visual Technology and Intelligent Control System Ministry of Culture Beijing China

ISBN: (纸本)9798350380347

Oral history involves conveying experienced events through spoken narratives, typically preserved and later studied by converting spoken language into text. Named entity recognition (NER) is the recognition of meaningful named entities in text, such as person names, locations, organizations, etc. Conducting NER research on oral history text is significant as it not only facilitates the verification of named entities by researchers but also allows for the extraction of key information from texts through named entities. By collecting oral history corpus, we establish an oral history text dataset, manually annotating all texts with six entity labels and repeatedly verifying them manually. Utilizing the concept of machine reading comprehension (MRC), a semantic fusion module was added, integrating label information into text information using a cross-attention mechanism, and decoding the corresponding entity labels through span decoding. The final model performs well on the oral history text dataset and also showed good performance on general Chinese datasets. © 2024 IEEE.

关键词： Decoding

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Evaluating the Impact of DoS Attacks on Connected and Autonomous Vehicle Traffic Flow with Car-following Theory 8

Evaluating the Impact of DoS Attacks on Connected and Autono...

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8th CAA International Conference on Vehicular control and Intelligence, CVCI 2024

作者： Jing, Penghao Zhu, Yongxin Li, Yongfu Huang, Xin School of Automation Chongqing University of Posts and Telecommunications Key Laboratory of Intelligent Air Ground Cooperative Control for Universities in Chongqing Chongqing400065 China School of Computer Science and Technology Chongqing University of Posts and Telecommunications Key Laboratory of Intelligent Air Ground Cooperative Control for Universities in Chongqing Chongqing400065 China

ISBN: (纸本)9798331504892

This paper aims to assess the impact of denial-of-service (DoS) attacks on the traffic flow of connected and autono-mous vehicles (CAVs). With this aim, a novel car-following model is presented to depict the dynamics of CAVs to support a compre-hensive analysis of the CAV traffic performance. Particularly, the model uniquely integrates the information delay, the optimal ve-locity changes with memory and the malicious effect of manipu-lated information. Then, the linear stability of the proposed model is derived using the perturbation method. By introducing small perturbations into the system, the stability conditions for a platoon of CAVs are determined. Finally, extensive simulations are con-ducted to explore the impact of DoS attack on CAV traffic. Our experimental results demonstrate that the model accurately captures real traffic flow, particularly the behavior of a CAV platoon under a DoS network attack. © 2024 IEEE.

关键词： Autonomous vehicles

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Curve-Suppression-Based Event-Triggered Mechanisms for Quasi-Synchronization of Fuzzy Delayed Neural Networks on Time Scales

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IEEE Transactions on Systems, Man, and Cybernetics: Systems 2025年第5期55卷 3174-3187页

作者： Wan, Peng Zhou, Yufeng Zeng, Zhigang Lai, Jingang Wuhan University of Science and Technology School of Artificial Intelligence and Automation Engineering Research Center of Metallurgical Automation and Measurement Technology Wuhan430081 China Huazhong University of Science and Technology School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent control of Education Ministry of China Wuhan430074 China

The vast majority of published event-triggered mechanisms (ETMs) are constructed based on measurement errors, which introduces a problem naturally that they are updated when the measurement errors exceed the thresholds although the current obtained sampling states can make systems converge well. With this problem in mind, we redesign ETMs for quasi-synchronization of T-S fuzzy neural networks (FNNs) with time delays on time scales. First, a novel ETM is designed for continuous-time FNNs with time-varying delays to achieve quasi-synchronization, with which synchronization errors is suppressed to globally exponentially converge to a ball. Second, we introduce the ETM for continuous-time FNNs to discrete-time FNNs, owing to the existence of discrete-time states, the Lypunov function of synchronization errors run over the exponentially decay curve, but it can be suppressed to evolve under another exponentially decay curve. Third, for FNNs on time scales with constant and time-varying delays, we estimate the forward jump operator of the Lyapunov functions and design ETMs to guarantee that the Lypunov functions evolve under the exponentially decay curves, so quasi-synchronization can be achieved. Last but not least, we prove that Zeno behavior will not happen and four numerical examples are introduced to verify the validity and the superiority of the proposed ETMs in reducing information transmission. © 2013 IEEE.

关键词： Continuous time systems

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Enhanced Potato Detection Using an Improved YOLOv8 Algorithm 24

Enhanced Potato Detection Using an Improved YOLOv8 Algorithm

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9th International Conference on Multimedia Systems and Signal Processing, ICMSSP 2024

作者： Wang, Zihan Zhang, Yunxi Ma, Yuqi Zhao, Ya Liu, Jia School of Automation and Electrical Engineering Tianjin University of Technology and Education Tianjin China Tianjin Key Laboratory of Information Sensing and Intelligent Control Tianjin China

ISBN: (纸本)9798400716911

This paper proposes a novel potato detection algorithm YOLOv8-P, based upon the improved YOLOv8 framework to achieve enhanced detection accuracy. The algorithm incorporates the BiFormer attention mechanism and introduces an additional detection layer specifically designed for large targets, thereby significantly improving the model's performance in identifying larger potatoes. Additionally, a comprehensive dataset named potato-2024 was developed to address the limitations of existing public datasets in potato detection. To validate the effectiveness of YOLOv8-P, comparative experiments were conducted with YOLOv5 and YOLOv8 on the potato-2024 dataset. The results demonstrate that, under identical experimental conditions and parameters, YOLOv8-P exhibits superior recognition accuracy in potato identification, thus establishing a new benchmark in potato detection technology. © 2024 Copyright held by the owner/author(s).

关键词： YOLOv8

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Collaborative SLAM System Based on Shared Priori Maps

Collaborative SLAM System Based on Shared Priori Maps

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International Conference on Guidance, Navigation and control, ICGNC 2022

作者： Wu, Chunxu Shou, Yicheng Li, Zhen Liu, Xiangdong School of Automation and Key Laboratory for Intelligent Control and Decision on Complex Systems Beijing Institute of Technology Beijing100081 China

ISBN: (纸本)9789811966125

Multi-robot system outperforms in efficiency for cooperative task, where the challenging burden of information exchange for surrounding environment is facilitated by collaborative simultaneous localization and mapping (SLAM). However, the utilization of environmental information, i.e., mapping, should be specifically put into efficient use and merged from the viewpoint of computation saving and localization accuracy improvement. Towards these issues, this paper proposes the Collaborative SLAM system based on shared priori maps which includes multi-agent initialization and a new map sharing and reuse mechanism among various active agents in an incremental way. Finally, based on the data set recorded in the actual laboratory environment, the proposed Collaborative SLAM system is evaluated from the perspectives of localization accuracy and computational power consumption. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Mapping

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Retinex-based Illumination feature fusion network for low-light image enhancement 4

Retinex-based Illumination feature fusion network for low-li...

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4th International Conference on Artificial Intelligence and Computer Engineering, ICAICE 2023

作者： He, Tiange Liang, Yuqi Lanzhou Jiaotong University Key Laboratory of Optoelectronic Technology and Intelligent Control Ministry of Education Gansu Lanzhou730000 China

ISBN: (纸本)9798400708831

Vision is the main means for human beings to acquire external information, and with the arrival of the wave of informationization, digital images have become an important carrier of information dissemination. Due to the complexity of the imaging environment, as well as the lack of hardware conditions of the imaging equipment, the directly acquired images are often of poor quality, limiting their application in actual scenes, while the lack of light during the image acquisition process will greatly reduce the visibility of the image. Existing low-light image enhancement techniques are difficult to balance quality and efficiency, and are ineffective in complex scenes. In this paper, an image decomposition model is constructed based on Retinex theory to reduce the uncertainty of decomposition. In order to recover the degradation process existing in the reflection image, a light feature map is introduced to serve as a guide, and an image enhancement network module is constructed by using a light feature fusion module, and at the same time, a light tuning network is designed to adaptively adjust the lighting conditions of the light map. Experiments on a benchmark dataset show the effectiveness and rationality test of the method. © 2023 ACM.

关键词： Image enhancement

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