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Asymptotic Trajectory Tracking of Stratospheric Airships Based on RISE Controllers Under External Uncertainties

IEEE Transactions on Aerospace and Electronic Systems 2025年

作者： Liu, Can Li, Baoquan Dai, Fengzhi Shi, Wuxi Zhang, Xuebo Tiangong University School of Control Science and Engineering Tianjin Key Laboratory of Autonomous Intelligence Technology and Systems Tianjin300387 China Tianjin University of Science and Technology College of Electronic Information and Automation Tianjin300222 China Nankai University Institute of Robotics and Automatic Information Systems Tianjin Key Laboratory of Intelligent Robotics Tianjin300071 China

For a stratospheric airship under internal and external uncertainties, controller strategies with robust integral sign of the error (RISE) are proposed for asymptotic trajectory tracking control. Specifically, a fully connected neural network (FCNN) is constructed with adaptive weight updating to estimate internal uncertainties of the airship system. Then, a self-tuning RISE controller is designed to track trajectories with compensating external disturbance, which enables gain adjustment in realtime according to system feedback. Based on Lyapunov technique and LaSalle-Yoshizawa corollary extension, system stability is strictly analyzed, and asymptotic convergence performance is obtained for trajectory tracking by the designed controller. Moreover, a RISE controller is designed with fixed gain matrices for the airship based on classic control strategies, and asymptotic tracking is also proved. Finally, numerical simulations are conducted with spiral and hovering control to verify performance of the proposed strategies. © 1965-2011 IEEE.

关键词： Lyapunov methods

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A Gesture Recognition Method Based on Improved YOLOv8 Detection

A Gesture Recognition Method Based on Improved YOLOv8 Detect...

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Chinese Automation Congress (CAC)

作者： Haiming Li Yanfei Chen Lei Yang Jiaoxing Shi Tao Liu Yihui Zhou Hubei Key Laboratory of Intelligent Robot School of Electrical and Information Engineering Wuhan Institute of Technology Wuhan P. R. China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

Aiming at the existing hand gesture estimation algorithms with large computation and poor real-time performance, this paper proposes an improved YOLOv8 algorithm for hand recognition, which combines the improved YOLOv8 detection algorithm for the detection of the hand and combines it with gesture analysis to realize gesture recognition. The improved algorithm not only reduces the amount of floating-point arithmetic, but also increases the accuracy and robustness of detection. In the gesture detection part, the traditional YOLOv8 algorithm is improved by firstly adding the p6 detection head, secondly replacing the CBS module with RFSE module, and thirdly replacing the C2f module with CFE module. The gesture analysis part is performed by training the yolov8 classification model for gesture recognition. The test results of the improved model in TV-Hand and COCO-Hand datasets confirm the effectiveness of the improved YOLOv8 algorithm in this paper, which improves the accuracy by 2.3% and reduces the computational volume by 13.5% compared to the original YOLOv8n, and the correct rates of gesture recognition are all over 90%, and the improved algorithm, which ensures the lightweight and at the same time realizes the further enhancement of accuracy The improved algorithm can realize further improvement of accuracy while ensuring lightweight, and the application prospect is broader.

关键词： Hands Training Analytical models Solid modeling Accuracy Computational modeling Gesture recognition Robustness Real-time systems Classification algorithms

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Beyond MLE: Convex Learning for Text Generation

arXiv

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arXiv 2023年

作者： Shao, Chenze Ma, Zhengrui Zhang, Min Feng, Yang Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China School of Future Science and Engineering Soochow University China

Maximum likelihood estimation (MLE) is a statistical method used to estimate the parameters of a probability distribution that best explain the observed data. In the context of text generation, MLE is often used to train generative language models, which can then be used to generate new text. However, we argue that MLE is not always necessary and optimal, especially for closed-ended text generation tasks like machine translation. In these tasks, the goal of model is to generate the most appropriate response, which does not necessarily require it to estimate the entire data distribution with MLE. To this end, we propose a novel class of training objectives based on convex functions, which enables text generation models to focus on highly probable outputs without having to estimate the entire data distribution. We investigate the theoretical properties of the optimal predicted distribution when applying convex functions to the loss, demonstrating that convex functions can sharpen the optimal distribution, thereby enabling the model to better capture outputs with high probabilities. Experiments on various text generation tasks and models show the effectiveness of our approach. It enables autoregressive models to bridge the gap between greedy and beam search, and facilitates the learning of non-autoregressive models with a maximum improvement of 9+ BLEU points. Moreover, our approach also exhibits significant impact on large language models (LLMs), substantially enhancing their generative capability on various tasks. Source code is available at https://***/ictnlp/Convex-Learning. Copyright © 2023, The Authors. All rights reserved.

关键词： Maximum likelihood estimation

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Bilayer metal halide perovskite for efficient and stable solar cells and modules

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Materials Futures 2022年第4期1卷 53-72页

作者： Yanqing Zhu Min Hu Mi Xu Bo Zhang Fuzhi Huang Yi-Bing Cheng Jianfeng Lu State Key Laboratory of Silicate Materials for Architectures Wuhan University of TechnologyWuhan 430070People’s Republic of China School of Electronic and Electrical Engineering Hubei Province Engineering Research Centre for Intelligent Micro-Nano Medical Equipment and Key TechnologiesWuhan Textile UniversityWuhan 430200People’s Republic of China Foshan Xianhu Laboratory of the Advanced Energy Science and Technology Guangdong Laboratory Foshan 528216People’s Republic of China Shanxi Lu’An Photovoltaics Technology Co. LtdChangzhi 046011People’s Republic of China State Key Laboratory of Advanced Technology for Materials Synthesis and Processing Wuhan University of TechnologyWuhan 430070People’s Republic of China

To reach the target of carbon neutral,a transition from fossil energy to renewable energy is *** technology is considered one of the most prominent sources of renewable ***,metal halide perovskite materials have attracted tremendous interest in the areas of optoelectronic devices due to their ease of processing and outstanding *** date,perovskite solar cells(PSCs)have shown high power conversion efficiency up to 25.7%and 31.3%for the perovskite-silicon tandem solar cells,which promises to revolutionize the PV ***,the stability of PSCs under operating conditions has yet to match state-of-the-art silicon-based solar cell technology,in which the stability of the absorbing layer and relevant interfaces is the primary *** issues become more serious in the larger area solar modules due to the additional interfaces and more defects within the *** perovskite film composed of a thin low dimensional perovskite layer and a three-dimensional perovskite layer shows great potential in fabricating solar cells with high efficiency and stability *** this review,recent advancements,including composition design and processing methods for constructing bilayer perovskite films are *** then analyze the challenges and resolutions in deposition bilayer perovskite films with scalable *** summarizing the beneficial effect of the bilayer structure,we propose our thinking of feasible strategies to fabricate high efficiency perovskite solar modules with a long ***,we outline the directions for future work that will push the perovskite PV technology toward commercialization.

关键词： perovskite solar cell perovskite solar module bilayer stability scalable technique

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Few-Shot Object Detection Based on Generalized Features

Few-Shot Object Detection Based on Generalized Features

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Artificial Intelligence and intelligent information processing (AIIIP), International Conference on

作者： Qiuqin Chen Xiao Ke Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing College of Computer and Data Science Fuzhou University Fuzhou China Key Laboratory of Spatial Data Mining & Information Sharing Ministry of Education Fuzhou Fuzhou China

Few-shot object detection aims to rapidly detect novel classes of objects using a minimal number of annotated instances. Compared to methods such as meta-learning, few-shot object detection based on transfer learning can achieve better performance. However, when training on base-class datasets, the model gradually improves its detection performance on base classes but does not enhance its generalization. A base-class model lacking generalization tends to result in lower detection performance for novel classes during fine-tuning. To address the aforementioned issues, this paper tackles the problem from two aspects: firstly, we introduce the Confusion-Dropblock Module (CDM) to perturb the model's features, thus enhancing feature generalization. Secondly, we incorporate Deformable Convolution Modules to mitigate the impact of object shapes and sizes on detection performance, thereby improving overall detection performance. Experimental results on the COCO dataset demonstrate the effectiveness of the proposed algorithm, outperforming the compared methods.

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An Unbalanced Classification Model of Tibetan Medicine Journals with Improved Prompt Learning Method

An Unbalanced Classification Model of Tibetan Medicine Journ...

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Digital Society and intelligent Systems (DSInS), International Conference on

作者： Yi Gao Songbin Chen Tibet University for Nationalities Xianyang China Tibet Key Laboratory of Optical Information Processing and Visualization Technology Xianyang China

In order to solve the problem of low classification accuracy of Tibetan medicine-related journals using pre-training model under the condition of unbalanced sample size. This paper proposes an unbalanced data augmentation for prompt learning based on bert (Unbalanced Data Augmentation for Prompt Learning based on Bert) Based on the BERT model, the unbalanced Tibetan medicine journal data is sent to EDA and SimBERT to generate the balanced data, and then the prompt template is constructed to help understand the downstream tasks, so as to improve the model's extraction of journal subject features. Experiments show that the average subject classification accuracy of the model UDA-PL-BERT is 75%, the accuracy is 97%, the f1 value is 71%, and the recall rate is 60%, which is improved compared with the BiLSTM model, TextCNN model, FastText model and Transformer model. The data modes processed by this method are limited to text form, and the target task is mainly unbalanced multi-classification task of Tibetan medicine-related journal topics, and does not do other dataset classification problems. The UDA-PL-BERT proposed in this paper can effectively improve the extraction and classification performance of unbalanced subject features of Tibetan medicine-related journals.

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Blind Separation of Heart Sound Convolutive Mixtures Utilizing Independent Vector Analysis 40

Blind Separation of Heart Sound Convolutive Mixtures Utilizi...

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40th Chinese Control Conference, CCC 2021

作者： Xie, Yuan Xie, Kan Xie, Shengli Guangdong University of Technology Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing School of Automation Guangzhou510006 China Ministry of Education of the P.R.C. Guangdong Key Laboratory of IoT Information Processing Jt. Intl. Research Laboratory of Intelligent Information Processing and System Integration of IoT Guangzhou510006 China Ministry of Education 111 Center for Intelligent Batch Manufacturing Based on IoT Technology Key Laboratory of Intelligent Detection and the IoT in Manufacturing Guangzhou510006 China

ISBN: (纸本)9789881563804

Separation of heart sound signals from the recorded mixtures has become a hot research topic in the clinical diagnosis of heart diseases. To better understand the current health status of patients, it is of great significance to develop intelligent auscultation methods to improve the effectiveness of hearing and assist clinicians in practice. In this paper, an optimization convolutive blind source separation algorithm utilizing independent vector analysis is proposed for separation of the heart sound mixtures. In the algorithm, a denoising approach is firstly used to reduce the impact of the additive white Gaussian noise. Then, using the short time Fourier transform, the mixing signals in time domain are transmitted into the frequency domain. Afterwards, the unmixng matrix is updated using Newton's method, and the heart sound sources are reconstructed based on the estimated unmixing matrix. Experimental results show that the algorithm obtains better separation performance than the baseline methods. Especially, it has better superiority in the strong reverberant environment. © 2021 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Time domain analysis

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Multi-Functional Optical Fiber Tweezers Based on Annular Aperture Array Metasurface

Multi-Functional Optical Fiber Tweezers Based on Annular Ape...

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Cross Strait Quad-Regional Radio Science and Wireless technology Conference (CSRWTC)

作者： Zihui Zhao Niannian Song Chunling Long Guan Wang Wentao Zhang Libo Yuan Hongchang Deng Guangxi Key Laboratory of Optoelectronic Information Processing School of Optoelectronic Engineering Guilin University of Electronic Technology Guilin China

We propose and demonstrate numerically a kind of optical fiber tweezer based on the annular aperture array (AAA) metasurface at the end of an annular core fiber to implement multi-functional optical manipulation by using the finite-element method. The results show that the device can generate single optical potential well (OPW) and double optical potential wells by sharp-edge diffraction of the AAA metasurface under the excitation of short- and long-wavelength lights. Because the distance between the two OPWs remains constant with the incident wavelength, the double OPWs can be used for an optical stretcher. We also show that by switching the incident wavelength and optical power, the microparticle oscillation can be realized in the tunable single OPW. The device can greatly improve the optical trapping ability of single or multiple microparticles, and provides a new idea for the application of optical fiber tweezers in research on micro-nano objects such as cell surgery, DNA unwinding, and targeted drug testing.

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Air Pollution Monitoring By Integrating Local and Global information in Self-Adaptive Multiscale Transform Domain

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IEEE Transactions on Multimedia 2025年

作者： Gu, Ke Liu, Yuchen Liu, Hongyan Liu, Bo Qiao, Junfei Lin, Weisi Zhang, Wenjun Beijing University of Technology School of Information Science and Technology Engineering Research Center of Intelligent Perception and Autonomous Control of Ministry of Education Beijing Laboratory of Smart Environmental Protection Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing Artificial Intelligence Institute Beijing100124 China Massey University School of Mathematical and Computational Sciences New Zealand Nanyang Technological University College of Computing and Data Science Singapore Shanghai Jiao Tong University Institute of Image Communication and Information Processing Shanghai China

As the deadliest form of pollution, air pollution had a prolonged severe damage to the human health and life safety of nearly 99% of the world's population. Facing to the problem that billions of tons of pollutants discharged into the air per year by industrial activities, this paper proposed a novel image-based air pollution monitor (IAPM) by incorporating local and global information in the self-adaptive multiscale transform domain, so as to achieve the timely and effective leakage detection of typical air pollutants from a single image. To be specific, this paper first developed a screen-shaped module according to two significant findings in visual neuroscience, which include the high sensitivity of human eyes to horizontal and vertical stimuli and the center-surround inhibition, by designing and fusing the square module, horizontal strip module and vertical strip module parallelly for simulating the behaviour of human eyes to extract local features. Second, the learnable weights and proportional mapping were applied to incorporate the screen-shaped module and lightweight vision transformer as backbone, towards more richly exploiting and fusing local and global information just as the way a brain perceives external stimuli. Third, a new self-adaptive multiscale transform domain method was devised based on two motivations from the visual characteristics of multiscale perception and the brain characteristics of self-adaptive domain transform to modify the backbone by using the operations of pooling and pointwise convolution. Extensive experiments implemented on the datasets of carbon particulate matters and ethylene leakage confirmed the superior monitoring performance of the proposed IAPM model beyond the state-of-the-art (SOTA) peers by an accuracy gain of about 4%. Furthermore, the proposed IAPM model only required 0.089 GFLOPs and 0.15 million model parameters, remarkably outperforming SOTA competitors in computational efficiency and storage resources. © 1999-2

关键词： information leakage

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Distributed solving Sylvester equations with fractional order dynamics

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Control Theory and technology 2021年第2期19卷 249-259页

作者： Songsong Cheng Shu Liang Yuan Fan Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Electrical Engineering and AutomationAnhui UniversityHefei230601AnhuiChina Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education School of Automation and Electrical EngineeringUniversity of Science and Technology BeijingBeijing100083China

This paper addresses distributed computation Sylvester equations of the form AX+XB=C with fractional order *** partitioning parameter matrices A,B and C,we transfer the problem of distributed solving Sylvester equations as two distributed optimization models and design two fractional order continuous-time algorithms,which have more design freedom and have potential to obtain better convergence performance than that of existing first order ***,rewriting distributed algorithms as corresponding frequency distributed models,we design Lyapunov functions and prove that proposed algorithms asymptotically converge to an exact or least squares ***,we validate the effectiveness of proposed algorithms by providing a numerical example.

关键词： Fractional order calculus Distributed optimization Sylvester equation Frequency distributed model Convergence

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