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Adaptive Barrier-Lyapunov-Functions Based Control Scheme of Nonlinear Pure-Feedback Systems with Full State Constraints and Asymptotic Tracking Performance

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Journal of Systems science & Complexity 2024年第3期37卷 965-984页

作者： NIU Ben WANG Xiaoan WANG Xiaomei WANG Xinjun LI Tao Faculty of Electronic Information and Electrical Engineering Dalian University of TechnologyDalian 116024China School of Information Science and Engineering Shandong Normal UniversityJinan 250014China School of Mathematics Southeast UniversityNanjing 211189China School of Automation Nanjing University of Information Science and TechnologyNanjing 210044China

In this paper,the authors propose an adaptive Barrier-Lyapunov-Functions(BLFs)based control scheme for nonlinear pure-feedback systems with full state *** to the coexist of the non-affine structure and full state constraints,it is very difficult to construct a desired controller for the considered *** to the mean value theorem,the authors transform the pure-feedback system into a system with strict-feedback structure,so that the well-known backstepping method can be ***,in the backstepping design process,the BLFs are employed to avoid the violation of the state constraints,and neural networks(NNs)are directly used to online approximate the unknown packaged nonlinear *** presented controller ensures that all the signals in the closed-loop system are bounded and the tracking error asymptotically converges to ***,it is shown that the constraint requirement on the system will not be violated during the ***,two simulation examples are provided to show the effectiveness of the proposed control scheme.

关键词： Asymptotic tracking control barrier Lyapunov functions full state constraints nonlinear pure-feedback systems

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DialectMoE: An End-to-End Multi-Dialect Speech Recognition Model with Mixture-of-Experts 23

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23rd Chinese National Conference on Computational Linguistics, CCL 2024

作者： Zhou, Jie Gao, Shengxiang Yu, Zhengtao Dong, Ling Wang, Wenjun Faculty of Information Engineering and Automation Kunming University of Science and Technology Yunnan Kunming650500 China Yunnan Key Laboratory of Artificial Intelligence Kunming University of Science and Technology Yunnan Kunming650500 China

ISBN: (纸本)9780000000002

Dialect speech recognition has always been one of the challenges in Automatic Speech Recognition (ASR) systems. While lots of ASR systems perform well in Mandarin, their performance significantly drops when handling dialect speech. This is mainly due to the obvious differences between dialects and Mandarin in pronunciation and the data scarcity of dialect speech. In this paper, we propose DialectMoE, a Chinese multi-dialects speech recognition model based on Mixture-of-Experts (MoE) in a low-resource conditions. Specifically, DialectMoE assigns input sequences to a set of experts using a dynamic routing algorithm, with each expert potentially trained for a specific dialect. Subsequently, the outputs of these experts are combined to derive the final output. Due to the similarities among dialects, distinct experts may offer assistance in recognizing other dialects as well. Experimental results on the Datatang dialect public dataset show that, compared with the baseline model, DialectMoE reduces Character Error Rate (CER) for Sichuan, Yunnan, Hubei and Henan dialects by 23.6%, 32.6%, 39.2% and 35.09% respectively. The proposed DialectMoE model demonstrates outstanding performance in multi-dialects speech recognition. © 2024 China National Conference on Computational Linguistics.

关键词： Dynamic routing algorithms

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CTFusion: CNN-transformer-based self-supervised learning for infrared and visible image fusion

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Mathematical Biosciences and engineering 2024年第7期21卷 6710-6730页

作者： Du, Keying Fang, Liuyang Chen, Jie Chen, Dongdong Lai, Hua Yunnan Key Laboratory of Digital Communications Yunnan Communications Investment & Construction Group Company Limited Kunming China Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming China CMA Meteorological Observation Centre Beijing China

Infrared and visible image fusion (IVIF) is devoted to extracting and integrating useful complementary information from muti-modal source images. Current fusion methods usually require a large number of paired images to train the models in supervised or unsupervised way. In this paper, we propose CTFusion, a convolutional neural network (CNN)-Transformer-based IVIF framework that uses self-supervised learning. The whole framework is based on an encoder-decoder network, where encoders are endowed with strong local and global dependency modeling ability via the CNN-Transformer-based feature extraction (CTFE) module design. Thanks to the development of self-supervised learning, the model training does not require ground truth fusion images with simple pretext task. We designed a mask reconstruction task according to the characteristics of IVIF, through which the network can learn the characteristics of both infrared and visible images and extract more generalized features. We evaluated our method and compared it to five competitive traditional and deep learning-based methods on three IVIF benchmark datasets. Extensive experimental results demonstrate that our CTFusion can achieve the best performance compared to the state-of-the-art methods in both subjective and objective evaluations. © 2024 the Author(s)

关键词： Image fusion

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An Online Detection and Remote Diagnosis System for Partial Discharge in Wind Farm Stations 5

An Online Detection and Remote Diagnosis System for Partial ...

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5th International Conference on Smart Grid and Energy engineering, SGEE 2024

作者： Ma, Jinxiong Xie, Peng Tang, Zhanjun Li, Xinghui Yunnan Longyuan New Energy Co. Ltd Kunming China Kunming University of Science and Technology Faculty of Information Engineering and Automation Kunming China

ISBN: (纸本)9798331530686

Partial discharge, as an early sign of decreased insulation performance of power equipment, if not treated in a timely manner, may lead to a series of serious consequences such as short circuit, equipment damage, and even production stoppage. Due to the wide distribution range of wind turbines, they face many challenges such as difficulty in manual real-time detection and low efficiency. In order to ensure the normal operation of wind farm equipment, a partial discharge unmanned online detection system based on power dedicated network technology with STM32 microcontroller and wifi module as the core was designed. The upper computer program of the monitoring center was developed and debugged and optimized according to national standards in the laboratory and on-site, realizing unmanned remote automatic detection and fault diagnosis of partial discharge. The upper computer has a user-friendly interface and can perform data collection, reception, processing, analysis, alarm and historical data inquiry functions in the monitoring center, which can achieve real-time monitoring and management of partial discharge situation in wind farm stations. © 2024 IEEE.

关键词： Wind turbines

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Study on optimization of multi-UAV nucleic acid sample delivery paths in large cities under the influence of epidemic environment

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Journal of Ambient Intelligence and Humanized Computing 2023年第6期14卷 7593-7620页

作者： Han, Yuhang Xiang, Hongyu Cao, Jianing Yang, Xiaohua Pan, Nan Huang, Linhai Mechanical Engineering Faculty of Civil Aviation and Aeronautics Kunming University of Science and Technology Yunnan China Computer Science and Technology Metrology Center Yunnan Power Grid Co. Ltd. Yunnan China Computer Science and Technology Faculty of Information Engineering and Automation Kunming University of Science and Technology Yunnan China

In the context of global novel coronavirus infection, we studied the distribution problem of nucleic acid samples, which are medical supplies with high urgency. A multi-UAV delivery model of nucleic acid samples with time windows and a UAV (Unmanned Aerial Vehicle) dynamics model for multiple distribution centers is established by considering UAVs’ impact cost and trajectory cost. The Golden Eagle optimization algorithm (SGDCV-GEO) based on gradient optimization and Corsi variation is proposed to solve the model by introducing gradient optimization and Corsi variation strategy in the Golden Eagle optimization algorithm. Performance evaluation by optimizing test functions, Friedman and Nemenyi test compared with Golden Jackal Optimization (GJO), Hunter-Prey Optimization (HPO), Pelican Optimization Algorithm (POA), Reptile Search Algorithm (RSA) and Golden Eagle Optimization (GEO), the convergence performance of SGDCV-GEO algorithm was demonstrated. Further, the improved RRT (Rapidly-exploring Random Trees) algorithm is used in the UAV path planning, and the pruning process and logistic chaotic mapping strategy are introduced in the path generation method. Finally, simulation experiments are conducted based on 8 hospitals and 50 randomly selected communities in the Pudong district of Shanghai, southern China. The experimental results show that the developed algorithm can effectively reduce the delivery cost and total delivery time compared with simulated annealing algorithm (SA), crow search algorithm (CSA), particle swarm algorithm (PSO), and taboo search algorithm (TS), and the developed algorithm has good uniformity, robustness, and high convergence accuracy, which can be effectively applied to the multi-UAV nucleic acid sample delivery path optimization in large cities under the influence of an epidemic environment. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： COVID-19

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Orientation-position coupling in the two-dimensional XY model

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Physical Review B 2024年第2期110卷 024312-024312页

作者： Zhufei Xiao (肖竹飞) Tao Huang (黄韬) Chunhua Zeng (曾春华) Faculty of Science/Faculty of Information Engineering and Automation Kunming University of Science and Technology

The XY model is a valuable tool for analyzing spin systems. While the two-dimensional (2D) classical XY model and its variations that only consider spin interactions have been extensively studied, limited exploration has been conducted on the 2D XY model with orientation-position coupled (OPC) interactions. In the present study, we extend the 2D classical XY model by making the exchange energy dependent on the spin position; this model is referred to as the 2D compressible XY (CXY) model. Compared to the 2D classical XY model, we find that the OPC interaction enhances the stability of the orientational structure of the system. The deformation induced effects on the rotational degrees of freedom of the system indicates that the increase in the stretching deformation enhances the orientational order of spins. Furthermore, the coupling between orientation and position is demonstrated by calculating the joint probability distribution of spin orientation and position fluctuations. This study advances comprehension of spin systems' elasticity and provides new ideas for controlling the stability of the orientational order of spin systems.

关键词： 2-dimensional systems Spin lattice models Stochastic differential equations

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A study on the application of contrastive learning in the brain-computer interface of motor imagery 6

A study on the application of contrastive learning in the br...

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6th International Conference on Advanced Electronic Materials, Computers, and Software engineering, AEMCSE 2023

作者： Chen, Siyi Kunming University of Science and Technology Faculty of Information and Automation Kunming650500 China

ISBN: (纸本)9781510668249

In brain-computer interface (BCI) systems, acquiring EEG signals is relatively easy, but labeling EEG signals is challenging. Supervised learning is widely used in existing BCI research, but its generalization ability is poor due to the influence of labeled data, which is not conducive to the promotion and application of BCI. In the fields of computer vision and natural language processing, self-supervised learning is becoming a popular research direction that can use unlabeled data for learning. In this study, we propose a self-supervised contrastive learning method for identifying motor imagery states by performing different data transformation operations on EEG data to generate positive and negative sample pairs. This allows the pre-Trained network to learn general feature representations from the signals, enabling it to converge faster and achieve better classification performance when performing downstream tasks. The network's performance is significantly related to the amount of unlabeled data used in the self-supervised learning process. Through the evaluation of the collected data, we achieved an accuracy of 82.56% in classifying motor imagery states with 40% amount of labeled data, demonstrating the practical value of our method. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Brain computer interface

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A method for selecting the position of each source in a multi-source microwave heating cavity

A method for selecting the position of each source in a mult...

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2023 IEEE International Workshop on Electromagnetics: Applications and Student Innovation Competition, iWEM 2023

作者： Yang, Biao Qian, Yudong Han, Zemin Kunming University of Science and Technology Faculty of Information Engineering and Automation Kunmin650500 China

ISBN: (纸本)9798350336740

For the problem of temperature uniformity in microwave heating, this paper proposes a method for selecting microwave source distribution within a multi-source microwave heating cavity. By arranging the position of each microwave source, using mutually perpendicular microwave sources on each exterior surface of the cavity and making the microwave sources on different surfaces orthogonal to each other, better temperature uniformity and heating efficiency can be obtained. The experiments analyzed the location distribution strategy according to the microwave heating cavity when using different numbers of microwave sources, and compare several typical microwave source layouts. The experimental results show that the combination of microwave sources selected using this method has more obvious advantages in heating efficiency and material temperature uniformity. © 2023 IEEE.

关键词： Efficiency

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A spatial-spectral classification framework for multispectral LiDAR

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地球空间信息科学学报（英文版） 2024年第5期27卷中插4,1460-1474页

作者： Shuo Shi Biwu Chen Sifu Bi Junkai Li Wei Gong Jia Sun Bowen Chen Lin Du Jian Yang Qian Xu Fei Wang Shalei Song State Key Laboratory of Information Engineering in Surveying Mapping and Remote SensingWuhan UniversityWuhanChina Scientific Laboratory Shanghai Radio Equipment Research InstituteShanghaiChina Industry Development and Planning Institute NFGABeijingChina State Key Laboratory of Information Engineering in Surveying Mapping and Remote SensingWuhan UniversityWuhanChina Electronic Information School Wuhan UniversityWuhanChina Faculty of Information Engineering China University of GeosciencesWuhanChina Faculty of Information Engineering and Automation Kunming University of Science and TechnologyKunmingChina Innovation Academy for Precision Measurement Science and Technology Chinese Academy of SciencesWuhanChina

Precise classification of Light Detection and Ranging(LiDAR)point cloud is a fundamental process in various applications,such as land cover mapping,forestry management,and autonomous *** to the lack of spectral information,the existing research on single wavelength LiDAR classification is *** information from images could address this limitation,but data fusion suffers from varying illumination conditions and the registration problem.A novel multi-spectral LiDAR successfully obtains spatial and spectral information as a brand-new data type,namely,multispectral point cloud,thereby improving classification ***,spa-tial and spectral information of multispectral LiDAR has been processed separately in previous studies,thereby possibly limiting the classification performance of multispectral *** explore the potential of this new data type,the current spatial-spectral classification framework for multispectral LiDAR that includes four steps:(1)neighborhood selection,(2)feature extraction and selection,(3)classification,and(4)label *** novel highlights were proposed in this spatial-spectral classification framework.(1)We improved the popular eigen entropy-based neighborhood selection by spectral angle match to extract a more precise neighborhood.(2)We evaluated the importance of geometric and spectral features to compare their contributions and selected the most important features to reduce feature redundancy.(3)We conducted spatial label smoothing by a conditional random field,accounting for the spatial and spectral informa-tion of the neighborhood *** proposed method demonstrated by a multispectral LiDAR with three channels:466 nm(blue),527 nm(green),and 628 nm(red).Experimental results demonstrate the effectiveness of the proposed spatial-spectral classification ***,this research takes advantages of the complementation of spatial and spectral infor-mation,which could benefit more precise neighbor

关键词： multispectral Light Detection and Ranging(LiDAR) point cloud classification neighborhood selection feature selection condition random field

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Secure Channel Estimation Using Norm Estimation Model for 5G Next Generation Wireless Networks

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Computers, Materials & Continua 2025年第1期82卷 1151-1169页

作者： Khalil Ullah Song Jian Muhammad Naeem Ul Hassan Suliman Khan Mohammad Babar Arshad Ahmad Shafiq Ahmad Faculty of Information Engineering and Automation Kunming University of Science and TechnologyKunming650500China Department of Computer Science Abbottabad University of Science and TechnologyHavelianAbbottabad22500Pakistan Department of Computing and Electronics Engineering Middle East CollegeMuscat124Oman Department of CS&IT Pak-Austria Fachhochschule Institute of Applied Sciences and Technology MangHaripur22621Pakistan Industrial Engineering Department College of EngineeringKing Saud UniversityRiyadh11421Saudi Arabia

The emergence of next generation networks(NextG),including 5G and beyond,is reshaping the technological landscape of cellular and mobile *** networks are sufficiently scaled to interconnect billions of users and *** in academia and industry are focusing on technological advancements to achieve highspeed transmission,cell planning,and latency reduction to facilitate emerging applications such as virtual reality,the metaverse,smart cities,smart health,and autonomous *** continuously improves its network functionality to support these *** input multiple output(MIMO)technology offers spectral efficiency,dependability,and overall performance in *** article proposes a secure channel estimation technique in MIMO topology using a norm-estimation model to provide comprehensive insights into protecting NextG network components against adversarial *** technique aims to create long-lasting and secure NextG networks using this extended *** viability of MIMO applications and modern AI-driven methodologies to combat cybersecurity threats are explored in this ***,the proposed model demonstrates high performance in terms of reliability and accuracy,with a 20%reduction in the MalOut-RealOut-Diff metric compared to existing state-of-the-art techniques.

关键词： Next generation networks massive mimo communication network artificial intelligence 5G adversarial attacks channel estimation information security

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