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UAV Localization with Unreliable Observations in Hostile Underground Environments

Journal of Computer science & technology 2024年第6期39卷 1401-1418页

作者： Peng-Peng Chen Kui-Yuan Zhang Shou-Wan Gao Si-Yi Ren School of Computer Science and Technology China University of Mining and TechnologyXuzhou 221116China Mine Digitization Engineering Research Center Ministry of EducationXuzhou 221116China Shenzhen Research Institute China University of Mining and TechnologyShenzhen 518057China

The accurate and robust unmanned aerial vehicle(UAV)localization is significant due to the requirements of safety-critical monitoring and emergency wireless communication in hostile underground *** range-based localization approaches fundamentally rely on the assumption that the environment is relatively ideal,which enables a precise range for ***,radio propagation in the underground environments may be dramatically influenced by various equipments,obstacles,and ambient *** this case,inaccurate range measurements and intermittent ranging failures inevitably occur,which leads to severe localization performance *** address the challenges,a novel UAV localization scheme is proposed in this paper,which can effectively handle unreliable observations in hostile underground *** first propose an adaptive extended Kalman filter(EKF)based on the fusion of ultra-wideband(UWB)and inertial measurement unit(IMU)to detect and adjust the inaccurate range *** to deal with intermittent ranging failures,we further design the constraint condition by limiting the system ***,the auto-regressive model is proposed to implement the localization in the ranging blind areas by reconstructing the lost ***,extensive simulations have been conducted to verify the *** carry out field experiments in an underground garage and a coal mine based on P440 UWB *** show that the localization accuracy is improved by 16.9%compared with the recent methods in the hostile underground environments.

关键词： unmanned aerial vehicle(UAV)localization Kalman filter ultra-wideband(UWB)observation hostile underground environment

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Simultaneously improving color purity, stability, and health-friendliness of OLEDs via optimally designed color filters

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Optics Express 2025年第6期33卷 13506-13518页

作者： Chen, Linya Gu, Honggang Guo, Xiaoke Xu, Ming Shi, Ting Li, Jinchuan Cao, Weiran Liu, Shiyuan School of Optical and Electronic Information Huazhong University of Science and Technology Hubei Wuhan430074 China Innovation Institute Huazhong University of Science and Technology Hubei Wuhan430074 China School of Mechanical Science and Engineering Huazhong University of Science and Technology Hubei Wuhan430074 China Guangdong HUST Industrial Technology Research Institute Guangdong Provincial Key Laboratory of Manufacturing Equipment Digitization Guangdong 523003 China Optics Valley Laboratory Wuhan430074 China Shenzhen China Star Optoelectronics Semiconductor Display Technology Co. Ltd. Guangdong Shenzhen518132 China

The widespread application of high-performance organic light-emitting diodes (OLEDs) remains hampered by persistent challenges, such as enhancing color purity, ensuring long-term stability, and mitigating health risks. While existing methods have made progress in addressing specific challenges, achieving concurrent improvements remains difficult. In this paper, we develop an optical model for OLEDs integrated with optimally designed color filters (CFs) and propose systematic CF optimization strategies to address these challenges simultaneously. A series of CFs compatible with OLEDs of varying peak wavelengths were designed to enhance color purity, stability, and health-friendliness while preserving intrinsic device properties, such as microcavity structures and material composition. The optimized CF structure comprises high-reflectivity films, a spacer layer, and a traditional interference filter. Results demonstrate that green OLEDs incorporating the optimized CF increase color purity from 0.899 to 0.992 in the forward direction, approaching the theoretical maximum, with only a 1.13% intensity loss at the peak wavelength. Furthermore, under multiple viewing angles, color purity improves by ∼0.1, and the effective viewing range expands from 40° to 50°, enhancing angular performance. RGB-pixel OLEDs with optimized CFs exhibit a color gamut expansion from 90.9% to 95.8% NTSC, paving the way for more vivid and realistic color reproduction. Notably, the CFs suppress ultraviolet-induced degradation by maintaining © 2025 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.

关键词： Organic light emitting diodes (OLED)

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Insight into microstructure evolution on anti-corrosion property of Al_(x)CoCrFeNiC_(0.01) high-entropy alloys using scanning vibration electrode technique

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Rare Metals 2023年第10期42卷 3455-3467页

作者： Bo-Kai Liao Zhan-Xiang Liang Zhi-Gang Luo Yue Liu Hao-Wei Deng Tao Zhang Xing-Peng Guo Qi-Sen Ren Hong-En Ge School of Chemistry and Chemical Engineering Guangzhou UniversityGuangzhou510006China School of Physics and Materials Science Center of Advanced Functional MaterialsGuangzhou UniversityGuangzhou510006China Joint Institute of Guangzhou University and Institute of Corrosion Science and Technology Guangzhou UniversityGuangzhou510006China Science and Technology Digitization Department China General Nuclear Power CorporationShenzhen518038China Institute of Nuclear Fuel and Materials China Nuclear Power Technology Research InstituteShenzhen518031China Hubei Key Laboratory of Material Chemistry and Service Failure School of Chemistry and Chemical EngineeringHuazhong University of Science and TechnologyWuhan430074China

The effect of micro structure on the corrosion resistance of Al_(x)CoCrFeNiC_(0.01)(x=0.2,0.7,and 1.2)high-entropy alloys(HEAs) was systematically studied in this *** microstructure evolution by regulating the Al content was analyzed in *** behavior was in situ monitored using the scanning vibration electrode technique,as well as some traditional electrochemical *** is interesting to find that the compositions of body-centered cubic(bcc) and face-centered cubic(fcc)phases changed with the rising Al content,while the corresponding electrochemical responses for both phases were discriminated using the scanning Kelvin probe force microscopy *** element was mainly distributed in the bcc phase for Al0.2(x=0.2) alloy,while its distribution changed to the fcc phase for the A10.7 and *** micro-galvanic corrosion cells formed between Cr-depleted and Cr-rich phases,resulting in the localized corrosion behaviors for the Al_(x)CoCrFeNiC_(0.01) HEAs,and the order for anti-corrosion property was Al0.2>Al1.2> Al0.7 HEAs.

关键词： High-entropy alloy Corrosion Mechanical property Scanning vibration electrode technique

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Multi-level self attention for unsupervised learning person re-identification

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Multimedia Tools and Applications 2024年第26期83卷 68855-68874页

作者： Zheng, Yi Zhao, Jiaqi Zhou, Yong Liu, Fayao Yao, Rui Zhu, Hancheng El Saddik, Abdulmotaleb Engineering Research Center of Mine Digitization of the Ministry of Education of the People’s Republic of China China University of Mining and Technology No. 1 Daxue Road Jiangsu Xuzhou221116 China School of Computer Science and Technology China University of Mining and Technology No. 1 Daxue Road Jiangsu Xuzhou221116 China Institute for Infocomm Research Agency for Science Technology and Research Singapore Singapore University of Ottawa 800 King Edward OttawaONK1N 6N5 Canada

In recent years, the task of person re-identification (ReID) has placed a critical demand on accurately describing image features. Attention mechanisms, particularly Transformer-like self-attention (TLSA), have gained favor among researchers due to their outstanding feature descriptive performance. However, due to their intricate structures, TLSA models typically require more computational resources. Simultaneously, contrastive learning has significantly enhanced the performance of unsupervised person re-identification. Nevertheless, contrastive learning originates from deep exploration of relationships among multiple samples, making batch size a crucial factor influencing deep learning methods based on the contrastive learning paradigm. Therefore, under the constraint of limited computational resources, traditional TLSA models often struggle to effectively adapt to unsupervised person ReID methods based on the contrastive learning paradigm. In response to the aforementioned issues, we propose a novel and lightweight Multi-Level Attention (MLA) method in this paper, which effectively mitigates the computational resource conflicts of the TLSA model during training under the contrastive learning paradigm. MLA comprises a lightweight multi-head attention module, complemented by a spatial feature weighting module, and an inter-feature cross-attention module to assist it. By fully leveraging the complementary strengths of these attention mechanisms, our approach achieves significant performance improvements in the ReID task. We evaluated the proposed approach on three large-scale real person ReID datasets, namely Market-1501, DukeMTMC-reID, MSMT17, and the virtual person ReID dataset, PersonX. The experimental results demonstrate that our method outperforms state-of-the-art approaches without relying on supplemental pre-training procedures or additional training data. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Natur

关键词： Unsupervised learning

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On-Orbit Identification of Unstable Micro-Propulsion System Parameters Using Drag-Free Control 13

On-Orbit Identification of Unstable Micro-Propulsion System ...

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13th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2024

作者： Xu, Nuo Xia, Xiwang Li, Zhaoxiong He, Xiongfeng Guo, Ning Wang, Pengcheng Zhang, Yonghe Chinese Academy of Sciences Innovation Academy for Microsatellites Shanghai201203 China University of Chinese Academy of Sciences Beijing100049 China Key Laboratory for Satellite Digitization Technology Chinese Academy of Sciences Shanghai201203 China Lanzhou Institute of Physics Science and Technology on Vacuum Technology and Physics Lab Lanzhou730000 China

ISBN: (纸本)9798350361674

Space gravitational wave detection spacecraft is equipped with a drag-free control and micro-propulsion system, and adopts thrust closed-loop feedback control structure. An on-orbit high accuracy estimation algorithm of micro-thruster parameters based on drag-free control model is proposed in this paper in order to deal with the problem of unsteady nominal thrust caused by thruster failure or aging and and extend thrusters lifespan. In this method, the recursive gain and covariance are designed to estimate the parameter matrix of system. Adaptive forgetting factors are added to the covariance matrix for designing a fading memory window to determine the influence of prior data on the results. At the same time, the thruster state is deconstructed into three parts: thrust gain, thrust direction and lever arm to facilitate the redistribution of control force and torque. Simulations show that the on-orbit thrust identification errors of the proposed method in different micro-propulsion states are all at sub-micro-newton level, and the noise is less than 0.1μ N/Hz which has capability to meet the requirements corresponding to drag-free control system. © 2024 IEEE.

关键词： Covariance matrix

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Analysis of GFRP hybrid laser and mechanical cutting of large wind turbine blade flashing

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Journal of Manufacturing Processes 2025年 149卷 1025-1031页

作者： Chunmeng Chen Long Chen Wenyuan Li Yu Huang Youmin Rong Guojun Zhang State Key Laboratory of Intelligent Manufacturing Equipment and Technology Huazhong University of Science and Technology Wuhan China School of Mechanical Science and Engineering Huazhong University of Science and Technology Wuhan China Guangdong HUST Industrial Technology Research Institute Dongguan China Guangdong Provincial Key Laboratory of Manufacturing Equipment Digitization Dongguan China

The development of offshore wind turbines has spurred the advancement of large-scale wind turbine blades. Mechanical cutting of wind turbine blade flashing faces several challenges, such as low efficiency, poor flexibility, loud noise, serious dust pollution, and significant tool wear. In this paper, a hybrid laser and mechanical cutting method for wind turbine blades flashing was proposed. Initially, the high-power continuous fiber lasers and carbon dioxide (CO 2 ) lasers were used to cut the blade flashing. Subsequently, the mechanical sanding was utilized to eliminate heat-induced damage from the cross-section of the cut flashing. The entrance heat-affected zone (HAZ) and ablation pit were measured to evaluate the cutting quality. The results showed that the blade flashing could be cut by laser in a single pass, and the entrance HAZ and pit depth could be completely eliminated by the mechanical sanding. The cutting speed is increased to 1.2 m/min while ensuring the cutting quality, which is improved (50 %) compared with manual grinding. Furthermore, upon comparing the cutting morphologies cut by fiber laser and CO 2 laser, it is evident that selecting an appropriate laser beam wavelength and achieving high beam quality can significantly enhance the cutting quality of wind turbine blades. The HAZ on the upper surface and the ablation pit in the cross-section produced by CO 2 laser cutting were 42.3 % and 61.3 % smaller, respectively. These results demonstrate that the hybrid CO 2 laser-mechanical cutting method is a feasible solution for processing large wind turbine blade flashing.

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Surface roughness and microstructure of 3D Cf/C composites by square flat-top femtosecond laser scribing

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Ceramics International 2025年

作者： Wang, Ningxiao Rong, Youmin Zhang, Guangyi Chen, Hang Huang, Yu Wu, Congyi School of Mechanical Science and Engineering Huazhong University of Science and Technology Wuhan430074 China Guangdong HUST Industrial Technology Research Institute Guangdong Provincial Key Laboratory of Manufacturing Equipment Digitization Dongguan China Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Zhejiang Ningbo315201 China

Carbon fiber reinforced carbon matrix (Cf/C) composites are of significant importance in aircraft as heat shields due to their exceptional heat shock resistance and ablation resistance. However, these properties can be further enhanced by improving surface quality, particularly by reducing surface roughness. Femtosecond laser processing technology, especially when combined with beam shaping, has been identified as a viable approach to achieve this reduction. In this study, both Gaussian beam and square flat-top beam laser scribing treatments were applied to Cf/C composites. The effects of scribing parameters on surface quality were systematically investigated. The results indicated that square flat-top beams significantly reduce surface roughness to 0.63 ± 0.09 μm, compared to 0.78 ± 0.08 μm achieved with Gaussian beams. This improvement is attributed to the complete and homogeneous microstructure induced by the square flat-top beam. This study highlights the importance of beam shaping technology in enhancing surface quality through femtosecond laser scribing, offering a method to improve the surface characteristics of Cf/C composites. © 2025 Elsevier Ltd and Techna Group S.r.l.

关键词： Femtosecond lasers

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Achieving Cross-domain NLOS Localization via Edge-assisted Semi-supervised Learning

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IEEE Transactions on Wireless Communications 2025年

作者： Chen, Pengpeng Zhang, Kuiyuan Gao, Shouwan He, Kangjia Lv, Junpeng China University of Mining and Technology Mine Digitization Engineering Research Center Ministry of Education School of Computer Science and Technology Xuzhou221116 China China University of Mining and Technology Shenzhen Research Institute Shenzhen518057 China China University of Mining and Technology School of Computer Science and Technology Xuzhou221116 China

The localization of coal mine robots (CMRs) serves as the foundation for intelligent mines. Despite various approaches proposed by academia and industry, existing range-based localization methods generally encounter the non-line-ofsight (NLOS) problem, leading to severe accuracy degradation in complicated underground mines. Facing the above challenge, this paper proposes an edge-assisted cross-domain NLOS localization (CrossDNL) framework based on semi-supervised learning. Specifically, we analyze the channel impulse responses (CIRs) of ultra-wideband (UWB) to identify the multi-channel NLOS conditions and further mitigate ranging errors via the deep neural network (DNN). To achieve reliable localization across various scenarios, CrossDNL adopts an edge-assisted semi-supervised learning architecture that enables it to optimize DNN models by self-training on the edge server and achieve the real-time service on the CMR. Besides, we propose a range-based error state Kalman filter (ESKF) scheme to improve the localization performance. We implement CrossDNL on the CMR and the edge server and evaluate it in various scenarios. Results show that CrossDNL can achieve a 14cm localization error in real time, outperforming the latest solutions by more than 20%. © 2002-2012 IEEE.

关键词： Semi-supervised learning

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AUTODEEPSLICE: A Data Driven Network Slicing Technique of 5G network using Automatic Deep Learning

AUTODEEPSLICE: A Data Driven Network Slicing Technique of 5G...

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2022 IEEE GLOBECOM Workshops, GC Wkshps 2022

作者： Chowdhury, Deepraj Das, Rupanjan Rana, Risav Dwivedi, Ashutosh Dhar Chatterjee, Pushpita Mukkamala, Raghava Rao International Institute of Information Technology Department of Electronics and Communication Naya Raipur India Copenhagen Business School Centre for Business Data Analytics Department of Digitization Frederiksberg Denmark Tennessee State University Department of Computer Science Nashville United States

ISBN: (纸本)9781665459754

With the advancement of mobile networks, and the revolution of mobile communication from 1G to 5G, human dependency has increased, so it has become the most priority to provide an efficient and highly reliable network. The 5G networks need high capacity and high-speed data transfer, with the least latency and increased security. 5G service providers are trying to find a programmable solution that can handle a huge number of independent service users on the same physical infrastructure. Network slicing is used to provide end-to-end network resource allocation. To accelerate the 5G network performance, Artificial Intelligence (AI) powered data-driven based decisions will be used for future communication. In this proposed work, Automatic deep learning has been implemented, where the hyperparameter will automatically select the algorithm and tune the hyperparameter. The proposed model AUTODEEPSLICE efficiently makes intelligent decisions and will choose the best network slice at the time of network breakdown. © 2022 IEEE.

关键词： 5G mobile communication systems

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Design and Implementation of an Industry Data Analysis Model Based on Cloud Computing 4

Design and Implementation of an Industry Data Analysis Model...

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4th International Symposium on Computer Engineering and Intelligent Communications, ISCEIC 2023

作者： Wang, Shi Hussin, Norriza Yang, Jing Yang, Xiaoqin SEGi University Faculty of Engineering Built Environment and Information Technology Kota Damansara47810 Malaysia Hainan Vocational University of Science and Technology Haikou571126 China Hainan Free Trade Port Key Lab. of International Shipping Development and Real Right Digitization Haikou571126 China Nanjing Tech University Pujiang Institute College of Computer and Communication Engineering Nanjing211200 China

ISBN: (纸本)9798350306323

Cloud computing technology can effectively solve resource allocation problems that traditional technologies cannot solve. In order to better analyze the development status of Hainan Free Trade Port industry, this paper unifies the management of Hainan Free Trade Port industry data, establishes an industry data cluster, uses a Map-Reduce model to map, classify, preprocess, and then dynamically train using a BP network. Finally, the weight is used to aggregate the output results. This paper proposes research on the construction of Hainan Free Trade Port industry data analysis model based on cloud computing. Compared to traditional data analysis models, the proposed model achieves dynamic adjustment of computing resources during network training, effectively shortens training cycles, optimizes and improves BP network structure, effectively improves overall operational efficiency, and provides valuable reference for data analysis work. © 2023 IEEE.

关键词： Cloud computing

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