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Wind disturbance compensated path-following control for fixed-wing UAVs in arbitrarily strong winds

Chinese Journal of Aeronautics 2024年第2期37卷 431-445页

作者： Hao LU Lan GAO Yunda YAN Mingzhe HOU Chenliang WANG Hangzhou Innovation Institute Beihang UniversityHangzhou 310051China School of Information Science and Technology Hangzhou Normal UniversityHangzhou 311121China Department of Computer Science University College LondonLondon WC1E 6BTUnited Kingdom Center for Control Theory and Guidance Technology Harbin Institute of TechnologyHarbin 150001China School of Automation Science and Electrical Engineering Beihang UniversityBeijing 100191China

Wind is the primary challenge for low-speed fixed-wing unmanned aerial vehicles to follow a predefined flight *** cope with various wind conditions,this paper proposes a wind disturbance compensated path following control strategy where the wind disturbance estimate is incorporated with the nominal guiding vector field to provide the desired airspeed direction for the *** the control input vector for the outer-loop kinematic subsystem needs to satisfy a magnitude constraint,a scaling mechanism is introduced to tune the proportions of the compensation and nominal ***,an optimization problem is formulated to pursue a maximum wind compensation in strong winds,which can be solved analytically to yield two scaling factors.A cascaded inner-loop tracking controller is also designed to fulfill the outer-loop wind disturbance compensated guiding vector ***-fidelity simulation results under sensor noises and realistic winds demonstrate that the proposed path following algorithm is less sensitive to sensor noises,achieves promising accuracy in normal winds,and mitigates the deviation from a desired path in wild winds.

关键词： Path following Guiding vector field Wind disturbance Disturbance rejection Fixed wings

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A Deep Learning Approach to Industrial Corrosion Detection

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computers, Materials & Continua 2024年第11期81卷 2587-2605页

作者： Mehwash Farooqui Atta Rahman Latifa Alsuliman Zainab Alsaif Fatimah Albaik Cadi Alshammari Razan Sharaf Sunday Olatunji Sara Waslallah Althubaiti Hina Gull Department of Computer Engineering(CE) College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia Department of Computer Science(CS) College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia Department of Computer Information Systems(CIS) College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia

The proposed study focuses on the critical issue of corrosion,which leads to significant economic losses and safety risks worldwide.A key area of emphasis is the accuracy of corrosion detection *** recent studies have made progress,a common challenge is the low accuracy of existing detection *** models often struggle to reliably identify corrosion tendencies,which are crucial for minimizing industrial risks and optimizing resource *** proposed study introduces an innovative approach that significantly improves the accuracy of corrosion detection using a convolutional neural network(CNN),as well as two pretrained models,namely YOLOv8 and *** leveraging advanced technologies and methodologies,we have achieved high accuracies in identifying and managing the hazards associated with corrosion across various industrial *** advancement not only supports the overarching goals of enhancing safety and efficiency,but also sets a new benchmark for future research in the *** results demonstrate a significant improvement in the ability to detect and mitigate corrosion-related concerns,providing a more accurate and comprehensive solution for industries facing these *** CNN and EfficientNetB0 exhibited 100%accuracy,precision,recall,and F1-score,followed by YOLOv8 with respective metrics of 95%,100%,90%,and 94.74%.Our approach outperformed state-of-the-art with similar datasets and methodologies.

关键词： Deep learning YOLOv8 EfficientNetB0 CNN corrosion detection Industry 4.0 sustainability

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Research on incentive mechanism and evaluation of cross-enterprise distributed research and development resource sharing under networked collaborative design mode

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Soft Computing 2024年第21期28卷 12543-12559页

作者： Hu, Yongqing Su, Weixing Chen, Hanning He, Maowei Liu, Fang College of Control Science and Engineering Tiangong University Tianjin China College of Computer Science and Technology Tiangong University Tianjin China

The promotion and application of model-based systems engineering (MBSE) suffer from the lack of effective sharing of research and design (R&D) resources among enterprises in the networked collaborative design environment. This paper establishes a cross-enterprise R&D resource sharing incentive evolutionary game model, which considers multiple factors about resource sharers, sharing process and shared resource value information. The model analyses how various factors influence the evolution trend of sharing behavior. In addition, in order to realize the dynamic multidimensional evaluation and feedback of shared R&D resources and promote the positive development of resource sharing, this paper establishes a new dynamic double-objective resource evaluation model. The digital information resource in R&D resources is taken as an example to carry out experimental simulation and verification. Compared with the traditional evaluation methods, the new dynamic double-objective evaluation model established in this paper owns better performance on improving the cross-enterprise R&D resources collaborative sharing and further promoting the deep integration between R&D resources and R&D process under the networked collaborative design and MBSE development mode than others. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Evolutionary game model Incentive mechanism Multi-objective evaluation Networked collaborative design Resource sharing

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An EnKF-LSTM Assimilation Algorithm for Crop Growth Model

IEEE Transactions on AgriFood Electronics

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IEEE Transactions on AgriFood Electronics 2024年第2期2卷 372-380页

作者： Zhou, Siqi Wang, Ling Liu, Jie Tang, Jinshan Harbin Institution of Technology Department of Computer Science and Technology Harbin150001 China Harbin Institute of Technology Department of Computer Science and Technology Harbin150001 China National Key Laboratory of Smart Farming Technology and System Heilongjiang Harbin150000 China George Mason University Department of Health Administration and Policy College of Public Health FairfaxVA22033 United States

Accurate and timely prediction of crop growth is of great significance to ensure crop yields, and researchers have developed several crop models for the prediction of crop growth. However, there are large differences between the simulation results obtained by the crop models and the actual results;thus, in this article, we proposed to combine the simulation results with the collected crop data for data assimilation so that the accuracy of prediction will be improved. In this article, an EnKF-LSTM data assimilation method for various crops is proposed by combining an ensemble Kalman filter and long short-term memory (LSTM) neural network, which effectively avoids the overfitting problem of the existing data assimilation methods and eliminates the uncertainty of the measured data. The verification of the proposed EnKF-LSTM method and the comparison of the proposed method with other data assimilation methods were performed using datasets collected by sensor equipment deployed on a farm. © 2024 IEEE.

关键词： Crops

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Underwater Pulse Waveform Recognition Based on Hash Aggregate Discriminant Network

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Journal of Ocean university of China 2024年第3期23卷 654-660页

作者： WANG Fangchen ZHONG Guoqiang WANG Liang College of Computer Science and Technology Ocean University of ChinaQingdao 266100China College of Marine Technology Ocean University of ChinaQingdao 266100China

Underwater pulse waveform recognition is an important method for underwater object *** existing works focus on the application of traditional pattern recognition methods,which ignore the time-and space-varying characteristics in sound propagation channels and cannot easily extract valuable waveform *** propagation channels in seawater are time-and space-varying convolutional *** the extraction of the waveform features of underwater acoustic signals,the effect of high-accuracy underwater acoustic signal recognition is identified by eliminating the influence of time-and space-varying convolutional channels to the greatest extent *** propose a hash aggregate discriminative network(HADN),which combines hash learning and deep learning to minimize the time-and space-varying effects on convolutional channels and adaptively learns effective underwater waveform features to achieve high-accuracy underwater pulse waveform *** the extraction of the hash features of acoustic signals,a discrete constraint between clusters within a hash feature class is *** constraint can ensure that the influence of convolutional channels on hash features is *** addition,we design a new loss function called aggregate discriminative loss(AD-loss).The use of AD-loss and softmax-loss can increase the discriminativeness of the learned hash *** results show that on pool and ocean datasets,which were collected in pools and oceans,respectively,by using acoustic collectors,the proposed HADN performs better than other comparative models in terms of accuracy and mAP.

关键词： convolutional channel hash aggregate discriminative network aggregate discriminant loss waveform recognition

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Biometric-Based Secure Encryption Key Generation Using Convolutional Neural Networks and Particle Swarm Optimization

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第16期49卷 213-234页

作者： Almola, Sahera A.S. Khudeyer, Raidah S. Younis, Hameed Abdulkareem Department of Computer Information Systems College of Computer Science and Information Technology University of Basrah Basrah Iraq

With the rapid expansion of computer networks and information technology, ensuring secure data transmission is increasingly vital—especially for image data, which often contains sensitive information. This research presents a biometric-based encryption system that uses fingerprint recognition and deep learning to generate strong, random encryption keys. Two convolutional neural networks (CNNs) are employed: one to verify identity based on a user’s ID and another to extract fingerprint features for key generation. These keys are optimized using Particle Swarm Optimization (PSO), enhancing their randomness and resistance to brute-force attacks. The system generates keys in real-time, eliminating the need for storage and minimizing the risk of theft or leakage. To further improve security, encryption keys are automatically updated after every ten messages, with different keys generated from multiple fingerprints of the same individual. Testing with the SOCOFing dataset (6,000 original and 49,270 synthetic images) achieved 99.75% identity verification and 99.83% classification accuracy. Performance metrics—entropy of 7.89, correlation factor of 0.00628, and zero repetition—demonstrate high robustness. This approach offers a secure, adaptive, and personalized encryption method ideal for sensitive domains like finance and healthcare. © 2025 Slovene Society Informatika. All rights reserved.

关键词： Convolutional neural networks

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Privacy-Preserving Federated Deep Learning Diagnostic Method for Multi-Stage Diseases

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computer Modeling in Engineering & sciences 2024年第6期139卷 3085-3099页

作者： Jinbo Yang Hai Huang Lailai Yin Jiaxing Qu Wanjuan Xie School of Computer Science and Technology Harbin University of Science and TechnologyHarbin150080China Shanghai Futures Information Technology Co. Ltd.Shanghai201201China Heilongjiang Province Cyberspace Research Center Harbin150001China Information Network Engineering and Research Center South China University of TechnologyGuangzhou510641China

Diagnosing multi-stage diseases typically requires doctors to consider multiple data sources,including clinical symptoms,physical signs,biochemical test results,imaging findings,pathological examination data,and even genetic *** applying machine learning modeling to predict and diagnose multi-stage diseases,several challenges need to be ***,the model needs to handle multimodal data,as the data used by doctors for diagnosis includes image data,natural language data,and structured ***,privacy of patients’data needs to be protected,as these data contain the most sensitive and private ***,considering the practicality of the model,the computational requirements should not be too *** address these challenges,this paper proposes a privacy-preserving federated deep learning diagnostic method for multi-stage *** method improves the forward and backward propagation processes of deep neural network modeling algorithms and introduces a homomorphic encryption step to design a federated modeling algorithm without the need for an *** also utilizes dedicated integrated circuits to implement the hardware Paillier algorithm,providing accelerated support for homomorphic encryption in ***,this paper designs and conducts experiments to evaluate the proposed *** experimental results show that in privacy-preserving federated deep learning diagnostic modeling,the method in this paper achieves the same modeling performance as ordinary modeling without privacy protection,and has higher modeling speed compared to similar algorithms.

关键词： Vertical federation homomorphic encryption deep neural network intelligent diagnosis machine learning and big data

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Cross-domain Fake Review Detection Based on Deep Learning MultiLevel Generic Features Extraction Fusion

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第18期49卷 99-110页

作者： Ren, Ge Wang, Huajun Yang, Yong College of Computer Science and Technology Xinjiang Normal University Xinjiang Urumqi830054 China

The fake review detection aims to identify fake reviews that affect regular competition of online marketplaces. Existing research on fake review detection mainly focuses on deep learning and feature-based methods. Feature-based methods make it difficult to obtain potential semantic information, while deep learning methods are less likely to consider multi-granularity information based on text structure. Neither is very satisfactory when it comes to cross-domain detection. In this paper, we present a cross-domain fake review detection method based on multi-level generic feature extraction fusion. The important information prevalent in different domains is purposefully extracted from the review text based on its structure for cross-domain fake review detection. At the word level, in order to obtain potential semantic information, the paper combined GloVe and TF-IDF weights as well as CNN to extract multi-granularity semantic information from the word level. In order to obtain sentence-level generic features, sentence-level syntactic information of comments is extracted through lexical annotation techniques. In order to obtain finer-grained emotion features at the document level, the paper annotates the dataset comments with distilBERT, a pre-trained language model that has been fine-tuned on the emotion classification task. Moreover, the generic features extracted at each layer that are applicable across domains are fused using a multi-head attention mechanism. Finally, classification is performed in the classification layer. Experimental results on public datasets show that the model proposed in this paper has significantly improved performance in cross-domain detection, achieving 83% and 78.0% accuracy on the restaurant and doctor domain datasets, respectively. It outperforms the state-of-the-art method by 10.7% on the restaurant dataset. © 2025 Slovene Society Informatika. All rights reserved.

关键词： Contrastive Learning

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Resource Allocation in UAV-Assisted Mobile Edge Computing Systems

Resource Allocation in UAV-Assisted Mobile Edge Computing Sy...

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2024 OES China Ocean Acoustics, COA 2024

作者： Han, Xiao Wang, Huiqiang Wang, Chengbo Computer Science and Technology Harbin Engineering University Harbin China

ISBN: (纸本)9798350340297

In a crowd-sensing system, effective transmission of sensing data is an important step in improving task distribution efficiency. In real-life scenarios, when resources are limited or geographic conditions are harsh, unmanned aerial vehicles (UAV) can often be used for data transmission. This paper proposes a group intelligence sensing task distribution method under dynamic topology conditions to solve the problems of unreachable links or high network overhead caused by the dynamic topology of UAVs. This method uses the Markov model to model the data transmission process and, on this basis, proposes an improved task distribution method based on topology awareness to achieve efficient task forwarding. The final experimental results show that compared with the traditional self-organizing network data transmission scheme for opportunistic networks, the scheme proposed in this paper can better solve the problems of routing voids and blind paths, and effectively improve the network lifetime while realizing a lower end-to-end delay, with better transmission performance. © 2024 IEEE.

关键词： Markov processes

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Achieving ultrahigh strength and ductility via high-density nanoprecipitates triggering multiple deformation mechanisms in a dual-aging high-entropy alloy with precold deformation

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材料科学技术（英文版） 2025年第2期205卷 27-41页

作者： Liyuan Liu Yang Zhang Zhongwu Zhang Key Laboratory of Superlight Materials and Surface Technology Ministry of EducationCollege of Materials Science and Chemical EngineeringHarbin Engineering UniversityHarbin 150001China Key Laboratory of Superlight Materials and Surface Technology Ministry of EducationCollege of Materials Science and Chemical EngineeringHarbin Engineering UniversityHarbin 150001China State Key Laboratory of Metal Materials for Marine Equipment and Application Anshan 114009China

How to achieve high-entropy alloys(HEAs)with ultrahigh strength and ductility is a challenging *** strengthening is one of the methods to significantly enhance strength,but unfortunately,ductility will be *** overcome the strength-ductility trade-off,the strategy of this study is to induce the formation of high-density nanoprecipitates through dual aging(DA),triggering multiple deformation mechanisms,to obtain HEAs with ultrahigh strength and ***,the effect of precold deforma-tion on precipitation behavior was studied using Ni35(CoFe)55V5Nb5(at.％)HEAas the *** results reveal that the activation energy of recrystallization is 112.2 kJ/*** the precold-deformation amount increases from 15％to 65％,the activation energy of precipitation gradually decreases from 178.8 to 159.7 kJ/*** precipitation time shortens,the size of the nanoprecipitate decreases,and the den-sity ***,the thermal treatment parameters were optimized,and the DA process was customized based on the effect of precold deformation on precipitation ***-density L12 nano-precipitates(～3.21 × 1025 m-3)were induced in the 65％precold-deformed HEA,which led to the si-multaneous formation of twins and stacking fault(SF)networks during *** yield strength(YS),ultimate tensile strength,and ductility of the DA-HEA are～2.0 GPa,～2.2 GPa,and～12.3％,*** with the solid solution HEA,the YS of the DA-HEA increased by 1,657 MPa,possessing an astonishing increase of～440％.The high YS stems from the precipitation strengthening contributed by the L12 nanoprecipitates and the dislocation strengthening contributed by precold *** synergistically enhanced ductility stems from the high strain-hardening ability under the dual support of twinning-induced plasticity and SF-induced plasticity.

关键词： High-entropy alloy Precold deformation Precipitation behavior Ultrahigh strength Deformation mechanism

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