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Deep learning-driven interval uncertainty propagation for aeronautical structures

Chinese Journal of Aeronautics 2024年第12期37卷 71-86页

作者： Yan SHI Michael BEER Institute for Risk and Reliability Leibniz Universitat HannoverHannover 30167Germany Department of Civil and Environmental Engineering University of LiverpoolLiverpool L693BXUnited Kingdom International Joint Research Center for Resilient Infrastructure&International Joint Research Center for Engineering Reliability and Stochastic Mechanics Tongji UniversityShanghai 200092China

Interval Uncertainty Propagation(IUP)holds significant importance in quantifying uncertainties in structural outputs when confronted with interval input *** the aviation field,the precise determination of probability models for input parameters of aeronautical structures entails substantial costs in both time and *** an alternative,the use of interval variables to describe input parameter uncertainty becomes a pragmatic *** complex task of solving the IUP for aeronautical structures,particularly in scenarios marked by pronounced nonlinearity and multiple outputs,necessitates innovative *** study introduces an efficient deep learning-driven approach to address the challenges associated with *** proposed approach combines the Deep Neural Network(DNN)with intelligenta* optimization algorithms for dealing with the IUP in aeronautical *** inventive extremal value-oriented weighting technique is presented,assigning varying weights to different training samples within the loss function,thereby enhancing the computational accuracy of the DNN in predicting extremal values of structural ***,an adaptive framework is established to strategically balance the global exploration and local exploitation capabilities of the DNN,resulting in a predictive model that is both robust and *** illustrate the effectiveness of the developed approach,various applications are explored,including a high-dimensional numerical example and two aeronautical *** obtained results highlight the high computational accuracy and efficiency achieved by the proposed approach,showcasing its potential for addressing complex IUP challenges in aeronautical engineering.

关键词： Uncertainty propagation Interval variable Deep learning optimization algorithm Aeronautical structure

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A CNN-Based Approach for Classical Music Recognition and Style Emotion Classification

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IEEE ACCESS 2025年 13卷 20647-20666页

作者： Shi, Yawen Henan Univ Sci & Technol Sch Art & Design Luoyang 471000 Henan Peoples R China

Music recognition refers to the process of automatically recognizing and classifying the musical content in audio signals using computer technology and algorithms. Music recognition technology can help people recognize information such as the music title, artist, musical style, rhythm, and the emotions conveyed by the music in the audio, thus enabling applications like automated music information retrieval and recommendation systems. Classical music, due to its vast quantity, diverse types, and time span covering several centuries, presents challenges that existing music recognition software and traditional music recognition algorithms cannot effectively address. In this study, The model based on convolutional neural networks (CNNs) is proposed, allowing people to recognize the classical music title, style, and emotions contained in a piece of music. The proposed model is particularly beneficial for individuals who are interested in classical music but lack extensive knowledge about it, as it provides essential information about the pieces. By extracting multidimensional features from classical music, the model can recognize the title, style, and emotions expressed. To improve the model's recognition accuracy, various noises are introduced to the dataset. Meanwhile, in this study, a novel loss function has been devised to more effectively assess the model's performance. For searching for optimal performance of the model, a novela* optimization algorithm also be proposed to find optimal hyperparameters of loss function. The experiment results show average title recognition accuracy is 0.98, average style recognition accuracy is 0.89 and average emotion recognition accuracy is 0.93. These results adequately demonstrate that the proposal model significantly enhances the model's ability to accurately recognize the titles, styles, and emotions of classical music, achieving high recognition rates even in noisy environments.

关键词： Emotion recognition Accuracy Feature extraction Multiple signal classification Classification algorithms Noise Deep learning Recommender systems Proposals Heuristic algorithms CNN deep learning music recognition music retrieval optimization algorithm

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Attitude estimation using an adaptive generalized complementary filter

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MEASUREMENT 2025年 251卷

作者： Wu, Wenyu Jin, Zhonghe A, Zeya Zhejiang Univ Microsatellite Res Ctr Hangzhou 310058 Peoples R China Huanjiang Lab Hangzhou 310058 Peoples R China Key Lab Micronano Satellite Res Zhejiang Prov Hangzhou 310058 Peoples R China

A precise and reliable attitude estimation system is essential for the accurate motion control of micro-/nano-satellites. This paper proposes a generalized attitude estimation framework that integrates optimization techniques with an adaptive nonlinear complementary filter for multi-sensor systems. The framework utilizes the Levenberg-Marquardta* optimization algorithm to derive quaternion measurements, which are used as attitude error inputs for an adaptive nonlinear filter. Compared with traditional complementary filters, the proposed approach offers broader applicability and enhanced adaptability through a parameter adjustment strategy. Comprehensive experiments, including performance evaluation, fault tolerance analysis, and real on-orbit data validation, confirm the framework's effectiveness and robustness. Results demonstrate that the proposed method provides efficient attitude angle estimations and gyro bias information, even in disturbed environments. Consequently, this scheme is well-suited for micro-/nano-satellite applications.

关键词： Attitude estimation Micro-/nano-satellites Gyro bias determination optimization algorithm Complementary filter

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Optimal energy management system and techno-economic assessment in Urban and Rural AC microgrids

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JOURNAL OF ENERGY STORAGE 2025年 114卷

作者： Montano, Jhon Gonzalez-Montoya, Daniel Candelo-Becerra, John E. Herrera-Jaramillo, Diego A. Inst Tecnol Metropolitano Dept Elect & Telecommun Medellin 050028 Colombia Univ Nacl Colombia Sede Medellin Dept Energia Electr & Automat Medellin 050034 Colombia Inst Univ Envigado Lab Desarrollo Tecnol Sostenible Envigado 055420 Colombia

This paper introduces an energy management system that incorporates a model for managing urban and rural alternating current (AC) microgrids (MGs), integrating renewable energy sources and energy storage systems. The proposed model aims to improve key technical, economic, and environmental performance indicators. It employs a mono-objective optimization framework, focusing on the independent minimization of operating costs, power losses, and CO2 emissions. To solve the optimization problem, seven bio-inspired algorithms are implemented and compared: Black Hole Optimizer (BHO), Crow Search algorithm (CSA), Salp Swarm algorithm (SSA), Equilibrium Optimizer (EO), Generalized Normal Distribution Optimizer (GNDO), Particle Swarm optimization (PSO), and Grasshoppera* optimization algorithm (GOA). The effectiveness of the proposed model is validated through a comparative analysis against a baseline scenario that represents conventional MG operation without optimization. This baseline scenario includes photovoltaic distributed generators and energy storage systems operating under static dispatch strategies. The results demonstrate that EO, SSA, and GNDO are the most effective algorithms for optimizing the specified objectives. For urban MGs, the proposed model achieves reductions of up to 7.16% in power losses, 0.163% infixed costs, 1.436% invariable costs, and 0.165% in CO2 emissions when compared to the baseline. Similarly, for rural MGs, the proposed approach yields reductions of 10.938% in power losses, 0.095% in energy costs, and 0.145% in CO2 emissions relative to the baseline scenario. These findings confirm the innovation and effectiveness of the proposed energy management model and itsa* optimization algorithms. The study highlights the model's capability to ensure technical efficiency while significantly reducing economic and environmental impacts. Moreover, the adaptability of the model to both urban and rural settings demonstrates its potential as a robust framework

关键词： Energy management system Techno-economic analysis AC MGs optimization algorithm Variable power demand Variable renewable generation

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Air quality index prediction through TimeGAN data recovery and PSO-optimized VMD-deep learning framework

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APPLIED SOFT COMPUTING 2025年 170卷

作者： Wang, Kenan Yang, Tianning Kong, Shanshan Li, Mingduo North China Univ Sci & Technol Coll Sci Tangshan 063210 Peoples R China North China Univ Sci & Technol Coll Elect Engn Tangshan 063210 Peoples R China

With the rapid development of the economy, air pollution has become increasingly severe. Accurate prediction of the Air Quality Index (AQI) is crucial for safeguarding public health and the environment. However, AQI time series exhibit strong randomness and volatility, posing challenges for traditional forecasting methods to achieve precise AQI predictions. Therefore, we propose a new AQI hybrid prediction model, TG-Hybrid model, which integrates generative artificial intelligence, signal decomposition techniques, artificial intelligence methods, anda* optimization algorithms. In the proposed model, missing values in the data are handled using generative adversarial networks, effectively addressing the issue of a large number of missing values in time series data. Autoregressive integrated moving average is employed to forecast the linear components of the data, while variational mode decomposition decomposes AQI into multiple modes. Particle swarm optimization is used to combine the prediction results of convolutional neural network combined with bidirectional long short-term memory and extreme gradient boosting. Additionally, AQI prediction experiments were conducted using air pollution data from Tangshan and Beijing, and compared with fifteen other models. The results indicate that the root mean square error for Tangshan and Beijing are 6.407 and 7.485, respectively, significantly outperforming other baseline models.

关键词： Time Generative Adversarial Network Air Quality Index Prediction Deep learning model optimization algorithm Variational Mode Decomposition

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Nonlinear Trim and Stability Analysis for a Wingtip-Chained Aircraft

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JOURNAL OF GUIDANCE CONTROL AND DYNAMICS 2025年第5期48卷 1187-1195页

作者： He, Jiamin Liu, Haojie Huang, Rui Zhao, Yonghui Nanjing Univ Aeronaut & Astronaut Sch Aeronaut & Astronaut State Key Lab Mech & Control Aerosp Struct Coll Aerosp Engn Nanjing 210016 Peoples R China

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Performance investigation and power prediction of a trifunctional heat pump system

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APPLIED THERMAL ENGINEERING 2025年 266卷

作者： Han, Youhua Zhang, Jili Ma, Liangdong Lu, Shixiang Lv, Hui Gong, Jian Nanchang Hangkong Univ Sch Civil Engn & Transportat Nanchang 330063 Jiangxi Peoples R China Dalian Univ Technol Dept Civil Engn Dalian 116024 Liaoning Peoples R China Zhongyuan Univ Technol Sch Energy & Environm Zhengzhou 450007 Peoples R China

In this work, a trifunctional direct-expansion photovoltaic thermal heat pump system was constructed to provide domestic hot water, photovoltaic power, and chilled water. Experiments and power prediction models of the trifunctional direct-expansion photovoltaic thermal heat pump system were conducted. First, the photovoltaic, heating, and cooling performances were investigated to evaluate the comprehensive performance of the system with a vapor injection cycle. The experimental results revealed that the average electrical power and photoelectric conversion efficiency of the photovoltaic thermal array were 1.01 kW and 14.71 %, respectively. The average heating power, coefficient of performance, equivalent coefficient of performance, cooling power, and energy efficiency ratio of the system were 7.46 kW, 3.64, 6.87, 4.07 kW, and 1.94, respectively. The electrical and heating performance of the system was sensitive to solar irradiation, and the cooling performance was sensitive to the ambient temperature and wind speed. Afterwards, based on the experimental data, a back- propagation neural network model combined with particle swarm optimization, a genetic algorithm, and a time correlation series were proposed to forecast the tri-generation of the direct-expansion photovoltaic thermal heat pump system. The prediction results show that the proposed neural network prediction model has high prediction accuracy and robustness. The normalized root mean square error and mean absolute percentage error of the model were 2.00 % and 2.23 %, respectively, for electrical power prediction;1.03 % and 1.28 %, respectively, for heating power prediction;and 3.34 % and 4.29 %, respectively, for cooling power prediction.

关键词： Photovoltaic thermal heat pump system Tri-generation performance Experimental study Artificial neural network optimization algorithm Prediction accuracy

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Multi-Agent Grouping and Routing with Cooperative Tasks

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JOURNAL OF AEROSPACE INFORMATION SYSTEMS 2025年第3期22卷 163-176页

作者： Choi, Euihyeon Ahn, Jaemyung Korea Adv Inst Sci & Technol KAIST Dept Aerosp Engn 291 Daehak Ro Daejeon 34141 South Korea

This paper introduces a grouping and routing problem of multiple agents for cooperative missions. The introduced problem, referred to as the Vehicle Grouping and Routing Problem with Profits, aims to maximize the total reward obtained by conducting a multi-agent mission (e.g., cooperative reconnaissance) while reducing its makespan by appropriately grouping the agents and determining their routes under operational constraints (e.g., fuel, endurance). A mixed-integer linear programming formulation and a conservative column generation-based solution procedure for the problem are proposed. A case study with homogeneous and heterogeneous agents and numerical experiments involving a cooperative reconnaissance mission with multiple unmanned aerial vehicles demonstrate the validity of the proposed formulation and solution procedure.

关键词： Unmanned Aerial Vehicle Mixed Integer Linear Programming optimization algorithm Flight Dynamics Autonomous Technology Planetary Science and Exploration Aerospace Engineering

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Innovative Study on Volatility Prediction Model for New Energy Stock Indices

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IEEE ACCESS 2025年 13卷 29754-29777页

作者： Li, Yanguo Long, Chao Yunnan Univ Finance & Econ Sch Econ Kunming 650221 Peoples R China Yunnan Univ Finance & Econ Finance Res Inst Kunming 650221 Peoples R China

Stock market volatility is a pivotal research area in finance, and accurately forecasting stock market volatility has long been a challenge for both academia and practice. The emergence of the new energy industry has drawn widespread attention to new energy stock indices;however, research on forecasting their volatility remains limited. To address this, this paper proposes a deep learning ensemble model based on decomposition optimization for predicting the volatility of new energy stock indices. The model comprises three components: variational mode decomposition (VMD), sparrow search algorithm (SSA), and echo state network (ESN). Initially, this paper employs VMD to decompose the original volatility series of new energy stock indices into multiple subsequences. Subsequently, SSA is utilized to optimize ESN. Finally, the constructed VMD-SSA-ESN model is employed to forecast the volatility of new energy stock indices. Through comparative analysis with other forecasting models, this paper finds that the VMD-SSA-ESN model exhibits significantly better forecasting performance across all selected new energy stock index volatility predictions. The research results indicate that the model constructed in this paper can adequately capture the characteristics of the volatility series, and both VMD and SSA effectively enhance the model's forecasting accuracy and stability. This study can provide robust support for investment decision-making and risk management in the new energy stock market.

关键词： Predictive models Indexes Forecasting Prediction algorithms Industries Stock markets Deep learning Computational modeling Analytical models Accuracy Volatility prediction deep learning variational mode decomposition optimization algorithm new energy

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Designing Routes in Terminal Maneuvering Area Using Hybrid Simulated Annealing

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JOURNAL OF GUIDANCE CONTROL AND DYNAMICS 2025年第3期48卷 615-629页

作者： Zhou, Jun Wang, Xujie Hu, Xiaobing Civil Aviat Univ China Sino European Inst Aviat Engn Coll Safety Sci Lab Complex Syst Safety & Intelligent Decis Tianjin 300300 Peoples R China Civil Aviat Univ China Coll Safety Sci & Engn Lab Complex Syst Safety & Intelligent Decis Tianjin 300300 Peoples R China

To ensure the safe and efficient operation of flights in terminal maneuvering areas, airports establish standard arrival and departure procedures. However, designing these procedures is typically time-consuming and challenging for quantitative path length optimization. This paper presents an efficient optimization method for designing multiple arrival and departure procedures, taking into account the configuration of the airport and the nearby environment. The objective is to minimize the total length of routes while satisfying related operational constraints, such as obstacle avoidance and route separation. Each route is modeled in three dimensions, consisting of a horizontal polygonal line and a vertical cone. Considering the actual procedures' merging or diverging configuration characteristics, multiple arrival or departure routes are then modeled as a tree-like topology structure. A hybrid algorithm, integrating simulated annealing and an improved A* algorithm, is developed for automated problem-solving. Simulation results, based on tests at Sweden's Arlanda Airport, exhibit advantages over existing related research in terms of both total route length and computing time. Tests at Shanghai Pudong Airport show an 8% reduction in total path length compared to actual procedures, demonstrating the proposed method's effectiveness and potential contributions to energy savings and emissions reduction.

关键词： Annealing Terminal Manoeuvring Area Obstacle Avoidance Computing and Informatics optimization algorithm Initial Approach Fix Standard Terminal Arrival Route Air Navigation Service Provider Standard Instrument Departure Airspace Management

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