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Attitude estimation using an adaptive generalized complementary filter

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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A High-Accuracy Advanced Persistent Threat Detection Model: Integrating Convolutional Neural Networks with Kepler-Optimized Bidirectional Gated Recurrent Units

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ELECTRONICS 2025年第9期14卷 1772-1772页

作者： Hu, Guangwu Sun, Maoqi Zhang, Chaoqin Shenzhen Inst Informat Technol Sch Comp & Software Shenzhen 518029 Peoples R China Zhengzhou Univ Light Ind Coll Comp Sci & Technol Zhengzhou 450002 Peoples R China

Advanced Persistent Threat (APT) refers to a highly targeted, sophisticated, and prolonged form of cyberattack, typically directed at specific organizations or individuals. The primary objective of such attacks is the theft of sensitive information or the disruption of critical operations. APT attacks are characterized by their stealth and complexity, often resulting in significant economic losses. Furthermore, these attacks may lead to intelligence breaches, operational interruptions, and even jeopardize national security and political stability. Given the covert nature and extended durations of APT attacks, current detection solutions encounter challenges such as high detection difficulty and insufficient accuracy. To address these limitations, this paper proposes an innovative high-accuracy APT attack detection model, CNN-KOA-BiGRU, which integrates Convolutional Neural Networks (CNN), Bidirectional Gated Recurrent Units (BiGRU), and the Keplera* optimization algorithm (KOA). The model first utilizes CNN to extract spatial features from network traffic data, followed by the application of BiGRU to capture temporal dependencies and long-term memory, thereby forming comprehensive temporal features. Simultaneously, the Keplera* optimization algorithm is employed to optimize the BiGRU network structure, achieving globally optimal feature weights and enhancing detection accuracy. Additionally, this study employs a combination of sampling techniques, including Synthetic Minority Over-sampling Technique (SMOTE) and Tomek links, to mitigate classification bias caused by dataset imbalance. Evaluation results on the CSE-CIC-IDS2018 experimental dataset demonstrate that the CNN-KOA-BiGRU model achieves superior performance in detecting APT attacks, with an average accuracy of 98.68%. This surpasses existing methods, including CNN (93.01%), CNN-BiGRU (97.77%), and Graph Convolutional Network (GCN) (95.96%) on the same dataset. Specifically, the proposed model demonstrates an acc

关键词： APT detection imbalanced dataset SMOTE-Tomek combined sampling optimization algorithm deep learning networks

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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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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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Active learning Kriging approach for creep-fatigue reliability assessment of turbine disk

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STRUCTURAL AND MULTIDISCIPLINARY optimization 2025年第4期68卷 1-22页

作者： Huang, Ying Zhang, Jianguo Wang, Bowei Song, Lukai Wei, Yanxu Zhang, Wei Beihang Univ Sch Reliabil & Syst Engn Beijing 100191 Peoples R China Beihang Univ Sch Comp Sci & Engn Beijing 100191 Peoples R China Hong Kong Polytech Univ Dept Mech Engn Hong Kong 100872 Peoples R China Beihang Univ Res Inst Aero Engine Beijing 102206 Peoples R China Taiyuan Univ Technol Coll Mech & Vehicle Engn Taiyuan 030024 Peoples R China

The active metamodel is hard to represent the creep-fatigue failure, which hinders the application of efficient active metamodeling technique in creep-fatigue reliability estimation. To improve the computing efficiency and accuracy of creep-fatigue reliability assessment for turbine disk involving complex coupling of multi-layer, multi-disciplinary, and multi-uncertainty, the efficient active metamodeling technique is first pushed deep into complex creep-fatigue reliability evaluation. By integrating the synergic surrogate strategy into the active metamodeling, a multi-layer surrogate control-based synergic enhanced Kriging (MSC-SEK) approach is proposed: Firstly, to precisely describe the complicated creep-fatigue strong-coupling relationships, a synergic enhanced Kriging (SEK) is established by organically synergizing multiple Kriging models, where a multi-colony multi-mutation artificial bee colony algorithm is designed to enhance the Kriging surrogate quality;further, to obtain high-quality modeling dataset, a novel MSC learning function is developed by synthetically considering multi-surrogate entropy and reliability-sensitive information. The superiority of MSC-SEK is validated by studying the creep-fatigue reliability evaluation of a typical aeroengine turbine disk. The current efforts open up an effective way to achieve high-accuracy and high-efficiency engineering creep-fatigue reliability evaluation.

关键词： Reliability evaluation Creep-fatigue Kriging Active metamodeling Turbine disk optimization algorithm

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Solving Multi-Objective Optimal Control Problems Using a Multiresolution Approach

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JOURNAL OF GUIDANCE CONTROL AND DYNAMICS 2025年第1期48卷 32-45页

作者： Parsonage, Ben Maddock, Christie Univ Strathclyde Dept Mech & Aerosp Engn Glasgow Scotland

This paper presents an adaptive multiresolution strategy for multi-objective optimal control problems. The optimal control problem is solved using a direct approach, with individualistic grid adaptation facilitated by a local error analysis at element boundaries. Multiple objectives are considered using a dominance-based approach applying both local and global search methods to a collaborative population of unique solutions. These aspects are simultaneously incorporated via a novel application of evolutionary algorithms for adaptive optimal control problems. Together, this avoids the need for a priori specification of the quantity and temporal location of element boundaries and the set of scalarization weights defining the multi-objective descent directions. Solution fidelity can thus increase concurrently with the exploration of the design space, which leads to increased numerical efficiency while propagating and maintaining population diversity. The benefits of the proposed approach over traditional uniform-grid implementations are demonstrated. Results show that the multiresolution approach is capable of striking an effective balance between solution fidelity, population diversity, and computational cost unachievable using uniform grids.

关键词： Evolutionary algorithm Adaptive Mesh Refinement Multidisciplinary Design and optimization optimization algorithm Genetic algorithm Multi-Objective Evolutionary algorithms Optimal Control Problem

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