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Decentralized Observation Allocation for a Large-Scale Constellation

JOURNAL OF AEROSPACE INFORMATION SYSTEMS 2023年第8期20卷 447-461页

作者： Parjan, Shreya Chien, Steve A. CALTECH Jet Prop Lab Pasadena CA 91109 USA

Increased space sensing enables new measurements of a wide range of Earth science phenomena including volcanism, flooding, wildfires, and weather. Large-scale observation constellations of hundreds of assets have already been deployed (for example, Planet Labs's Dove satellites), and several constellations of tens of thousands of assets are planned. New challenges exist to rapidly assimilate available data and to optimize measurements by directing spacecraft assets to best observe complex Earth science phenomena. Centralized approaches to managing request allocation in these large constellations are constrained by 1) the need to assign/elect a central node to assign requests to spacecraft and 2) reliance on a single agent communicating with potentially thousands of dependent agents. On the other hand, entirely decentralized approaches to request allocation and observation are prone to oversatisfaction of some requests and undersatisfaction of others due to a lack of communication among agents. In large constellations, an intermediary method is necessary to solve the request allocation problem in a distributed manner. We present distributed artificial intelligence/multiagent methods that leverage existing work on distributed constraint optimization to allocate observations in a satellite constellation. We compare their performance to centralized and highly decentralized approaches using realistic orbits and observation request distributions. Our distributed algorithms can find approximate solutions to the large-scale constellation request allocation problem with low data volume for agent coordination and extend to continuous planning problems with varying request sets and availability of spacecraft agents.

关键词： Distributed Artificial Intelligence algorithms and Data Structures Computing and Informatics optimization algorithm Satellite Remote Sensing Earth Observation Satellite Multi-Agent System

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Identifying optimal ground feature classification and assessing leaf nitrogen status based on UAV multispectral images in an apple orchard

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PLANT AND SOIL 2024年 1-20页

作者： Sun, Guangzhao Chen, Shuaihong Hu, Tiantian Zhang, Shaowu Li, Hui Li, Aoqi Zhao, Lu Liu, Jie Northwest A&F Univ Key Lab Agr Soil & Water Engn Arid Semiarid Areas Minist Educ Xianyang Shaanxi Peoples R China Northwest A&F Univ Coll Water Resources & Architectural Engn Xianyang Shaanxi Peoples R China

Aims In the case of uneven vegetation coverage, it is still facing many problems that using UAV-remote sensing to assess the canopy nutrient status. The research objective is to determine the optimal ground feature classification and establish the leaf nitrogen concentration (LNC) inversion model. Methods UAV-Phantom 4 multispectral platform was used to acquire apple orchard images. The ground features of remote sensing image were classified with the minimum distance, maximum likelihood, and object-oriented feature extraction classifications (MDC, MLC, OFEC), while spectral vegetation indices were used to perform LNC inversion using the backpropagation neural network (BP) and extreme learning machine (ELM). Further, genetic algorithm (GA) and particle swarm optimization (PSO) were used to optimize two inversion models. Results Compared with the MDC, the overall accuracy of MLC increased 20.30% and 26.69% in 2021 and 2022, while the OFEC increased 30.48% and 31.04%, respectively. The LNC inversion model of BP and ELM produced an acceptable performance (R-c (2)> R(2)p > 0.60, RRMSE < 15%). The use of GA and PSO algorithms did not improve the prediction performance of the BP inversion model. Compared with ELM, the RRMSE of GA_ELM and PSO_ELM inversion models decreased 2.63% and 20.39%. Furthermore, the PSO_ELM inversion model decreased the RRMSE 10.95% compared to the BP model. ConclusionThe combination of reverse thinking iterative approach with the PSO algorithm significantly enhances the predictive performance of the ELM inversion model, which can present a rapid assessment method for leaf nitrogen nutrition diagnosis based on the relationship between LNC and fruit yield.

关键词： UAV-remote sensing Vegetation index optimization algorithm Foliar nitrogen Apple trees

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Command Governors with Inexact optimization and Without Invariance

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JOURNAL OF GUIDANCE CONTROL AND DYNAMICS 2022年第8期45卷 1523-1528页

作者： Garone, Emanuele Kolmanovsky, Ilya V. Univ Libre Bruxelles Serv Automat & Anal Syst B-1050 Brussels Belgium Univ Michigan Aerosp Engn Ann Arbor MI 48109 USA

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Neural Network Classification-Regression Method Applied to Approach Speed Control Strategy

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JOURNAL OF AEROSPACE INFORMATION SYSTEMS 2024年第4期21卷 362-366页

作者： Mori, Ryota Kobe Univ Grad Sch Maritime Sci Kobe 6580022 Japan

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Tracking performance of the coaxial counter-paddle flight system using fractional active disturbance rejection controller

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APPLIED MATHEMATICAL MODELLING 2023年第1期121卷 800-827页

作者： Gao, Yong Zhang, Hao Chen, Xiao Lu, Tingting Tan, Shizhe Yang, Hua Gulliver, T. aaron Ocean Univ China Fac Informat Sci & Engn Sch Elect Engn Qingdao 266100 Peoples R China Univ Victoria Dept Elect & Comp Engn Victoria BC V8W 2Y2 Canada

Stable aerial flight capability is crucial for amphibious vehicles to perform cross-domain motion, yet it is often influenced by unpredictable cross-domain environments and model uncertainties. A fractional-order active disturbance rejection controller based on finite-time convergent extended state observer is proposed for the anti-interference control problem in the attitude trajectory tracking of bullet-shaped trans-domain amphibious vehicle in complex environments. Firstly, a complete dynamic model of the airborne flight system of the bullet-shaped trans-domain amphibious vehicle is established. A hierarchical control scheme based on the proposed controller is designed assuming that all states of the system are available, in order to improve the control reliability and robustness of the flight system in multi-source interference environments. Then, based on a novel hybrid particle swarm search technique called the Particle Swarm optimization with Levy Flight and Wavelet Mutation algorithm, which combines Levy flight and wavelet mutation, a new parameter identification and tuning algorithm is proposed for the proposed controller to obtain optimal control parameters for attitude and position. To provide a comprehensive comparison, the stability of thea* optimization algorithm and the robustness performance of the controller were evaluated. Finally, all controllers were tested under three different disturbance scenarios. The results show that the proposed controller can achieve better stability in flight attitude and trajectory tracking tasks compared to Proportion Integration Differentiation controller, Fractional-order Proportion Integration Differentiation controller, and Active Disturbance Rejection controller in all scenarios. & COPY;2023 Elsevier Inc. All rights reserved.

关键词： Coaxial counter-paddle Trajectory tracking control Anti-disturbance control Bullet-shaped amphibious vehicle optimization algorithm

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Hierarchical Method for Mining a Prevailing Flight Pattern in Airport Terminal Airspace

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JOURNAL OF AEROSPACE INFORMATION SYSTEMS 2023年第11期20卷 702-718页

作者： Chu, Xiao Zeng, Weili Tan, Xianghua Zhou, Yadong Zhu, Dan Nanjing Univ Aeronaut & Astronaut Coll Civil Aviat Nanjing 211106 Peoples R China Natl Key Lab Air Traff Flow Management Nanjing 210016 Peoples R China

Due to the variety of flight patterns in airport terminal airspace, as well as the high global similarity of different flight patterns entering and leaving from the same runway or corridor, it is difficult for current mainstream methods to achieve good clustering. To this end, this paper first constructs a truncated dynamic time warping (TDTW) trajectory similarity measurement to characterize different trajectory patterns with high global similarity and large local differences. Furthermore, a hierarchical flight pattern mining method is proposed, which is divided into four layers according to different characteristics. The first three layers of the method classify trajectories according to takeoff and landing types, runways, and corridors;whereas the fourth layer uses a K-medoid clustering method based on TDTW, thereby making the mining process more controllable and in line with actual operation. Compared to dynamic time warping, the experimental results show that the intraclass compactness and interclass separation of the cluster obtained by the proposed method have decreased and increased by 44.6 and 20.1%, respectively, and the overall performance has improved by 54.1%. More refined and reasonable flight patterns have been obtained.

关键词： Airport Terminal Airspace Runway Configuration Take off and Landing optimization algorithm Secondary Radar Flight Trajectory Gaussian Mixture Models Artificial Neural Network Probability Density Functions

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Automated Exploration of Economical and Safe Well Trajectories in Brown Oil

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SPE DRILLING & COMPLETION 2023年第4期38卷 631-644页

作者： Pathan, Rizwan Singh, Umesh Joshi, Amol Dilip Cave, Adrien Fouchard, Cedric Baume, Antonin TCS Mumbai India TotalEnergies Courbevoie France

Due to volatile and uncertain oil and gas prices, the need to reduce well planning and drilling cost is a crucial concern in the oil and gas exploration and production industry. Therefore, the focus has increased on brownfield sites for new oil and gas explorations. However, the presence of previously drilled wellbores, also known as legacy wells, in brownfield sites increases the risk of collision incidents during the drilling process. Thus, avoiding collisions is also an important objective in the well trajectory design process. Moreover, designing well trajectories is a challenging and time-consuming process. Such designs demand multiple interdependent iterations to arrive at a required cost-effective solution that meets the drillability and safety requirements. In this study, we developed a novel framework to automate the well trajectory design process, including a well trajectory optimization technique to generate safer and more economical well trajectories. The developed framework was tested on two live oil-and gasfield cases. The first field case involved designing a single well trajectory in a crowded field, and the second field case involved designing 36 well trajectories in a field that consists of 13 legacy wells. The developed framework has a high impact on reducing the well trajectory design time and drilling length of trajectories. For example, in the second field case study, the obtained solution resulted in total length savings of 2.3% compared to an existing industry-standard tool solution, and it avoided collision with 13 wells. Also, the total time taken for designing 36 collision -free optimized wells was reduced from months (as per the industry standard) to a couple of days.

关键词： united states government optimization problem evolutionary algorithm optimization algorithm trajectory algorithm upstream oil & gas well planning machine learning consideration Trajectory design Well Planning

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State-of-the-art advanced hybrid ANNs paradigm for assessment and prediction of slope stability

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MULTISCALE AND MULTIDISCIPLINARY MODELING EXPERIMENTS AND DESIGN 2024年第4期7卷 3483-3509页

作者： Kumar, Nitish Kumari, Sunita Natl Inst Technol Patna Dept Civil Engn Patna India

The study presents advanced hybrid artificial neural network (ANN) models to enhance the analysis of slope stability and the prediction of the factor of safety (FOS). Conventional methodologies, such as analytical procedures and numerical simulations, encounter difficulties in accurately representing intricate interactions. Consequently, machine learning (ML) simulation methods are being introduced as contemporary alternatives. This paper provides a comparative examination of hybrid artificial neural network (ANN) models utilizing severala* optimization algorithms (OAs) like Firefly (FF), Ant Lion (ALO), shuffled complex evolution (SCE), and the imperialist colony algorithm (ICA), simulated on a dataset of 349 slope cases. The performance of the ML models is enhanced by hyperparameter tuning and verified using various proven statistical indicators, error matrix, and external validation. It is concluded that all the applied models are robust for practical applications. The comparative analysis is carried out using rank analysis which proposes ANN-FF (rank = 64, RTR2 = 0.983 and RTS2 = 0.943) as the best performing model, followed by ANN-ICA (rank = 42, RTR2 = 0.9772 and RTS2 = 0.937) and ANN-ALO (rank = 37, RTR2 = 0.973 and RTS2 = 0.909). The developed hybrid ANN models demonstrate significant potential as a novel tool to aid engineers in estimating rock strain during the design phase of various engineering projects.

关键词： Slope stability Machine learning Factor of safety Artificial neural network optimization algorithm

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Deep Diffusion for Training Chatter Detection Systems for Machine Tools

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IEEE SENSORS JOURNAL 2024年第19期24卷 29814-29823页

作者： Kuo, Ping-Huan Wu, Hao-Hsuan Yau, Her-Terng Natl Chung Cheng Univ Dept Mech Engn Chiayi 62102 Taiwan Natl Chung Cheng Univ Adv Inst Mfg High Tech Innovat AIM HI Chiayi 62102 Taiwan

In machine tool operations, chatter can cause machining errors and reduce a tool's lifespan and the production quality, and artificial intelligence (AI) is commonly used for predicting vibrations and chatter. In this study, 1-D chatter data were preprocessed through time delay mapping (TDM) to generate 2-D images. Diffusion models were then trained on the images and used to generate additional images to augment a small training dataset. The dataset was further used to train a convolutional neural network (CNN) model for detecting chatter. The data augmentation method can reduce data imbalance and improve a model's feature recognition capability, enhancing its accuracy on small datasets. Ana* optimization algorithm was used in this study to obtain the CNN hyperparameters that maximized the model's robustness and accuracy. Overall, the TDM, model-based augmentation, and optimization methods were discovered to effectively enhance the chatter detection accuracy of the final model. The results of image generation can be used to construct a large database, providing more training data for models and reducing time and labor costs. In addition, because the images retain critical features, the model's complexity can be substantially reduced.

关键词： Diffusion models Data models Training Accuracy Predictive models Time division multiplexing Machining Chatter detection convolutional neural network (CNN) diffusion models optimization algorithm time delay mapping (TDM)

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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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