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Real-time drone detection framework based on advanced texture feature extraction and pattern recognition model using GUI

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Neural Computing and Applications 2025年第5期37卷 3435-3454页

作者： Hussen, Noha Salem, Mofreh Eldesouky, Ali I. Sakr, Noha Elghamrawy, Sally Computers and Control Systems Engineering Department Mansoura University Mansoura Egypt Computer Engineering Department Misr Higher Institute for Engineering and Technology Mansoura Egypt

The increasing prevalence of drones has raised significant concerns regarding their potential for misuse in activities such as smuggling, terrorism, and unauthorized access to restricted airspace. Consequently, the development of robust and efficient drone detection systems has become paramount. Traditional detection methods such as radar and acoustic sensors have limitations in detecting small drones and can be costly to implement. This can lead to lower detection rates and increased security breaches and safety concerns. However, prevailing methodologies often falter in achieving real-time detection with requisite precision and efficacy, particularly within complex operational environments. Motivated by the imperative to surmount these challenges, our research endeavors to introduce a pioneering real-time drone detection framework. Our study encapsulates several seminal advancements. Firstly, we proffer a groundbreaking framework that synergizes advanced integration technique based on texture feature extraction and pattern recognition techniques for real-time drone detection to increase accuracy to detect drones in different conditions such as bad weather and low resolution. Secondly, we introduce an intuitive graphical user interface, enhancing the usability and accessibility of the system in real-time scenario. Lastly, through exhaustive evaluation and comparative analysis, we substantiate the superior performance of our framework in terms of accuracy, precision, and real-time detection capabilities compared to conventional DDS methodologies. Its stability and effectiveness render it a compelling solution for security-focused entities, notably those within air force and military systems. Our experimental results reveal a commendable accuracy rate of 97%, affirming the reliability and precision 98% and recall parameter 98% of our framework in accurately identifying and detecting drones, thus surpassing recent models in the field. © The Author(s), under exclusive

关键词： Graphical user interfaces

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Data-driven output regulation control for constrained linear systems

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Science China(Information Sciences) 2025年第3期68卷 338-353页

作者： Chaoyu XIA Yi DONG Chaoli WANG Shengyuan XU Shanghai Research Institute for Intelligent Autonomous Systems Tongji University College of Electronic and Information Engineering Shanghai Research Institute for Intelligent Autonomous SystemsTongji University Department of Control Science and Engineering School of Optical-Electrical and Computer EngineeringUniversity of Shanghai for Science and Technology Department of Automation Nanjing University of Science and Technology

This study introduces a data-driven approach for state and output feedback control addressing the constrained output regulation problem in unknown linear discrete-time systems. Our method ensures effective tracking performance while satisfying the state and input constraints, even when system matrices are not available. We first establish a sufficient condition necessary for the existence of a solution pair to the regulator equation and propose a data-based approach to obtain the feedforward and feedback control gains for state feedback control using linear programming. Furthermore, we design a refined Luenberger observer to accurately estimate the system state, while keeping the estimation error within a predefined set. By combining output regulation theory, we develop an output feedback control strategy. The stability of the closed-loop system is rigorously proved to be asymptotically stable by further leveraging the concept of λ-contractive sets.

关键词： output regulation constrained system data-driven.

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Vibration-induced bias error reduction using loop gain compensation for high-precision fiber optic gyroscopes

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Journal of Systems engineering and Electronics 2025年第1期36卷 224-232页

作者： CHEN Heyu QIN Xuexin XIE Huan KONG Linghai ZHENG Yue School of Instrumentation Science and Opto-Electronics Engineering Beihang UniversityBeijing 100191China Beijing Institute of Computer Technology and Application Beijing 100854China Hunan Shuaiwei Control Technology Co. LTDChangsha 421000China Beijing Institute of Control and Electronic Technology Beijing 100038China

Vibration-induced bias deviation,which is generated by intensity fluctuations and additional phase differences,is one of the vital errors for fiber optic gyroscopes(FOGs)operating in vibration environment and has severely restricted the applications of high-precision *** conventional methods for suppressing vibration-induced errors mostly concentrate on reinforcing the mechanical structure and optical path as well as the compensation under some specific operation parameters,which have very limited effects for high-precision FOGs maintaining performances under *** this work,a technique of suppressing the vibration-induced bias deviation through removing the part related to the varying gain from the rotation-rate output is put ***,the loop gain is extracted out by adding a gain-monitoring *** demodulating the loop gain and the rotation rate simultaneously under distinct frequencies and investigating their quantitative relationship,the vibrationinduced bias error is compensated without limiting the operating parameters or environments,like the applied modulation *** experimental results show that the proposed method has achieved the reduction of bias error from about 0.149°/h to0.014°/h during the random vibration with frequencies from20 Hz to 2000 *** technique provides a feasible route for enhancing the performances of high-precision FOGs heading towards high environmental adaptability.

关键词： vibration-induced error fiber optic gyroscope(FOG) loop gain

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Communication Countermeasure Strategy Decision-Making Based on Incomplete Information Game 27

Communication Countermeasure Strategy Decision-Making Based ...

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27th International Conference on Advanced Communications Technology, ICACT 2025

作者： Liu, Jinyue Gao, Xiang Yang, Haowei Zhang, Jihao Liu, Yuhan Gong, Peng National Key Laboratory of Mechatronic Engineering and Control School of Mechatronical Engineering Beijing Institute of Technology Beijing China Institute of Computer Application Technology Norinco Group Beijing China

ISBN: (纸本)9791188428137

In modern battlefields, the stability of wireless communications is crucial for intelligence transmission, command coordination, and maintaining strategic advantage. However, with the rapid advancement of communication technologies, the electromagnetic environment has become increasingly complex, making intelligent decision-making in communication countermeasures a prominent research focus. However, most existing studies adopt deep learning and reinforcement learning methods, which require extensive data samples and computational resources. To address these limitations, this paper proposes a game-theoretic model for intelligent radio communication countermeasures under incomplete information. It quantitatively evaluates common attack and defense tactcis used in communication countermeasures and explores the mixed-strategy equilibrium of the game when both the attackers and defenders consist of multiple types. Furthermore, simulation-based comparative analyses are conducted to assess the factors influencing the utility of both the attacker and defender. The findings offer theoretical insights and decision-making support for the deployment of communication countermeasure strategies in practical military operations, contributing to significant strategic value. © 2025 institute of Electrical and Electronics Engineers Inc.. All rights reserved.

关键词： Deep reinforcement learning

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A Note on the Local Observability of Uniform Hypergraphs

A Note on the Local Observability of Uniform Hypergraphs

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94th Annual Meeting of the International Association of Applied Mathematics and Mechanics, GAMM 2024

作者： Gerbet, Daniel Röbenack, Klaus Institute of Control Theory Faculty of Electrical and Computer Engineering TUD Dresden University of Technology Dresden Germany

Hypergraphs generalize graphs in such a way that edges may connect any number of nodes. If all edges are adjacent to the same number of nodes, the hypergraph is called uniform. Thus, a graph is a 2-uniform hypergraph. Each uniform hypergraph can be identified with an autonomous dynamical state-space system, whose vector field is composed of homogenous polynomials. We consider the observability of dynamical systems, which possess this special structure. In control theory, the observability of a dynamical system asks for the possibility to reconstruct the systems state from an output trajectory, where the output is a projective measurement of the systems state. In addition, one can ask for the observable subspace of the state. Nonlinear systems, although observable at almost all points in the state space, may possess not-locally observable ones. The existence and location of such states are important for observability analysis and observer design. Recently, the observability of these hypergraph systems has been studied for the general case. We apply an extended deterministic observability test to uniform hypergraphs and also compute the not-observable states for different linear output maps. We are able to confirm the results of the recent study, at least for graphs with a small number of nodes, due to the computational complexity of our method. In addition, the not-locally observable states are computed for different combinations of measured nodes. © 2025 The Author(s).

关键词： Dynamical systems

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Invariant Set Analysis and control Synthesis of Multi-Equilibrium Switched Systems under control Constraints 13

Invariant Set Analysis and Control Synthesis of Multi-Equili...

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13th International Conference on Intelligent control and Information Processing, ICICIP 2025

作者： Han, Yuejiang Zhang, Lixian Ding, Yihang Dong, Yifei Liang, Ye School of Astronautics Harbin Institute of Technology Harbin China College of Computer and Control Engineering Northeast Forestry University Harbin China

ISBN: (纸本)9798331516147

This paper investigates the problems of invariant set analysis and control synthesis for multi-equilibrium switched systems under control constraints. A control strategy based on the invariant set method is proposed, accompanied by criteria formulated as linear matrix inequalities, ensuring that the state remains within the mode-dependent invariant sets. Furthermore, considering the control constraints in practical scenarios, a multi-equilibrium switched controller with control constraints (MESCC controller) is designed. Compared to existing works, the proposed method incorporates mode-dependent dwell time conditions and mode-dependent invariant sets, thereby offering enhanced design flexibility. An example of an aero-engine control system is presented to demonstrate the effectiveness and potential of the theoretical results. © 2025 IEEE.

关键词： control system synthesis

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Research on indoor temperature and humidity coupling system based on fuzzy adaptive PD control 9

Research on indoor temperature and humidity coupling system ...

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2024 9th International Seminar on computer Technology, Mechanical and Electrical engineering, ISCME 2024

作者： Li, Hui Yang, Hao Zhang, Suli Miao, Jiahao School of Information and Control Engineering Jilin Institute of Chemical Technology Jilin Jilin132022 China School of Computer Technology and Engineering Changchun Institute of Technology Jilin Changchun130012 China

In this research, a fuzzy adaptive PD control approach is introduced for managing the coupled indoor temperature and humidity system. Initially, the mathematical framework of indoor temperature and humidity is analyzed. For the coupled system, a decoupling control strategy is introduced, which eliminates the mutual influence between temperature and humidity by incorporating decoupling components, thereby enabling the system to be equivalently controlled as two independent single-input single-output systems. Secondly, a fuzzy adaptive PD control method is employed to achieve parameter self-tuning. Finally, to assess the efficacy of the introduced control approach, the present study undertakes a simulation analysis, utilizing a medium-sized office as a case study. The outcomes of the simulation suggest that upon implementing the fuzzy adaptive PD control methodology, the modulation of indoor temperature and humidity achieves enhanced precision and swiftness, notably demonstrating greater robustness in the presence of disturbance signals. © 2025 institute of Physics Publishing. All rights reserved.

关键词： Self tuning control systems

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Enhancing deformation characteristics prediction of coarse-grained soils with time-series generative adversarial network-based data augmentation and pre-training

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Frontiers of Structural and Civil engineering 2025年第3期19卷 396-410页

作者： Ying ZHANG Meng JIA Xuedong ZHANG Liping CAO Ziying AN Hongchao WANG Jinyu WANG School of Control and Computer Engineering North China Electric Power UniversityBeijing 102206China The State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin China Institute of Water Resources and Hydropower ResearchBeijing 100038China

Coarse-grained soils are fundamental to major infrastructures like embankments,roads,and *** their deformation characteristics is essential for ensuring structural *** methods,such as triaxial compression tests and numerical simulations,face challenges like high costs,time consumption,and limited generalizability across different soils and *** address these limitations,this study employs deep learning to predict the volumetric strain of coarse-grained soils as axial strain changes,aiming to obtain the axial strain(ε_(a))-volumetric strain(ε_(v))curve,which helps derive key mechanical parameters like cohesion(c),and elastic modulus(E).However,the limited data from triaxial tests poses challenges for training deep learning *** propose using a Time-series Generative Adversarial Network(TimeGAN)for data ***,we apply feature importance analysis to assess the quality of the numerical augmented data,providing feedback for improving the TimeGAN *** further enhance model performance,we introduce the pre-training strategy to reduce bias between augmented and real *** results demonstrate that our approach effectively predictscurve,with the mean absolute error(MAE)of 0.2219 and the R^(2) of *** analysis aligns with established findings in soil mechanics,underscoring the potential of our method in engineering applications.

关键词： coarse-grained soils deformation characteristics TimeGAN data augmentation pre-training

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Perception of premium services based on the evolution of hyper-service network in mobile edge computing

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Journal of Cloud Computing 2025年第1期14卷 1-17页

作者： Zhang, Xiaojun Liu, Zhizhong Chu, Dianhui Zhao, Meiyue School of Computer and control Engineering Yantai University Yantai264005 China School of Computer and control Engineering Harbin Institute of Technology Weihai264209 China

Recently, massive services from multiple domains are available on the network with the rapid development of Mobile Edge Computing (MEC) technology. These various services construct a service network through services’ collaboration, which can provide users with rich resources at anytime and anywhere. However, in MEC, due to the proliferation of services, as well as the dynamics of Quality of Service (QoS), it becomes more and more difficult for users to discover the premium services effectively, which seriously impacts users’ satisfaction and the utilization of resources. Therefore, how to proactively perceive the emergence of premium services in the service networks has become an challenge. To tackle this issue, we propose a method for perception of the premium services based on the evolution of hyper-service network (named as PPSHSN). First, based on the hypergraph theory, we construct a hyper-service network with the optimal connection method. Then, we develop the evolution mechanism of the hyper-service network and optimize the hyper-service network with the hyperdegree premium value evolution algorithm, thus to conduct the evolution of the service networks. Next, we devise the evaluation indexes based on the topological characteristics of hypergraphs, which are updated according to the evolution of the hyper-service network. Finally, we complete perception of premium services based on the evolution results of the hyper-service network. The effectiveness and feasibility of our proposed method are verified through extensive simulation experiments. © The Author(s) 2025.

关键词： Hypermedia systems

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Binary Stochastic Flip Optimization for Training Binary Neural Networks

Binary Stochastic Flip Optimization for Training Binary Neur...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Shibuya, Tatsukichi Inoue, Nakamasa Kawakami, Rei Sato, Ikuro Department of Computer Science Institute of Science Tokyo Tokyo Japan Department of Systems and Control Engineering Institute of Science Tokyo Tokyo Japan Denso IT Laboratory Tokyo Japan

ISBN: (纸本)9798350368741

For deploying deep neural networks on edge devices with limited resources, binary neural networks (BNNs) have attracted significant attention, due to their computational and memory efficiency. However, once a neural network is binarized, finetuning it on edge devices becomes challenging because most conventional training algorithms for BNNs are designed for use on centralized servers and require storing real-valued parameters during training. To address this limitation, this paper introduces binary stochastic flip optimization (BinSFO), a novel training algorithm for BNNs. BinSFO employs a parameter update rule based on Boolean operations, eliminating the need to store real-valued parameters and thereby reducing memory requirements and computational overhead. In experiments, we demonstrated the effectiveness and memory efficiency of BinSFO in finetuning scenarios on six image classification datasets. BinSFO performed comparably to conventional training algorithms with a 70.7% smaller memory requirement. Code is released at https://***/TatsukichiShibuya/ICASSP2025_BinSFO. © 2025 IEEE.

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