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42nd Chinese control Conference, CCC 2023

作者： Zhang, Faxiang Gao, Guanbin Na, Jing Wu, Jiande Kunming University of Science and Technology Faculty of Mechanical and Electrical Engineering Yunnan International Joint Laboratory of Intelligent Control and Application of Advanced Equipment Kunming650500 China Yunnan University Yunnan Key Laboratory of Intelligent Systems and Computing Kunming650500 China

ISBN: (纸本)9789887581543

This paper solves the neural network (NN) tracking control problem for uncertain nonlinear servo system with time-varying parameters and uncertainties. To address the uncertain nonlinear function with unknown time-varying parameters (i.e., unknown nonlinear spatiotemporal function), the time-varying parameter extraction method is used to separate the time-varying parameters from uncertain nonlinear spatiotemporal function, which yields an unknown state-based boundary function. By the tools of NN and adaptive technology, an adaptive neural tracking controller is designed, which guarantees the uniformly ultimately bounded (UUB) performance of the resulting closed-loop system. The effectiveness of the designed method is verified by simulations. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Servomechanisms

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Air Traffic Management and Communication over ATN/IPS for Future Datalink Communication

Air Traffic Management and Communication over ATN/IPS for Fu...

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2025 Integrated Communications, Navigation and Surveillance Conference, ICNS 2025

作者： Aydogan, Emre Ozmen, Sergun Cetek, Fulya Aybek Valdes, Rosa Maria Arnaldo Jurado, Raquel Delgado-Aguilera Fernandez, Angel Ernesto Carmona Miralles, Adrian Martinez Vendruscolo, Tommaso Bonelli, Stefano Delahaye, Daniel Chaimatanan, Supatcha Chen, Feng Hamzaoui, Raouf Eskişehir Technical University Faculty of Aeronautics and Astronautics Air Traffic Control Department Eskişehir Turkey Turkish Airlines Îstanbul Turkey Universidad Politėcnica de Madrid Department of Aerospace Systems Air Transport and Airports Madrid Spain Servicios Aeronáuticos Control y Navegación Madrid Spain Deep Blue srl Rome Italy OPTIM Lab Toulouse France De Montfort University Faculty of Computing Engineering and Media Leicester United Kingdom

ISBN: (纸本)9798331534738

The growing demand for air traffic presents challenges in air traffic management, making seamless gate-to-gate communication essential. Traditional radio frequency communication faces limitations such as weather dependency and frequency restrictions. To address these issues, data link communications have gained importance, using VHF channels, satellite systems, and ATN/IPS-based networks. This study introduces the ATMACA (Air Traffic Management and Communication Over ATN/IPS) protocol, an advanced context management framework for ATN/IPS, designed to enhance aviation communications. ATMACA integrates instant messaging and software-defined nodes to improve connectivity, session continuity, and mobility management across networks and devices. It ensures seamless user interaction, reduces pilot workload, and enhances flight safety through automated Air Traffic control (ATC) sector handoff in controller-Pilot Data Link Communications (CPDLC) and Data Link Initiation Capability (DLIC) applications. Another key innovation of the ATMACA framework is Green Route Operations (GRO), which enables real-time trajectory prediction and optimization. © 2025 IEEE.

关键词： Air traffic control

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Bio-inspired Swarm Intelligence: A Flocking Project with Group Object Recognition 3

Bio-inspired Swarm Intelligence: A Flocking Project with Gro...

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3rd International Conference on Consumer Electronics and Computer Engineering, ICCECE 2023

作者： Tianbo, Song Weijun, Hu Jiangfeng, Cai Weijia, Liu Quan, Yuan Kun, He Ira A. Fulton Schools of Engineering Arizona State University Tempe United States Saint Petersburg State University Faculty of Applied Mathematics and Control Processes Saint Petersburg Russia Lomonosov Moscow State University Department of Chemistry Moscow Russia Allen School of Engineering and Computing Trine University Angola United States The Open University of China Faculty of Engineering Hangzhou China College of Computing Illinois Institute of Technology Chicago United States

ISBN: (纸本)9798350331578

With the help of the adaptive gain control algorithm and the flocking SWARM algorithm with adversarial agents, the project described in this article makes the entire system communication-free while maintaining the same functionality and performance. It outlines the entirety of this biologically inspired swarm intelligence's thought process, from inception to conclusion (Flocking algorithm). The entire project is dependable and practically impenetrable under a variety of conditions. The neighbor observation for group conjecture is then strengthened by the use of the adaptive gain control technique and a partial Kalman filter. After the system has been stabilized, use the built-in camera of the SWARM drone to introduce the Object Recognition method in computer vision. As a result, the system enters the real world from the simulation level. © 2023 IEEE.

关键词： Object recognition

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Improving Data Transmission Efficiency in UAV Networks Through Adaptive Coding

Improving Data Transmission Efficiency in UAV Networks Throu...

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International Youth Conference on Radio Electronics, Electrical and Power Engineering (REEPE)

作者： Majd M. Safadi Vladislav B. Amoursky Mathematical and Software of Computing Systems Complexes and Computer Networks Faculty of Information Science and Control Systems Bauman Moscow State Technical University Moscow Russian Federation

ISBN: (数字)9798331531836

ISBN: (纸本)9798331531843

In recent years, unmanned aerial vehicles (UAV) have become widely used in civil and military applications such as surveillance, data collection, and cargo delivery. With the increasing use of this technology, there is a need for improved communication systems to ensure reliable and efficient data transmission. UAV networks face unique challenges due to dynamic motion, environmental interference, and energy and bandwidth limitations. The objective of this study is to propose an adaptive coding scheme using Rate-Compatible Punctured Coding (RCPC) technology, which assigns different coding rates to different data depending on their importance and channel conditions. The proposed scheme includes a mechanism to dynamically adjust the coding rates based on the change in link quality. This study demonstrates that adaptive coding can improve resource efficiency, reduce latency, and enhance communication reliability.

关键词： Surveillance Interference Autonomous aerial vehicles Encoding Reliability Data communication Time factors Vehicle dynamics Adaptive coding Signal to noise ratio

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Simulation analysis of erosion characteristics of slurry pipelines based on CFD-DDPM pipeline parameters 43

Simulation analysis of erosion characteristics of slurry pip...

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43rd Chinese control Conference, CCC 2024

作者： Yuan, Haoran Wu, Jiande Xiong, Xin Yunnan University Yunnan Key Laboratory of Intelligent Systems and Computing Yunnan Kunming650500 China Kunming University of Science and Technology Faculty of Information Engineering and Automation Yunnan Kunming650500 China Kunming University of Science and Technology Yunnan Key Laboratory of Intelligent Control and Application Yunnan Kunming650500 China

ISBN: (纸本)9789887581581

In order to explore the impact of pipe diameter, bend-diameter ratio and bend angle on the erosion of slurry pipe, a numerical simulation method approach grounded in Computational Fluid Dynamics - Dense Discrete Phase Model (CFD-DDPM) is proposed. Firstly, the geometric models of bends with different pipe diameters, bend-diameter ratios, and bend angles are constructed;secondly, the slurry pipe erosion model based on Dense Discrete Phase Model (DDPM) is constructed;then, model validation is conducted on the slurry pipe erosion model to affirm the accuracy of the model;finally, the computational fluid dynamics method is employed to simulate the erosion and wear behavior of the pipeline, to investigate the impact of pipe diameter, bend-diameter ratio and bend angle on the erosion and wear of the slurry pipeline. At the same time, gray correlation analysis was also used to analyze the significant relationship between the three pipeline factors and erosion and wear. The results show that the main erosion area is not determined, with the change of the pipeline parameters, the most serious erosion area will move;the effect of different pipeline parameter variations on the erosion rate is different, in which the pipe diameter of the largest impact, the second largest bend-diameter ratio, the smallest impact of the bend angle. Therefore, in the design of slurry pipeline, should first consider the influence of pipe diameter factors. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Slurry pipelines

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A Search Strategy and Vessel Detection in Maritime Environment Using Fixed-Wing UAVs

A Search Strategy and Vessel Detection in Maritime Environme...

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International Symposium on Underwater Technology

作者： Marijana Peti Ana Milas Natko Kraševac Marko Križmančić Ivan Lončar Nikola Mišković Stjepan Bogdan Laboratory for Robotics and Intelligent Control Systems (LARICS) Faculty of Electrical Engineering and Computing University of Zagreb Zagreb Croatia Laboratory for Underwater Systems and Technologies (LABUST) Faculty of Electrical Engineering and Computing University of Zagreb Zagreb Croatia

In this paper, we address the problem of autonomous search and vessel detection in an unknown GNSS-denied maritime environment with fixed-wing UAVs. The main challenge in such environments with limited localization, communication range, and the total number of UAVs and sensors is to implement an appropriate search strategy so that a target vessel can be detected as soon as possible. Thus we present informed and non-informed methods used to search the environment. The informed method relies on an obtained probabilistic map, while the non-informed method navigates the UAVs along predefined paths computed with respect to the environment. The vessel detection method is trained on synthetic data collected in the simulator with data annotation tools. Comparative experiments in simulation have shown that our combination of sensors, search methods and a vessel detection algorithm leads to a successful search for the target vessel in such challenging environments.

关键词： Location awareness Navigation Annotations Computational modeling Search problems Probabilistic logic Sensors

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A novel analytical model for estimating vehicle delay at isolated signalised intersections

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International Journal of Simulation and Process Modelling 2022年第1-2期19卷 71-85页

作者： Qiao, Feng Liu, Huixin Luo, Dan Sun, Haochen Chen, Yinong School of Information and Control Engineering Shenyang Institute of Technology Fushun113122 China Faculty of Information and Control Engineering Shenyang Jianzhu University Shenyang100168 China School of Information Engineering Henan University of Science and Technology Henan Luoyang471000 China School of Computing Informatics and Decision Systems Engineering Arizona State University TempeAZ85287-8809 United States

This paper proposes a novel analytical model to estimate the average vehicle delay at signalised intersections under saturated or oversaturated conditions based on the investigation and analysis of the existing methods to deal with the problems arising in the process of acceleration, deceleration, and the transmissibility of the cycle-by-cycle average vehicle delay. The proposed model employs and combines the operating and queuing characteristics of vehicles to produce the analytic formula. To verify the effectiveness of the proposed model, simulation experiments are conducted, and the error rates and the correlation coefficients are investigated, which confirm that the proposed model possesses certain significant advantages over the existing models in saturated and oversaturated conditions. The results of research work show that the proposed model can provide transportation engineers or professionals with an effective tool for analysing, timing and managing the saturated or oversaturated signalised intersections. © 2022 Inderscience Enterprises Ltd.

关键词： Analytical models

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An Improved dynamic functional connectivity and deep neural network model for Autism Spectrum Disorder Classification 22

An Improved dynamic functional connectivity and deep neural ...

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6th International Conference on Deep Learning Technologies, ICDLT 2022

作者： Li, Ming Xiu Tu, Shan Shan Rehman, Sadaqat Ur Yang, Yong Jie Engineering Research Center of Intelligent Perception and Autonomous Control Faculty of Information Technology Beijing University of Technology Beijing100124 China School of Natural and Computing Sciences University of Aberdeen United Kingdom

ISBN: (纸本)9781450396936

Brain disorders such as autism spectrum disorder (ASD) is still difficult to diagnose. In the recent years, different novel deep learning algorithms have been applied to detect ASD. Most studies use the functional connectivity (FC) pattern to represent the brain activities. However, it has been investigated that dynamic functional connectivity (dFC) which represent more features than FC can characterize the intrinsic brain organization changes over time. The goal of this paper is to determine that dFC features are more successful than FC features in the classification of ASD using deep learning. In this paper, we propose a classification model using dFC and deep neural network. Firstly, we used windowed k-means (WKM) approach to compute the sub-state of the brain and extract the main features of the functional magnetic resonance imaging(fMRI). Then, two stacked denoising autoencoders were applied to extract the features and reduce the dimension. At last, the MLP was utilized to complete the classification task and do fine-tuning based on the autoencoder encoders weights. The experiments were carried out on the Autism Brain Imaging Data Exchange (ABIDE) datasets. Result shows that we acquired a mean accuracy of 68.51%. Overall, our proposed classification is effective and provide evidence that dFC contains more brain states features. © 2022 ACM.

关键词： Deep neural networks

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Facial Expression Recognition Model Depending on Optimized Support Vector Machine

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Computers, Materials & Continua 2023年第7期76卷 499-515页

作者： Amel Ali Alhussan Fatma M.Talaat El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Doaa Sami Khafaga Mona Alnaggar Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Machine Learning&Information Retrieval Department Faculty of Artificial IntelligenceKafrelsheikh UniversityKafrelsheikh33516Egypt Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Robotics and Intelligent Machines Department Faculty of Artificial IntelligenceKafrelsheikh UniversityKafrelsheikh33516Egypt

In computer vision,emotion recognition using facial expression images is considered an important research *** learning advances in recent years have aided in attaining improved results in this *** to recent studies,multiple facial expressions may be included in facial photographs representing a particular type of *** is feasible and useful to convert face photos into collections of visual words and carry out global expression *** main contribution of this paper is to propose a facial expression recognitionmodel(FERM)depending on an optimized Support Vector Machine(SVM).To test the performance of the proposed model(FERM),AffectNet is *** uses 1250 emotion-related keywords in six different languages to search three major search engines and get over 1,000,000 facial photos *** FERM is composed of three main phases:(i)the Data preparation phase,(ii)Applying grid search for optimization,and(iii)the categorization *** discriminant analysis(LDA)is used to categorize the data into eight labels(neutral,happy,sad,surprised,fear,disgust,angry,and contempt).Due to using LDA,the performance of categorization via SVM has been obviously *** search is used to find the optimal values for hyperparameters of SVM(C and gamma).The proposed optimized SVM algorithm has achieved an accuracy of 99%and a 98%F1 score.

关键词： Facial expression recognition machine learning linear dis-criminant analysis(LDA) support vector machine(SVM) grid search

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Application of Extreme Learning Machine for Shunt Faults Detection and Classification in Three-phase Transmission Line Systems 3

Application of Extreme Learning Machine for Shunt Faults Det...

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3rd IEEE International Conference on Signal, control and Communication, SCC 2023

作者： Assadi, Khaoula Slimane, Jihane Ben Salhi, Salah University of Tunis El Manar National Engineering School of Tunis LR11ES20 Analysis Design and Control of Systems Laboratory Tunis1002 Tunisia Northern Border University Faculty of Computing and InformationTechnology Department of Computer Sciences Rafha91911 Saudi Arabia

ISBN: (纸本)9798350326390

The electrical power transmission lines play a crucial role in maintaining a continuous electricity supply. However, the exposed environment of these lines increases the risks of faults occurrence, necessitating prompt detection, and classification, to eliminate them within a specified time. In light of this, the present study introduces a methodology for the detection and classification of faults in a three-phase transmission line system employing the Extreme Learning Machine (ELM). A 220 kV, 300 km three-phase transmission line system was simulated in MATLAB/Simulink software, where the current and voltage signals were generated under different fault conditions including fault types, fault inception time, and fault resistance. The fundamental components of these signals were used as inputs in the ELM model. Different shunt fault types, including line-to-ground, line-to-line, double-line-to-ground, triple-line, and triple-line-to-ground were applied for fault detection and classification framework. Furthermore, the optimum number of hidden neurons in the ELM model was investigated in this study. In terms of the Mean Squared Error (MSE) and correlation coefficient, four ELM variants based on Sine, Sigmoid, Radial Basis, and Triangular Basis transfer functions were assessed for each module. The outcome of these metrics indicates that the ELM with the sigmoid model demonstrated superior performance compared to the other ELM models. Based on statistical metrics MSE, Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE), and also the computational time, the efficiency of ELM-Sigmoid and state-of-the-art methods of Multi-Layer Perceptron Neural Network (MLPNN) and Radial Basis Function Neural Network (RBFNN) were compared and analyzed during training and testing phases. The results obtained revealed that the ELM-Sigmoid model outperforms the other models in the detection and classification of shunt faults with significant accuracy and fast computational speed. ©

关键词： Fault detection

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