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Automatic Modulation Classification in Wireless Communications Using Multi-Aspect Graph Attention Network with Avg-TopK Pooling and Osprey Optimization 4

Automatic Modulation Classification in Wireless Communicatio...

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4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Mohandas, R. Elangovan, Bharath Sivasubramanian, Soorya Sridhar, S. Dhanadhya, Trupti Chennai Institute of Technology Department of Electronics & Communication Engineering Tamil Nadu Chennai India C K College of Engineering & Technology Department of Artificial Intelligence & Data Science Tamil Nadu Cuddalore India V.R.S College of Engineering & Technology Department of Computer Science and Engineering Tamil Nadu Arasur India Rajalakshmi Institute of Technology [Autonomous Department of Research Tamil Nadu Chennai India Dr. D Y Patil Institute of Technology Department of Electrical Engineering Maharashtra Pune India

ISBN: (纸本)9798331529635

AMC stands for automatic modulation classification, a significant tool in wireless communication for identification of the signal modulations of immense relevance to dynamic spectrum management, interference identification and cognitive radio networks. Many of the previous AMC approaches include feature extraction and manual classification which are not particularly portable and accurate in other contexts of communications. Recently, with the development of deep leaning, attention based methods have brought obviously enhanced performance in perceiving comprehensive signal characteristics;but there are still problems in focusing to multi-aspect features and training at a large scale signal data with high computational complexity. In order to address this problem, this research introduces a Avg-TopK pooling layer along with a Multi-Aspect Graph Attention Network (MAGAN) for the purpose of the AMC while using the Osprey Optimization Algorithm for the optimization of the proposed model. The Avg-TopK pooling only keeps K most important feature representations and decreases noise levels for better signal modulation classification. MAGAN uses multi-aspect attention mechanism allowing the emphasize various properties of the signal while perceiving mutual connections between the features of the signal. Additionally, the proposed Osprey Optimization Algorithm improves the training time of the model through optimization of hyperparameters. The presented results obtained from experimental analysis of standard AMC performance metrics show that the proposed model provides better AMC classification capability and insensitivity to the noise and existence of multiple types of modulation modes in contrast to other AMC models. In light of this, this work aims at solving some of the major challenges facing AMC through presenting a highly adaptive, efficient and accurate solution for the ever evolving wireless communication systems. The introduced approach attains higher accuracy as 99%

关键词： Radio interference

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Improvement of the Predictive control Method for the High-Level controller 17

Improvement of the Predictive Control Method for the High-Le...

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17th IEEE International Conference on Advanced Trends in Radioelectronics, Telecommunications and computer engineering, TCSET 2024

作者： Budashko, Vitalii Sandler, Albert Glazeva, Oksana Educational and Scientific Institute of Automation and Electrical Engineering National University 'Odessa Maritime Academy' NUOMA Odesa Ukraine National University 'Odessa Maritime Academy' NUOMA Department of the theory of automatic control and computer technology Odesa Ukraine National University 'Odessa Maritime Academy' NUOMA Department of electrical engineering and electronics Odesa Ukraine

ISBN: (纸本)9798331520564

Automation of ship maneuvering in limited sailing conditions usually requires 100% redundancy of thrusters (THRs) of various modifications and their locations in accordance with the matrix. The hierarchy of the motion control system is branched by levels using high-level motion controllers and THRs control distribution algorithms, which allows for a modular design with software, where the high-level controller (HLC) can be designed without comprehensive information about the thruster motors, and the input signal branching and speed limits are handled by the motion controller. But, for certain THRs configurations, this branching leads to the decrease in control efficiency due to the limitation of data on the physical characteristics of the ships and the operation of the motion controller. This research examines different approaches to improving control efficiency using methods of nonlinear predictive control models as a basis for developing motion controllers with decision optimization based on relevant constraints. The implemented branched system is the result of solving two simple problems of ship's motion, which highlight the related problems. The use of different approaches to eliminate the identified shortcomings made it possible to develop the nonlinear controller that combines the motion controller with distributed control of the THRs and ensuring the certain level of system reliability. The modularity of the control system is provided by the expansion of the closed system, which made it possible to achieve an increase in efficiency during the combined mode of operation. The obtained solution of nonlinear control with time-varying constraints made it possible to increase the accuracy of the control with a decrease in the duration of the pause in response to the change of the disturbance within 10% © 2024 IEEE.

关键词： Predictive control systems

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A Primal-Dual SGD Algorithm for Distributed Nonconvex Optimization

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IEEE/CAA Journal of Automatica Sinica 2022年第5期9卷 812-833页

作者： Xinlei Yi Shengjun Zhang Tao Yang Tianyou Chai Karl Henrik Johansson Division of Decision and Control Systems School of Electrical Engineering and Computer ScienceKTH Royal Institute of Technologyand also affiliated with the Digital FuturesStockholm 10044Sweden Department of Electrical Engineering University of North TexasDentonTX 76203 USA IEEE State Key Laboratory of Synthetical Automation for Process Industries Northeastern UniversityShenyang 110819China

The distributed nonconvex optimization problem of minimizing a global cost function formed by a sum of n local cost functions by using local information exchange is *** problem is an important component of many machine learning techniques with data parallelism,such as deep learning and federated *** propose a distributed primal-dual stochastic gradient descent(SGD)algorithm,suitable for arbitrarily connected communication networks and any smooth(possibly nonconvex)cost *** show that the proposed algorithm achieves the linear speedup convergence rate O(1/(√nT))for general nonconvex cost functions and the linear speedup convergence rate O(1/(nT)) when the global cost function satisfies the Polyak-Lojasiewicz(P-L)condition,where T is the total number of *** also show that the output of the proposed algorithm with constant parameters linearly converges to a neighborhood of a global *** demonstrate through numerical experiments the efficiency of our algorithm in comparison with the baseline centralized SGD and recently proposed distributed SGD algorithms.

关键词： Distributed nonconvex optimization linear speedup Polyak-Lojasiewicz(P-L)condition primal-dual algorithm stochastic gradient descent

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Individually-guided Evolutionary Algorithm for Solving Multi-task Optimization Problems 19

Individually-guided Evolutionary Algorithm for Solving Multi...

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19th IEEE International Conference on Networking, Sensing and control, ICNSC 2022

作者： Wang, XiaoLing Kang, Qi Zhou, MengChu Tongji University Department of Control Science and Engineering Shanghai China New Jersey Institute of Technology Department of Electrical and Computer Engineering Newark United States

ISBN: (纸本)9781665472432

Multi-task optimization (MTO) is a novel emerging evolutionary computation paradigm that is used for solving multiple optimization tasks concurrently. Most MTO algorithms limit each individual to one task, and thus weaken the performance of information exchange. To address this issue and improve the efficiency of knowledge transfer, this work proposes an efficient MTO framework named individually-guided multi-task optimization (IMTO). It divides evolutions into vertical and horizontal ones. To further improve the efficiency of knowledge transfer, a partial individuals' learning scheme is used to choose suitable individuals to learn from other tasks. Experimental results show its superior advantages over the multifactorial evolutionary algorithm and its variants. © 2022 IEEE.

关键词： Evolutionary algorithms

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Cascaded Design for Finite-Time Trajectory Tracking control of an Underactuated Surface Vehicle 8

Cascaded Design for Finite-Time Trajectory Tracking Control ...

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8th International Conference on control Science and Systems engineering, ICCSSE 2022

作者： Chen, Ziyin Ge, Shuzhi Sam Beijing Institute of Space Mechanics & Electricity Department of Control Engineering Beijing China National University of Singapore Department of Electrical & Computer Engineering Singapore Singapore

ISBN: (纸本)9781665484138

This paper discusses the cascaded design for finitetime trajectory tracking control of an underactuated unmanned surface vehicle (USV). The tracking error dynamics is established with the guidance of a virtual reference vehicle, and transformed into a new cascaded structure, so the control of the overall system can be equivalent to stabilization of two interconnected subsystems separately, which can facilitate the control design by verifying the properties of the interconnection instead of iterative constructing Lyapunov functions for the whole system. The nonsingular fast terminal sliding mode (NFTSM) method is applied to stabilize the kinematic and dynamic subsystems in the present of model uncertainties, which can drive the states to the origin in finite time and avoid the possible singularity during the control phase. The stability of the cascaded system is guaranteed by verifying the satisfaction of the sufficient conditions. Numerical simulation results are provided to demonstrate the effectiveness of the proposed cascaded design. © 2022 IEEE.

关键词： Unmanned surface vehicles

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Rule-Based Out-of-Distribution Detection

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第6期5卷 2627-2637页

作者： De Bernardi, Giacomo Narteni, Sara Cambiaso, Enrico Mongelli, Maurizio CNR-Istituto di Elettronica Ingegneria Dell' Informazione e Delle Telecomunicazioni Genoa16152 Italy Universitá Degli Studi di Genova The Electrical Electronics and Telecommunication Engineering and Naval Architecture Department Genova16145 Italy CNR-Istituto di Elettronica Ingegneria dell'Informazione e Delle Telecomunicazioni Genoa16152 Italy Politecnico di Torino Department of Control and Computer Engineering Turin10129 Italy

Out-of-distribution detection is one of the most critical issue in the deployment of machine learning. The data analyst must assure that data in operation should be compliant with the training phase as well as understand if the environment has changed in a way that autonomous decisions would not be safe anymore. The method of the paper is based on eXplainable Artificial Intelligence (XAI);it takes into account different metrics to identify any resemblance between in-distribution and out of, as seen by the XAI model. The approach is nonparametric and distributional assumption free. The validation over complex scenarios (predictive maintenance, vehicle platooning, covert channels in cybersecurity) corroborates both precision in detection and evaluation of training-operation conditions proximity. © 2020 IEEE.

关键词： Probability distributions

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Atmosphere-dependent combustion of Ganoderma lucidum biomass toward its enhanced transformability into green energy

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Environmental Science and Pollution Research 2024年第29期31卷 42372-42387页

作者： Cao, Hanlin Zhan, Hongda Qi, Jianying Lin, Sen Ren, Mingzhong Liang, Jiayu Evrendilek, Fatih He, Yao Liu, Jingyong Technical Centre for Soil Agriculture and Rural Ecology and Environment Ministry of Ecology and Environment Beijing100012 China Guangdong Key Laboratory of Environmental Catalysis and Health Risk Control School of Environmental Science and Engineering Guangdong University of Technology Guangzhou510006 China Ministry of Ecology and Environment South China Institute of Environmental Sciences Guangzhou510275 China Department of Electrical and Computer Engineering College of Engineering Boston University BostonMA02215 United States

Globally, the circular efficiency of biomass resources has become a priority due to the depletion and negative environmental impacts of fossil fuels. This study aimed to quantify the atmosphere-dependent combustion of Ganoderma lucidum (GL) biomass and its thermodynamic and kinetic parameters toward enhancing its circularity and transformability characteristics. The GL combustion occurred in the three stages of moisture removal, volatile release, and coke combustion. Combustion performance characteristics were more favorable in the N2/O2 atmosphere than in the CO2/O2 atmosphere under the same heating rates. The rising heating rate facilitated the release of volatiles. According to the model-free methods of Ozawa-Flynn-Wall and Kissinger–Akahira–Sunose, the activation energies essential for the primary reaction were 283.09 kJ/mol and 288.28 kJ/mol in the N2/O2 atmosphere and 233.09 kJ/mol and 235.64 kJ/mol in the CO2/O2 atmosphere. The gaseous products of the GL combustion included CH4, H2O, C = O, CO, CO2, NH3, C = C, and C–O(H). Ash prepared in both atmospheres exhibited a tendency for slag formation, with oxy-fuel combustion lowering its risk. This study thus provides a theoretical and practical basis for transforming GL residues into a sustainable energy source. Graphical abstract: (Figure presented.) © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Combustion

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Partially Double-Pass Structure in Serial Hybrid Fiber Amplifier

Partially Double-Pass Structure in Serial Hybrid Fiber Ampli...

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2023 International Conference on engineering, Science and Advanced Technology, ICESAT 2023

作者： Ali, Ali Yaseen Salh Al-Mashhadani, Mohammed Kamil Al-Mashhadani, Thamer Fahad Hammudi, Rabi Noori Bouteraa, Yassine Hammudi, Rabi Noori Tunisia Tikrit University Department of Petroleum Systems Control Engineering Saladdin Iraq Tikrit University Department of Electrical Engineering Saladdin Iraq Kut Univeristy College Department of Laser Engineering and Optoelectronics Alkut Wasit52001 Iraq Prince Sattam Bin Abdulaziz University Department of Computer Engineering Al-Kharj11942 Saudi Arabia

ISBN: (纸本)9798350344400

In this paper, a partially double pass configuration in serial hybrid fiber amplifier is experimentally demonstrated. In the proposed design, a double pass erbium gain and single pass Raman gain are achieved serially. A total pump power of 450 mW (400 mW for 1495 nm Raman amplifier and 50 mW for1480 nm in erbium amplifier) were used. At -30 dBm input signal power and optimum pumps conditions, the achieved flatness bandwidth is 80 nm (1530-1610 nm) in the conventional and long bands (C+L) bands. In addition, the obtained average gain level is 33 dB. While the obtained flatness gain is 85 nm (1525-1610 nm) within the large input signal power region at -5 dBm. By choosing a proper pump wavelength that avoid the overlapping between Raman and erbium peaks gain, a wide flatness gain is obtained. © 2023 IEEE.

关键词： Raman scattering

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Integrated Routing and Traffic Signal control for CAVs via Reinforcement Learning Approach 27

Integrated Routing and Traffic Signal Control for CAVs via R...

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27th IEEE International Conference on Intelligent Transportation Systems, ITSC 2024

作者： Park, Jiho Zhang, Guohui Wang, Chieh [Ross] Wang, Hong Jiang, Zhong-Ping Tandon School of Engineering New York University Control and Networks Lab Department of Electrical and Computer Engineering BrooklynNY11201 United States University of Hawaii at Manoa Department of Civil and Environmental Engineering HonoluluHI96822 United States Oak Ridge National Laboratory Oak RidgeTN37831 United States

ISBN: (纸本)9798331505929

Incorporating Connected and Automated Vehicles (CAVs) into urban traffic networks presents opportunities and challenges for traffic management systems. This paper aims to develop an integrated routing and traffic signal control system designed explicitly for CAVs, utilizing a Reinforcement Learning (RL) approach. The objective is to enhance traffic flow and improve overall transportation efficiency in the controlled areas. We propose an innovative framework that employs the Deep Reinforcement Learning (DRL) algorithm, especially the Deep Q-network (DQN), to dynamically adjust the number of vehicles in the routes and the duration of traffic signals. Our simulation results demonstrate that a DQN agent successfully optimizes the number of vehicles in the routes and traffic signal timings of traffic signal controllers, eventually reducing total travel time. The study illustrates the potential usage of RL-based systems in managing routing and traffic signals for CAVs, offering a promising opportunity for future urban traffic management strategies. © 2024 IEEE.

关键词： Travel time

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Efficient Augmentation of Human Action Recognition Datasets with Warped Windows 26

Efficient Augmentation of Human Action Recognition Datasets ...

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26th International Conference on Knowledge-Based and Intelligent Information and engineering Systems, KES 2022

作者： Warchol, Dawid Oszust, Mariusz Department of Computer and Control Engineering Faculty of Electrical and Computer Engineering Rzeszów University of Technology W. Pola 2 Rzeszów35-959 Poland

In this paper, an approach to augment action recognition time series datasets, devoted to improving the accuracy of deep learning classifiers, is proposed. In the introduced method, two operators are sequentially introduced that perform linear and nonlinear modifications in the time scale of the input time series. The resulting data samples contribute to the variability within classes and allow a deep learning-based classifier to better capture their boundaries, leading to a significant improvement in the classification accuracy. The extensive experiments performed on eight publicly available action recognition datasets using the popular Bidirectional Long Short-Term Memory (BiLSTM) classifier reveal the superiority of the proposed algorithm over related approaches. © 2022 The Authors. Published by Elsevier B.V.

关键词： Time series

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