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UAV based device to device communication for 5G/6G networks using optimized deep learning models

WIRELESS NETWORKS 2024年第8期30卷 7137-7151页

作者： Luo, Yixin Fu, Gui Civil Aviat Flight Univ China Inst Elect & Elect Engn Guanghan 618300 Peoples R China

The success of future 5G/6G communication relies heavily on efficient and low-latency wireless cellular device-to-device communication, which enhances network spectrum, energy efficiency, and data transfer rate. To achieve this, advanced technologies such as deep learning and swarm optimization are utilized to improve energy-efficient communication while considering latency. The fifth and beyond-generation networks require intelligent and efficient technologies to control and transfer data. Unmanned Aerial Vehicles (UAVs) are employed to establish high-transfer-rate data communication between devices using minimal energy consumption. This study also focuses on integrating UAV-based D2D communication with other advanced technologies, highlighting the significance of integration with possible solutions to enhance network performance. The development of 5G/6G communication technologies aims to meet the increasing demands of wireless networks, such as faster data rates, lower latency, improved reliability, and more excellent device and application support. However, the current network advantages need to be enhanced to meet the needs of the upcoming digital environment. Thus, to address these challenges, we propose a novel approach that utilizes optimized deep learning models in three ways for UAV-based device-to-device communication: 1. Improved Hybrid particle swarm optimization and Effective K-means clustering approach (IHPSO-K) 2. Hybrid Fuzzy C means (HFCM) approach, and 3-greedy algorithm to overcome the restrictions faced by UAVs in meeting the latest technological requirements. With the above considering features, we use two methods to execute the efficient performance of the proposed algorithm, namely the device-centric approach and the network-centric approach. Combining these techniques can help achieve a more accurate and efficient clustering of data points in UAV-based D2D communication. This can lead to improved performance in terms of throughput, latency,

关键词： UAV 6G communication Device-to-device communication Energy efficiency Beyond 5G networks particle swarm optimization algorithm K means clustering Fuzzy C means

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Structural optimization Design of MEMS Piezoelectric Hydrophones Based on Intelligent Learning algorithm

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IEEE SENSORS JOURNAL 2024年第13期24卷 20769-20776页

作者： Zhang, Ying Yang, Hua Mao, Beibei Sun, Fei Chen, Zhe Ocean Univ China Dept Elect Engn Qingdao 266100 Peoples R China

Hydrophones rely on high sensitivity to detect weak hydroacoustic signals, with the diaphragm structural design significantly impacting the sensitivity of MEMS piezoelectric hydrophones. Traditional optimization methods for hydrophone structure involve creating an equivalent circuit mathematical model or a finite-element analysis (FEA) model through simulation, starting with a structural model based on equivalent analogy or empirical knowledge, and proceeding with a parametric analysis of geometric structural parameters. However, these methods are computationally intensive and time-consuming, impeding structural design optimization. This article presents an intelligent learning (IL) algorithm-based method for optimizing MEMS hydrophone structures, which aims to simplify the design process and enhance hydroacoustic reception. Orthogonal tests and FEA models are utilized to collect the simulation data, and neural network models are developed to illustrate the relationship between the structural parameters and hydrophone performance. The diaphragm electrode thickness, piezoelectric layer thickness, diaphragm radius, and top electrode radius are optimized through a backpropagation (BP) neural network enhanced by the particle swarm optimization algorithm. The model's accuracy is validated through simulation testing, with the optimized hydrophone sensitivity and effective electromechanical coupling coefficient averaging 99.77% and 96.03%, respectively. A MEMS hydrophone is fabricated based on the design model's optimization results, and experimental testing confirmed the design performance. This method can meet the design requirements of MEMS hydrophones with improved efficiency and precision and can be extended to other MEMS sensors and actuators requiring structural parametric design.

关键词： Echo sounders Micromechanical devices optimization Sensitivity Electrodes Acoustics Resonant frequency MEMS hydrophone particle swarm optimization algorithm sensitivity structural optimization design

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Two-Stage Optimal Scheduling Based on the Meteorological Prediction of a Wind-Solar-Energy Storage System with Demand Response

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ENERGIES 2024年第6期17卷 1286页

作者： Wei, Lu Li, Yiyin Xie, Boyu Xu, Ke Meng, Gaojun Henan Meteorol Serv Ctr Zhengzhou 450003 Peoples R China Nanjing Inst Technol Sch Elect Power Engn Nanjing 211167 Peoples R China

With large-scale wind and solar power connected to the power grid, the randomness and volatility of its output have an increasingly serious adverse impact on power grid dispatching. Aiming at the system peak shaving problem caused by regional large-scale wind power photovoltaic grid connection, a new two-stage optimal scheduling model of wind solar energy storage system considering demand response is proposed. There is a need to comprehensively consider the power generation cost of various types of power sources, day-ahead load forecasting information, and other factors and plan the day-ahead output plan of the energy storage system with the minimum system operation cost as the optimization objective of day-ahead dispatching. The demand response strategy is introduced into the time-ahead optimal scheduling, and the optimization of the output value of the energy storage system in each period is studied with the goal of minimizing the system adjustment cost. The particle swarm optimization algorithm is used to solve the model, and the IEEE33 node system is used for an example simulation. The results show that using the demand response and the collaborative effect of the energy storage system can suppress the uncertainty of wind power and photovoltaic power, improve the utilization rate of the system, reduce the power generation cost of the system, and achieve significant comprehensive benefits.

关键词： demand response energy storage system two-stage scheduling particle swarm optimization algorithm

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FPGA-Based particle swarm Collaborative Target Localization algorithm for UAV swarms

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SENSORS 2025年第8期25卷 2462-2462页

作者： Zhang, Chuanhao Li, Changsheng Chen, Zhipeng Li, Haojie Yu, Hang Nanjing Univ Sci & Technol Sch Mech Engn Nanjing 210094 Peoples R China

To achieve precise collaborative localization of multiple unmanned aerial vehicles (UAVs) in hardware environments, this paper presents an field-programmable gate array-based particle swarm optimization (PSO) algorithm aimed at enhancing the localization efficiency of multiple nodes targeting a specific object. By leveraging the unique computational capabilities of FPGA, the proposed algorithm integrates optimization strategies, including particle mutation, variable crossover probabilities, and adjustable weights. These strategies collectively enhance the performance of the PSO algorithm in localization tasks. Comparative simulations conducted across a range of operational scenarios demonstrate that the algorithm not only ensures high localization accuracy but also delivers excellent real-time performance and rapid convergence. To further validate the algorithm's practical applicability, a four-node collaborative localization platform was developed, and experiments were carried out. The results confirmed the feasibility of multi-node collaborative localization, underscoring the advantages of the proposed algorithm, such as high accuracy, fast convergence, and robust stability.

关键词： cooperative target localization particle swarm optimization algorithm FPGA deployment and verification algorithm localization capability analysis

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Forecasting carbon dioxide emissions using adjacent accumulation multivariable grey model

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GONDWANA RESEARCH 2024年 134卷 107-122页

作者： Yang, Wei Qiao, Zhengran Wu, Lifeng Ren, Xiaohang Taghizadeh-Hesary, Farhad Shanxi Univ Inst Management & Decis Taiyuan 030006 Peoples R China Shanxi Univ Sch Econ & Management Taiyuan 030006 Peoples R China Hebei Univ Engn Coll Management Engn & Business Handan 056038 Peoples R China Cent South Univ Business Sch Changsha 410083 Peoples R China Tokai Univ TOKAI Res Inst Environm & Sustainabil TRIES Tokyo Japan Lebanese Amer Univ Adnan Kassar Sch Business Beirut Lebanon

Carbon dioxide (CO2) emissions, the primary greenhouse effect catalyst, command global attention due to associated environmental challenges. Urgent carbon reduction is imperative, particularly with scholarly discourse emphasizing the criticality of peak emissions and carbon neutrality. Accurate CO2 emission prediction holds immense importance for shaping effective management policies aimed at emission reduction and environmental mitigation. This study introduces an enhanced multivariable grey prediction model (AGMC(1,N)), utilizing the particle swarm optimization (PSO) algorithm based on artificial intelligence to determine its optimal order. Rigorous analysis, including a disturbance bound classification discussion, validates the superior stability and outstanding predictive capability of the AGMC(1,N) model, as exemplified in a detailed case study. Applying the AGMC(1,N) model to forecast CO2 emissions in the Beijing-Tianjin-Hebei region and Shanxi Province reveals a correlation between energy, primary and secondary industry growth, GDP per capita, and increased emissions, while rising urbanization and natural gas consumption correlate with emission decline. The study concludes with actionable proposals derived from predictive insights, providing valuable support for decision-making by management departments focused on emission reduction. (c) 2024 International Association for Gondwana Research. Published by Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

关键词： Emission reduction Grey multivariable prediction model Adjacent accumulation particle swarm optimization algorithm Carbon dioxide prediction

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Research on optimization Method of Discrete Point Cloud Registration Based on Improved particle swarm optimization 23

Research on Optimization Method of Discrete Point Cloud Regi...

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Proceedings of the 7th International Conference on algorithms, Computing and Systems

作者： Rong Guo Yunsheng Mao Zuquan Xiang Xiaosa Zhao School of Naval Architecture Ocean and Energy Power Engineering Wuhan University of Technology China

ISBN: (纸本)9798400709098

The plate forming process based on a three-core rolling bending machine is a crucial technology in industries such as shipbuilding, aerospace, and boiler manufacturing. It enables the formation of single curvature plates like cylinders and cones. However, traditional sheet metal forming inspection methods rely on manual sample testing, resulting in low efficiency, poor precision, and an inability to automate or digitize the forming process. This paper introduces a novel automatic plate curvature detection device that measures discrete point data from sheet metal forming and requires rapid registration of this data for evaluating forming errors. To achieve rapid registration, a particle swarm optimization algorithm based on simulated annealing evolution principles is employed. This algorithm facilitates quick alignment between the discrete point data and theoretical values while analyzing sheet forming errors to guide subsequent automated processing steps. Experimental results demonstrate that the improved particle swarm optimization (PSO) effectively evaluates forming errors, providing valuable guidance for automating steel sheet forming processes.

关键词： curvature detection error evaluation particle swarm optimization algorithm registration calculation

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Inatorial forecasting method considering macro and micro characteristics of chaotic traffic flow

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Chinese Physics B 2023年第10期32卷 350-362页

作者：侯越张迪李达杨萍 School of Electronics and Information Engineering Lanzhou Jiaotong UniversityLanzhou 730070China

Traffic flow prediction is an effective strategy to assess traffic conditions and alleviate traffic congestion. Influenced by external non-stationary factors and road network structure, traffic flow sequences have macro spatiotemporal characteristics and micro chaotic characteristics. The key to improving the model prediction accuracy is to fully extract the macro and micro characteristics of traffic flow time sequences. However, traditional prediction model by only considers time features of traffic data, ignoring spatial characteristics and nonlinear characteristics of the data itself, resulting in poor model prediction performance. In view of this, this research proposes an intelligent combination prediction model taking into account the macro and micro features of chaotic traffic data. Firstly, to address the problem of time-consuming and inefficient multivariate phase space reconstruction by iterating nodes one by one, an improved multivariate phase space reconstruction method is proposed by filtering global representative nodes to effectively realize the high-dimensional mapping of chaotic traffic flow. Secondly, to address the problem that the traditional combinatorial model is difficult to adequately learn the macro and micro characteristics of chaotic traffic data, a combination of convolutional neural network(CNN) and convolutional long short-term memory(ConvLSTM) is utilized for capturing nonlinear features of traffic flow more comprehensively. Finally,to overcome the challenge that the combined model performance degrades due to subjective empirical determined network parameters, an improved lightweight particle swarm is proposed for improving prediction accuracy by optimizing model hyperparameters. In this paper, two highway datasets collected by the Caltrans Performance Measurement System(PeMS)are taken as the research objects, and the experimental results from multiple perspectives show that the comprehensive performance of the method proposed in this

关键词： traffic flow prediction phase space reconstruction particle swarm optimization algorithm deep learning models

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New weighted coefficients of the average-derivative modeling method based on global optimization algorithms

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ACTA GEOPHYSICA 2024年第2期72卷 619-636页

作者： He, Chen Chen, Jing-Bo Chinese Acad Sci Inst Geol & Geophys Key Lab Petr Resources Res Beijing 100029 Peoples R China Chinese Acad Sci Innovat Acad Earth Sci Beijing 100029 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

The average-derivative optimal method (ADM) is widely applied in frequency-domain forward modeling for its high accuracy and simplicity. Since tuning weighted coefficients can suppress the numerical dispersion, it is extremely important to adopt a suitable optimization algorithm to determine the ADM coefficients. To date, most schemes associated with the ADM have adopted the conventional local optimization algorithms, which are sensitive to the initial value and easy to converge on local optimum. The motivation of this paper is to derive new and more accurate ADM coefficients for 2D frequency-domain elastic-wave equation by the global optimization algorithms, which can escape from the local optimum with a certain probability. We adopt simulated annealing (SA) and particle swarm optimization (PSO) algorithms for global optimization and numerical modeling. Compared with the conventional local optimization algorithm, the global optimization algorithms have smaller phase errors, especially for S-wave phase velocity. Numerical examples demonstrate that the global optimization algorithms produce more accurate results than the local optimization algorithm.

关键词： Numerical modeling Average-derivative method Global optimization Simulated annealing algorithm particle swarm optimization algorithm

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Research on Deformation Prediction of VMD-GRU Deep Foundation Pit Based on PSO optimization Parameters

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MATERIALS 2024年第10期17卷 2198页

作者： Liu, Ronggui Zhang, Qing Jiang, Feifei Zhou, Juan He, Jianxia Mao, Zhongyang Jiangsu Univ Fac Civil Engn & Mech Zhenjiang 212013 Peoples R China China State Construction Engn Macau Co Ltd Macau 999078 Peoples R China Nantong Inst Technol Sch Civil Engn Nantong 226000 Peoples R China Nanjing Tech Univ Coll Mat Sci & Engn Nanjing 211800 Peoples R China

As a key guarantee and cornerstone of building quality, the importance of deformation prediction for deep foundation pits cannot be ignored. However, the deformation data of deep foundation pits have the characteristics of nonlinearity and instability, which will increase the difficulty of deformation prediction. In response to this characteristic and the difficulty of traditional deformation prediction methods to excavate the correlation between data of different time spans, the advantages of variational mode decomposition (VMD) in processing non-stationary series and a gated cycle unit (GRU) in processing complex time series data are considered. A predictive model combining particle swarm optimization (PSO), variational mode decomposition, and a gated cyclic unit is proposed. Firstly, the VMD optimized by the PSO algorithm was used to decompose the original data and obtain the Internet Message Format (IMF). Secondly, the GRU model optimized by PSO was used to predict each IMF. Finally, the predicted value of each component was summed with equal weight to obtain the final predicted value. The case study results show that the average absolute errors of the PSO-GRU prediction model on the original sequence, EMD decomposition, and VMD decomposition data are 0.502 mm, 0.462 mm, and 0.127 mm, respectively. Compared with the prediction mean square errors of the LSTM, GRU, and PSO-LSTM prediction models, the PSO-GRU on the PTB0 data of VMD decomposition decreased by 62.76%, 75.99%, and 53.14%, respectively. The PTB04 data decreased by 70%, 85.17%, and 69.36%, respectively. In addition, compared to the PSO-LSTM model, it decreased by 8.57% in terms of the model time. When the prediction step size increased from three stages to five stages, the mean errors of the four prediction models on the original data, EMD decomposed data, and VMD decomposed data increased by 28.17%, 3.44%, and 14.24%, respectively. The data decomposed by VMD are more conducive to model prediction and

关键词： deformation prediction of deep foundation pit particle swarm optimization algorithm variational mode decomposition gated recurrent unit

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Optimal Scheduling of Off-Site Industrial Production in the Context of Distributed Photovoltaics

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ENERGIES 2024年第9期17卷 2156页

作者： Xie, Sizhe Li, Yao Wang, Peng North China Elect Power Univ Dept Elect & Elect Engn Beijing 102206 Peoples R China

A reasonable allocation of production schedules and savings in overall electricity costs are crucial for large manufacturing conglomerates. In this study, we develop an optimization model of off-site industrial production scheduling to address the problems of high electricity costs due to the irrational allocation of production schedules on the demand side of China's power supply, and the difficulty in promoting industrial and commercial distributed photovoltaic (PV) projects in China. The model makes full use of the conditions of different PV resources and variations in electricity prices in different places to optimize the scheduling of industrial production in various locations. The model is embedded with two sub-models, i.e., an electricity price prediction model and a distributed photovoltaic power cost model to complete the model parameters, in which the electricity price prediction model utilizes a Long Short-Term Memory (LSTM) neural network. Then, the particle swarm optimization algorithm is used to solve the optimization model. Finally, the production data of two off-site pharmaceutical factories belonging to the same large group of enterprises are substituted into the model for example analysis, and it is concluded that the optimization model can significantly reduce the electricity consumption costs of the enterprises by about 7.9%. This verifies the effectiveness of the optimization model established in this paper in reducing the cost of electricity consumption on the demand side.

关键词： optimized scheduling distributed photovoltaic LSTM neural network particle swarm optimization algorithm master-slave game electricity market

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