To achieve precise collaborative localization of multiple unmanned aerial vehicles (UAVs) in hardware environments, this paper presents an field-programmable gate array-based particleswarmoptimization (PSO) algorith...
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To achieve precise collaborative localization of multiple unmanned aerial vehicles (UAVs) in hardware environments, this paper presents an field-programmable gate array-based particleswarmoptimization (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.
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...
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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 particleswarmoptimization (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.
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 pla...
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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 particleswarmoptimization (PSO) effectively evaluates forming errors, providing valuable guidance for automating steel sheet forming processes.
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 ...
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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 optimizationalgorithm to determine the ADM coefficients. To date, most schemes associated with the ADM have adopted the conventional local optimizationalgorithms, 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 optimizationalgorithms, which can escape from the local optimum with a certain probability. We adopt simulated annealing (SA) and particleswarmoptimization (PSO) algorithms for global optimization and numerical modeling. Compared with the conventional local optimizationalgorithm, the global optimizationalgorithms have smaller phase errors, especially for S-wave phase velocity. Numerical examples demonstrate that the global optimizationalgorithms produce more accurate results than the local optimizationalgorithm.
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 characteristi...
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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 particleswarmoptimization (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
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 produc...
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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.
AC-DC converter has the advantages of high power density, stable output, easy to control, etc., and is widely used in many industrial fields. In this paper, the two-stage isolated AC-DC converter is the object of stud...
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AC-DC converter has the advantages of high power density, stable output, easy to control, etc., and is widely used in many industrial fields. In this paper, the two-stage isolated AC-DC converter is the object of study, and the converter consists of a front-stage Boost PFC converter and a rear-pole LLC resonant converter. The small signal perturbation analysis method and the extended function method are used to construct the equivalent mathematical models of the PFC converter and the LLC resonant converter, and analyze their input and output characteristics and frequency response characteristics. The control strategy based on particleswarmalgorithm optimized PI is proposed, and the PSO algorithm optimized PI loop control system is designed on this basis. Simulation software is used to compare the performance indexes of PSO optimized PI control method, and an experimental platform is built to verify it. The results show that the isolated converter can not only meet the requirement of sinusoidal input current on the grid side, but also realize the electrical isolation and ensure the output voltage stability. The PSO algorithm is also introduced to automatically adjust the PI parameters according to the operating state of the converter, so that a faster regulation speed can be obtained during the startup of the converter and the sudden change of the load, and the system as a whole has a better dynamic characteristic and steady state characteristic, and the simulation analysis and experimental sessions verify the correctness of the circuit design and the effectiveness of the control strategy.
Nuclear reactor control is pivotal for the safe and efficient operation of nuclear power plants, facilitating the regulation of reactor reactivity. This study introduces an optimized fractional-order proportional-inte...
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Nuclear reactor control is pivotal for the safe and efficient operation of nuclear power plants, facilitating the regulation of reactor reactivity. This study introduces an optimized fractional-order proportional-integral-derivative (FOPID) controller tailored for maintaining reactivity levels in nuclear power plants, particularly during load-following operations. The controller adjusts the position of control rod to regulate power output effectively. We enhance FOPID controller's performance using a modification of Planet optimizationalgorithm (POA-M), leveraging the strengths of the Arithmetic optimizationalgorithm (AOA) to improve its exploitation capabilities. We evaluate the efficacy of POA-M-FOPID controller against traditional techniques, including POA, AOA, and particleswarmoptimization (PSO). We assess its performance using the Egyptian Testing Research Reactor (ETRR-2) as a case study. Our results demonstrate that the POA-M-FOPID controller outperforms alternative algorithms across various control metrics, exhibiting superior resilience and efficiency. Notably, the utilization of the POA-M-FOPID controller yields remarkable improvements in reactor power performance, achieving significantly reduced settling time (25.27 sec) and maximum overshoot (0.67 %) compared to conventional FOPID controllers incorporating POA, AOA, and PSO methods. These findings underscore the effectiveness of POA-MFOPID in enhancing nuclear reactor control systems, offering potential benefits for broader nuclear power industry in terms of safety, stability, and operational efficiency.
Sliding Mode Control (SMC) has gained significant attention due to its simplicity, robustness, and rapid response in ensuring system stability, particularly with the Lyapunov approach. Despite its advantages, SMC face...
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Sliding Mode Control (SMC) has gained significant attention due to its simplicity, robustness, and rapid response in ensuring system stability, particularly with the Lyapunov approach. Despite its advantages, SMC faces challenges such as chattering near equilibrium, sensitivity to parameter variations, and delayed convergence. To address these issues, advanced techniques like Terminal Sliding Mode Control (TSMC) and Integral Terminal Sliding Mode Control (ITSMC) have been proposed. TSMC ensures finite-time convergence while mitigating chattering, while ITSMC further handles singularities and disturbances. Additionally, Adaptive Switching Control (ASC) based on particleswarmoptimization (PSO) is applied to achieve faster convergence, suppress chattering, and enhance system robustness. The adaptive control law, utilizing a Lyapunov-based approach, is employed to estimate and compensate for external disturbances, further improving system performance under uncertainties. Gain tuning, essential for optimizing system performance and reducing tracking errors, is achieved using the efficient Teaching-Learning-Based optimization (TLBO) algorithm. This study applies TSMC, ITSMC, and ASC-based PSO to an Anti-Lock Braking System (ABS), aiming to enhance robustness, stability, and finite-time convergence while reducing chattering. Stability is analyzed through the Lyapunov theory, ensuring rigorous validation. MATLAB simulations demonstrate the effectiveness of the proposed methods in improving ABS performance, offering a valuable contribution to robust control techniques for systems operating under dynamic and uncertain conditions.
This brief introduces a digital calibration technique to boost the input impedance of instrumentation amplifiers (IAs) with digitally tunable input impedance. The technique employs two machine learning-driven optimiza...
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This brief introduces a digital calibration technique to boost the input impedance of instrumentation amplifiers (IAs) with digitally tunable input impedance. The technique employs two machine learning-driven optimizationalgorithms, the genetic algorithm (GA) and the particleswarmoptimization (PSO) algorithm, to efficiently control integrated capacitor banks within the IA for the determination of the optimal input impedance. These algorithms offer a significant time reduction compared to a calibration with an exhaustive search, reducing calibration time by a factor of over 10(6) (with four 9-bit digital control words) while conserving computational resources. A prototype platform was developed to automatically optimize a fabricated IA test chip designed with 65-nm CMOS technology, which allows to test the machine learning algorithms using a microcontroller to control the digitally tunable input impedance. With an extra input capacitance of 100 pF, the GA algorithm achieved an input impedance of 1.75 G Omega after four generations (iterations), while the PSO algorithm achieved 1.27 G Omega with five iterations.
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