To understand the anisotropic propagation characteristics of acoustic emission (AE) signals in finger-joint-laminated boards, an improved planar localization method for AE sources is proposed. Initially, the AE source...
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To understand the anisotropic propagation characteristics of acoustic emission (AE) signals in finger-joint-laminated boards, an improved planar localization method for AE sources is proposed. Initially, the AE source was generated by the pencil-lead break (PLB) at a fixed position on the surface of the glued wood specimen, and two AE sensors were arranged in different directions at a fixed distance of 60 mm, and the AE propagation velocity was calculated according to the time difference of arrival (TDOA). For clarity, the grain parallel direction was defined as 0 degrees, with velocities measured every 10 degrees through a counterclockwise rotation. Using these measurements, two angular anisotropic wave velocity models were developed. Subsequently, three AE sensors arranged linearly on the specimen surface pinpointed the AE source's location. This source location problem was formulated as a multi-parameter optimization model, constrained by the geometric relationships between the AE source and the sensors. A particleswarmoptimization (PSO) algorithm was utilized to estimate the AE source's angles relative to each sensor. The findings indicate that the located AE sources deviated by 6.0-19.0 mm from the predetermined PLB positions, with inaccuracies largely attributed to the anisotropic AE wave velocity models' precision.
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 ac...
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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 swarmoptimization 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 particleswarmoptimization 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,
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 metho...
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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.
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 p...
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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.
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.
Rhizoma Coptidis is a Chinese herbal medicine with antibacterial and anti-inflammatory properties. It has extensive applications in modern medicine. The content of berberine in Rhizoma Coptidis directly determines its...
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Rhizoma Coptidis is a Chinese herbal medicine with antibacterial and anti-inflammatory properties. It has extensive applications in modern medicine. The content of berberine in Rhizoma Coptidis directly determines its quality. Fourier transforms near-infrared (FT-NIR) spectroscopy is a commonly used non-destructive method for rapidly detecting berberine content. In contrast to single-supervised learning algorithms in machine learning, ensemble learning combines individual learning algorithms to create a stable and better-performing strong-supervised model. This study collected spectral data of Rhizoma Coptidis using FT-NIR spectroscopy technology and established a chemometric model using a stacking ensemble approach with multiple models. Partial Least Squares (PLS), Adaptive Boosting (AdaBoost), Gradient boosting decision trees (GBDT), random forest (RF), and extreme gradient boosting (XGBoost) regression models were chosen as alternative base models, different Stacking models were established by random combinations. To fully leverage the strengths of each model and enhance predictive capability, an adaptive inertia weight particle swarm optimization algorithm (AWPSO) was used to search for the optimal parameters. The correlation coefficient of the test (RT) and the root mean square error of the test (RMSET) systematically evaluated the model performance. Finally, AWPSO-RF, AWPSOXGBoost, and AWPSO-AdaBoost were selected as the base models. The RMSET and RT for RF, XGBoost, and AdaBoost were 0.226, 0.250, 0.195, and 0.871, 0.830, 0.927. After optimizing with the AWPSO algorithm, the RMSET and RT for AWPSO-RF, AWPSO-XGBoost, and AWPSO-AdaBoost were 0.226, 0.245, 0.194, and 0.871, 0.843, 0.922, respectively. The RMSET and RT values for the stacking ensemble were 0.174 and 0.932. The prediction accuracy and generalization ability of multi -model fusion stacking ensemble learning are superior to the single -model regression methods. Therefore, the stacking ensemble learn
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.
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