Aiming at solving the problem that existing artificial neural networks (ANNs) still have low accuracy in predicting yarn strength, this study combines traditional expert experience and an ANN to propose a hybrid netwo...
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Aiming at solving the problem that existing artificial neural networks (ANNs) still have low accuracy in predicting yarn strength, this study combines traditional expert experience and an ANN to propose a hybrid network, named the expert weighted neural network. Many studies have shown that it is reliable to predict yarn strength based on ANN technology. However, most ANN training models face with problems of low accuracy and easy trapping into their local minima. The strength prediction of traditional yarns relies on expert experience. Obvious expert experience can help the model perform preliminary learning and help the algorithm model achieve higher accuracy. Therefore, this study proposes a neural network model that combines expert weights and particleswarmoptimization (PSO). The model uses PSO to optimize the weights of experts and investigates its effectiveness in yarn strength prediction.
To the best of our knowledge, currently the physical model based method is still an ill posed problem. Additionally, the image enhancement approaches also suffer from the texture preservation issue. Retinex-based appr...
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To the best of our knowledge, currently the physical model based method is still an ill posed problem. Additionally, the image enhancement approaches also suffer from the texture preservation issue. Retinex-based approach is proved its effectiveness in image dehazing while the parameter should be turned properly. Therefore, in this paper, the particleswarmoptimization (PSO) algorithm is firstly performed to optimize the parameter and the hazed image is converted into hue, saturation, intensity(HSI) for color compensation, In the other hand, the multi-scale local detail upgrading and the bilateral filtering approaches are designed to overcome the dehazing artefacts and edge preservation, which could further improve the overall visual effect of images. Experimental results on natural and synthetic images by using qualitative analysis and frequently used quantitative evaluation metrics illustrate the approving defogging effect of the proposed method. For instance, in a natural image road, our method achieves the higher e for 0.63, gamma for 3.21 andHfor 7.81, respectively and lower sigma for 0.04. In a synthetic image poster, the higher PSNR for 18.17 and SSIM for 0.78 are also acquired compared to other explored approaches in this paper. Besides, the results performed on other underwater and aerial images in this study further demonstrates its defog effectiveness.
This paper focuses on particle swarm optimization algorithm (PSOA)-based H infinity tracking fault-tolerant control for batch processes to resist the influence of actuator faults and unknown disturbances. First, accor...
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This paper focuses on particle swarm optimization algorithm (PSOA)-based H infinity tracking fault-tolerant control for batch processes to resist the influence of actuator faults and unknown disturbances. First, according to a given actual process model, by introducing output tracking error, state difference and new states including output tracking error, an extended equivalent model is constructed. Then, a linear-quadratic performance function is introduced. By using the PSOA to adjust those parameters in the function, a new state space H infinity tracking fault-tolerant control law is proposed under optimal control theory. Actuator faults are regarded as uncertainties here. The Lyapunov stability theory is used to solve the allowable disturbances in a certain range. The greatest merit of this design is that it has better tracking performance and stronger anti-fault and interference ability. Finally, the injection molding process and nonlinear batch reactor are taken as examples to compare with the genetic algorithm method (GA) and the traditional control method (TC), which shows that the method proposed is more practical and effective.
We propose a novel iterative thresholding approach based on firefly and particleswarmoptimization to be used for the detection of hemorrhages, one of the signs of diabetic retinopathy disease. This approach consists...
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We propose a novel iterative thresholding approach based on firefly and particleswarmoptimization to be used for the detection of hemorrhages, one of the signs of diabetic retinopathy disease. This approach consists of the enhancement of the image using basic preprocessing methods, the segmentation of vessels with the help of Gabor and Top-hat transformation for the removal of the vessels from the image, the determination of the number of regions with hemorrhages and pixel counts in these regions using firefly algorithm (FFA) and particle swarm optimization algorithm (PSOA)-based iterative thresholding, and the detection of hemorrhages with the help of a support vector machine (SVM) and linear regression (LR)-based classifier. In the preprocessing step, color space selection, brightness and contrast adjustment, and adaptive histogram equalization are applied to enhance retinal images, respectively. In the step of segmentation, blood vessels are detected by using Gabor and Top-hat transformations and are removed from the image to avoid confusion with hemorrhagic regions in the retinal image. In the iterative thresholding step, the number of hemorrhagic regions and pixel counts in these regions are determined by using an iterative thresholding approach that generates different thresholding values with the FFA/PSOA. In the classification step, the hemorrhagic regions and pixel counts obtained by the iterative thresholding are used as inputs in the LR/SVM-based classifier. PSOA-based iterative thresholding and the SVM classifier achieved 96.7% sensitivity, 91.4% specificity, and 94.1% accuracy for hemorrhage detection. Finally, the experiments show that the correct classification rates and time performances of the PSOA-based iterative thresholding algorithm are better than those of the FFA in hemorrhage detection. In addition, the proposed approach can be used as a diagnostic decision support system for detecting hemorrhages with high success rate.
Location privacy protection is an essential but challenging topic in the field of network security. Although the existing research methods, such as k-anonymity, mix zone, and differential privacy, show significant suc...
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Location privacy protection is an essential but challenging topic in the field of network security. Although the existing research methods, such as k-anonymity, mix zone, and differential privacy, show significant success, they usually neglect the location semantic and the proper trade-off between privacy and utility, which may allow attackers to obtain user privacy information by revealing the semantic correlation between the anonymous region and user's real location, thus causing privacy leakage. To solve this problem, we propose a location privacy protection scheme based on the k-anonymity technique, which provides practical location privacy-preserving through generating an anonymous set. This paper proposes a new location privacy attack strategy termed semantic relativity attack (SRA), which considers the location semantic problem. Correspondingly, a semantic and trade-off aware location privacy protection mechanism (STA-LPPM) is presented to achieve privacy protection with both high-level privacy and utility. To be specific, we model the location privacy protection as a multi-objective optimization problem and propose the Improved Multi-Objective particleswarmoptimization (IMOPSO) to generate the optimal anonymous set calculating the well-design fitness functions of the multi-objective optimization problem. In this way, the privacy scheme can provide mobile users with the right balance of privacy protection and service quality. Experiments reveal that our privacy scheme can effectively resist the semantic relativity attack while preventing significant utility degrading.
Precise regional ionospheric total electron content (TEC) models play a crucial role in correcting ionospheric delays for single-frequency receivers and studying variations in the Earth's space environment. A part...
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Precise regional ionospheric total electron content (TEC) models play a crucial role in correcting ionospheric delays for single-frequency receivers and studying variations in the Earth's space environment. A particleswarmoptimization neural network (PSO-NN)-based model for ionospheric TEC over China has been developed using a long-term (2008-2021) ground-based global positioning system (GPS), COSMIC, and Fengyun data under geomagnetic quiet conditions. In this study, a spatial gridding approach is utilized to propose an improved version of the PSO-NN model, named the PSO-NN-GRID. The root-mean-square error (RMSE) and mean absolute error (MAE) of the TECs estimated from the PSO-NN-GRID model on the test data set are 3.614 and 2.257 TECU, respectively, which are 7.5% and 5.5% smaller than those of the PSO-NN model. The improvements of the PSO-NN-GRID model over the PSO-NN model during the equinox, summer, and winter of 2015 are 0.4-22.1%, 0.1-12.8%, and 0.2-26.2%, respectively. Similarly, in 2019, the corresponding improvements are 0.5-13.6%, 0-10.1%, and 0-16.1%, respectively. The performance of the PSO-NN-GRID model is also verified under different solar activity conditions. The results reveal that the RMSEs for the TECs estimated by the PSO-NN-GRID model, with F10.7 values ranging within [0, 80), [80, 100), [100, 130), [130, 160), [160, 190), [190, 220), and [220, +), are, respectively, 1.0%, 2.8%, 4.7%, 5.5%, 10.1%, 9.1%, and 28.4% smaller than those calculated by the PSO-NN model.
This article presents the problem of the energy system optimization for wind generators. The goal of this work is to maximize power extraction for a permanent magnet synchronous generator-based wind turbine with maxim...
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This article presents the problem of the energy system optimization for wind generators. The goal of this work is to maximize power extraction for a permanent magnet synchronous generator-based wind turbine with maximum power point technique. This goal is achieved using a proportional-integral controller for optimal torque tuning with the particle swarm optimization algorithm. In order to indicate the effectiveness and superiority of the particle swarm optimization algorithm-based proposal, a comparison with the genetic algorithm and the artificial bee colony algorithm is studied. The system is modeled and tested under MATLAB/Simulink environment. Simulation results validate the advantages of the designed particleswarmoptimization-tuned proportional-integral controller compared to P&O and the proportional-integral controller manually in terms of performance index.
This paper compares the geometrical model of a 2DTBC with its actual model generated by the Micro-Computed Tomography (& mu;CT) method. First, the geometrical models' equations are edited to simulate the manuf...
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This paper compares the geometrical model of a 2DTBC with its actual model generated by the Micro-Computed Tomography (& mu;CT) method. First, the geometrical models' equations are edited to simulate the manufacturing process more accurately, and the equations are incorporated into the TBC-Gen program. Then, a & mu;CT scan is done on a cured carbon fiber TBC, and 2D and 3D models are created. Next, two yarns of the scanned models are extracted and modelled. The segmented yarns are analyzed, and their geometrical data, including cross-section area, major and minor yarn diameters, orientation, mandrel diameter, portion angle and centre points, are extracted. Next, a yarn path simulating the scanned yarn is generated using the TBC-Gen and the extracted parameters. Then, the generated geometrical model is compared with the & mu;CT model. To do that, a parameter named portion angle is introduced to help the geometrical model better fit the & mu;CT model. Finally, particleswarmoptimization (PSO) is used to optimize the portion angle. The result of the fitting algorithm shows the accuracy of the geometrical model (less than 1% error) to simulate actual TBC. Understanding the amount of error between a geometrical model and the actual model will help to evaluate the application of geometrical models more thoroughly. Also, the more accurate geometrical model will contribute less error to the FEM simulation. The quantitative comparison between these two models can give a clear understanding of the amount of error existing in the geometrical model compared to an accurate model generated by & mu;CT.
Kinematic calibration is necessary for the pose accuracy improvement of the parallel robots. However, difficulties occur in traditional calibration methods, such as exceeding a large number of error parameters and err...
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Kinematic calibration is necessary for the pose accuracy improvement of the parallel robots. However, difficulties occur in traditional calibration methods, such as exceeding a large number of error parameters and error accumulation. In this paper, a 3-PUU parallel robot is taken as the research object, and the closed-loop vector method and differential theory are employed to establish an error model. Through quantitative analysis of geometric parametric errors, redundant parameters within the calibration algorithm are meticulously eliminated. By treating the dominant error terms as optimization variables for the mechanism's parameters, the calibration problem is transformed into a nonlinear system optimization challenge, which effectively avoids the problem of multiple error parameters and accumulation of errors in the traditional method. Further, using the particleswarmalgorithm to compute the minimum value of the objective function, one can obtain the actual structural parametric errors of the robot. These errors are then utilized to correct the kinematic model, enabling the calibration of the mechanism's parameters and ultimately enhancing operation accuracy. Lastly, simulation and experimental validation of the algorithm are carried out. The simulation results indicate that the end-effector position error converges to zero infinitely;and the experimental results indicate that the maximum error of the end-effector in the x, y, z directions and the maximum position error are reduced from 8.53, 11.67, 3.29, and 12.56 mm to 1.09, 1.32, 0.98, and 1.75 mm, respectively. The standard deviation of the position error is reduced from 2.43 to 0.32 mm. The mean error is reduced from 7.76 to 1.02 mm. To sum up, the operation accuracy and stability of the robot are greatly improved.
With the increasing emphasis on environmental issues, the utilization of renewable energy has been recognized as a feasible solution to address the energy crisis and reduce environmental pollution. In view of this, th...
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With the increasing emphasis on environmental issues, the utilization of renewable energy has been recognized as a feasible solution to address the energy crisis and reduce environmental pollution. In view of this, this article proposes a multi-modal renewable energy hybrid power supply optimization model based on heterogeneous cloud wireless access. The model innovatively combines heterogeneous cloud wireless access technology and various intelligent optimizationalgorithms, including k-clustering algorithm, particle swarm optimization algorithm, and whale optimizationalgorithm, forming a hybrid optimizationalgorithm. In order to comprehensively evaluate the actual performance of the model, this study recruited 20 experts to provide detailed ratings on four core dimensions: cost-benefit ratio, reliability, robustness, and user satisfaction. The results showed that the model scored 95.1, 96.4, 95.6, and 96.2 in the four dimensions of cost-benefit ratio, reliability indicators, robustness, and user satisfaction, respectively. This series of significant data not only confirms the theoretical superiority of the model, but also demonstrates its strong potential and practical value in practical applications. In summary, this study provides a promising and innovative solution for the field of renewable energy supply.
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