Although the technical requirements for the feature extraction of ship radiated noise (SRN) in the fields of national defense and economy increase with each passing day, the complexity of the marine environment makes ...
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Although the technical requirements for the feature extraction of ship radiated noise (SRN) in the fields of national defense and economy increase with each passing day, the complexity of the marine environment makes the feature extraction of SRN difficult. The traditional feature extraction method based on variational mode decomposition (VMD) is widely used in the feature extraction of SRN. Nevertheless, the use of VMD is greatly affected by parameters. In this paper, the butterfly optimization algorithm (BOA) is introduced to optimize VMD, which is called BOA-VMD algorithm, and realizes the optimal selection of VMD parameters K and alpha . To further improve the efficiency of feature extraction method, combined with slope entropy (SE), a feature extraction method of SRN based on BOA-VMD and SE is proposed. The experimental results of the simulated signal show that the BOA-VMD algorithm has a smaller envelope entropy value and better decomposition effect than the genetic algorithm (GA) and particle swarm optimization (PSO). The experimental results of feature extraction of SRN show that the highest recognition rate of the four entropy values improve with the increase of the number of extracted features, compared with the three entropy values of dispersion entropy (DE), fluctuation dispersion entropy (FDE) and permutation entropy (PE), the SRN feature extraction method based on BOA-VMD and SE has the highest recognition rate under different quantity features, and the recognition rate has reached 100% under three features.
In wireless sensor networks, non-linear distance information characterized by uncertainty and randomness creates the deviation and significantly target nodes position is affected. Non-linearity effect among distance a...
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In wireless sensor networks, non-linear distance information characterized by uncertainty and randomness creates the deviation and significantly target nodes position is affected. Non-linearity effect among distance and received signal strength (RSS) of target nodes is minimized by modelling edge weights using knowledge base in fuzzy logic system. Further, optimum membership functions bases' of RSS and edge weights using application of butterfly optimization algorithm are evolved to minimize the node position error. Here, a single anchor node is used to estimate target nodes 3D coordinates in anisotropic environment using range free localization methods. The anchor node has been deployed at top layer and over beneath layers target nodes are distributed equally. In This paper, simulation results of the proposed method attain substantial performance improvement in target node 3D position accuracy than the earlier proposed range-free methods. Proposed technique is useful for mapping of several instances like, fire hazards in forests, tracking of workers at different installation sites, solar plant tracking in smart cities and so forth.
As the second largest bond market in the world, China's bond market has attracted extensive attention in recent years. Given its importance in facilitating financing arrangements and informing investment decisions...
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As the second largest bond market in the world, China's bond market has attracted extensive attention in recent years. Given its importance in facilitating financing arrangements and informing investment decisions, accurate bond coupon prediction is valuable. This paper proposes an ensemble model combining TabNet, DeepFM, and XGBoost for predicting the coupons of investment-grade corporate bonds. Specifically, to optimize the hyperparameters of the proposed model, an improved butterfly optimization algorithm incorporating the concepts of good point sets, refraction opposition-based learning, switching probability adjustment, and Solis & Wets search strategies is developed. Extensive experiments using data on China's investment-grade corporate bonds demonstrate the superior performance of the proposed model in the accuracy of bond coupon predictions. Additionally, the importance of various features has been discussed. The results show that the base interest rate for valuation and term to maturity are important to bond coupon predictions obtained by the proposed model.
Achieving high absolute positioning accuracy is crucial for obtaining aspheric optical components with remarkable surface quality using a robotic smoothing system. Robot kinematic calibration is an effective means of ...
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Achieving high absolute positioning accuracy is crucial for obtaining aspheric optical components with remarkable surface quality using a robotic smoothing system. Robot kinematic calibration is an effective means of improving absolute positioning accuracy. The calibration algorithms that use gradient direction have been shown to significantly improve computational efficiency compared to other calibration methods. However, these algorithms usually suffer from gradient degradation or vanishing after several iterations. In particular, the extended Kalman filter depends on the initial covariance matrix, which must be continually adjusted to reasonable values using artificial means. To address this challenge, an adaptive residual extended Kalman filter is proposed for robot kinematic calibration. This method involves using the residual generated from the current iteration to avoid gradient degradation or vanishing in the next iteration. An improved butterfly optimization algorithm is also used to adapt the system covariance matrix, the covariance matrix of system noises, and the covariance matrix of measurement noises of the extended Kalman filter to improve the identification accuracy. Finally, the proposed method's feasibility is demonstrated through sufficient calibration experiments. The method improved the RMSE positioning accuracy from 0.9328 to 0.4786 mm, a 48.69 % increase from before calibration. The smoothing compensation experimental results show that the proposed method achieves optical components with excellent surface quality. The PV and RMS are, respectively 10.46 % and 20.96 % lower than before compensation, and the PSD curve is superior to before compensation.
Providing communication between two remote points via a medium that is disturbed or distorted by noise or dispersion is the purpose of a communication system. In comparison to traditional approaches, metaheuristics in...
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Providing communication between two remote points via a medium that is disturbed or distorted by noise or dispersion is the purpose of a communication system. In comparison to traditional approaches, metaheuristics inspired by nature have shown better performance. In this works, butterfly optimization algorithm (BOA), an algorithm inspired by nature is presented as training algorithm for ANN. Here, we apply the training strategy for BOA in channel equalization. The proposed equalizer was found to perform better than previously known NN-based equalizers based on Bit Error Rate (BER) and Mean Square Error (MSE).
The imbalance between modes of transport in our country appears as the most important problem. Therefore, in air transportation, which has a significant increasing trend, estimating the passenger demand with directly ...
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The imbalance between modes of transport in our country appears as the most important problem. Therefore, in air transportation, which has a significant increasing trend, estimating the passenger demand with directly related parameters and novel algorithms is important for Turkey. In this study, different prediction models were developed applying for the first time with five different meta-heuristic algorithms which are Flower Pollination algorithm (FPA), Artificial Bee Colony algorithm (ABC), Crow Search algorithm (CSA), Krill Herd algorithm (KH), and the butterfly optimization algorithm (BOA) to estimate Turkey's air transport demand. While developing the models, Fuel Price, Gross Domestic Product per Capita, Seat Capacity, and Annual Fuel Consumption were selected as the model parameters. Although each model developed using different approaches is applicable, quadratic and power models developed using CSA showed the highest performance. For this reason, future projections were based on these models. Air transport passenger demand was examined using two scenarios in a process until 2035. In the first scenario, according to model forms, Turkey's future air transport passenger demand will reach about 460 and 490 million passengers, respectively. In the second scenario, the number of passengers will reach approximately 375 and 660 million for quadratic and power models, respectively. The results of this study will contribute to the evaluation of the current investment plans and the development of strategic plans that will meet the demands. Additionally, they will help take necessary measures and introduce some necessary regulations to ensure the income and expense balance so that the efficiency of airline companies can be improved.
The equalization of digital channels is widely recognized as a nonlinear classification problem. In such scenarios, utilizing networks that approximate nonlinear mappings can be highly advantageous. There has also bee...
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The equalization of digital channels is widely recognized as a nonlinear classification problem. In such scenarios, utilizing networks that approximate nonlinear mappings can be highly advantageous. There has also been extensive research on equalizers based on Radial Basis Function Neural Networks (RBFNNs). This study introduces a training methodology centred on the Improved butterfly optimization algorithm (IBOA) for channel equalization using RBFNN. This approach aims to optimize the performance of RBFNN equalizers by leveraging the IBOA algorithm for training. Previous literature primarily approached the equalization problem as an optimization challenge. In contrast, this study addresses it as a classification problem. This training approach exhibits substantial enhancements compared to conventional metaheuristic algorithms.
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