In the urban rail transit (URT) environment, the radio wave propagation prediction model and communication system planning are very important. However, due to the complexity of the tunnel propagation environment, the ...
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In the urban rail transit (URT) environment, the radio wave propagation prediction model and communication system planning are very important. However, due to the complexity of the tunnel propagation environment, the current prediction model can not fully cover the radio wave propagation process in the tunnel. In this paper, the propagation mechanism area is divided based on the segmentation approach. Different propagation models are used for different propagation mechanism areas to predict path loss more quickly and accurately. To improve the accuracy of the prediction model, this paper proposes an improved seagull optimization algorithm (ISOA). First, to address the shortcomings of the seagull optimization algorithm (SOA) such as easy premature convergence and slow convergence speeds, two improved methods of random search and periodic disturbance are proposed. Then, in order to verify the effectiveness and feasibility of the improved algorithm, the benchmark function is used to test the optimization performance of the ISOA and gray wolf optimization, the SOA, and particle swarm optimization. The results show that the optimization performance of ISOA is the most significant. Finally, the ISOA is used to fit and correct the continuous wave test data for a rectangular tunnel and an arch tunnel. The results show that the corrected propagation model has a higher degree of fit with the measured data than the single standard propagation model (SPM) model. The modified propagation model thus has guiding significance for the deployment of time-division long-term (TD-LTE) evolution networks in the tunnel environment.
The composition of base oils affects the performance of lubricants made from *** paper proposes a hybrid model based on gradient-boosted decision tree(GBDT)to analyze the effect of different ratios of KN4010,PAO40,and...
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The composition of base oils affects the performance of lubricants made from *** paper proposes a hybrid model based on gradient-boosted decision tree(GBDT)to analyze the effect of different ratios of KN4010,PAO40,and PriEco3000 component in a composite base oil system on the performance of *** study was conducted under small laboratory sample conditions,and a data expansion method using the Gaussian Copula function was proposed to improve the prediction ability of the hybrid *** study also compared four optimization algorithms,sticky mushroom algorithm(SMA),genetic algorithm(GA),whale optimization algorithm(WOA),and seagull optimization algorithm(SOA),to predict the kinematic viscosity at 40℃,kinematic viscosity at 100℃,viscosity index,and oxidation induction time performance of the *** results showed that the Gaussian Copula function data expansion method improved the prediction ability of the hybrid model in the case of small *** SOA-GBDT hybrid model had the fastest convergence speed for the samples and the best prediction effect,with determination coefficients(R^(2))for the four indicators of lubricants reaching 0.98,0.99,0.96 and 0.96,***,this model can significantly reduce the model’s prediction error and has good prediction ability.
seagull Optimization algorithm (SOA) is a metaheuristic algorithm that mimics the migrating and hunting behaviour of seagulls. SOA is able to solve continuous real-life problems, but not to discrete problems. The eigh...
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seagull Optimization algorithm (SOA) is a metaheuristic algorithm that mimics the migrating and hunting behaviour of seagulls. SOA is able to solve continuous real-life problems, but not to discrete problems. The eight different binary versions of SOA are proposed in this paper. The proposed algorithm uses four transfer functions, S-shaped and V-shaped, which are used to map the continuous search space into discrete search space. Twenty-five benchmark functions are used to validate the performance of the proposed algorithm. The statistical significance of the proposed algorithm is also analysed. Experimental results divulge that the proposed algorithm outperforms the competitive algorithms. The proposed algorithm is also applied on data mining. The results demonstrate the superiority of binary seagull optimization algorithm in data mining application.
This paper proposes a method to improve the efficiency of distribution network planning and investment by using TOPSIS and binary seagull optimization algorithm. The core idea lies in the following methods: quantitati...
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In this study, eight parameters are selected and their historical data are collected to predict the future of the energy demand of Turkey. The initial eight parameters were the gross domestic product (GDP) of Turkey, ...
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In this study, eight parameters are selected and their historical data are collected to predict the future of the energy demand of Turkey. The initial eight parameters were the gross domestic product (GDP) of Turkey, average annual US crude oil price (COP), inflation for Turkey in percentages (INF), the population of Turkey, total vehicle travel in kilometers for Turkey, total amount of goods transported on motorways, employment for Turkey, and trade of Turkey. However, after these eight parameters data are analyzed using Pearson and Spearman correlation methods, it is found out that five of these parameters are highly correlated. The remaining three parameters are the GDP of Turkey, COP, and INF for Turkey. Afterward, five separate scenarios are developed to forecast the future of the energy demand of Turkey. The first two scenarios involve the third- and fourth-order polynomial fitting, the third and fourth scenarios employ static and recurrent neural networks, and the fifth scenario utilizes autoregressive models to predict the future energy demand of Turkey. The efficient hybridization of the seagull optimization and very optimistic method of minimization metaheuristic algorithms is carried out to achieve the polynomial fitting of the data. The optimization performance of the hybrid algorithm is assessed by applying the algorithm on benchmark optimization problems and comparing the results with that of some other metaheuristic optimizers. Moreover, it is seen that the forecasts of the first scenario agree well with the Ministry of the Energy and Natural Resources estimates.
In educational facility interiors, the risk of congestion and trampling among occupants during the evacuation process presents a significant safety concern. Therefore, assessing the risk of the evacuation process is o...
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In educational facility interiors, the risk of congestion and trampling among occupants during the evacuation process presents a significant safety concern. Therefore, assessing the risk of the evacuation process is of great practical and academic importance. To meet the requirements of rapid and timely risk assessment, this article proposes an emergency evacuation risk assessment model based on the Improved Extreme Learning Machine (ELM). The ELM with fast learning speed and good generalization performance is improved to form the Deep Extreme Learning Machine (DELM) and Kernel Based Extreme Learning Machine (KELM) models, and the Improved seagull Optimization algorithm (ISOA) was used to constitute the ISOA-DELM and ISOA-KELM models for training. Taking a university library as an example, the evaluation process of model data acquisition, training, and testing is analyzed and compared. The prediction accuracy of the ISOA-DELM and ISOA-KELM models proposed in this paper reached more than 92%. The results show that improved extreme learning machine models can enable an efficient and fast risk assessment.
Aiming at mitigating the fluctuation of distributed photovoltaic power generation, a segmented compensation strategy based on the improved seagull algorithm is proposed in this paper. In this regard, a hybrid energy s...
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Aiming at mitigating the fluctuation of distributed photovoltaic power generation, a segmented compensation strategy based on the improved seagull algorithm is proposed in this paper. In this regard, a hybrid energy storage system comprising a lithium battery and supercapacitor is utilized. The internal power distribution of the hybrid energy storage system is adjusted using wavelet packet decomposition, and the state of charge is employed to adapt the primary power distribution. The start and end times for charging and discharging are determined by combining the time of use, electricity price, state-of-charge information, and load size at night to realize the economic operation of the system. The opposing search operator strategy and mutation operation are used to improve the seagull algorithm, optimize the controller parameters of the DC/DC converter, and improve its response time. Combined with the historical measured data of a distributed photovoltaic in Hubei Province, simulation results show that the proposed strategy can effectively smoothen the fluctuation of distributed photovoltaic generated power while reducing the charging and discharging frequencies of the energy storage system, hence improving its stability and service life.
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