Parameters estimation of horizontal multilayer soils using a heuristic algorithm

Dan, Yihua; Zhang, Zhanlong; Yin, Junjun; Yang, Junan; Deng, Jun

doi:10.1016/j.epsr.2021.107661

Cited by 6 publications

(5 citation statements)

References 20 publications

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“…At last, The target performs a uniform linear motion within 50∼100 s. Take the maneuvering frequency as

, the maximum acceleration as

m/s 2 , the maximum acceleration probability as

, and the probability that the acceleration is equal to zero as

. The sampling period

s, it is assumed that the measurement periods of the nodes are the same, and the data are aligned in space and time [ 30 ].…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…At last, The target performs a uniform linear motion within 50∼100 s. Take the maneuvering frequency as µ = 0.05, the maximum acceleration as a max = 10 m/s 2 , the maximum acceleration probability as P max = 0.25, and the probability that the acceleration is equal to zero as P 0 = 0.75. The sampling period T = 1 s, it is assumed that the measurement periods of the nodes are the same, and the data are aligned in space and time [30]. In each simulation experiment, to mitigate the impact of initial parameters on the analysis results, the initial parameters of both the target and the bearing-only sensors are kept consistent [31].…”

Section: Experiments and Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Distributed Cubature Information Filtering Method for State Estimation in Bearing-Only Sensor Network

Chen,

Fu,

Zhang

et al. 2024

Entropy

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The problem of state estimation based on bearing-only sensors is increasingly important while existing research on distributed filtering solutions is rather limited. Therefore, this paper proposed the novel distributed cubature information filtering (DCIF) method for addressing the state estimation challenge in bearing-only sensor networks. Firstly, the system model of the bearing-only sensor network was constructed, and the observability of the system was analyzed. The sensor nodes are paired to measure relative angle information. Subsequently, the coordinated consistency theory is employed to achieve a unified state estimation of the maneuvering target. The DCIF method enhances the observability of the system, addressing the issues of large accuracy errors and divergence in traditional nonlinear filtering algorithms. Building upon the theoretical proof of consistency convergence in DCIF, four simulation experiments were conducted for comparison. These experiments validate the effectiveness and superiority of the DCIF method in bearing-only sensor networks.

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“…At last, The target performs a uniform linear motion within 50∼100 s. Take the maneuvering frequency as

, the maximum acceleration as

m/s 2 , the maximum acceleration probability as

, and the probability that the acceleration is equal to zero as

. The sampling period

s, it is assumed that the measurement periods of the nodes are the same, and the data are aligned in space and time [ 30 ].…”

Section: Experiments and Resultsmentioning

confidence: 99%

Section: Experiments and Resultsmentioning

confidence: 99%

Distributed Cubature Information Filtering Method for State Estimation in Bearing-Only Sensor Network

Chen,

Fu,

Zhang

et al. 2024

Entropy

View full text Add to dashboard Cite

show abstract

“…There are many evaluation indexes to evaluate the forecasting effect of the model [36][37][38][39][40], such as the mean absolute error (MAE), root-mean-square error (RMSE), mean absolute percentage error (MAPE), mean squared error (MSE), and R-squared (R2). In this paper, the three most widely used evaluation indicators MAE, RMSE and R 2 were used.…”

Section: Evaluation Indicatorsmentioning

confidence: 99%

DBSTGNN-Att: Dual Branch Spatio-Temporal Graph Neural Network with an Attention Mechanism for Cellular Network Traffic Prediction

Cai,

Tan,

Zhang

et al. 2024

Applied Sciences

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As network technology continues to develop, the popularity of various intelligent terminals has accelerated, leading to a rapid growth in the scale of wireless network traffic. This growth has resulted in significant pressure on resource consumption and network security maintenance. The objective of this paper is to enhance the prediction accuracy of cellular network traffic in order to provide reliable support for the subsequent base station sleep control or the identification of malicious traffic. To achieve this target, a cellular network traffic prediction method based on multi-modal data feature fusion is proposed. Firstly, an attributed K-nearest node (KNN) graph is constructed based on the similarity of data features, and the fused high-dimensional features are incorporated into the graph to provide more information for the model. Subsequently, a dual branch spatio-temporal graph neural network with an attention mechanism (DBSTGNN-Att) is designed for cellular network traffic prediction. Extensive experiments conducted on real-world datasets demonstrate that the proposed method outperforms baseline models, such as temporal graph convolutional networks (T-GCNs) and spatial–temporal self-attention graph convolutional networks (STA-GCNs) with lower mean absolute error (MAE) values of 6.94% and 2.11%, respectively. Additionally, the ablation experimental results show that the MAE of multi-modal feature fusion using the attributed KNN graph is 8.54% lower compared to that of the traditional undirected graphs.

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“…The soil parameter calculation method is a typical single‐objective multi‐parameter non‐linear optimization algorithm, the PSO‐DE optimization algorithm is adopted in this work [23]. The basic iteration update strategy is [15]

\left\{\begin{array}{l}{V}_{i}^{k+1}={\omega }^{k}{V}_{i}^{k}+{c}_{1}{r}_{1}\cdot \left({P}_{\mathrm{b}\mathrm{e}\mathrm{s}\mathrm{t},i}^{k}-{X}_{i}^{k}\right)+{c}_{2}{r}_{2}\cdot \left({G}_{\mathrm{b}\mathrm{e}\mathrm{s}\mathrm{t},i}^{k}-{X}_{i}^{k}\right)\\ {X}_{i}^{k+1}={X}_{i}^{k}+{V}_{i}^{k}\end{array}\right.

where X i and V i are the position and velocity of the particles.…”

Section: Basic Theory Of Grounding Parameter Calculation In Horizonta...mentioning

confidence: 99%

“…F is the mutation factor, taking 0.5. The crossover probability is taken at 0.9 [23]. Then, calculating the intermediate variable

{S}_{i}^{k+1}

through crossover operations between

{R}_{\mathrm{i}}^{k+1}

and

{X}_{i}^{k+1}

, and obtaining the intermediate variable

{T}_{i}^{k+1}

through crossover operations between

{Q}_{\mathrm{i}}^{k+1}

and

{V}_{i}^{k+1}

.…”

Section: Basic Theory Of Grounding Parameter Calculation In Horizonta...mentioning

confidence: 99%

Influence analysis of calculated horizontally layered soil parameters on grounding parameters

et al. 2022

Self Cite

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Calculated parameters (soil layer resistivity, soil layer thickness, and the number of soil layers) of horizontally layered soil are usually obtained based on the measured apparent resistivity under different measurement distances, which are significant for the design, operation, and maintenance of grounding grids. Most existing calculation methods of soil parameters are just trying to make the calculation results approach the measurement data, ignoring the relationship among measurement data, the calculated soil parameters, and grounding parameters, which would increase the workload of the measurement. To better balance the distance range of the measurement data and the influence of the calculated horizontally layered soil on grounding parameters, this paper systematically studies the relationship among measured apparent soil resistivity, calculated horizontally layered soil parameters, and grounding parameters. The basic theories of apparent resistivity measurement, soil parameter calculation, and grounding parameter calculation are given, the influence of soil layer thickness on the measured apparent resistivity is studied, and the influence of the calculated soil parameters on the grounding resistance of different grounding models is analysed. Based on different scales of grounding grids, the results give a corresponding reference distance range of measured apparent soil resistivity. Therefore, this paper can help decrease the workload of soil resistivity measurement during grounding parameters analysis, which has far-reaching engineering significance. | INTRODUCTIONHorizontally layered soil parameters are usually calculated based on a series of discrete measured data of apparent soil resistivity (referred to as calculated soil parameters). The accuracy of calculated soil parameters has a great influence on the grounding parameter calculations [1-3]. Therefore, it is significant to study the influence of calculated soil parameters on grounding parameters [4].Numerous scholars have studied the calculation of the parameters of horizontally layered soil. T. Takahashi gave the expressions for parameters estimation of arbitrary horizontally n-layer soil [5]. F. P. Dawalibi analysed the grounding parameters in different horizontally layered soils [6]. However, Ref. [5] and Ref. [6] did not focus on the influence of the measured apparent soil resistivity on the estimation results. J. L. del Alamo used 8 different mathematical methods to calculate the parameters of horizontally layered soil, and gave the advantages and disadvantages of those methods [7]. P. J. Lagace studied the combination method of the static image during the calculation, improved calculation efficiency, and effectively solved the interpolation problem in different methods [8]. In recent years, heuristic optimization algorithms are widely used in the calculation of soil parameters. I. F. Gonos and W. P. Calixto improved the genetic algorithm (GA) to calculate soil parameters, and the results of the This is an open access article under the terms of...

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Parameters estimation of horizontal multilayer soils using a heuristic algorithm

Cited by 6 publications

References 20 publications

Distributed Cubature Information Filtering Method for State Estimation in Bearing-Only Sensor Network

Distributed Cubature Information Filtering Method for State Estimation in Bearing-Only Sensor Network

DBSTGNN-Att: Dual Branch Spatio-Temporal Graph Neural Network with an Attention Mechanism for Cellular Network Traffic Prediction

Influence analysis of calculated horizontally layered soil parameters on grounding parameters

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