Search alternatives:
1_decoding algorithm » modeling algorithm (Expand Search)
modelling algorithm » modeling algorithm (Expand Search), processing algorithm (Expand Search)
table 1_decoding » table 1_developing (Expand Search)
1_decoding algorithm » modeling algorithm (Expand Search)
modelling algorithm » modeling algorithm (Expand Search), processing algorithm (Expand Search)
table 1_decoding » table 1_developing (Expand Search)
-
1
-
2
-
3
-
4
Algorithmic experimental parameter design.
Published 2024“…The results of numerical simulations and sea trial experimental data indicate that the use of subarrays comprising 5 and 3 array elements, respectively, is sufficient to effectively estimate 12 source angles. …”
-
5
Spatial spectrum estimation for three algorithms.
Published 2024“…The results of numerical simulations and sea trial experimental data indicate that the use of subarrays comprising 5 and 3 array elements, respectively, is sufficient to effectively estimate 12 source angles. …”
-
6
-
7
Risk element category diagram.
Published 2025“…It can be summarized that the algorithmic model has good accuracy and robustness. …”
-
8
The run time for each algorithm in seconds.
Published 2025“…The goal of this paper is to examine several extensions to KGR/GPoG, with the aim of generalising them a wider variety of data scenarios. The first extension we consider is the case of graph signals that have only been partially recorded, meaning a subset of their elements is missing at observation time. …”
-
9
-
10
-
11
-
12
Scatter diagram of different principal elements.
Published 2025“…The experimental results show that the SSA-LightGBM model proposed in this paper has an average fault diagnosis accuracy of 93.6% after SSA algorithm optimization, which is 3.6% higher than before optimization. …”
-
13
-
14
-
15
-
16
-
17
-
18
-
19
Comparison results of prediction model with single algorithm and combination algorithm.
Published 2025Subjects: -
20
Model-Based Clustering of Categorical Data Based on the Hamming Distance
Published 2024“…The proposed method exploits the Hamming distance to define a family of probability mass functions to model the data. The elements of this family are then considered as kernels of a finite mixture model with an unknown number of components. …”