Acoustic Seabed Classification Based on Multibeam Echosounder Backscatter Data Using the PSO-BP-AdaBoost Algorithm: A Case Study From Jiaozhou Bay, China

Ji, Xue; Wang, Jinhu; Tang, Qiuhua

doi:10.1109/joe.2020.2989853

Cited by 36 publications

(15 citation statements)

References 45 publications

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“…Finally, the best weak classifier for each iteration is selected to construct a strong classifier. However, AdaBoost combines weak classifiers to construct a strong classifier [ 54 ]. The weights of each weak classifier are not equal, and the stronger classifier will be assigned the high weight [ 55 ].…”

Section: Ai-based Discriminant Algorithmsmentioning

confidence: 99%

“…The weights of each weak classifier are not equal, and the stronger classifier will be assigned the high weight [ 55 ]. Specifically, the weighted error of the k -th weak classifier

is written as [ 54 ]

where w indicates the output weight. The weight coefficient of the k -th

is defined as [ 54 ]

…”

Section: Ai-based Discriminant Algorithmsmentioning

confidence: 99%

“…Specifically, the weighted error of the k -th weak classifier

is written as [ 54 ]

where w indicates the output weight. The weight coefficient of the k -th

is defined as [ 54 ]

…”

Section: Ai-based Discriminant Algorithmsmentioning

confidence: 99%

See 2 more Smart Citations

Remote Interference Discrimination Testbed Employing AI Ensemble Algorithms for 6G TDD Networks

Zhang

Zhou

et al. 2023

Sensors

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The Internet-of-Things (IoT) massive access is a significant scenario for sixth-generation (6G) communications. However, low-power IoT devices easily suffer from remote interference caused by the atmospheric duct under the 6G time-division duplex (TDD) mode. It causes distant downlink wireless signals to propagate beyond the designed protection distance and interfere with local uplink signals, leading to a large outage probability. In this paper, a remote interference discrimination testbed is originally proposed to detect interference, which supports the comparison of different types of algorithms on the testbed. Specifically, 5,520,000 TDD network-side data collected by real sensors are used to validate the interference discrimination capabilities of nine promising AI algorithms. Moreover, a consistent comparison of the testbed shows that the ensemble algorithm achieves an average accuracy of 12% higher than the single model algorithm.

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Section: Ai-based Discriminant Algorithmsmentioning

confidence: 99%

“…The weights of each weak classifier are not equal, and the stronger classifier will be assigned the high weight [ 55 ]. Specifically, the weighted error of the k -th weak classifier

is written as [ 54 ]

where w indicates the output weight. The weight coefficient of the k -th

is defined as [ 54 ]

…”

Section: Ai-based Discriminant Algorithmsmentioning

confidence: 99%

See 1 more Smart Citation

Remote Interference Discrimination Testbed Employing AI Ensemble Algorithms for 6G TDD Networks

Zhang

Zhou

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…22 The Adaboost algorithm can make good use of different algorithms as its weak classifiers, through repeated learning, while constantly changing the probability distribution of the training sample data and the proportion of the weak classifier in the strong classifier, the final output is determined. The key core of the Adaboost algorithm lies in the selection of weak classifiers, choosing a suitable weak classifier can greatly improve the accuracy of Adaboost classification, 23,24 and the error rate can be adjusted adaptively according to the feedback results of the weak classifier, improving classification accuracy and high execution efficiency, so this algorithm is widely used by people. 25 Its model structure is shown in Figure 2.…”

Section: Improved Bp-adaboost Algorithmmentioning

confidence: 99%

Research on fault diagnosis method of aviation cable based on improved Adaboost

Wang

Yuan

Guo

et al. 2022

Advances in Mechanical Engineering

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In order to solve the problems of short circuit, open circuit and insulation faults in aviation cables, a fault diagnosis method based on BP-Adaboost algorithm is proposed in this paper. The BP neural network is used as the weak classifier in the Adaboost algorithm, and many weak classifiers are composed a strong classifier with stronger classification performance to diagnose fault categories. The BP-Adaboost fault diagnosis model is established, and the BP-Adaboost algorithm is improved to adapt to the multi-classification faults of cables, so as to identify the short circuit, open circuit, and insulation faults in the aircraft cable as well as normal working conditions. The accuracy of classification is analyzed; the results of the algorithm are analyzed by Matlab software, and the analysis results show that the improved BP-Adaboost algorithm has a relatively good classification performance for multi-class aviation cable fault diagnosis. Finally, the feasibility of the algorithm proposed is verified through an example combined with cable fault detection equipment.

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“…Therefore, in the LSSVM model based on the radial basis function (RBF), the kernel width  of the radial basis function (RBF) and the penalty factor C are the main parameters that affect its performance. In order to E3S Web of Conferences 185, 01051 (2020) ICEEB 2020 http://doi.org/10.1051/e3sconf/202018501051 improve the prediction accuracy, the above-mentioned particle swarm optimization is used The algorithm optimizes these two parameters [10].…”

Section: Least Squares Support Vector Machine (Lssvm) Support Vectormentioning

confidence: 99%

Ultra-short-time prediction technology of wind power station output based on variational mode decomposition and particle swarm optimization least squares vector machine

et al. 2020

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Wind power is developing rapidly in the context of sustainable development, and a series of problems such as wind curtailment and power curtailment have gradually emerged. The forecast of power generation output has become one of the hotspots of current research. This paper proposes a wind power plant output ultra-short-time prediction technology based on variational modal decomposition and particle swarm optimization least squares vector machine. Variational Modal Decomposition (VMD) method decomposes the historical output data of wind power plants at multiple levels. At the same time, it explores the impact of various decomposition methods such as EMD decomposition on the prediction accuracy, and uses the least squares support vector machine based on particle swarm optimization algorithm. Predictive summation is performed on each level of data separately to obtain a more accurate prediction effect, which has a certain improvement in prediction accuracy compared with traditional prediction algorithms.

show abstract

Acoustic Seabed Classification Based on Multibeam Echosounder Backscatter Data Using the PSO-BP-AdaBoost Algorithm: A Case Study From Jiaozhou Bay, China

Cited by 36 publications

References 45 publications

Remote Interference Discrimination Testbed Employing AI Ensemble Algorithms for 6G TDD Networks

Remote Interference Discrimination Testbed Employing AI Ensemble Algorithms for 6G TDD Networks

Research on fault diagnosis method of aviation cable based on improved Adaboost

Ultra-short-time prediction technology of wind power station output based on variational mode decomposition and particle swarm optimization least squares vector machine

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