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A method of badminton video motion recognition based on adaptive enhanced adaboost algorithm

INTERNATIONAL JOURNAL OF BIOMETRICS 2025年第1-2期17卷 86-101页

作者： Chang, Yuntao Hunan City Univ Dept Phys Educ Yiyang 413000 Hunan Peoples R China

To overcome the problems of low recognition accuracy, poor recognition recall, and long recognition time in traditional badminton video action recognition methods, a badminton video action recognition method based on an adaptive enhanced adaboost algorithm is proposed. Firstly, the badminton video actions are collected through inertial sensors, and the badminton action videos are captured to construct an action dataset. The data in this dataset is normalised, and then the badminton video action features are extracted. The weighted fusion method is used to fuse the extracted badminton video action features. Finally, the fused action features are used as the basis, Construct a badminton video action classifier using the adaptive enhanced adaboost algorithm, and output the badminton video action recognition results through the classifier. The experimental results show that the proposed method has good performance in recognising badminton video actions.

关键词： inertial sensor weighted fusion method adaboost algorithm motion recognition data standardisation

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

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IEEE JOURNAL OF OCEANIC ENGINEERING 2021年第2期46卷 509-519页

作者： Ji, Xue Yang, Bisheng Tang, Qiuhua Key State Key Lab Informat Engn Surveying Mapping Wuhan 430079 Hubei Peoples R China MNR Inst Oceanog 1 Qingdao 266061 Peoples R China

When backpropagation neural network (BPNN) is often applied to supervised classification, problems arise, including a slow convergence rate, local extremum, and difficulty in determining the number of hidden layers and hidden nodes that affect the classification accuracy and efficiency. These problems can be overcome by using smarter network designs. Adaptive boosting (adaboost), which combines multiple weak classifiers to create a strong classifier, has a strong classification advantage. In this article, we propose an acoustic seabed classification method that combines adaboost with the particle swarm optimization (PSO). The PSO-BP-adaboost algorithm uses multibeam echosounder backscatter data to solve the multiclassification problem of diverse seafloor sediment types with small differences between types. We optimize a BPNN using the PSO algorithm to obtain the optimal initial weight and threshold and combine these to form an adaboost strong classifier. The input data is obtained from the sonar mosaic from multibeam echosounder backscatter data collected in Jiaozhou Bay using a series of fine processing techniques. These processing techniques result in 34-dimensional (34-D) features using ReliefF analysis. The most advantageous 8-D features are used as input into the adaboost algorithm based on one-level decision tree, PSO-BP algorithm, support vector machine (SVM), and PSO-BP-adaboost algorithm. The PSO-BP-adaboost classification model has better classification accuracy. The overall accuracy is improved by 12.68%, 6.78%, and 3.56%, respectively, which demonstrates that the PSO-BP-adaboost algorithm can be effectively applied to acoustic seabed classification and identification and achieves high precision.

关键词： Backscatter Sediments Acoustics Discrete wavelet transforms Sonar Microscopy Neural networks Acoustic seabed classification adaboost algorithm back-propagation neural network (BPNN) multibeam echosounder particle swarm optimization (PSO)

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Intelligent Island detection method of DC microgrid based on adaboost algorithm

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ENERGY REPORTS 2023年 9卷 970-982页

作者： Zhi, Na An, Yawei Zhao, Yan Qiu, Jilin Xian Univ Technol Xian 710048 Shaanxi Peoples R China

Aiming at the problems of large detection blind areas and difficult threshold settings in traditional passive islanding detection methods, this paper proposes an intelligent passive islanding detection method based on the adaptive boosting (adaboost) algorithm. This method first uses feature screening technology to obtain the key feature electrical quantities of DC microgrid islands and form a sample set, and then rely on the adaboost algorithm to generate a high-precision island classification model to realize the detection and classification of DC microgrid operation status. It has the advantages of automatic setting threshold, small detection blind area, and high accuracy. In addition, aiming at the problem that the classification results of the intelligent islanding detection algorithm based on machine learning cannot be directly used as the islanding discrimination results, an islanding criterion method based on the sliding window algorithm is added on the basis of the classification algorithm. This method realizes the accurate detection of the DC microgrid islanding operation state by the secondary analysis and judgment of the classification results of the islanding classification model. Finally, a DC microgrid model is built to simulate and verify the proposed method. The results show that the method can quickly and accurately complete the islanding detection of the DC microgrid.(c) 2023 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： DC microgrid Islanding detection adaboost algorithm Island discrimination

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A random feature mapping method based on the adaboost algorithm and results fusion for enhancing classification performance

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 256卷

作者： Shan, Wangweiyi Li, Dong Liu, Shulin Song, Mengmeng Xiao, Shungen Zhang, Hongli Changzhou Univ Sch Petr & Nat Gas Engn Changzhou 213164 Peoples R China Shanghai Univ Sch Mechatron Engn & Automat Shanghai 200444 Peoples R China Ningde Normal Univ Sch Mech & Elect Engn Ningde 352100 Peoples R China Ningde Normal Univ Sch Informat Engn Ningde 352100 Peoples R China

The feature mapping method can improve data separability, enhance data representation ability, and reduce data processing complexity. However, on the one hand, the existing feature mapping methods have difficulty processing datasets of different dimensions and distributions adaptively, limiting the scope of application;on the other hand, a single feature mapping method has the problem of instability and poor generalization ability, weakening the classification ability of subsequent classifiers. This paper proposes a random feature mapping method based on the adaboost algorithm and results fusion to enhance classification performance. The method adopts horizontal expansion, fine-tuning weights through sparse autoencoders, and uses input-mapped features as feature nodes to generate multiple feature subsets for increasing stability. After training weak classifiers on each multiple feature subset, the weights of classifiers are adjusted adaptively by the adaboost ensemble algorithm. Finally, the method fuses weak classifiers twice to enhance classification performance, which abandons the traditional voting method and uses the weighted probability selection method. Experiments on twenty classic datasets show that the proposed method can effectively mine essential features and enhance classification accuracy compared with original datasets. For instance, the Balance dataset has an average classification accuracy of more than 20% higher than the original dataset on the KNN classifier. The proposed method outperforms alternative feature mapping methods in terms of performance and efficiency on different classifiers in most cases.

关键词： Random feature mapping Multiple feature subsets adaboost algorithm Results fusion Classification performance

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Generalized radial basis function neural network based on an improved dynamic particle swarm optimization and adaboost algorithm

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NEUROCOMPUTING 2015年 152卷 305-315页

作者： Lu, Jinna Hu, Hongping Bai, Yanping North Univ China Sch Informat & Commun Engn Taiyuan 030051 Shanxi Peoples R China North Univ China Dept Math Taiyuan 030051 Shanxi Peoples R China

This paper proposes an improved dynamic particle swarm optimization algorithm, which uses a new and effective exponential decreasing inertia weight (EDIW) strategy. Based on the improved EDIW-PSO algorithm together with adaboost algorithm, we adjust the parameters (centers, widths, shape parameters and connection weights) of GRBF and present a novel hybrid EDIW-PSO-adaboost-GRBF model. Two application examples are given on the proposed model. The results obtained show that the proposed model is effective and feasible for prediction problems. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Generalized radial basis function Dynamic particle swarm optimization Exponential decreasing inertia weight adaboost algorithm

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Boosting scores fusion approach using Front-End Diversity and adaboost algorithm, for speaker verification

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COMPUTERS & ELECTRICAL ENGINEERING 2017年 62卷 648-662页

作者： Asbai, Nassim Amrouche, Abderrahmane USTHB Fac Elect & Comp Sci Speech Com & Signal Proc Lab Bab Ezzouar 16111 Algeria El Bachir El Ibrahimi Univ Bordj Bou Arreridj Algeria

A new speech feature extraction method called Mel Modified Group Delay coefficients (MMGDCs) is presented in this paper. In this method, the modified group delay spectrum detects the high formants frequencies, while the Mel filters select these desired formants in the high frequency regions. Also in this paper, a scores fusion approach is proposed between MMGDCs, Mel coefficients (MFCCs) and their extensions using the asymmetric tappers. The adaboost algorithm is used as strategy of this fusion. The performances evaluation of the proposed features and their extended variants are carried out on NIST 2000 corpus, and tested in both clean and simulated noisy conditions, using different noise categories extracted from the NOISEX-92 database. The obtained results show the superiority of the proposed MMGDCs against MFCCs in terms of error reduction, and the power of adaboost algorithm to make the fusion between MMGDCs and MFCCs better, especially under noisy environments. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Speaker verification Modified group delay spectra Mel filter MMGDCs MFCC adaboost algorithm Scores fusion Noisy conditions

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Application of KPCA and adaboost algorithm in classification of functional magnetic resonance imaging of Alzheimer's disease

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NEURAL COMPUTING & APPLICATIONS 2020年第10期32卷 5329-5338页

作者： Fan, Zhao Xu, Fanyu Li, Cai Yao, Lili Shanxi Med Univ Inst Geriatr Taiyuan 030001 Peoples R China Shanxi Med Univ Sch Stomatol Taiyuan 030001 Peoples R China Shanxi Med Univ Basic Med Sci Dept Physiol Taiyuan 030001 Peoples R China

With the rapid development of modern brain imaging techniques and big data analysis that measures brain processes, researchers are increasingly looking to reveal the pathogenesis of Alzheimer's disease. In order to find effective classification of magnetic resonance images of Alzheimer's disease, this paper constructed a feature classification model for Alzheimer's disease based on adaboost algorithm and KPCA algorithm, and selected 21 patients with Alzheimer's disease (AD). The trial included 6 patients with advanced Alzheimer's disease (LAD), 7 patients with early Alzheimer's disease, and 8 healthy individuals (HC) who underwent different levels of analysis. The results show that the article uses the KPCA algorithm to obtain the highest classification accuracy of the two groups: 94.77%, the single feature distinguishing ability is the node degree, and the accuracy of 90.94% can be achieved in the imaging diagnosis of AD. The article can significantly improve the classification of magnetic resonance images of Alzheimer's disease. This result is a good test of the effectiveness of the selected algorithm and has profound clinical significance for the diagnosis and classification of AD using magnetic resonance imaging.

关键词： Kernel principal component analysis (KPCA) adaboost algorithm Alzheimer's disease (AD) Image classification

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Fast Iris localization using Haar-like features and adaboost algorithm

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MULTIMEDIA TOOLS AND APPLICATIONS 2020年第45-46期79卷 34339-34362页

作者： Lin, Yi-Nan Hsieh, Tsang-Yen Huang, Jr-Jen Yang, Cheng-Ying Shen, Victor R. L. Bui, Hai Hoang Ming Chi Univ Technol Dept Elect Engn 84 Gongzhuan Rd New Taipei 243 Taiwan Univ Taipei Dept Comp Sci 1 Ai Kao W Rd Taipei 100 Taiwan Natl Taipei Univ Dept Comp Sci & Informat Engn 151 Univ Rd New Taipei 237 Taiwan Chaoyang Univ Technol Dept Informat Management 168 Jifeng E Rd Taichung 413 Taiwan

Traditional iris recognition methods, which are still preferred against artificial intelligence (AI) approaches in practical applications, are often required to capture high-grade iris samples by an iris scanner for accurate subsequent processing. To reduce the system cost for mass deployment of iris recognition, pricey scan devices can be replaced by the average quality cameras combined with additional processing algorithm. In this paper, we propose a Haar-like-feature-based iris localization method to quickly detect the location of human iris in the images captured by low-cost cameras for the ease of post-processing stages. The adaboost algorithm was chosen as a learning method for training a cascade classifier using Haar-like features, which was then utilized to detect the iris position. The experimental results have shown acceptable accuracy and processing speed for this novel cascade classifier. This achievement stimulates us to implement this novel capturing device in our iris recognition.

关键词： Object detection Iris localization Haar-like features adaboost algorithm Cascade classifier

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Islanding detection of synchronous distributed generation resources using adaboost algorithm

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INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS 2016年第8期26卷 1604-1624页

作者： Chavoshi, Seyyed Ali Noroozian, Reza Amiri, Ali Univ Zanjan Dept Elect & Comp Engn Fac Engn Univ Blvd Zanjan Iran

This paper presents a novel approach for intelligent islanding detection in a multi-machine distribution network using the Adaptive Boosting (adaboost) algorithm. The adaboost algorithm is an iterative procedure that improves the accuracy of classification models by combining multiple base classifiers. In this paper, two different case studies are specified so as to assess the performance of the adaboost in islanding detection. In order to train the adaboost algorithm, various scenarios are implemented in the case studies. A number of electrical parameters including frequency and voltage are utilized as a feature vector to describe the designed scenarios. In order to determine the accuracy of adaboost in detecting the islanding phenomenon, the proposed method is subsequently tested in further scenarios. Response time and non-detection zone of the suggested method are also evaluated in the case studies as the two other main criteria. The results indicate that this method can successfully detect islanding operation in a variety of operating states in the networks under study. All the simulations presented here are produced in the Simulink toolbox of MATLAB. Copyright (C) 2015 John Wiley & Sons, Ltd.

关键词： adaboost algorithm distributed generation resources islanding detection synchronous generator

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Research on Recognition of Nine Kinds of Fine Gestures Based on Adaptive adaboost algorithm and Multi-Feature Combination

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IEEE ACCESS 2019年 7卷 3235-3246页

作者： Liang, Shili Wang, Liangliang Zhang, Ling Wu, Yansheng Northeast Normal Univ Sch Phys Changchun 130022 Jilin Peoples R China Changchun Univ Sci & Technol Coll Sci Changchun 130022 Jilin Peoples R China

Accurate recognition of gestures based on surface EMG signals is of very importance in the study of human prosthetic interaction. In this paper, multi-feature combination and adaptive adaboost algorithm are used to identify nine kinds of fine gestures. First, in order to adaptively set the threshold for extracting the active segment, the algorithm for detecting the active segment of the surface myoelectric signal is improved, and the effective active segment value is obtained by rejecting the invalid active segment. Second, a fine gesture classification method based on multi-feature combination is proposed. In this paper, the recursive feature elimination algorithm based on cyclic feature selection is used to screen out the feature set with the top classification effect, and the feature set selected by eight experimental objects is counted, and the optimal feature combination is obtained according to the experimental analysis. Finally, nine fine gesture actions are identified through a weak classifier constructed by combining the adaboost algorithm and the single-layer decision tree. The experimental results show that the proposed algorithm can achieve an average recognition rate of 98.56 (+/- 1.44)% for nine fine gestures.

关键词： Gesture recognition adaboost algorithm multi-feature combination surface EMG signals

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