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20th IEEE International Conference on Computer Supported Cooperative Work in Design (CSCWD)

作者： Zhang, Xingqiang Ding, Jiajun Nanchang Univ Sch Informat Engn Nanchang Peoples R China

ISBN: (纸本)9781509019151

An improved face detection method is proposed on the basis of traditional adaboost algorithm. The training samples are not distinguished in the traditional face detection based on adaboost algorithm, which results in ignoring face samples in the process of training and the face feature information can't be fully shown. In addition, because face samples and non-face samples are treated equally, all samples must be calculated and the time of training classifier is extended. In order to improve the bad results, this paper proposes an improved strategy for implementation of algorithm. Face samples and non-face samples are set different initial weights when training classifier, so they attract different attention. And face and non-face samples are handled separately in order to reduce the complexity of the time. Compared with traditional methods, the improved method spends less time on training classifier.

关键词： face detection adaboost algorithm classifier haar feature

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Video Surveillance System of Substation Based on adaboost Pedestrian Anti-misjudgment algorithm 17th

Video Surveillance System of Substation Based on AdaBoost Pe...

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17th International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP) / 14th International Conference on Frontiers of Information Technology, Applications and Tools (FITAT)

作者： Huang, Xu-hong Tang, Sheng-ping Zheng, Shang Zhao, Nan Fujian Univ Technol Fuzhou 350000 Fujian Peoples R China Fujian Univ Technol Fujian Prov Key Lab Big Data Min & Applicat Fuzhou 350000 Fujian Peoples R China

ISBN: (纸本)9789811910579;9789811910562

In order to reduce the false alarm rate of the pedestrian monitoring system in the substation and accelerate the unmanned operation of the substation, this paper proposes a video surveillance system of substation based on the adaboost pedestrian anti-misjudgment algorithm. It use infrared temperature sensor and infrared life sensor to distinguish between plants and animals according to whether there are life characteristics, so as to solve the misjudgment of the alarm system caused by shaking plants, use directional gradient histogram (HOG) features, support vector machines (SVMs) classifier, and adaboost algorithm to distinguish pedestrians from other animals, and eliminate the misjudgment of other animals on the alarm system;if the detection result is a pedestrian, the data will be uploaded to the PC and synchronized to the cloud to remind the relevant staff in time. This system can effectively solve the problem of the high-false alarm rate of the current unattended substation monitoring system and accelerate the advancement of unattended substations.

关键词： adaboost algorithm Infrared SVM Temperature sensor

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Empirical Analysis of adaboost algorithms on License Plate Detection

Empirical Analysis of AdaBoost Algorithms on License Plate D...

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IEEE International Conference on Mechatronics and Automation

作者： Sun, Junxi Cui, Dong Gu, Dongbing Cai, Hua Liu, Guangwen Changchun Univ Sci & Technol Sch Elect & Informat Engn 7089 Weixing Rd Changchun 130022 Peoples R China Univ Essex Sch Elect Engn & Comp Sci Colchester C04 3SQ Essex England

ISBN: (纸本)9781424426928

adaboost algorithm is an effective license plate detection method in the field of license plate recognition technology. A through analysis of three boosting algorithms (namely Discrete, Real and Gentle adaboost) is presented for license plate detection, including the algorithm details and experiment comparisons. The experimental results show the Gentle adaboost algorithm obtains an overall better results in terms of high detection rate and low false positive rate than the discrete adaboost algorithm or real adaboost algorithm.

关键词： License plate detection adaboost algorithm weak classifier

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An Online Education Data Classification Model Based on TrMadaboost algorithm

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Chinese Journal of Electronics 2019年第1期28卷 21-28页

作者： YU Lasheng WU Xu YANG Yu School of Information Science & Engineering Central South University

With the rapid development of network information technology and the wide application of smart phones, tablet PCs and other mobile terminals, online education plays an increasingly important role in social life. This article focuses on mining useful data from the massive online education data, by using transfer learning, relying on Hadoop, to construct Online education data classification framework（OEDCF）, and design an algorithm TrMadaboost. This algorithm overcomes the traditional classification algorithms in which the required data must be restricted to independent and identically distributed data, since online education using this new algorithm can achieve the correct classification even it has different data distribution. At the same time, with the help of Hadoop's parallel processing architecture, OEDCF can greatly enhance the efficiency of data processing, create favorable conditions for learning analysis, and promote personalized learning and other activities of big data era.

关键词： Transfer learning Online education OEDCF adaboost algorithm Tr_Madaboost algorithm

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Hybrid adaboost based on Genetic algorithm for Gene Expression Data Classification 17

Hybrid Adaboost based on Genetic Algorithm for Gene Expressi...

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12th Chinese Conference on Computer Supported Cooperative Work and Social Computing (ChineseCSCW)

作者： Meng, Yaqiong Lu, Huijuan Yan, Ke Ye, Minchao China Jiliang Univ Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9781450353526

This paper presents a hybrid adaboost algorithm. The decision groups are chosen as weak classifiers, which consist of K nearest neighbor algorithm, Naive Bayes and decision tree. When the weak classifiers are promoted to strong classifier, the genetic algorithm is used to optimize the discourse right of each weak classifier. Experiments show proposed algorithm compared with the weak algorithm integration algorithm with only a single algorithm, the proposed algorithm is superior.

关键词： adaboost algorithm genetic algorithm K nearest neighbor algorithm Naive Bayes decision tree

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A Medical Diagnosis Model Based On Pnn-Cadaboost algorithm 11

A Medical Diagnosis Model Based On Pnn-Cadaboost Algorithm

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11th International Conference on Intelligent Computation Technology and Automation (ICICTA)

作者： Liu Shi-ya Cen Zhao-hua Liu Jing Zhang Zhi-fei Foshan Univ Sci & Technol Sch Automat Foshan 528000 Peoples R China

ISBN: (纸本)9781538683088

This paper focuses on research of an adaptive clustering algorithm and its application in medical diagnosis based on probabilistic neural networks. A PNN-Cadaboost medical diagnosis model is proposed on the standard adaboost algorithm together with clustering algorithm. Both PNN-adaboost model and PNN-Cadaboost model are established respectively. For testing our model validness, the experimental data are collected from the Wisconsin breast cancer data set in the UCI database, computations and comparisons with multiple indicators. It is proved that the PNN-Cadaboost medical diagnosis model can effectively improve the classification performance and has good robust stability.

关键词： Medical diagnosis Classification model Breast cancer data adaboost algorithm Pnn - Cadaboost model

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Based on adaboost Automatic Face Recognition System Based on algorithm and Local Feature Method 20

Based on AdaBoost Automatic Face Recognition System Based on...

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2020 3rd International Conference on Digital Medicine and Image Processing

作者： Ying Li Changsha Medical University China

ISBN: (纸本)9781450389044

For the recognition of feature positions and feature points in face images, it is often necessary to test and iteratively calculate the extracted face feature images with the help of various data extraction and information optimization algorithms, to obtain the global optimal solution inside the feature space. This article relies on adaboost the training algorithm uses several weak classifiers to expand the training of data samples, sets the same initialization weight for each sample, and expands the retrieval and exclusion operations of multi-layer target images in the Cascade Classifier, repeat the weighted summation of the calculated Image weight and the threshold, gradually eliminate the redundant data information in the face feature space, and ensure the optimal solution of the image algorithm test in the face recognition system.

关键词： adaboost algorithm Automatic Face recognition Local features

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The Design of a Car License Plate Identification System Based on Ada Boost algorithm

The Design of a Car License Plate Identification System Base...

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International Conference on Materials Science and Technology

作者： Hong, Wang Xian, Li Shuang, Liu Hebei Univ Coll Elect & Informat Engn Baoding Peoples R China Signalway Dev Co Ltd Beijing Peoples R China Univ Habana Coll Qual & Technol Supervis Baoding Peoples R China

ISBN: (纸本)9783037850152

Design and implement a car license plate identification system with the applications of Viola and Jones algorithm. This algorithm which is based on the adaboost method is trained and optimized for the best performance using large database of car license plate images. The final license plate identification system obtained a cascade of classifiers consisting of 8 stages with 1310 Haar-like features. Once the license plates have sufficient visibility and there are no other objects similar to the plate in images, this system operates perfectly and shows high correct identification rate with low false positive rate. And as integral image allows the Haar-like features to be calculated very fast, the system also finished the identification rapidly.

关键词： License plate identification Haar-like feature adaboost algorithm Cascade Classifiers

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The Ada Boost algorithm Applied in ATM Automatic Identification System

The Ada Boost algorithm Applied in ATM Automatic Identificat...

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3rd International Conference on Advanced Engineering Materials and Technology (AEMT 2013)

作者： Zhang, Ming-Hui Zhang, Yao-Yu Air Force Aviat Univ Changchun Peoples R China Chinese Acad Sci Changchun Inst Opt Fine Mech & Phys Changchun Peoples R China

ISBN: (纸本)9783037857649

Seeing that human face features are unique, an increasing number of face recognition algorithms on existing ATM are proposed. Since face detection is a primary link of face recognition, our system adopts adaboost algorithm which is based on face detection. Experiment results demonstrated that the computing time of face detection using this algorithm is about 70ms, and the single and multiple human faces can be effectively measured under well environment, which meets the demand of the system.

关键词： Haar-Like character adaboost algorithm face detection ATM

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State of Health Estimation for Lithium-Ion Batteries Based on Fusion Health Features and adaboost-GWO-BP Model

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JOURNAL OF THE ELECTROCHEMICAL SOCIETY 2024年第11期171卷 110528-110528页

作者： Tong, Liang Li, Yiyang Chen, Yong Kuang, Rao Xu, Yonghong Zhang, Hongguang Peng, Baoying Yang, Fubin Zhang, Jian Gong, Minghui Beijing Informat Sci & Technol Univ Mech Elect Engn Sch Beijing 100192 Peoples R China Hainan Univ Sch Marine Sci & Engn Haikou 570228 Hainan Peoples R China Beijing Univ Technol Fac Environm & Life Key Lab Enhanced Heat Transfer & Energy Conservat Beijing Key Lab Heat Transfer & Energy Convers Beijing 100124 Peoples R China Univ Wisconsin Green Bay Richard J Resch Sch Engn Mech Engn Green Bay WI 54311 USA

To accurately predict the state of health (SOH) of lithium-ion batteries and improve the safety and reliability of battery management systems, a new SOH estimation method based on fusion health features (HFs) and adaptive boosting integrated grey wolf optimizer to optimize back propagation neural network (adaboost-GWO-BP) is proposed. First, five kinds of multi-type HFs were extracted from the battery charging process, and the correlation between the proposed HFs and SOH was verified by Pearson and Spearman correlation coefficients. Then, the indirect health feature (IHF) was obtained by multidimensional scaling dimensionality reduction to reduce data redundancy and improve the correlation between HFs and SOH. The GWO-BP model was then used to establish the nonlinear mapping relationship between IHF and SOH. In order to overcome the problem of low accuracy of battery SOH estimation in a single model, the adaboost algorithm in ensemble learning is introduced to enhance the accuracy of the model estimation. Finally, the proposed method is verified by NASA dataset, and compared with other models. In the comparative experiments, mean absolute error and root mean square error of the proposed method for SOH estimation is less than 0.81% and 1.26%, which has higher accuracy compared to other models. (c) 2024 The Electrochemical Society ("ECS"). Published on behalf of ECS by IOP Publishing Limited. All rights, including for text and data mining, AI training, and similar technologies, are reserved. [DOI:10.1149/1945-7111/ad940c]

关键词： lithium-ion battery health features grey wolf optimizer adaboost algorithm state of health

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