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8th World Congress on Intelligent Control and Automation (WCICA)

作者： Li, Lingling Chen, Yangzhou Xin, Le Beijing Univ Technol Beijing Peoples R China

ISBN: (纸本)9781424467129

Driver's fatigue detection has been realized based on driver's mouth geometrical features. It can give some information when driver is fatigue. For better speed and reliability, a new method which was based on combined adaboost algorithm and particle filter was applied. Then the mouth verification was applied according to prior knowledge. Driver's state was judged by the geometrical features in a period of time. As a result, the detection accuracy was improved. This method can meet the requirement of driver's fatigue detection.

关键词： mouth detection particle filter adaboost algorithm two cameras driver fatigue

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Monitoring of Agglomeration in Fluidized-Bed Reactor Based on Voiceprint Features Recognition with Unbalanced Samples

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JOURNAL OF CHEMICAL ENGINEERING OF JAPAN 2022年第10期55卷 326-335页

作者： Zhao, Zhong Chen, Yanglihong Pang, Junqiu Beijing Univ Chem Technol Coll Informat Sci & Technol Beijing 100029 Peoples R China

When acoustic emission detection technology is applied to detect the agglomeration in fluidized bed reactors (FBRs), the collected acoustic emission samples are usually non-stationarity and unbalanced, making it difficult to extract stable and separable classification features. In this study, the voiceprint features of collected acoustic emission signals were extracted with the Mel Frequency Cepstrum Coefficients (MFCC) and Linear Prediction Cepstrum Coefficients (LPCC). Extracted voiceprint features of LPCC and MFCC were fused with RelieF algorithm to form the stable R-LPMFCC feature, which were then compressed with principal components analysis (PCA) as input data for classification. The cost factor and GINI index-based decision-making calculation were introduced to the adaboost algorithm to significantly improve its accuracy and F -score when classifying unbalanced samples. The comparative experimental results in a fluidized-bed pilot plant verify the effectiveness and feasibility of the proposed method.

关键词： FBR Agglomeration Monitoring R-LPMFCC ReliefF adaboost algorithm

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Hyperspectral Inversion of Soil Organic Matter Content Based on a Combined Spectral Index Model

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SENSORS 2020年第10期20卷 2777页

作者： Wei, Lifei Yuan, Ziran Wang, Zhengxiang Zhao, Liya Zhang, Yangxi Lu, Xianyou Cao, Liqin Hubei Univ Fac Resources & Environm Sci Wuhan 430062 Peoples R China Hubei Univ Hubei Key Lab Reg Dev & Environm Response Wuhan 430062 Peoples R China Minist Land & Resources Key Lab Urban Land Resources Monitoring & Simulat Shenzhen 518034 Peoples R China Anhui Acad Agr Sci Inst Soil & Fertilizer Hefei 230031 Peoples R China Wuhan Univ Sch Printing & Packaging Wuhan 430079 Peoples R China

Soil organic matter (SOM) refers to all carbon-containing organic matter in soil and is one of the most important indicators of soil fertility. The hyperspectral inversion analysis of SOM traditionally relies on laboratory chemical testing methods, which have the disadvantages of being inefficient and time-consuming. In this study, 69 soil samples were collected from the Honghu farmland area and a mining area in northwest China. After pretreatment, 10 spectral indicators were obtained. Ridge regression, kernel ridge regression, Bayesian ridge regression, and adaboost algorithms were then used to construct the SOM hyperspectral inversion model based on the characteristic bands, and the accuracy of the models was compared. The results showed that the adaboost algorithm based on a grid search had the best accuracy in the different regions. For the mining area in northwest China, Rp2 = 0.91, RMSEp = 0.22, and MAEp = 0.2. For the Honghu farmland area, Rp2 = 0.86, RMSEp = 0.72, and MAEp = 0.56. The detection of SOM content using hyperspectral technology has the characteristics of a high detection precision and high speed, which will be of great significance for the rapid development of precision agriculture.

关键词： hyperspectral remote sensing soil organic matter adaboost algorithm pearson correlation analysis

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Underwater acoustic target recognition using SVM ensemble via weighted sample and feature selection

Underwater acoustic target recognition using SVM ensemble vi...

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International Bhurban Conference on Applied Sciences & Technology, IBCAST

作者： Honghui Yang Anqin Gan Hanlu Chen Yue Pan Jansheng Tang Jiangqiao Li School of Marine Science and Technology Northwestern Polytechnical University Xi'an China Science and Technology on Underwater Acoustic Antagonizing Laboratory Institute of Systems Engineering Research Beijing China

The accuracy of underwater acoustic target recognition (UATR) system can be improved by ensemble of support vector machine (SVM) classifiers. However, the ensembles are often large, leading to extra high computational and storage cost. To solve this problem, we propose a novel adaboost method based on weighted sample and feature selection (WSFSelect-SVME). The adaboost method constructs an ensemble of classifiers iteratively focusing each new individual SVM classifier on the most difficult samples. Weighted immune clonal sample selection algorithm and mutual information sequential forward feature selection algorithm are utilized to keep the performance of each new individual SVM classifier while reducing the number of samples and features in the training set. The classification performance of the proposed method is examined on the UCI Sonar dataset and a real-world underwater acoustic target dataset. Experiment results on two datasets show that, compared to adaboost SVM ensemble (SVME) algorithm, the WSFSelect-SVME algorithm obtains better classification accuracy with the number of samples decreasing respectively to 45% and 50%, and the number of features decreasing to 33% and 51%. The experimental results revealed that the proposed algorithm can reduce the space complexity of the ensemble while improving the accuracy compared to the adaboost SVME algorithm.

关键词： adaboost algorithm classifier ensemble feature selection support vector machine underwater acoustic target recognition weighted sample selection Support Vector Network Feature extraction Classifier Ensembles Target recognition space complexities Ensembles Underwater Classifiers Acoustics

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Comparative Analysis of Various Face Detection Methods

Comparative Analysis of Various Face Detection Methods

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IEEE Punecon

作者： Deepali G. Ganakwar Vipulsangarm K. Kadam Department of Engineering & Technology Dr. Babasaheb Ambedkar Marathwada University Aurangabad India Department of Electronics Engineering P.E.S College of Engineering Nagsnvana Aurangabad India

ISBN: (数字)9781728119243

ISBN: (纸本)9781728119250

With wide variety of increase in image and video database, the demand raises for automatic examination of this database as it is cumbersome in manual understanding and examination. This paper provides brief insights into some of renowned and mostly accepted Techniques of face detection. Face detection technique can be simply defined as a technology used by computer system that detects one or several human faces resulting in digital image. Recognizing and tracking the face, estimating pose and expressions, analysis of face and detecting any other features of face are the steps included in face detection method. Nowadays, face detection techniques owes one of the most active research areas of computer vision. Considering the face as an object that grabs countless applications in image processing makes it challenging task in computer vision. This paper provides a survey of existing literature on human face detection system. Three commonly used methods have been considered for comparative analysis in this paper.

关键词： Face detection integral image adaboost algorithm Hausdorff distance Facial Recognition images computer vision video databases

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Pedestrian detection with infrared videos for night-time surveillance

Pedestrian detection with infrared videos for night-time sur...

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2014 International Conference on Artificial Intelligence and Industrial Application (AIIA2014)

作者： B.J.Choi J.S.Park J.K.Song B.W.Yoon Department Electronics Engineering Kyungsung University

ISBN: (纸本)9781784660529

This paper presents a real-time infrared pedestrian detection algorithm based on Haar-like features. The detection phase is performed by a cascade classifier that is trained with datasets generated from infrared images. The experimental results under different urban street scenarios prove that our method is robust and efficient for the infrared images.

关键词： pedestrian detection Haar-like feature infrared video cascade classifier adaboost algorithm

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A novel head gesture recognition means in the human-wheelchair interaction

A novel head gesture recognition means in the human-wheelcha...

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The 2010 International Conference on Computer Application and System Modeling(2010计算机应用与系统建模国际会议 ICCASM 2010)

作者： Yuan Luo Zhang-fang Hu Lin li yizhang Chongqing University of Posts and Telecommunications Research Center of Intelligent System and Robot Chongqing University of Posts and Telecommunications Research Center of Intelligent System and Robe

ISBN: (纸本)9781424472352

As an intelligent human-wheelchair interaction ways, head gesture becomes one of the most pop research topics, and the head gesture recognition is an important aspect of the interactions. In this paper, kalman filter forecast the lips position detected by adaboost algorithm may be appeared in the next frames first, and then detect the lips in the next frame. Compare the lips window position with a fixed point to confirm the head gesture correspondingly. Kalman filter overcome detect all the possible lips position by just use the adaboost algorithm in every frame, greatly improve the lips detection precision and reduce the detection time, solve the wheelchair's time delay.

关键词： component: Kalman filter human-wheelchair interaction adaboost algorithm

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Research and application of a hybrid system based on interpolation for forecasting direct economic losses of marine disasters

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INTERNATIONAL JOURNAL OF DISASTER RISK REDUCTION 2019年 37卷 101121-000页

作者： Zhao, Xin Li, Hui Ding, Lili Liu, Mengxi Ocean Univ China Sch Econ Qingdao 266100 Shandong Peoples R China Minist Educ Marine Dev Studies Inst OUC Key Res Inst Univ Qingdao 266100 Shandong Peoples R China

The accurate prediction of the direct economic losses of marine disasters (DELMD) is critical for allowing policy makers to take proper measures in managing a marine disaster. The prediction of the annual DELMD is hindered by its characteristically small samples, nonlinearity and indetermination. Developing ways to create an efficient system to forecast DELMD is challenging work. For this reason, this paper introduces a hybrid forecasting system that uses an adaptive boosting (adaboost) algorithm and a back propagation neural network (BPNN) based on interpolation to predict DELMD. In this paper, four interpolations are employed to expand the original small sample with virtual points, then the adaboost algorithm is used to optimize the results obtained by the weak predictor, BPNN, and produce the final forecasting results. Furthermore, to verify the prediction performance of the developed forecasting system, traditional models are used as comparisons to the new forecasting system. The experimental evidence shows that (a) the cubic spline interpolation is the most effective way to solve the small sample problem for forecasting DELMD, (b) the proposed hybrid forecasting system not only outperforms other traditional models but also is robust for other samples.

关键词： Interpolation adaboost algorithm BPNN DELMD Virtual point

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Study Remote Video Sorting in the Communication Networks

Study Remote Video Sorting in the Communication Networks

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2011 IEEE International Conference on Information Theory and Information Security(ICITIS 2011)

作者： Yun Wang Institute of Mathematics and Information Science of Langfang Teachers College Hebei Langfang China

In this paper, a communication network can be carried out in the sorting of innovative remote video applications. To make a huge video data can be transmitted in a communications networks, we propose a new idea that in the original traditional C/S on the basis of networks transmission mode, an increase of middleware, and integrated the current pattern recognition advanced adaboost algorithm, the remote video sorting in-depth study points. This remote video sorting system has a certain reference value.

关键词： adaboost algorithm C/S Video Sorting.

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Ensemble Learning for Detecting Remote Access Trojans

Ensemble Learning for Detecting Remote Access Trojans

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作者： Khin Swe Yin May Aye Khine Faculty of Computing University of Computer Studies

Machine learning algorithms for network traffic classification has been researched for several years. They are useful for both encrypted and unencrypted network traffic classification. Nowadays malicious malware like Remote Access Trojans go through network, and they are secretly installed in a victim's computer, they stay in the victim host and communicate back to the attacker. The command and control traffic of Remote Access Trojans can be differentiated from normal traffic using machine learning based techniques. This paper compares the performance of nine supervised machine learning algorithms for detection of Remote Access Trojans. Both unbalanced and balanced dataset are applied for building model. Four ensemble learning methods give high detection rate. Among them, adaboost ensemble learning outperforms the competing methods, and it gets the best accuracy, least false negative rate and least false positive rate.

关键词： machine learning algorithms Remote Access Trojans adaboost algorithm

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