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A Face Detection Method Based on Skin Color Model and Improved adaboost algorithm

TRAITEMENT DU SIGNAL 2020年第6期37卷 929-937页

作者： Yang, Xiaoying Liang, Nannan Zhou, Wei Lu, Hongmei Suzhou Univ Sch Informat Engn Suzhou 234000 Peoples R China

This paper integrates skin color model and improved adaboost into a face detection method for high-resolution images with complex backgrounds. Firstly, the skin color areas were detected in a multi-color space. Each image was subject to adaptive brightness compensation, and converted into the YCbCr space, and a skin color model was established to solve face similarity. After eliminating the background interference by morphological method, the skin color areas were segmented to obtain the candidate face areas. Next, the inertia weight control factors and random search factor were introduced to optimize the global search ability of particle swami optimization (PSO). The improved PSO was adopted to optimize the initial connection weights and output thresholds of the neural network. After that, a strong adaboost classifier was designed based on optimized weak BPNN classifiers, and the weight distribution strategy of adaboost was further improved. Finally, the improved adaboost was employed to detect the final face areas among the candidate areas. Simulation results show that our face detection method achieved high detection rate at a fast speed, and lowered false detection rate and missed detection rate.

关键词： face detection image processing skin color model adaboost algorithm

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Lead-Acid Battery SOC Prediction Using Improved adaboost algorithm

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ENERGIES 2022年第16期15卷 5842-5842页

作者： Sun, Shuo Zhang, Qianli Sun, Junzhong Cai, Wei Zhou, Zhiyong Yang, Zhanlu Wang, Zongliang Navy Submarine Acad Dept Power Manipulat Qingdao 266042 Peoples R China Ocean Univ China Coll Engn Qingdao 266042 Peoples R China

Research on the state of charge (SOC) prediction of lead-acid batteries is of great importance to the use and management of batteries. Due to this reason, this paper proposes a method for predicting the SOC of lead-acid batteries based on the improved adaboost model. By using the online sequence extreme learning machine (OSELM) as its weak learning machine, this model can achieve incremental learning of the model, which has a high computational efficiency, and does not require repeated training of old samples. Through improvement of the adaboost algorithm, the local prediction accuracy of the algorithm for the sample is enhanced, the scores of the proposed model in the maximum absolute error (AEmax) and maximum absolute percent error (APEmax) indicators are 6.8% and 8.8% lower, and the accuracy of the model is further improved. According to the verification with experimental data, when there are a large number of prediction samples, the improved adaboost model can reduce the prediction accuracy indicators of mean absolute percent error (MAPE), mean absolute error (MAE), and mean square error (MSE) to 75.4%, 58.3, and 84.2%, respectively. Compared with various other prediction methods in the prediction accuracy of battery SOC, the prediction accuracy indicators MAE, MSE, MAPE, AEmax, and APEmax of the model proposed in this paper are all optimal, which proves the validity and adaptive ability of the model.

关键词： lead-acid battery state of charge (SOC) adaboost algorithm online sequence extreme learning machine (OSELM) incremental learning

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State of Charge Estimation of Lithium-Ion Battery Based on Back Propagation Neural Network and adaboost algorithm

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ENERGIES 2023年第23期16卷 7824页

作者： Cai, Bingzi Li, Mutian Yang, Huawei Wang, Chunsheng Chen, Yougen Costa, Carlos Miguel Guangdong Power Grid Corp Huizhou Power Supply Bur Huizhou 516000 Peoples R China Cent South Univ Sch Automat Changsha 410083 Peoples R China Florida State Univ Dept Elect & Comp Engn Tallahassee FL 32304 USA

The accurate estimation of the state of charge (SOC) of lithium-ion batteries is critical in battery energy storage systems. This paper introduces a novel approach, the adaboost-BPNN model, to overcome the limitations of traditional data-driven estimation methods, such as a low estimation accuracy and poor generalization ability. The proposed model employs a back propagation neural network (BPNN) for the preliminary estimation. Subsequently, an adaboost-BPNN model is developed as a strong learner using the adaboost integration algorithm. Each BPNN sub-model serves as a weak learner within the adaboost framework. The final output of the strong learner is obtained by combining the individual outputs from the weak learners using weighting factors. This adaptive adjustment of weighting factors enhances the accuracy of SOC estimation. The proposed SOC estimation algorithm is evaluated and validated through experimental analysis. Throughout the paper, theoretical analysis is conducted, and the proposed adaboost-BPNN model is validated and verified using experimental results. The results demonstrate that the adaboost-BPNN model outperforms traditional methods in accurately estimating SOC under various conditions, including constant current-constant voltage (CCCV) charging, dynamical stress testing (DST), US06, a federal urban driving schedule (FUDS), and pulse discharge conditions.

关键词： adaboost algorithm back propagation neural network lithium-ion battery state of charge

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CNVABNN: An adaboost algorithm and neural networks-based detection of copy number variations from NGS data

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COMPUTATIONAL BIOLOGY AND CHEMISTRY 2022年第0期99卷 107720页

作者： Wang, Xuan Li, Junqing Huang, Tihao Liaocheng Univ Sch Comp Sci & Technol Liaocheng Peoples R China HengXing Univ Sch Informat Engn Qingdao Peoples R China

Copy number variation (CNV) is a non-negligible structural variation on the genome. And next-generation sequencing (NGS) technology is widely used to detect CNVs due to the feature of high throughput and low cost on the whole genome. Based on the original MFCNV method, this paper proposes an improved CNV detection method, which is called CNVABNN. In comparison to the MFCNV method, CNVABNN has three ad-vantages: (1) It adds detectable categories, and refines the categories of loss into hemi_loss and homo_loss. (2) It utilizes the idea of integrated learning. The adaboost algorithm is used as the core framework and neural net-works are used as weak classifiers, then CNVABNN combines all of the weak classifiers into a strong classifier. The overall performance of CNV detection is improved by using the strong classifier. (3) The detection is opti-mized by predicting CNVs twice through neural networks and voting mechanisms. To evaluate the performance of CNVABNN, six existing detection methods are used for comparison. The experimental results show that CNVABNN achieves better results in terms of precision, sensitivity, and F1-score for both simulated and real samples.

关键词： Copy number variation adaboost algorithm Next -generation sequencing Neural network Read depth

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Impacts of Electric Vehicle Connected with Charging Station using Student Psychology Optimization algorithm (SPOA) and adaboost algorithm

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JOURNAL OF CIRCUITS SYSTEMS AND COMPUTERS 2023年第9期32卷 2350153-2350153页

作者： Murugan, M. Kumar, S. Satheesh Selvam, M. Panneer Rajesh, P. Shajin, Francis H. H. Govt Coll Engn Dept Elect & Elect Engn Bodinayakkanur India Univ Technol & Appl Sci Dept Elect & Elect Engn Al Khuwair Oman Sona Coll Technol Dept Elect & Elect Engn Salem Tamil Nadu India Anna Univ Dept Elect & Elect Engn Chennai India Anna Univ Dept Elect & Commun Engn Chennai India

This paper presents an electric vehicle connected to a charging station based on the proposed method. The proposed technique is the joint execution of the Student Psychology Optimization algorithm (SPOA) and the adaboost algorithm and is therefore called the SPOA-adaboost algorithm. In particular, the annualized social cost depends on CS and EVCS set from the objective function of the allocation model. The EVCS is linked with the CS and allows the charging service for electric vehicles. The vehicle-to-grid functions of electric vehicles are properly considered under the present optimization model. The load demands are considered as controllable resources, and EV optimal optimization problems are connected with allocation problems. When EV arrives at the charging station, it reports its own energy demand and expected departure time using the EVCS operator. Every EVCS could attack the details of electric vehicles via the proposed method. With this proper action, this method manages the energy demand and the total supply. The constraints are the power flow equations, equivalent load demands on the buses, branch current constraints, discrete size restrictions for CS, constraints on CS outputs, EV participation on V2G activities, mutual exclusivity of the EV charge and discharge statuses, EV Owner charge satisfaction, EV Owner satisfaction charge, EV SOC restrictions, Occupied CF quantities, and EVCS CF sufficiency. Among these, the execution of the present model done by the MATLAB/Simulink platform and the performance of the proposed model is likened with other systems.

关键词： Electric vehicle charging station vehicle to grid student psychology optimization algorithm adaboost algorithm

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Detection of Two-wheeled Vehicles based on Gaussian Mixture Model and adaboost algorithm 11

Detection of Two-wheeled Vehicles based on Gaussian Mixture ...

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11th World Congress on Intelligent Control and Automation

作者： Kuang, Xianyan Wang, Chengkun Xu, Lunhui Xiao, Dinghua Jiangxi Univ Sci & Technol Sch Elect Engn & Automat Ganzhou Jiangxi Peoples R China South China Univ Technol Sch Civil Engn & Transportat Guangzhou Guangdong Peoples R China

ISBN: (纸本)9781479958252

The paper describes a video detection method for two-wheeled vehicles appearing especially in the small-medium cities. The multi Gaussian mixture model is used to build the background and foreground. The single threshold method is efficiently used in shadow removal. Then Gaussian smooth filter processing and morphological image processing are used to filter out the noises in the foreground. The target region is obtained by comparing the difference of physical characteristics computed by labeled connecting area between general vehicles (cars) and two-wheeled vehicles. In the target region, the offline classifier based trained with local binary pattern (LBP) feature and adaboost algorithm, is used to accurate object detection. Experimental results show that the proposed method has a good performance in real-time and accurate detection of two-wheeled vehicles.

关键词： two wheels vehicles detection Gaussian mixture model local binary pattern features adaboost algorithm

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Quantum adaboost algorithm via cluster state

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INTERNATIONAL JOURNAL OF MODERN PHYSICS B 2017年第6期31卷 1750040-1750040页

作者： Li, Yuan Shanghai Dianji Univ Dept Commun Engn Shanghai 200240 Peoples R China

The principle and theory of quantum computation are investigated by researchers for many years, and further applied to improve the efficiency of classical machine learning algorithms. Based on physical mechanism, a quantum version of adaboost (Adaptive Boosting) training algorithm is proposed in this paper, of which purpose is to construct a strong classifier. In the proposed scheme with cluster state in quantum mechanism is to realize the weak learning algorithm, and then update the corresponding weight of examples. As a result, a final classifier can be obtained by combining efficiently weak hypothesis based on measuring cluster state to reweight the distribution of examples.

关键词： Quantum compute adaboost algorithm cluster state

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Application of adaboost algorithm and Immune algorithm in Telecommunication Fraud Detection

Application of Adaboost Algorithm and Immune Algorithm in Te...

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2018 International Conference on Network, Communication, Computer Engineering (NCCE 2018)

作者： Bing Wu Mengtao Li Chunlai Zhou School of Communication University of China

ISBN: (纸本)9781510871076

Fraud detection is one of the biggest challenges facing the telecommunication industry now and,in the future,the fight against fraud and anti-fraud has also reached a new *** a time-sensitive fraud detection method is an important way for operators to solve this *** this article,to solve the low accuracy of the general algorithm,an adaptive improvement algorithm is *** common algorithms are combined and enhanced,which greatly improves the accuracy of the detection *** we take the artificial immune algorithm as an *** main application is combining machine learning and immune algorithm to apply to telecommunication fraud *** combination of the two is more conducive to pushing research on telecommunication fraud to a new stage for the future telecommunication industry.

关键词： adaboost algorithm Artificial immune algorithm Fraud detection

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Face Detection Based on Skin Color Segmentation and adaboost algorithm

Face Detection Based on Skin Color Segmentation and AdaBoost...

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2011 3rd IEEE International Conference on Information Management and Engineering(ICIME 2011)

作者： Chunlei Shi Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences Graduate University of Chinese Academy of Sciences Changchun Normal University Changchun, China, Beijing, China, Changchun, China Longxu Jin, Ke Zhang Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences Changchun, China

Face detection technology is a research hot spot in computer vision and pattern recognition field. In this paper, we proposed a face detection method based on skin color segmentation and adaboost algorithm. First, skin color segmentation is used to detect skin regions which form the candidate face regions in terms of statistical characteristics of human face. Then, we scan these candidate face regions using cascade classifier which is consisted of many strong classifiers based on adaboost algorithm to get more accurate face detection result. Experimental results show that, the proposed method can detect face with high speed and low false acceptance rate. It can be effectively applied to the images of multi-pose and multi-face.

关键词： face detection skin color segmentation Haar-like featrue adaboost algorithm cascade classifier Summed Area Table

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Advanced Pedestrian Detection system using combination of Haar-like features, adaboost algorithm and Edgelet-Shapelet

Advanced Pedestrian Detection system using combination of Ha...

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IEEE International Conference on Computational Intelligence and Computing Research

作者： G. R. Rakate S. R. Borhade P. S. Jadhav M. S. Shah Vishwakarma Institute of Technology Pune India

ISBN: (纸本)9781467313421

The basic task in various applications like automotive control, video surveillance, etc is human body detection. For such applications to be successful, high accuracy and high speed performance are crucial. Image feature description determines accuracy and hence it should be robust against occlusion, rotation, and changes in object shapes and illumination conditions. Till date, many such feature descriptors have been proposed. Many of them are based on histogram of oriented gradients (HOG) along with support vector machine (SVM) classifier. Limitation of this method is high time consumption though it achieved good performance for Pedestrian Detection. To counter this limitation, a Two-step framework was proposed. It consisted two steps - full-body detection (FBD) and head-shoulder detection (HSD). Zhen Li proposed fusion of Haar-like and HOG features for better performance, and HSD step utilizes Edgelet features for classification and detection. But this method results in low detection rate and less computation speed. To counter these limitations, we have proposed an advanced method to improve both detection rate and speed. We achieve this by combination of Haar-like and Triangular features for FBD and Edgelet/Shapelet for HSD. We have achieved an average 95% detection rate and 60% faster speed for this proposed method.

关键词： Pedestrian Detection Histogram of Oriented Gradients Haar-like Features adaboost algorithm Edgelet/Shapelet

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