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Antenna Resonant Frequency Modeling based on adaboost Gaussian Process Ensemble

APPLIED COMPUTATIONAL ELECTROMAGNETICS SOCIETY JOURNAL 2020年第12期35卷 1485-1492页

作者： Zhang, Tianliang Tian, Yubo Chen, Xuezhi Gao, Jing Jiangsu Univ Sci & Technol Sch Elect & Informat Zhenjiang 212003 Jiangsu Peoples R China

The design of electromagnetic components generally relies on simulation of full-wave electromagnetic field software exploiting global optimization methods. The main problem of the method is time consuming. Aiming at solving the problem, this study proposes a regression surrogate model based on adaboost Gaussian process (GP) ensemble (AGPE). In this method, the GP is used as the weak model, and the adaboost algorithm is introduced as the ensemble framework to integrate the weak models, and the strong learner will eventually be used as a surrogate model. Numerical simulation experiment is used to verify the effectiveness of the model, the mean relative error (MRE) of the three classical benchmark functions decreases, respectively, from 0.0585, 0.0528, 0.0241 to 0.0143, 0.0265, 0.0116, and then the method is used to model the resonance frequency of rectangular microstrip antenna (MSA) and coplanar waveguide butterfly MSA. The MRE of test samples based on the APGE are 0.0069, 0.0008 respectively, and the MRE of a single GP are 0.0191, 0.0023 respectively. The results show that, compared with a single GP regression model, the proposed AGPE method works better. In addition, in the modeling experiment of resonant frequency of rectangular MSA, the results obtained by AGPE are compared with those obtained by using neural network (NN). The results show that the proposed method is more effective.

关键词： adaboost algorithm Gaussian process ensemble microstrip antenna resonant frequency

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Transfer adaboost SVM for Link Prediction in Newly Signed Social Networks using Explicit and PNR Features

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Procedia Computer Science 2015年 60卷 332-341页

作者： Anh-Thu Nguyen-Thi Phuc Quang Nguyen Thanh Duc Ngo Tu-Anh Nguyen-Hoang University of Information Technology Vietnam National University HCMC Quarter 6 Linh Trung Ward Thu Duc Dist Ho Chi Minh city 700000 Vietnam

In signed social network, the user-generated content and interactions have overtaken the web. Questions of whom and what to trust has become increasingly important. We must have methods which predict the signs of links in the social network to solve this problem. We study signed social networks with positive links (friendship, fan, like, etc) and negative links (opposition, anti-fan, dislike, etc). Specifically, we focus how to effectively predict positive and negative links in newly signed social networks. With SVM model, the small amount of edge sign information in newly signed network is not adequate to train a good classifier. In this paper, we introduce an effective solution to this problem. We present a novel transfer learning framework is called Transfer adaboost with SVM (TAS) which extends boosting-based learning algorithms and incorporates properly designed RBFSVM (SVM with the RBF kernel) component classifiers. With our framework, we use explicit topological features and Positive Negative Ratio (PNR) features which are based on decision-making theory. Experimental results on three networks (Epinions, Slashdot and Wiki) demonstrate our method that can improve the prediction accuracy by 40% over baseline methods. Additionally, our method has faster performance time.

关键词： Link Prediction Signed Social Network adaboost algorithm

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Research on low-speed performance of continuous rotary electro-hydraulic servo motor based on robust control with adaboost prediction

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JOURNAL OF ENGINEERING-JOE 2019年第13期2019卷 60-67页

作者： Jing, Wang X. Zhen, Liu M. Wei, Sun Y. Wang, Xin Harbin Univ Sci & Technol Sch Mech & Power Engn Harbin Heilongjiang Peoples R China Jilin Univ Sch Mech & Aerosp Engn Changchun Jilin Peoples R China

In order to improve the robustness and low-speed performance of continuous rotary electro-hydraulic servo system under influences of dynamic uncertainties, parametric perturbation, friction, other non-linear properties, and uncertainties, the robust control strategy was proposed with adaboost prediction. Firstly, basing on the system mathematic model, the model with structured uncertainty and generalised state equation was established with parametric perturbation and external disturbances, and then the robust controller was developed by adopting $H_\infty $H infinity theory. Furthermore, adaboost algorithm based on radial basis function (RBF) neural network was applied to design the system feedback mechanism, so the multiple weak neural network learners were obtained by using adaboost algorithm to train system actual output and input. Also, these weak neural network learners constituted a strong learner to predict the electro-hydraulic servo system output and calculate the predictive error so as to adjust the system robust control output, so the real-time control was carried out by the robust controller. Some comparative simulated results are obtained to verify the proposed controller guarantees performances of low speed, tracking accuracy, and ability of anti-interference, which greatly expands the band of frequency response and improve the system robustness.

关键词： control system synthesis electrohydraulic control equipment hydraulic motors adaptive control servomotors robust control servomechanisms radial basis function networks feedback learning (artificial intelligence) neurocontrollers frequency response H∞ theory external disturbances continuous rotary electro-hydraulic servomotor system low speed performance system robustness real-time control system robust control output predictive error system actual output multiple weak neural network learners system feedback mechanism RBF neural network adaboost algorithm generalised state equation structured uncertainty system mathematic model robust control strategy nonlinear properties parametric perturbation dynamic uncertainties adaboost prediction low-speed performance

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Crude oil price prediction: A comparison between adaboost-LSTM and adaboost-GRU for improving forecasting performance

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COMPUTERS & CHEMICAL ENGINEERING 2021年 155卷 107513-107513页

作者： Busari, Ganiyu Adewale Lim, Dong Hoon Gyeongsang Natl Univ Dept Bio & Med Big Data 501 Jinju Daero Jinju 52828 South Korea Gyeongsang Natl Univ Dept Bio & Med Big Data Dept Informat & Stat 501 Jinju Daero Jinju 52828 South Korea Gyeongsang Natl Univ RINS 501 Jinju Daero Jinju 52828 South Korea

Crude oil plays an important role in the world economy and contributes to more than one third of energy consumption worldwide. The better forecasting of its fluctuating price is crucial for policymaking. Although various methods had been previously applied for forecasting crude oil price which including both statistical models and Artificial Neural Network models but we find that there is no single paper that compares the adaboost-LSTM and adaboost-GRU models for improving forecasting performance. We proposed adaboost-GRU in which the GRU model was built, put inside sklearn wrapped and finally boost by adaboost Regressor. The predictive power of the proposed model was compared with adaboost-LSTM using daily Crude oil prices from October 23, 2009, to June 23, 2021, and single LSTM and single GRU were used as the benchmarking models. Forecasting performances were measured using five different metrics namely;the mean absolute error (MAE), the root mean squared error (RMSE), the Scatter Index (SI), the mean absolute percentage error (MAPE), and the weighted mean absolute percentage error (WMAPE). We have demonstrated that the adaboost-LSTM and the adaboost-GRU models outperform the benchmarking models as expected, and the empirical results show that the adaboost-GRU is superior to all models studied in this research. (c) 2021 Elsevier Ltd. All rights reserved.

关键词： Crude Oil Price Forecast adaboost algorithm Long short-term memory Gated recurrent unit Forecasting

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Underwater Acoustic Target Recognition Using SVM Ensemble via Weighted Sample and Feature Selection 13

Underwater Acoustic Target Recognition Using SVM Ensemble vi...

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13th International Bhurban Conference on Applied Sciences and Technology (IBCAST)

作者： Yang, Honghui Gan, Anqin Chen, Hanlu Pan, Yue Tang, Jansheng Li, Jiangqiao Northwestern Polytech Univ Sch Marine Sci & Technol Xian Peoples R China Inst Syst Engn Res Sci & Technol Underwater Acoust Antagonizing Lab Beijing Peoples R China

ISBN: (纸本)9781467391276

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： underwater acoustic target recognition classifier ensemble adaboost algorithm support vector machine weighted sample selection feature selection

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Face Recognition and Smile Detection as an Appraisal of Customer Satisfaction Instrument

Face Recognition and Smile Detection as an Appraisal of Cust...

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International Conference on Applied Engineering (ICAE)

作者： Hardianti, Sri Diah Sugandi, Budi Futra, Asrizal Deri Politekn Negeri Batam Elect Engn Batam Indonesia

ISBN: (纸本)9781538680667

The spreading of cafes, restaurant, and food courts in a small market area can create a high-level competitive situation among food serving industries. As customer satisfaction is the main key to reach the glory, making up or keeping up a service or product quality is a critical decision. In this research, we presented a steady and easy way out to solve this problem based on face recognition and smile detection method. We implemented an adaboost ( adaptive boosting) algorithm by Viola-Jones which is the robust algorithm for recognizing the face and detecting a smile. It works completely by cascade classifier step to ensure the object detected is a face. Our system performance attained 96,43% of accuracy in real time condition. It is able to detect a face in a range of 25-100 cm, and it can detect a smile from 25-45 cm in a certain position and conditions.

关键词： face recognition smile detection adaboost algorithm real-time satisfaction instrument

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Low-Illumination Environment Vehicle Detection Based on Optimized Adaptive Image Enhancement

Low-Illumination Environment Vehicle Detection Based on Opti...

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International Conference on Image, Video Processing, and Artificial Intelligence

作者： Zhao, Kun Liu, Jian Xiao, Xiaocheng Xidian Univ Sch Microelect Xian 710071 Peoples R China Chery New Energy Automot Technol Co Ltd Wuhu 241000 Peoples R China

ISBN: (数字)9781510650282

ISBN: (纸本)9781510650282;9781510650275

With the increasing number of vehicles, vehicle detection has become an important part of intelligent transportation system. At present, most detection algorithms are only suitable for normal light conditions, but the detection performance is poor for low illumination conditions. In order to achieve effective vehicle detection under low illumination conditions, this paper proposes an image enhancement algorithm to increase the contrast of the image, thereby greatly improving the effect of vehicle detection. First, the image contrast is enhanced through an adaptive contrast stretching algorithm. Secondly, the bilateral filtering algorithm is used to filter out the noise in the image. Finally, the detection system based on Haar features and adaboost classifier is used to detect vehicles. Experimental results show that the proposed algorithm can effectively enhance image contrast, highlight vehicle information, and the vehicle detection accuracy rate under low illumination conditions reaches 87.04%.

关键词： vehicle detection adaptive contrast stretching bilateral filtering Haar characteristics adaboost algorithm

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3D-Reconstruction of Synapses Based on EM Images 13

3D-Reconstruction of Synapses Based on EM Images

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IEEE International Conference on Mechatronics and Automation

作者： Sun, Mingsong Zhang, Dandan Guo, Hongchen Deng, Hao Li, Weifu Xie, Qiwei Harbin Univ Sci & Technol Software Sch Harbin 150040 Peoples R China Chinese Acad Sci Inst Automat Beijing 100190 Peoples R China Harbin Univ Sci & Technol Sch Comp Sci & Technol Harbin 150080 Peoples R China Beijing Univ Posts & Telecommun Beijing 100190 Peoples R China Hubei Univ Appl Math & Fac Math & Stat Wuhan 430062 Peoples R China Chinese Acad Sci Ctr Excellence Brain Sci & Intelligence Technol Beijing Peoples R China

ISBN: (纸本)9781509023967

3D reconstruction from the microscopy images of serial sections plays an important role in analyzing structure of biological specimens, such as neuronal circuits in brain tissue. This paper is focusing on the 3D reconstruction of synapses which is a branch of micro reconstruction of brain. Having analysed the structure of synapses, we first detect and locate them in serial sections with cascade adaboost algorithm, then optimize the shape of synapses by constructing suitable fitting functions and segment them with morphology processing method. Taking the connection of adjacent sections and synaptic extensibility in space into consideration, we complete the reconstruction of synapses which is based on the segmentation in serial sections. Actually all our works are based on the registration of serial sections.

关键词： 3D reconstruction adaboost algorithm fitting function registration of serial sections

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Segmentation of Mitochondria Based on SEM Images 13

Segmentation of Mitochondria Based on SEM Images

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IEEE International Conference on Mechatronics and Automation

作者： Li, Weifu Rao, Qiang Chen, Xi Li, Guoqing Zhang, Dandan Hubei Univ Appl Math & Fac Math & Stat Wuhan 430062 Peoples R China Chinese Acad Sci Inst Automat Beijing 100190 Peoples R China Chinese Acad Sci Ctr Excellence Brain Sci & Intelligence Technol Beijing Peoples R China Harbin Univ Sci & Technol Sch Comp Sci & Technol Harbin 150080 Peoples R China

ISBN: (纸本)9781509023967

Recently, due to the rapid development of scanning electron microscope (SEM) with its very high resolution in three dimensions, we can look more closely into those fine structures related to brain function. However, high resolution will inevitably produce huge amounts of data. Considerable attention has been paid for the automated data analysis, which is massively lagging behind data acquisition. In this paper, we focus on the segmentation of mitochondria for its important role in neural function and its homogeneous shape as preferably corresponding landmarks for image registration. Different from previous methods, we present a coarse-to-fine strategy which utilizes the adaptive boosting (adaboost) algorithm for detection, multi-layer information fusion for the connection across adjacent sections, and sparse region growing for fine segmentation. Finally we show the 3D visualization in ImageJ. Experimental results on automated tape-collecting ultramicrotome scanning electron microscopy (ATUM-SEM) images demonstrate the effectiveness of our proposed method.

关键词： SEM adaboost algorithm multi-layer information fusion sparse region growing

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A dynamic image recognition method of sleeper springs trouble of moving freight cars based on Haar features

A dynamic image recognition method of sleeper springs troubl...

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6th International Symposium on Instrumentation and Control Technology

作者： Zhou Fuqiang Jiang Yuan Zhang Guangjun Beihang Univ Sch Instrument Sci & Optoelect Engn Beijing 100083 Peoples R China

ISBN: (纸本)0819464538

A novel conception of automatic recognition for free-trouble sleeper springs is proposed and adaboost algorithm based on Haar features is applied for the sleeper springs recognition in Trouble of moving Freight car Detection System (TFDS). In the recognition system, feature set of sleeper springs is determined by Haar features and selected by adaboost algorithm. In order to recognize and select the free-trouble sleeper springs from all the captured dynamic images, a cascade of classifier is established by searching dynamic images. The amount of detected images is drastically reduced and the recognition efficiency is improved due to the conception of free-trouble recognition. Experiments show that the proposed method is characterized by simple feature, high efficiency and robustness. It performs high robustness against noise as well as translation, rotation and scale transformations of objects and indicates high stability to images with poor quality such as low resolution, partial occlusion, poor illumination and overexposure etc. The recognition time of a 640x480 image is about 16ms, and Correct Detection Rate is high up to about 97%, while Miss Detection Rate and Error Detection Rate are very low. The proposed method can recognize sleeper springs in all-weather conditions, which advances the engineering application for TFDS.

关键词： free-trouble recognition Haar features adaboost algorithm dynamic image sleeper springs

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