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Classification of Tectonic and Nontectonic Earthquakes by an integrated learning algorithm

PURE AND APPLIED GEOPHYSICS 2020年第1期177卷 455-467页

作者： Ren, Tao Wang, Pengyu Lin, Mengnan Liu, Xiaoyu Chen, Hongfeng Liu, Jie Northeastern Univ Software Coll Shenyang 110819 Peoples R China China Earthquake Networks Ctr Beijing 100029 Peoples R China

A machine learning model for accurate and quick classification of tectonic and nontectonic earthquakes is proposed. Firstly, an improved method is proposed for detection of first arrivals. An iterative approach is applied for the multiwindow algorithm to decrease its computational cost, then a new method for detection of first arrivals is proposed by combining the improved multiwindow algorithm with a recursive least-squares filter and the Akaike information criterion. Secondly, it is shown that the integrated learning algorithm is a suitable method for classification, then four suitable features are manually selected to train it. Thirdly, the three parameter values of the integrated learning model are determined to improve its accuracy. Finally, based on seismic data from the China Earthquake Networks Center, simulations are conducted to test the validity of the proposed method for detection of first arrivals, then the classification accuracy is tested. The results show an accuracy of the model of 88.88%, indicating effective classification performance.

关键词： integrated learning algorithm detection of first arrivals tectonic earthquake nontectonic earthquake seismic attribute

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Predicting the NOx emissions of low heat value gas rich-quench-lean combustor via three integrated learning algorithms with Bayesian optimization

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ENERGY 2023年 273卷

作者： Yan, Peiliang Fan, Weijun Zhang, Rongchun Beihang Univ Sch Energy & Power Engn Natl Key Lab Sci & Technol Aeroengines Aerothermod Beijing 100191 Peoples R China Beihang Univ Res Inst Aeroengine Beijing 100191 Peoples R China

With the increased attention to low heat value gas fuels in recent years, research on NOx emissions from the combustors of low heat value gas fuels is necessary. This study introduces integrated learning algorithms to the NOx pollution emission prediction from gas turbine combustors. The combustor is a self-designed low heat value gas rich-quench-lean combustor and 92 sets of emission data have been obtained using simulation methods. The simulation data has been experimentally verified. The effect of four control parameters on NOx emissions has been investigated: inlet air temperature, inlet air mass flow rate, swirler installation angle, and combustor lean burn zone length. Pearson correlation coefficients show that four control parameters have very low correlations. The three integrated learning algorithms used in this study are the random forest regression algorithm, the extreme gradient boosting algorithm, and the natural gradient boosting algorithm. The hyperparameters of the three integrated learning algorithms are optimised using the Bayesian optimization algorithm. Three integrated learning algorithms are utilized to predict pollutant emission characteristics and the results show that NGboost provides the best predictions and Random Forest the worst, with NGboost suffering from overfitting problems. The results of the algorithm predictions were analyzed to understand the impact of different control parameters on the NOx emissions. A feature importance evaluation shows that the influence of inlet air temperature on NOx emissions far outweighs the other three parameters.

关键词： Low heat value gas RQL combustor integrated learning algorithm NOx emission Prediction

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An E-mail Classification algorithm based on Stacking integrated learning

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电脑学刊 2022年第2期33卷 105-114页

作者： Li-Xia Wan Wei-Xing Huang Qing-Hua Tang

The text filtering of traditional anti spam system mainly focuses on keyword matching and text fingerprint analysis, which is difficult to accurately identify and classify spam. Therefore, an integrated learning algorithm based on stacking is proposed in this paper. Firstly, the algorithm takes the manually marked text data of various categories as samples, uses TF-IDF algorithm to train the word vector space model, then selects linear SVC, xgboost and logistic regression algorithm to structure the base classifier, uses random forest algorithm to structure the meta classifier, and combines the stacking ensemble learning algorithm to structure the classification model. It achieves the function of dividing e-mail into five categories: illegal, advertisement, news, bill and recruitment. From the simulation results, the AUC values of the stacking integrated learning classification algorithm for each category are 0.92, 0.95, 1.00, 0.93 and 0.97 respectively, and the AP values are 0.86, 0.88, 1.00, 0.88 and 0.94 respectively, which realizes the high performance and high precision of text classification.

关键词： anti spam system integrated learning algorithm TF-IDF algorithm word vector space model e-mail classification

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Performance Evaluation of Mangrove Species Classification Based on Multi-Source Remote Sensing Data Using Extremely Randomized Trees in Fucheng Town, Leizhou City, Guangdong Province

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REMOTE SENSING 2023年第5期15卷 1386页

作者： Wang, Xinzhe Tan, Linlin Fan, Jianchao Dalian Polytech Univ Inst Informat Sci & Engn Dalian 116034 Peoples R China Natl Marine Environm Monitoring Ctr Dept Marine Remote Sensing Dalian 116023 Peoples R China

Mangroves are an important source of blue carbon that grow in coastal areas. The study of mangrove species distribution is the basis of carbon storage research. In this study, we explored the potential of combining optical (Gaofen-1, Sentinel-2, and Landsat-9) and fully polarized synthetic aperture radar data from different periods (Gaofen-3) to distinguish mangrove species in the Fucheng town of Leizhou, Guangdong Province. The Gaofen-1 data were fused with Sentinel-2 and Landsat-9 satellite data, respectively. The new data after fusion had both high spatial and spectral resolution. The backscattering coefficient and polarization decomposition parameters of the fully polarized SAR data which could characterize the canopy structure of mangroves were extracted. Ten different feature combinations were designed by combining the two types of data. The extremely randomized trees algorithm (ERT) was used to classify the species, and the optimal feature subset was selected by the feature selection algorithm on the basis of the ERT, and the importance of the features was sorted. Studies show the following: (1) When controlling a single variable, the higher the spatial resolution of the multi-spectral data, the higher the interspecific classification accuracy. (2) The coupled Sentinel-2 and Landsat-9 data with a 2 m resolution will have higher classification accuracy than a single data source. (3) The selected feature subset contains all types of features in the optical data and the polarization decomposition features of the SAR data from different periods: multi-spectral band > texture feature > polarization decomposition parameter > vegetation index. Among the optimized feature combinations, the classification accuracy of mangrove species was the highest, the overall classification accuracy was 90.13%, and Kappa was 0.84, indicating that multi-source and SAR data from different periods coupling could improve the discrimination of mangrove species. (4) The ERT classificatio

关键词： mangrove community integrated learning algorithm full polarimetric SAR polarimetric decomposition multispectral

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NAO Robot Limb Control Method Based on Motor Imagery EEG

NAO Robot Limb Control Method Based on Motor Imagery EEG

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International Symposium on Computer, Consumer and Control (IS3C)

作者： Guo, Yuan Wang, Mei Zheng, Tianwei Li, Yuancheng Wang, Pai Qin, Xuebin Xian Univ Sci & Technol Coll Elect & Control Engn Xian Peoples R China Xian Univ Sci & Technol Coll Comp Sci Xian Peoples R China

ISBN: (纸本)9781728193625

Brain computer interface (BCI) technology refers to the technology that helps establish direct communication and control channels between the human brain and the computer. This technology can meet the control requirements of external equipment without passing through the patient's damaged nerves and declining muscle system. Electroencephalogram (EEG) contains a variety of information, including motor imagination information, steady-state visually evoked EEG information, emotional information, and fatigue information, etc. This paper designs a brain-computer interface system based on motor imagination and uses it to control the body movements of the NAO robot. The system is mainly divided into three modules: signal acquisition, signal processing and robot control. In terms of signal acquisition, electrode caps, conductive glue, EEG amplifiers and Curry7 software are used. In this paper, we design a frequency band feature selection algorithm based on an improved sub-band filter bank (FB)-Common Space Pattern (CSP). These features are classified using Bagging ensemble classification algorithm and converted to use Commands for controlling the NAO robot. In terms of communication control, the instruction codes are preset, and the classification results are corresponding to instructions for controlling different actions of the robot. It is transmitted to the robot by using wireless communication technology. The Bagging ensemble classification method proposed in this paper classifies EEG features. The accuracy of the four types of motor imaging EEG classification is as high as 78.29%, and the accuracy of NAO robot action execution experiments is up to 86.33%, which proves the correctness of the algorithm studied in this paper. The reliability and feasibility of the NAO robot brain control system designed in this paper.

关键词： Brain-computer interface EEG subband filter co-space algorithm integrated learning algorithm motion imagination NAO robot

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Light-stacking strengthened fusion based building energy consumption prediction framework via variable weight feature selection

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APPLIED ENERGY 2021年 303卷 117694-117694页

作者： Sun, Jian Liu, Gang Sun, Boyang Xiao, Gang Shanghai Univ Elect Power Sch Automat Engn Shanghai 200000 Peoples R China Shanghai Elect Power Co Engn Construct Consulting Branch State Grid Shanghai 200122 Peoples R China Shanghai Jiao Tong Univ Sch Aeronaut & Astronaut Shanghai 200040 Peoples R China

Building energy consumption prediction plays an irreplaceable role in energy resource management and planning. Continuous improvement in the performance of predictive models is the key to ensure energy management and deployment operations. The imbalance between the speed and the accuracy for hyperparameter optimization is an important factor that limits the performance of the model. A Light-Stacking Strengthened Fusion Framework (LSStFu) is proposed to solve this problem. The optimization and fusion of the multi-type hyperparameter model obtained by random search can greatly improve the accuracy of the model prediction. This process can also assure a reduction in time consumption when compared with grid search. Moreover, a feature selection algorithm can only describe a single aspect of a bunch of multiple types of data sets, which limits the generalization performance ability of the algorithm. In order to address the above limitation, a Variable Weight Feature Selection (VWFS) method is proposed to fuse the contribution of three feature selection algorithms based on particle swarm optimization. To evaluate the robustness of the proposed LSStFu algorithm, it is compared with other algorithms through eight evaluation indicators. This evaluation process verifies the reliability and stability of the Light-Stacking framework to provide an efficient, accurate, and stable hyperparameter optimization framework for energy predictive models. From the ablation analysis, it can be observed that with the optimal subset of features obtained through the VWFS, the accuracy of the prediction models is improved. In addition, the model construction process has also sped up at the same time.

关键词： Short-term building energy forecasting integrated learning algorithm Light-Stacking Strengthened Fusion Framework Variable Weight Feature Selection

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Nondestructive Classification of Potatoes Based on HSI and Clustering 4

Nondestructive Classification of Potatoes Based on HSI and C...

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4th International Conference on Measurement, Information and Control (ICMIC)

作者： Ji, Yamin Sun, Laijun Heilongjiang Univ Coll Elect Engn Harbin Peoples R China

ISBN: (纸本)9781728130354

A rapid classification method of potatoes based on the combination of hyperspectral imaging(HSI) technology and integrated learning algorithm was proposed in this paper. Here, potatoes were divided into six types: intact ones, green skin, germination, dry rot, wormhole and damage. Firstly, visible-near infrared (VNIR) hyperspectral imaging system with the band range of 400-1000nm was used to collect the potato hyperspectral image information in the experiment. Further, after the image masked, K-means clustering method was used to segmentate images. Extract the average spectrum of the defect areas and the intact areas as the classification data set. Based on the traditional machine learning algorithm (support vector machine, decision tree) and the integrated learning algorithm (random forest, gradient promotion decision tree), the classification model of potato defects was established and compared. The results show that among all classification algorithms, the classification accuracy of potato defects can be significantly improved by using the decision tree of gradient lifting. By comparing the feature importance of each band, the model accuracy was maintained above 80%. Furthermore, in order to improve the discrimination ability of data and reduce the dimension of data, linear discrimination analysis method was used to process spectral data, and the accuracy of the established model was finally improved to 84.62%.

关键词： Potatoes classification Hyperspectral imaging K-Means clustering integrated learning algorithm

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Nondestructive Classification of Potatoes Based on HSI and Clustering

Nondestructive Classification of Potatoes Based on HSI and C...

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作者： Yamin Ji Laijun Sun College of Electronic Engineering Heilongjiang University

A rapid classification method of potatoes based on the combination of hyperspectral imaging(HSI) technology and integrated learning algorithm was proposed in this paper. Here, potatoes were divided into six types: intact ones, green skin, germination, dry rot, wormhole and damage. Firstly, visible-near infrared(VNIR) hyperspectral imaging system with the band range of 400-1000 nm was used to collect the potato hyperspectral image information in the experiment. Further, after the image masked, K-means clustering method was used to segmentate images. Extract the average spectrum of the defect areas and the intact areas as the classification data set. Based on the traditional machine learning algorithm(support vector machine, decision tree) and the integrated learning algorithm(random forest, gradient promotion decision tree), the classification model of potato defects was established and compared. The results show that among all classification algorithms, the classification accuracy of potato defects can be significantly improved by using the decision tree of gradient lifting. By comparing the feature importance of each band, the model accuracy was maintained above 80%. Furthermore, in order to improve the discrimination ability of data and reduce the dimension of data, linear discrimination analysis method was used to process spectral data, and the accuracy of the established model was finally improved to 84.62%.

关键词： Potatoes classification Hyperspectral imaging K-Means clustering integrated learning algorithm

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