Automated diagnosis systems are necessary for the maintenance of superannuated social infrastructure. This paper presents a methodology for detecting material defects using acoustic signals in a hammering test. The ap...
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ISBN:
(纸本)9781479969685
Automated diagnosis systems are necessary for the maintenance of superannuated social infrastructure. This paper presents a methodology for detecting material defects using acoustic signals in a hammering test. The approach comprises a feature extraction step using Short-Time Fourier Transform (STFT) and a classifier training step based on AdaBoost, an ensemble learning algorithm. Especially, we use weak learners based on a simple template matching method that can consider both the variable scale of amplitude and the variable frequency band. The experiments discriminate between defective and clean materials using different hammering test methods: rubbing and tapping.
Existing intelligent theoretical line losses calculation methods that prevalent on worse line calculation error, are all based on single learning algorithm. In order to overcome this defect, a novel intelligent calcul...
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ISBN:
(纸本)9781509040933
Existing intelligent theoretical line losses calculation methods that prevalent on worse line calculation error, are all based on single learning algorithm. In order to overcome this defect, a novel intelligent calculation method based on boosting algorithm is proposed. In this calculation method, the theoretical line losses calculation is abstracted into function fitting problem, in addition, the sample set - which is structured by the lines' information of known theoretical line losses - is input to many single learning algorithms of boosting algorithm for training many sub-calculation model and constituting them as a sequence, which sequence is the final theoretical line losses calculation model. In the sub-calculation model training process, this intelligent method effectively reduces the calculation error by the boosting algorithm's internal mechanism that the large calculation error lines are constantly reinforcement training. Finally the experiment shows that this intelligent calculation method based on boosting algorithm has lower calculation error than traditions.
COVID-19 is a disease caused by a virus from the coronavirus group, namely severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The Sars-CoV-2 virus has 5 variants that are included in the variant of concern ...
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Big multi-step ship motion forecasting plays an essential role as ship safety warning in maritime operations. However, big multi-step prediction will weaken the correlation of time series and introduce large error acc...
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Big multi-step ship motion forecasting plays an essential role as ship safety warning in maritime operations. However, big multi-step prediction will weaken the correlation of time series and introduce large error accumulation. Real-time prediction also requires a simple prediction model structure, data input-output relationship in line with the actual application, and a high prediction accuracy. To realize the above requirements, a hybrid big multi-step forecasting model is proposed based on real-time wavelet packet decomposition (RTWPD), outlier robust extreme learning machine (ORELM), boosting algorithm, and least squares support vector machine (LSSVM)based error correction method. Taking 10-step, 20-step, and 30-step as the research objects of big multi-step prediction, two sets of ship roll and pitch motion data obtained by practical experiment are provided to complete four comparison experiments, including the comparison of different decomposition algorithms, the comparison of different classical forecasting models, the comparison of different error correction methods and the comparison of the proposed model with different benchmark models. Taking the 10-step, 20-step, and 30-step prediction results of the pitch of dataset #2 as an example, the mean absolute errors (MAEs) of the proposed model are 0.1619 degrees, 0.1742 degrees, and 0.1808 degrees, respectively;and the minimum Diebold-Mariano (DM) value is 4.88, which is greater than the upper limit of the 1 % confidence level, indicating that the prediction results of the proposed model differ significantly from those of the other models. And the root mean square error (RMSE) of the proposed model is about 0.20 degrees for different data sets and different ship motions. The experimental results indicate that the proposed ***-LSSVM (RWOALS) model is stable and feasible in practical applications, and it can provide reliable guidance for maritime operations.
作者：
Yin, ShiLiu, HuiDuan, ZhuCent South Univ
Inst Artificial Intelligence & Robot IAIR Key Lab Traff Safety Track Minist EducSch Traff & Transportat Engn Changsha 410075 Hunan Peoples R China
The multi-step forecasting of PM2.5 concentration is helpful to realize the early warning of air pollution, but the accurate multi-step forecasting of PM2.5 has certain difficulties. In this paper, a novel multistep f...
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The multi-step forecasting of PM2.5 concentration is helpful to realize the early warning of air pollution, but the accurate multi-step forecasting of PM2.5 has certain difficulties. In this paper, a novel multistep forecasting method of hourly PM2.5 concentration is proposed. Two boosting algorithms, Modified *** and Gradient boosting, are used to enhance the extreme learning machine (ELM) for ensemble prediction of the PM2.5. Then two multi-step forecasting strategies, multiple-input multiple output (MIMO) and recursive, are used. Finally, through error correction model (ECM) the prediction error is corrected to obtain the hourly PM2.5 multi-step forecasting results. Corresponding experiments are carried out through the PM2.5 data sets of four cities, and the results show that: (1) the forecasting method proposed in this study can achieve a good multi-step forecasting effect of PM2.5, and changing the forecasting strategy or boosting algorithm has little influence on the forecasting effect;(2) the use of ECM can improve the PM2.5 forecasting accuracy of the model, and as the forecasting steps increase, the improvement effect of ECM is more significant;(3) the forecasting framework proposed in this paper is effective, and the forecasting accuracy of the proposed method is significantly better than the corresponding single models and the existing models. (C) 2021 Elsevier Inc. All rights reserved.
In order to improve the estimation accuracy of a soft sensor in the chemical process, an ensemble model is proposed based on boosting and Gaussian process algorithms. Using Gaussian process as a base learner, a levera...
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ISBN:
(纸本)9781479900305
In order to improve the estimation accuracy of a soft sensor in the chemical process, an ensemble model is proposed based on boosting and Gaussian process algorithms. Using Gaussian process as a base learner, a leveraging learner is constructed by boosting algorithm. The ensemble model is obtained by dynamically averaging the regression functions trained by leveraging learners. Finally, the algorithm is applied to a soft sensor model for a production plant of Bisphenol A. Simulation results show that the integration algorithm has higher accuracy and generalization ability comparing to a single Gaussian process model.
In this paper, a new Hierarchical fuzzy classifier based on evolutionary boosting algorithms is proposed. The main goal of this paper is to improve the performance of fuzzy rule based classifiers through utilizing hie...
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ISBN:
(纸本)9781424481835
In this paper, a new Hierarchical fuzzy classifier based on evolutionary boosting algorithms is proposed. The main goal of this paper is to improve the performance of fuzzy rule based classifiers through utilizing hierarchical structure for achieving fuzzy rules. The advantages of hierarchical fuzzy rules generated by evolutionary boosting algorithms are evaluated by comparison between the performance of proposed algorithm and other classifications methods on a set of standard classification tasks.
The application of machine learning was investigated for predicting end-point temperature in the basic oxygen furnace steelmaking process, addressing gaps in the field, particularly large-scale dataset sizes and the u...
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The application of machine learning was investigated for predicting end-point temperature in the basic oxygen furnace steelmaking process, addressing gaps in the field, particularly large-scale dataset sizes and the underutilization of boosting algorithms. Utilizing a substantial dataset containing over 20,000 heats, significantly bigger than those in previous studies, a comprehensive evaluation of five advanced machine learning models was conducted. These include four ensemble learning algorithms: XGBoost, LightGBM, CatBoost (three boosting algorithms), along with random forest (a bagging algorithm), as well as a neural network model, namely the multilayer perceptron. Our comparative analysis reveals that Bayesian-optimized boosting models demonstrate exceptional robustness and accuracy, achieving the highest R-squared values, the lowest root mean square error, and lowest mean absolute error, along with the best hit ratio. CatBoost exhibited superior performance, with its test R-squared improving by 4.2% compared to that of the random forest and by 0.8% compared to that of the multilayer perceptron. This highlights the efficacy of boosting algorithms in refining complex industrial processes. Additionally, our investigation into the impact of varying dataset sizes, ranging from 500 to 20,000 heats, on model accuracy underscores the importance of leveraging larger-scale datasets to improve the accuracy and stability of predictive models.
Rapid and economical classification of transgenic soybean and non-transgenic soybean is highly important for food processing and handling. This paper developed an efficient and low-cost identification method for diffe...
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Rapid and economical classification of transgenic soybean and non-transgenic soybean is highly important for food processing and handling. This paper developed an efficient and low-cost identification method for different categories of soybeans on the basis of a handheld miniature near-infrared spectrometer. The dataset consists of transgenic modified and non-transgenic soybeans from soybean breeders, and different pretreatment methods and classifiers are used to establish models. The identification model with the best performance is selected for the boosting models. After the data are compared by different pretreatment methods and classifiers, SG+SNV is the best, and the performance of the model constructed by the gradient lifting tree is optimized. The accuracy is 98.03 % and the F1 score is 96.74 %. The results show that the near-infrared spectrum can be used to collect the all-band spectrum of soybean, and the model can be used to classify the soybean category accurately, and quickly via a handheld miniature spectrometer.
Existing soil texture mapping methods cannot accurately predict soil texture in complex geographical environments. To address this challenge, we propose a method that combines a kernel temperature-vegetation dryness i...
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Existing soil texture mapping methods cannot accurately predict soil texture in complex geographical environments. To address this challenge, we propose a method that combines a kernel temperature-vegetation dryness index (kTVDI) with a gradient boosting algorithm to accurately predict the spatial distribution of soil texture. In this study, we collected 399 soil samples collected from Mingguang City in southeast China and made spatial predictions of soil texture based on remote sensing indices such as the kernel normalized difference vegetation index computed from Landsat8 data and topographic attributes computed via digital elevation model as environmental covariates. We validated model performance by mapping the spatial distributions of sand, silt, and clay particle fractions in the city (30-m resolution), using the boosting algorithms adaptive boosting (AdaBoost), gradient boosting decision tree (GBDT), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and categorical boosting (CatBoost). Among the environmental covariates, the kTVDI, digital elevation index, and salinity index have the highest importance values for soil texture prediction. The kTVDI is better for sand and silt prediction (especially sand). When combined with AdaBoost, the kTVDI can effectively improve the accuracy and consistency of the prediction model. Uncertainty analyses showed that the kTVDI was more effective at modeling soil texture in the plains. In summary, we present a new approach for accurately predicting the spatial distribution of soil texture and empirically validate its effectiveness and advantages for practical applications.
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