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Research on a Gas Concentration Prediction algorithm Based on Stacking

SENSORS 2021年第5期21卷 1597-1597页

作者： Xu, Yonghui Meng, Ruotong Zhao, Xi Harbin Inst Technol Inst Automat Testing & Control Harbin 150080 Peoples R China

Machine learning algorithms play an important role in the detection of toxic, flammable and explosive gases, and they are extremely important for the study of mixed gas classification and concentration prediction methods. To solve the problem of low prediction accuracy of gas concentration regression prediction algorithms, a gas concentration prediction algorithm based on a stacking model is proposed in the current research. In this paper, the stochastic forest, extreme random regression tree and gradient boosting decision tree (GBDT) regression algorithms are selected as the base learning devices and use the stacking algorithm to take the output of each base learning device as input to train a new model to produce a final output. Through the stacking model, the grid search algorithm is studied to automatically optimize the parameters so that the performance of the entire system can reach the optimal parameters. Through experimental simulation, the gas concentration prediction algorithm based on stacking model has better prediction effect than other integrated frame algorithms and the accuracy of mixed gas concentration prediction is improved.

关键词： ensemble learning model fusion regression algorithm automatic grid search algorithm

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Parametric regression of a multiparameter thixotropic model for waxy crude oil based on multiobjective strategy

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JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING 2019年 173卷 287-297页

作者： Yuan, Qing Li, Jingfa Liu, Hongfei Yu, Bo Sun, Dongliang Deng, Yajun China Univ Petr Natl Engn Lab Pipeline Safety Beijing Key Lab Urban Oil & Gas Distribut Technol Beijing 102249 Peoples R China Beijing Inst Petrochem Technol Sch Mech Engn Beijing Key Lab Pipeline Crit Technol & Equipment Beijing 102617 Peoples R China

The waxy crude oil exhibits a time- and shear-dependent non-Newtonian behavior called thixotropic behavior below the gelation temperature. This behavior can be quantitatively described by using a thixotropic model. However, many parameters are commonly contained in a thixotropic model with good-description capacity, and it still remains challenging to obtain high fitting precision and reasonable physical interpretation simultaneously in the process of parametric regression. In this study, based on a representative multiparameter thixotropic model, a multiobjective regression strategy is put forward. The fitting precision and physical interpretation are taken into account in this regression strategy, and their importance can be adjusted dynamically with the structure breakdown of waxy crude oil. In order to successfully achieve the multiobjective regression strategy, a self-adaptive nondominated sorting differential evolution algorithm (self-adaptive NSDE) is proposed. This regression algorithm is independent on the expressions of the regression model, and thus it can also be applied to the multiobjective parametric regression of other complex models including thixotropic models in rheology field and even other models in different fields. Based on experimental data, the parametric regression of a thixotropic model is performed, and the obtained results are analyzed and discussed in detail. It is found that the regressive data exhibit a small deviation at the starting stage of structure breakdown and the equilibrium flow curve can be ensured at the ending stage of structure breakdown. This finding well verifies considerations of multiobjective regression strategy. Compared with previous three regression strategies in the literature reports, the multi-objective regression strategy exhibits advantages in the fitting precision and physical interpretation.

关键词： Waxy crude oil Thixotropic model Parametric regression Multiobjective regression strategy regression algorithm

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Global Intercomparison of Hyper-Resolution ECOSTRESS Coastal Sea Surface Temperature Measurements from the Space Station with VIIRS-N20

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REMOTE SENSING 2021年第24期13卷 5021-5021页

作者： Weidberg, Nicolas Wethey, David S. Woodin, Sarah A. Univ South Carolina Dept Biol Sci Columbia SC 29208 USA Univ Vigo Fac Marine Sci Coastal Ecol Grp Vigo 36310 Spain

The ECOSTRESS multi-channel thermal radiometer on the Space Station has an unprecedented spatial resolution of 70 m and a return time of hours to 5 days. It resolves details of oceanographic features not detectable in imagery from MODIS or VIIRS, and has open-ocean coverage, unlike Landsat. We calibrated two years of ECOSTRESS sea surface temperature observations with L2 data from VIIRS-N20 (2019-2020) worldwide but especially focused on important upwelling systems currently undergoing climate change forcing. Unlike operational SST products from VIIRS-N20, the ECOSTRESS surface temperature algorithm does not use a regression approach to determine temperature, but solves a set of simultaneous equations based on first principles for both surface temperature and emissivity. We compared ECOSTRESS ocean temperatures to well-calibrated clear sky satellite measurements from VIIRS-N20. Data comparisons were constrained to those within 90 min of one another using co-located clear sky VIIRS and ECOSTRESS pixels. ECOSTRESS ocean temperatures have a consistent 1.01 degrees C negative bias relative to VIIRS-N20, although deviation in brightness temperatures within the 10.49 and 12.01 mu m bands were much smaller. As an alternative, we compared the performance of NOAA, NASA, and U.S. Navy operational split-window SST regression algorithms taking into consideration the statistical limitations imposed by intrinsic SST spatial autocorrelation and applying corrections on brightness temperatures. We conclude that standard bias-correction methods using already validated and well-known algorithms can be applied to ECOSTRESS SST data, yielding highly accurate products of ultra-high spatial resolution for studies of biological and physical oceanography in a time when these are needed to properly evaluate regional and even local impacts of climate change.

关键词： SST upwelling ECOSTRESS VIIRS spatial autocorrelation regression algorithm

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Quality assessment tool for performance measurement of image contrast enhancement methods

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IET IMAGE PROCESSING 2019年第5期13卷 833-842页

作者： Abdoli, Mohsen Nasiri, Fatemeh Brault, Patrice Ghanbari, Mohammad ATEME Rennes France CentraleSupelec CNRS UMR 8506 L2S Gif Sur Yvette France Univ Paris Saclay Paris France Natl Inst Appl Sci INSA Rennes France IRT B Com Inst Res & Technol Cesson Sevigne France Aviwest St Gregoire France Univ Tehran Dept Elect & Comp Engn Coll Engn Tehran Iran Univ Essex Sch Comp Sci & Elect Engn Colchester Essex England

An objective image quality assessment tool is proposed to measure image enhancement quality with emphasis on contrast. In the proposed tool, which is based on maximizing contrast with minimum artefact (MCMA), local and global properties of an image are measured through pixel-wise and histogram-wise features, respectively. To this aim, three sub-measures are introduced, each of which able to detect one contrast-related quality aspect: (i) low dynamic range of image;(ii) histogram shape preservation during image enhancement process;and (iii) local pixel diversity. These sub-measures are combined through a subjective test to adapt them to the mean opinion scores (MOSs) of a diverse set of training contrast-enhanced images. A regression algorithm performs the adaptation by fitting the three sub-measures to the MOS values and finding an optimal linear combination by maximizing the Pearson correlation. In order to evaluate the performance of the MCMA algorithm, another independent, subsequent, subjective test was performed on a set of images enhanced by various known contrast enhancement algorithms to obtain MOS values and to compare them with the output of the proposed MCMA method. The experimental results show that MCMA has the highest correlation to the MOS when compared to the existing tested contrast measurement tools.

关键词： regression analysis image enhancement feature extraction Pearson correlation regression algorithm mean opinion scores maximising contrast minimum artefact tested contrast measurement tools contrast enhancement algorithms subsequent test independent test MCMA algorithm optimal linear combination MOS values training contrast-enhanced images local pixel diversity histogram shape preservation low dynamic range contrast-related quality aspect histogram-wise features image enhancement quality objective image quality assessment tool image contrast enhancement methods performance measurement MCMA method

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EVALUATION OF DAMAGE SEVERITY OF TEA PLANTS USING ELECTRONIC NOSE

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TRANSACTIONS OF THE ASABE 2019年第1期62卷 95-104页

作者： Sun, Y. Wang, J. Cheng, S. Wang, Y. Zhejiang Univ Dept Biosyst Engn Hangzhou Zhejiang Peoples R China

In this study, an electronic nose (E-nose) was used to evaluate the damage severity of tea plants, and a new evaluation index (mass loss) was introduced to reflect damage severity. Gas chromatography-mass spectrometer (GC-MS) was employed for proving the potential of the E-nose to detect tea plants with different damage severities. The number of pests attacking tea plants and the time under attack are two traditional evaluation indexes that are widely applied. The prediction performance of mass loss was compared with the number of pests and time under attack based on partial least squares regression (PLSR) according to the correlation coefficient (R-2) and root mean square error (RMSE), and the results showed that the prediction performance of mass loss was better than that of the other two indexes. Three regression algorithms, namely PLSR, extreme learning machine for regression (ELMR), and support vector regression (SVR), were applied to predict mass loss, and their performances were compared. The results indicated that these three algorithms all had good performances, and SVR was the best. It could be concluded that E-nose is a feasible technique for evaluating the damage severity of tea plants, and mass loss is an appropriate evaluation index for damage severity.

关键词： Damage severity Electronic nose Mass loss regression algorithm

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Towards an in-situ evaluation methodology of thermal resistance of basement walls in buildings

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ENERGY AND BUILDINGS 2020年 208卷

作者： Bienvenido-Huertas, David Luis Perez-Ordonez, Juan Moyano, Juan Seara-Paz, Sindy Univ Seville Dept Graph Express & Bldg Engn Higher Tech Sch Bldg Engn Ave Reina Mercedes 4A E-41012 Seville Spain Univ A Coruna Dept Civil Engn ETSI Caminos Canales & Puertos Campus Elvina S-N La Coruna 15071 Spain

One of the essential aspects in building energy analysis is determining correctly the thermal characteristics of envelopes. Characterizing envelope elements accurately allows energy consumption to be predicted and energy saving measures to be established. Basement walls are among those common envelope elements in buildings. The estimation of their thermal resistance is a challenge due to the difficulties and the lack of methodologies to know the wall stratigraphy. This study establishes a methodology to characterize the thermal resistance of this kind of walls. Such methodology consists in monitoring easily measurable variables (internal air temperature, external air temperature, ground temperature, internal surface temperature, and heat flux) which, together with the use of regression models, estimate thermal resistance. The methodology is validated in 2 phases: (i) in a calibrated hot-box adapted to elements in contact with the ground, and (ii) by analysing actual case studies. With the data obtained, various regression models are created based on time windows. The M5 Prime algorithm with a time window of 2 days of observation made accurate estimations in the case studies. An efficient methodology is therefore developed for estimating the thermal resistance in actual case studies. Also, the calibrated hot-box adapted could be used to analyse samples under controlled conditions in laboratory. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Thermal resistance Basement walls regression algorithm Calibrated hot-box

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An Improved Skeleton Extraction Method via Multi-Task and Variable Coefficient Loss Function in Natural Images

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IEEE ACCESS 2019年 7卷 171272-171284页

作者： Xiao, Youqing Cai, Zhanchuan Yuan, Xixi Macau Univ Sci & Technol Fac Informat Technol Macau 999078 Peoples R China

Extracting object skeleton and its scale features in natural images is helpful for object detection and recognition in computer vision. In order to advance the location accuracy of object skeleton pixels, a new method via multi-task and variable coefficient loss function is proposed in this paper. Adopting the hierarchical integration mechanism to mutually refine captured features at different network layers;a specific variable coeffecient loss function is designed for multi-class imbalanced data handling problem, such as the skeleton pixels in natural images are always far less than the non-skeleton pixels;the regression algorithm is an added deep learning branch in the skeleton extraction network assisting the improvement of recognition accuracy. Besides, not only the skeleton pixels and its classification can be obtained, but also its scales are predicted without disturbing skeleton acquisition process. The experimental results verify that both the skeleton accuracy and the generalization abilities are promoted benefiting from the regression task and the new loss function in the new method, as satisfactory results are achieved on three public datasets, i.e., SK-LARGE, SK-SMALL, and WH-SYMMAX, which are indicated by F-measures and precision/recall curves. The results further demonstrate that the proposed method is superior to the best skeleton extraction method available currently.

关键词： Skeleton extraction Softmax function regression algorithm skeleton scale prediction deep learning

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Bioimpedance Parameter Estimation using Fast Spectral Measurements and Regularization

Bioimpedance Parameter Estimation using Fast Spectral Measur...

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18th IFAC Symposium on System Identification (SYSID)

作者： Ramirez-Chavarria, Roberto G. Quintana-Carapia, Gustavo Mueller, Matias I. Mattila, Robert Matatagui, Daniel Sanchez-Perez, Celia Univ Nacl Autonoma Mexico Inst Ciencias Aplicadas & Tecnol Cd Mx Mexico Univ Nacl Autonoma Mexico Fac Ingn Cd Mx Mexico Vrije Univ Brussel Dept ELEC Brussels Belgium KTH Royal Inst Technol Sch Elect Engn Dept Automat Control Stockholm Sweden

This work proposes an alternative framework for parametric bioimpedance estimation as a powerful tool to characterize biological media. We model the bioimpedance as an electrical network of parallel RC circuits, and transform the frequency-domain estimation problem into a time constant domain estimation problem by means of the distribution of relaxation times (DRT) method. The Fredholm integral equation of the first kind is employed to pose the problem in a regularized least squares (RLS) form. We validate the proposed methodology by numerical simulations for a synthetic biological electrical circuit, by using a multisine signal in the frequency range of 1kHz to 853kHz and considering an error in variables (EIV) problem. Results show that the proposed method outperforms the state-of-the-art techniques for spectral bioimpedance analysis. We also illustrates its potentiality in terms of accurate spectral measurements and precise data interpretation, for further usage in biological applications. (C) 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Biomedical Systems Frequency Measurements Impedance Spectroscopy Parameter Estimation regression algorithm Spectrum Analysis

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High-Resolution Automated Fugl-Meyer Assessment Using Sensor Data and regression Model 3

High-Resolution Automated Fugl-Meyer Assessment Using Sensor...

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3rd International Conference on Science and Technology - Computer (ICST)

作者： Julianjatsono, Ricky Ferdiana, Ridi Hartanto, Rudy Univ Gadjah Mada Dept Elect Engn & Informat Technol Yogyakarta Indonesia

ISBN: (纸本)9781538618745

Motor function assessment is a critical component in post-stroke rehabilitation program. Fugl-Meyer Assessment (FMA) is regarded as the most comprehensive tool to describe post-stroke patient's motor function and is widely utilized. However, the FMA scoring system classifies patient's motor function within only three levels (0, 1, or 2) for each assessment item. Hence, it's difficult to observe minor improvement in patient's motor function. To address this issue, a high-resolution scoring system was proposed in this research. Six regression models were built for six upper-extremity FMA items. These models were trained using data gathered from Microsoft Kinect sensor and glove sensor. The trained models could predict FMA scores with the resolution of 14 fractional digits. The representativeness of the predicted scores was evaluated by calculating their Pearson's correlation with the actual kinematic variables. All predicted scores represent patient's motor function better than standard FMA score. In addition, the highest correlation's mean of 0.58 was found on shoulder elevation item which utilized Neural Network regression algorithm. This algorithm also outperformed the other in most assessment items. The ability to observe the changes in patient's motor function in detail helps therapists providing more responsive treatment and could likely increase the patient's adherence toward the given treatment.

关键词： Fugl-Meyer Assessment machine learning regression algorithm Kinect sensor

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Complete protection scheme for fault detection, classification and location estimation in HVDC transmission lines using support vector machines

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IET SCIENCE MEASUREMENT & TECHNOLOGY 2017年第3期11卷 279-287页

作者： Johnson, Jenifer Mariam Yadav, Anamika NIT Elect Engn GE Rd Raipur 492010 Chhattisgarh India

This paper presents a complete protection scheme for detecting, classifying and locating the fault in HVDC transmission lines using support vector machines (SVM). SVM has been used for protective relaying application in HVAC transmission line, however very limited works have been reported for HVDC transmission line. In this work, a +/- 500 kV HVDC transmission system is developed in PSCAD/EMTDC and the measurement signals obtained are analyzed in MATLAB. The rectifier side AC RMS voltage, DC voltage and current on both the poles are continuously monitored, and given as input to the SVM binary classifier in order to detect the presence of fault in the line. Once a fault is detected, the SVM multi-class classification module predicts the type of fault and the SVM regression algorithm predicts the location of fault. The feature vector used in the classification and location modules is the standard deviation of the signals over half cycle before and after the occurrence of fault. The method proposed is simple as it requires single-end data and a direct standard deviation of one cycle data gives very accurate results. The detection and classification modules are 100% accurate whereas the fault location module has a mean error of 0.03%.

关键词： fault diagnosis transmission lines support vector machines fault location complete protection scheme fault detection fault classification fault location estimation HVDC transmission lines support vector machines MATLAB rectifier side AC root mean square voltage DC voltage SVM binary classifier regression algorithm feature vector

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