- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Prediction of two-dimensional topography of laser cladding based on neural network
摘要： The two-dimensional morphology of the cladding layer has an important influence on the quality of the cladding layer and the crack tendency. Using the powerful nonlinear processing ability of the single hidden layer feedforward neural network, a prediction model between the cladding technological parameters and the two-dimensional morphology of the cladding layer is established. Taking the cladding parameters as the input and the two-dimensional morphology of the cladding as the output, the experimental data is used to train the network to achieve a high-level mapping of the input and output. On this basis, the algorithm of extreme learning machine is used to optimize the single hidden layer feedforward neural network to overcome the problems of slow convergence speed, more network training parameters and easy local convergence in back-propagation algorithm. The results show that the relationship between the cladding process parameters and the two-dimensional morphology of the cladding layer can be roughly reflected by the back-propagation algorithm. However, the prediction results are not stable and the error rate is between 10% and 40%. The neural network optimized by the extreme learning machine is utilized to get a better prediction result. The error rate is 10–20%.
关键词： extreme learning machine.,BP neural network,Layer cladding,morphology prediction
更新于2025-11-28 14:24:20
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Use of Hyperspectral Image Data Outperforms Vegetation Indices in Prediction of Maize Yield
摘要： Hyperspectral cameras can provide reflectance data at hundreds of wavelengths. This information can be used to derive vegetation indices (VIs) that are correlated with agronomic and physiological traits. However, the data generated by hyperspectral cameras are richer than what can be summarized in a VI. Therefore, in this study, we examined whether prediction equations using hyperspectral image data can lead to better predictive performance for grain yield than what can be achieved using VIs. For hyperspectral prediction equations, we considered three estimation methods: ordinary least squares, partial least squares (a dimension reduction method), and a Bayesian shrinkage and variable selection procedure. We also examined the benefits of combining reflectance data collected at different time points. Data were generated by CIMMYT in 11 maize (Zea mays L.) yield trials conducted in 2014 under heat and drought stress. Our results indicate that using data from 62 bands leads to higher prediction accuracy than what can be achieved using individual VIs. Overall, the shrinkage and variable selection method was the best-performing one. Among the models using data from a single time point, the one using reflectance collected at 28 d after flowering gave the highest prediction accuracy. Combining image data collected at multiple time points led to an increase in prediction accuracy compared with using single-time-point data.
关键词： maize yield,hyperspectral imaging,prediction accuracy,vegetation indices,Bayesian methods
更新于2025-09-23 15:23:52
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[IEEE 2018 International Joint Conference on Neural Networks (IJCNN) - Rio de Janeiro (2018.7.8-2018.7.13)] 2018 International Joint Conference on Neural Networks (IJCNN) - Multi-spectral missing label prediction via restoration using deep residual dictionary learning
摘要： Dictionary learning (DL) is one of the popular sparse coding machine learning techniques. In image processing literature, every input image is represented as the sparse linear combination of basis vectors. DL has been shown to have wide applications for image restoration as well as pattern recognition problems. In DL, the input image is factorized into dictionary and sparse codes. This factorization always leaves a residual or approximation error. Very few works in the literature had focused on to leverage the information present in this residual. In this paper, we use residuals within our framework and show that the restoration performance or accurate prediction of missing label in multi-spectral images can be significantly improved over conventional DL based techniques. We initially show that the higher order frequencies are propagated through residuals. Then we show that incorporating this residual in the image restoration methodology can significantly improve the outcomes. Finally, we propose a technique to solve the problem of missing label prediction by using a restoration based deep residual dictionary learning framework.
关键词： Dictionary learning,Restoration,Sparse coding,In-painting,Residuals,Label prediction
更新于2025-09-23 15:23:52
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OPD analysis and prediction in aero-optics based on dictionary learning
摘要： When aircraft ?ying at a high speed, the density and re?ective index of atmosphere around it become uneven. Thus images or videos observed from the observation window on the aircraft are usually blur or quivering, which is called the aero-optical effect. To recover the images from poor quality, it is necessary to learn about the wavefront distortion of the light, described as optical path difference (OPD). Among the existing methods, the method of computational ?uid dynamics (CFD) simulation followed by ray tracing is very time consuming, and the method of real-time OPD measurement with OPD sensor has a certain lag for OPD with high frequency. In this paper, a reconstructible dimension reduction method based on dictionary learning is employed to map the high-dimensional OPD data into a low-dimensional space, and the OPD data are calculated when rays travel across the supersonic shear layer. All the parameters of training and test datasets remain the same except the convective Mach numbers (Mc number). According to the dimension reduction results of training sets, we ?nd that OPD is obviously periodic and its distribution characteristics have a strong correlation with Mc number. By ?tting the OPD data in the low-dimensional space, every point on the ?tting curve can be reconstructed to the original high-dimensional space, which works as prediction. Compared with the truthful data, the average similarity coef?cient of the prediction for the test datasets is up to 83%, which means that the prediction result is credible.
关键词： Prediction,Aero-optics,Couple dictionary learning,Optical path difference
更新于2025-09-23 15:23:52
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A deep learning based feature extraction method on hyperspectral images for nondestructive prediction of TVB-N content in Pacific white shrimp (Litopenaeus vannamei)
摘要： Hyperspectral imaging (HSI) technique with spectral range of 900e1700 nm was implemented to predict total volatile basic nitrogen (TVB-N) content in Pacific white shrimp. Successive projections algorithm (SPA) and deep-learning-based stacked auto-encoders (SAEs) algorithm were comparatively used for spectral feature extraction. Least-squares support vector machine (LS-SVM), partial least squares regression (PLSR) and multiple linear regression (MLR) were used for prediction. The results demonstrated that the SAEs-based prediction models (SAEs-LS-SVM, SAEs-MLR and SAEs-PLSR) performed better than either full wavelengths-based or SPA-based prediction models. The SAEs-LS-SVM was considered to be the best model with RP2 value of 0.921, RMSEP value of 6.22 mg N [100 g]?1, RPD value of 3.58 and computational time of 3.9 ms for predicting TVB-N in prediction set. The results of this study indicated that SAEs has a high potential in the multivariate analysis of hyperspectral images for shrimp quality inspections.
关键词： Stacked auto-encoders,Pacific white shrimp,Total volatile basic nitrogen,Nondestructive prediction,Hyperspectral imaging
更新于2025-09-23 15:23:52
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Damage degree prediction method of CFRP structure based on fiber Bragg grating and epsilon-support vector regression
摘要： The assessment of structural damage is of great significance for ensuring the service safety of carbon fiber reinforced plastics (CFRP) structures. In this paper, the damage degree prediction method of CFRP structure based on fiber Bragg grating and epsilon-support vector regression was studied. The structural dynamic response signals were detected by fiber Bragg grating sensors. Then, the Fourier transform was used to extract the dynamic characteristics of the structure as the damage feature, and the damage feature dimensionality was reduced by using the RReliefF algorithm. On this basis, the damage degree prediction model of CFRP structure based on epsilon-support vector regression was established. Finally, the method proposed in this paper was experimentally verified. The results showed that the epsilon-support vector regression model can accurately predict the damage degree of unknown samples, and the absolute relative error of 27 experiments was less than 10% for 30 testing experiments. This paper provided a feasible method for predicting the damage degree of CFRP structures.
关键词： Frequency response,Carbon fiber reinforced plastics,Epsilon-support vector regression,RReliefF,Damage degree prediction
更新于2025-09-23 15:23:52
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The conditional mean spectra by disaggregating the eta spectral shape indicator
摘要： Conditional spectra are a recent development in this field, which utilizes the advantages of spectral shape indicators, for example, epsilon and eta. The application of an eta indicator in conditional spectra calculations depends mainly on calculating the peak ground velocity epsilon, data about which are not readily available in the current literature. This issue has been solved by linear regression between the conventional epsilon and the peak ground velocity epsilon. However, not enough attention has been paid in the literature to the disaggregation of the eta indicator. For this reason, the disaggregation of seismic hazard based on the use of an eta indicator has been investigated in this paper, based on a simplified linear seismic source. The obtained results were compared with the available approach in the literature, which shows that this refinement has a meaningful effect on the conditional spectra specifically in the short period range. Furthermore, eta‐based conditional spectra are used at different hazard levels to select ground‐motion records. A three‐storeyed building is then analysed, and the corresponding annual probability of failure is calculated based on the full dataset as well as on the records, which were selected based on conditional spectra.
关键词： genetic algorithm,eta,seismic hazard analysis,ground‐motion prediction equation (GMPE),epsilon
更新于2025-09-23 15:23:52
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Prediction model optimization using full model selection with regression trees demonstrated with FTIR data from bovine milk
摘要： Predictive modeling is the development of a model that is best able to predict an outcome based on given input variables. Model algorithms are different processes that are used to define functions that transform the data within models. Common algorithms include logistic regression (LR), linear discriminant analysis (LDA), classification and regression trees (CART), na?ve Bayes (NB), and k-nearest neighbor (KNN). Data preprocessing option, such as feature extraction and reduction, and model algorithms are commonly selected empirically in epidemiological studies even though these decisions can significantly affect model performance. Accordingly, full model selection (FMS) methods were developed to provide a systematic approach to select predictive modeling methods; however, current limitations of FMS, such as its dependency on user-selected hyperparameters, have prevented their routine incorporation into analyses for model performance optimization. Here we present the use of regression trees as an innovative method to apply FMS. Regression tree FMS (rtFMS) requires the development of a model for every combination of predictive modeling method options under consideration. The iterated, cross-validation performances of these models are then passed through a regression tree for selection of a final model. We demonstrate the benefits of rtFMS using a milk Fourier transform infrared spectroscopy dataset, wherein we build prediction models for two blood metabolic health parameters in dairy cows, nonesterified fatty acids (NEFA) and β-hydroxybutyrate acid (BHBA). The goal for building NEFA and BHBA prediction models is to provide a milk-based screening tool for metabolic health in dairy cattle that can be incorporated automatically in milk analysis routines. These models could be used in conjunction with physical exams, cow side tests, and other indications to initiate medical intervention. In contrast to previously reported FMS methods, rtFMS is not a black box, is simple to implement and interpret, it does not have hyperparameters, and it illustrates the relative importance of modeling options. Additionally, rtFMS allows for indirect comparisons among models developed using different datasets. Finally, rtFMS eliminates user bias due to personal preference for certain methods and rtFMS removes the dependency on published comparisons of methods. Thus, rtFMS provides clear benefits over the empirical selection of data preprocessing options and model algorithms.
关键词： Prediction model,Fourier-transform infrared spectra,Regression tree,Preprocessing,Full model selection
更新于2025-09-23 15:23:52
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Reconciling solar forecasts: Sequential reconciliation
摘要： When forecasting hierarchical photovoltaic (PV) power generation in a region and/or over several forecast horizons, reconciliation is needed to ensure the lower-level forecasts add up exactly to the upper-level forecasts. Previously in “Reconciling solar forecasts: Geographical hierarchy” [Sol. Energy 146 (2017) 276–286] and “Reconciling solar forecasts: Temporal hierarchy” [Sol. Energy 158 (2017) 332–346], forecast reconciliation has been demonstrated for geographical and temporal hierarchies, separately. This article follows such frameworks and extends the reconciliation to spatio-temporal cases. More specifically, sequential reconciliation is used for operational day-ahead forecasting of 318 PV systems in California. It is shown that by using sequential reconciliation, aggregate-consistent forecasts can be obtained across both the geographical and temporal hierarchies. In addition, the forecast accuracy can be further improved from that of the single-hierarchy cases.
关键词： Forecast reconciliation,Numerical weather prediction,Operational forecasting
更新于2025-09-23 15:23:52
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Outlier Events of Solar Forecasts for Regional Power Grid in Japan Using JMA Mesoscale Model
摘要： To realize the safety control of electric power systems under high penetration of photovoltaic power systems, accurate global horizontal irradiance (GHI) forecasts using numerical weather prediction models (NWP) are becoming increasingly important. The objective of this study is to understand meteorological characteristics pertaining to large errors (i.e., outlier events) of GHI day-ahead forecasts obtained from the Japan Meteorological Agency, for nine electric power areas during four years from 2014 to 2017. Under outlier events in GHI day-ahead forecasts, several sea-level pressure (SLP) patterns were found in 80 events during the four years; (a) a western edge of anticyclone over the Pacific Ocean (frequency per 80 outlier events; 48.8%), (b) stationary fronts (20.0%), (c) a synoptic-scale cyclone (18.8%), and (d) typhoons (tropical cyclones) (8.8%) around the Japanese islands. In this study, the four case studies of the worst outlier events were performed. A remarkable SLP pattern was the case of the western edge of anticyclone over the Pacific Ocean around Japan. The comparison between regionally integrated GHI day-ahead forecast errors and cloudiness forecasts suggests that the issue of accuracy of cloud forecasts in high- and mid-levels troposphere in NWPs will remain in the future.
关键词： outlier events,regional integration,global horizontal irradiance (GHI),photovoltaic (PV) power generation,numerical weather prediction (NWP),day-ahead forecast
更新于2025-09-23 15:23:52