- 标题
- 摘要
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- 实验方案
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Octane prediction from infrared spectroscopic data
摘要： A model for the prediction of research octane number (RON) and motor octane number (MON) of hydrocarbon mixtures and gasoline-ethanol blends has been developed based on infrared spectroscopy data of pure components. Infrared spectra for 61 neat hydrocarbon species were used to generate spectra of 148 hydrocarbon blends by averaging the spectra of their pure components on a molar basis. The spectra of 38 FACE (Fuels for Advanced Combustion Engines) gasoline blends were calculated using PIONA (Paraffin, Isoparaffin, Olefin, Naphthene, and Aromatic) class averages of the pure components. The study sheds light on the significance of dimensional reduction of spectra and shows how it can be used to extract scores with linear correlations to the following important features: molecular weight, paraffinic CH3 groups, paraffinic CH2 groups, paraffinic CH groups, olefinic -CH=CH2 groups, naphthenic CH-CH2 groups, aromatic C-CH groups, ethanolic OH groups, and branching index. Both scores and features can be used as input to predict octane numbers through nonlinear regression. Artificial Neural Network (ANN) was found to be the optimal method where the mean absolute error on a randomly selected test set was within the experimental uncertainty of RON, MON, and octane sensitivity.
关键词： octane prediction,infrared spectroscopy,hydrocarbon blends,artificial neural network,gasoline-ethanol blends,dimensional reduction
更新于2025-09-19 17:13:59
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[IEEE 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Chicago, IL, USA (2019.6.16-2019.6.21)] 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Sequential Application of Static and Dynamic Mechanical Stresses for Electrical Isolation of Cell Cracks
摘要： Short-term traf?c prediction plays a critical role in many important applications of intelligent transportation systems such as traf?c congestion control and smart routing, and numerous methods have been proposed to address this issue in the literature. However, most, if not all, of them suffer from the inability to fully use the rich information in traf?c data. In this paper, we present a novel short-term traf?c ?ow prediction approach based on dynamic tensor completion (DTC), in which the traf?c data are represented as a dynamic tensor pattern, which is able capture more information of traf?c ?ow than traditional methods, namely, temporal variabilities, spatial characteristics, and multimode periodicity. A DTC algorithm is designed to use the multimode information to forecast traf?c ?ow with a low-rank constraint. The proposed method is evaluated on real-world data sets and compared with other state-of-the-art methods, and the ef?cacy of the proposed approach is validated on the experiments of traf?c ?ow prediction, particularly when dealing with incomplete traf?c data.
关键词： missing data,dynamic tensor completion,Short-term traf?c ?ow prediction,multi-mode information
更新于2025-09-19 17:13:59
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Numerical Prediction and Experimental Validation of Multiple Phosphor White LED Spectrum
摘要： White LEDs with single phosphor usually have poor color rendering capability. The emission spectra of the blue LED and the yellow phosphor are narrow as compared with the reference light sources. The red light output is particular low. The color of the objects illuminated by such light source appear not accurate nor natural. Though the luminous efficiency is high, such poor color rendering light sources are not suitable for most general lighting applications. To improve the color rendering properties, multiple phosphors with different emission spectra should be used. For instance, a certain amount of red or orange phosphors may be used to mix with yellow phosphor to broaden the overall spectrum. In this paper, a numerical model is proposed to predict the emission spectra of LEDs with multiple phosphors. The model has considered the excitation and emission spectra of the phosphors, the mixing ratio and re-absorption between the phosphors. To validate the model, white light LEDs with multiple phosphors are fabricated. The spectra are measured and compared with the modeling results. It is found that the proposed model can estimate the emission spectra of LEDs with multiple phosphors with a high degree of accuracy.
关键词： Color Rendering,Spectrum Prediction,Phosphor Characterizations,White Light LED,Multiple Phosphors
更新于2025-09-19 17:13:59
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A Regional Photovoltaic Output Prediction Method Based on Hierarchical Clustering and the mRMR Criterion
摘要： Photovoltaic (PV) power generation is greatly affected by meteorological environmental factors, with obvious fluctuations and intermittencies. The large-scale PV power generation grid connection has an impact on the source-load stability of the large power grid. To scientifically and rationally formulate the power dispatching plan, it is necessary to realize the PV output prediction. The output prediction of single power plants is no longer applicable to large-scale power dispatching. Therefore, the demand for the PV output prediction of multiple power plants in an entire region is becoming increasingly important. In view of the drawbacks of the traditional regional PV output prediction methods, which divide a region into sub-regions based on geographical locations and determine representative power plants according to the correlation coefficient, this paper proposes a multilevel spatial upscaling regional PV output prediction algorithm. Firstly, the sub-region division is realized by an empirical orthogonal function (EOF) decomposition and hierarchical clustering. Secondly, a representative power plant selection model is established based on the minimum redundancy maximum relevance (mRMR) criterion. Finally, the PV output prediction for the entire region is achieved through the output prediction of representative power plants of the sub-regions by utilizing the Elman neural network. The results from a case study show that, compared with traditional methods, the proposed prediction method reduces the normalized mean absolute error (nMAE) by 4.68% and the normalized root mean square error (nRMSE) by 5.65%, thereby effectively improving the prediction accuracy.
关键词： minimum redundancy maximum relevance criterion,hierarchical clustering,photovoltaic,regional power output prediction
更新于2025-09-19 17:13:59
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[IEEE 2019 Photonics North (PN) - Quebec City, QC, Canada (2019.5.21-2019.5.23)] 2019 Photonics North (PN) - Optimizing Bifacial Silicon Heterojunction Solar Cells for High-Latitude
摘要： In this paper, we present a new motor imagery classification method in the context of electroencephalography (EEG)-based brain–computer interface (BCI). This method uses a signal-dependent orthogonal transform, referred to as linear prediction singular value decomposition (LP-SVD), for feature extraction. The transform defines the mapping as the left singular vectors of the LP coefficient filter impulse response matrix. Using a logistic tree-based model classifier; the extracted features are classified into one of four motor imagery movements. The proposed approach was first benchmarked against two related state-of-the-art feature extraction approaches, namely, discrete cosine transform (DCT) and adaptive autoregressive (AAR)-based methods. By achieving an accuracy of 67.35%, the LP-SVD approach outperformed the other approaches by large margins (25% compared with DCT and 6 % compared with AAR-based methods). To further improve the discriminatory capability of the extracted features and reduce the computational complexity, we enlarged the extracted feature subset by incorporating two extra features, namely, Q- and the Hotelling’s T 2 statistics of the transformed EEG and introduced a new EEG channel selection method. The performance of the EEG classification based on the expanded feature set and channel selection method was compared with that of a number of the state-of-the-art classification methods previously reported with the BCI IIIa competition data set. Our method came second with an average accuracy of 81.38%.
关键词： Brain-computer interface,feature extraction,linear prediction,orthogonal transform,channel selection
更新于2025-09-19 17:13:59
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[IEEE 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Chicago, IL, USA (2019.6.16-2019.6.21)] 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Field-Aging Test Bed for Behind-the-Meter PV + Energy Storage
摘要： Data missing in collections of time series occurs frequently in practical applications and turns out to be a major menace to precise data analysis. However, most of the existing methods either might be infeasible or could be inefficient to predict the missing values in large-scale coevolving time series. Also, the evolving of time series needs to be handled properly to adapt to the temporal characteristic. Furthermore, more massive volume of data is generated in many areas than ever before. In this paper, we have taken up the challenge of missing data prediction in coevolving time series by employing temporal dynamic matrix factorization techniques. First, our approaches are optimally designed to largely utilize both the interior patterns of each time series and the information of time series across multiple sources to build an initial model. Based on the idea, we have imposed hybrid regularization terms to constrain the objective functions of matrix factorization. Then, temporal dynamic matrix factorization is proposed to effectively update the initial already trained models. In the process of dynamic matrix factorization, batch updating and fine-tuning strategies are also employed to build an effective and efficient model. Extensive experiments on real-world data sets and synthetic data set demonstrate that the proposed approaches can effectively improve the performance of missing data prediction. Even when the missing ratio reaches as high as 90%, our proposed methods still show low prediction errors. Dynamic performance demonstrates that the methods can obtain satisfactory effectiveness and efficiency. Furthermore, we have also demonstrated how to take advantage of the high processing power of Apache Spark to perform missing data prediction in large-scale coevolving time series.
关键词： missing data prediction,time series,Apache Spark,Matrix factorization
更新于2025-09-19 17:13:59
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[IEEE 2019 Chinese Control Conference (CCC) - Guangzhou, China (2019.7.27-2019.7.30)] 2019 Chinese Control Conference (CCC) - Photovoltaic power generation probabilistic prediction based on a new dynamic weighting method and quantile regression neural network
摘要： Predicting the popularity of online videos is an important task for the service design, advertisement placement, network management, and so on. In this paper, we tackle the challenge head-on by casting the popularity prediction problem into two consecutive tasks: online video future popularity level prediction and online video future view count prediction. We first predict the future popularity levels of online videos, based on a rich set of features and effective classification technique. Then, according to the popularity level transitions, we build specialized regression models to predict the future view count values. We validate our approach on the exhaustive dataset of a leading online video service provider in China, namely, Youku. The experimental results show that comparing with two state-of-the-art baseline models, our proposed method can significantly decrease the relative prediction errors of 32.25% and 19.82%, respectively. At last, we also discuss the model setup and feature importance of our method. We believe our work can provide direct help in practical for the interested parties of online video service, such as service providers, online advisers, and network operators.
关键词： Online video service,video popularity prediction
更新于2025-09-19 17:13:59
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Parallel information processing using a reservoir computing system based on mutually coupled semiconductor lasers
摘要： Via the nonlinear channel equalization and the Santa-Fe time series prediction, the parallel processing capability of a reservoir computing (RC) system based on two mutually coupled semiconductor lasers is demonstrated numerically. The results show that, for parallel processing the prediction tasks of two Santa-Fe time series with rates of 0.25 GSa/s, the minimum prediction errors are 3.8 × 10?5 and 4.4 × 10?5, respectively. For parallel processing two nonlinear channel equalization tasks, the minimum symbol error rates (SERs) are 3.3 × 10?4 for both tasks. For parallel processing a nonlinear channel equalization and a Santa-Fe time series prediction, the minimum SER is 6.7 × 10?4 for nonlinear channel equalization, and the minimum prediction error is 4.6 × 10?5 for Santa-Fe time series prediction.
关键词： Santa-Fe time series prediction,parallel processing,reservoir computing,nonlinear channel equalization,semiconductor lasers
更新于2025-09-19 17:13:59
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Enhancing inertia of solar photovoltaic-based microgrid through notch filter-based PLL in SRF control
摘要： More and more wind turbine manufacturers turn to using the full-scale power electronic converter due to the stricter grid code requirements to thoroughly decouple the generator from the grid connection. However, a commonly used type of this generator is still unclear, where the selections of the low-speed (LS; direct-drive) and medium-speed (MS; one-stage) permanent-magnet synchronous generators (PMSGs) are both promising solutions. This paper will assess and compare the reliability metrics for the machine-side converter (MSC) for those two configurations. First, a translation from the mission profile of the turbine to the current and voltage loading of each power semiconductor is achieved based on synchronous generator modeling. Afterward, a simplified approach to calculate the loss profile and the thermal profile is used to determine the most stressed power semiconductors in the converter. Finally, according to the lifetime power cycles, the lifespan can be calculated when operating in various wind classes. It is concluded that, although the LS PMSG is able to eliminate the gearbox, the lifespan of its MSC is lower than the one-stage MS generator.
关键词： loss profile,power electronic converter,thermal profile,Lifetime prediction,permanent-magnet synchronous generator (PMSG)
更新于2025-09-19 17:13:59
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[IEEE 2019 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC) - Macao, Macao (2019.12.1-2019.12.4)] 2019 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC) - Photovoltaic Power Generation Prediction Using Data Clustering and Parameter Optimization
摘要： With the rapid development of the photovoltaic industry, photovoltaic power forecasting has become an urgent problem to be solved. In this paper, a method for predicting photovoltaic power based on data clustering and parameter optimization is proposed. The proposed method can be implemented as follows: firstly, the meteorological feature to be collected is determined by analyzing the physical model of the photovoltaic cell and the collected numerical weather information is divided into a set of categories by K-means. Then, the BP neural network is adopted and trained for individual categories, and an adaptive parameter optimization method is proposed to prevent model from local optimum. In the end, the proposed method is compared with other models to verify its effectiveness.
关键词： Photovoltaic Power Prediction,BP Neural Network,Data Clustering,Parameter Optimization
更新于2025-09-19 17:13:59