scholarly journals Prediction of Transient NOx Emission from Diesel Vehicles Based on Deep-Learning Differentiation Model with Double Noise Reduction

Atmosphere ◽  
2021 ◽  
Vol 12 (12) ◽  
pp. 1702
Author(s):  
Jiaqiang Li ◽  
Yang Yu ◽  
Yanyan Wang ◽  
Longqing Zhao ◽  
Chao He
Keyword(s):  
Time Series ◽  
Diesel Engines ◽  
High Frequency ◽  
Singular Spectrum Analysis ◽  
Low Frequency ◽  
Ensemble Empirical Mode Decomposition ◽  
Support Vector ◽  
Nox Emissions ◽  
Diesel Vehicles ◽  
Original Time

For diesel engines, accurate prediction of NOx (Nitrogen Oxides) emission plays an essential role in virtual NOx sensor development and engine design under situations of actual road driving. However, due to the randomness and uncertainty in the driving process of diesel vehicles, it is difficult to make predictions about NOx emissions. In order to solve this problem, this paper proposes differential models for noise reductions of NOx emissions in time series. First, according to the internal fluctuation of time series, use SSA (Singular Spectrum Analysis) to reduce the noises of the original time series; second, use ICEEMDAN (Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise) to decompose the noise-reducing data into several relatively stable subsequences; third, use the sample entropy to calculate the complexity of each subsequence, and divide the sequences into high-frequency ones and low-frequency ones; finally, use GRU (Gated Recurrent Unit) to complete the prediction of high-frequency sequences and SVR (Support Vector Regression) for the prediction of low-frequency sequences. To obtain the final models, integrate the prediction results of the subsequences. Make comparisons with five single models, SSA single-processing models, and ICEEMDAN single-processing models. The experimental results show that the proposed model can predict the instantaneous NOx emissions of diesel engines better than the single model and the model processed by SSA, and the differentiated model can effectively improve the execution speed of the model.

scholarly journals Rainfall and runoff forecasting with SSA–SVM approach

2001 ◽  
Vol 3 (3) ◽  
pp. 141-152 ◽  
Author(s):  
C. Sivapragasam ◽  
Shie-Yui Liong ◽  
M. F. K. Pasha
Keyword(s):  
Linear Prediction ◽  
Singular Spectrum Analysis ◽  
Low Frequency ◽  
Support Vector ◽  
Runoff Forecasting ◽  
Time Operation ◽  
Real Time Operation ◽  
Frequency Components ◽  
Hydrologic Variable ◽  
Original Time

Real time operation studies such as reservoir operation, flood forecasting, etc., necessitates good forecasts of the associated hydrologic variable(s). A significant improvement in such forecasting can be obtained by suitable pre-processing. In this study, a simple and efficient prediction technique based on Singular Spectrum Analysis (SSA) coupled with Support Vector Machine (SVM) is proposed. While SSA decomposes original time series into a set of high and low frequency components, SVM helps in efficiently dealing with the computational and generalization performance in a high-dimensional input space. The proposed technique is applied to predict the Tryggevælde catchment runoff data (Denmark) and the Singapore rainfall data as case studies. The results are compared with that of the non-linear prediction (NLP) method. The comparisons show that the proposed technique yields a significantly higher accuracy in the prediction than that of NLP.

Combustion Instability and Emission Control by Pulsating Fuel Injection

2008 ◽  
Vol 130 (1) ◽  
Author(s):  
Christian Oliver Paschereit ◽  
Ephraim Gutmark
Keyword(s):  
High Frequency ◽  
Fuel Injection ◽  
Low Frequency ◽  
Nox Emissions ◽  
Modulation Amplitude ◽  
Pulsation Frequency ◽  
Symmetric Mode ◽  
Instability Mode ◽  
Secondary Fuel ◽  
Symmetric Instability

Open-loop control methodologies were used to suppress symmetric and helical thermoacoustic instabilities in an experimental low-emission swirl-stabilized gas-turbine combustor. The controllers were based on fuel (or equivalence ratio) modulations in the main premixed combustion (premixed fuel injection (PMI)) or, alternatively, in the secondary pilot fuel. PMI included symmetric and asymmetric fuel injection. The symmetric instability mode responded to symmetric excitation only when the two frequencies matched. The helical fuel injection affected the symmetric mode only at frequencies that were much higher than that of the instability mode. The asymmetric excitation required more power to obtain the same amount of reduction as that required by symmetric excitation. Unlike the symmetric excitation, which destabilized the combustion when the modulation amplitude was excessive, the asymmetric excitation yielded additional suppression as the modulation level increased. The NOx emissions were reduced to a greater extent by the asymmetric modulation. The second part of the investigation dealt with the control of low frequency symmetric instability and high frequency helical instability by the secondary fuel injection in a pilot flame. Adding a continuous flow of fuel into the pilot flame controlled both instabilities. However, modulating the fuel injection significantly decreased the amount of necessary fuel. The reduced secondary fuel resulted in a reduced heat generation by the pilot diffusion flame and therefore yielded lower NOx emissions. The secondary fuel pulsation frequency was chosen to match the time scales typical to the central flow recirculation zone, which stabilizes the flame in the burner. Suppression of the symmetric mode pressure oscillations by up to 20dB was recorded. High frequency instabilities were suppressed by 38dB, and CO emissions reduced by using low frequency modulations with 10% duty cycle.

Taxi Booking Mobile App Order Demand Prediction Based on Short-Term Traffic Forecasting

2017 ◽  
Vol 2634 (1) ◽  
pp. 57-68 ◽  
Author(s):  
Yunxuan Li ◽  
Jian Lu ◽  
Lin Zhang ◽  
Yi Zhao
Keyword(s):  
High Frequency ◽  
Demand Forecasting ◽  
Low Frequency ◽  
Original Data ◽  
Mobile App ◽  
Support Vector ◽  
Short Term ◽  
Traffic Demand ◽  
Traffic Forecasting ◽  
Demand Prediction

The Didi Dache app is China’s biggest taxi booking mobile app and is popular in cities. Unsurprisingly, short-term traffic demand forecasting is critical to enabling Didi Dache to maximize use by drivers and ensure that riders can always find a car whenever and wherever they may need a ride. In this paper, a short-term traffic demand forecasting model, Wave SVM, is proposed. It combines the complementary advantages of Daubechies5 wavelets analysis and least squares support vector machine (LS-SVM) models while it overcomes their respective shortcomings. This method includes four stages: in the first stage, original data are preprocessed; in the second stage, these data are decomposed into high-frequency and low-frequency series by wavelet; in the third stage, the prediction stage, the LS-SVM method is applied to train and predict the corresponding high-frequency and low-frequency series; in the last stage, the diverse predicted sequences are reconstructed by wavelet. The real taxi-hailing orders data are applied to evaluate the model’s performance and practicality, and the results are encouraging. The Wave SVM model, compared with the prediction error of state-of-the-art models, not only has the best prediction performance but also appears to be the most capable of capturing the nonstationary characteristics of the short-term traffic dynamic systems.

An Adaptive Orthogonal SSA Decomposition Algorithm for a Time Series

2018 ◽  
Vol 10 (01) ◽  
pp. 1850002 ◽  
Author(s):  
Kenji Kume ◽  
Naoko Nose-Togawa
Keyword(s):  
Time Series ◽  
Correlation Matrix ◽  
Singular Spectrum Analysis ◽  
Dimensional Subspace ◽  
Orthogonal Decomposition ◽  
Gram Matrix ◽  
Diagonal Form ◽  
Window Length ◽  
Original Time ◽  
Basis Vectors

Singular spectrum analysis (SSA) is a nonparametric spectral decomposition of a time series into arbitrary number of interpretable components. It involves a single parameter, window length [Formula: see text], which can be adjusted for the specific purpose of the analysis. After the decomposition of a time series, similar series are grouped to obtain the interpretable components by consulting with the [Formula: see text]-correlation matrix. To accomplish better resolution of the frequency spectrum, a larger window length [Formula: see text] is preferable and, in this case, the proper grouping is crucial for making the SSA decomposition. When the [Formula: see text]-correlation matrix does not have block-diagonal form, however, it is hard to adequately carry out the grouping. To avoid this, we propose a novel algorithm for the adaptive orthogonal decomposition of the time series based on the SSA scheme. The SSA decomposition sequences of the time series are recombined and the linear coefficients are determined so as to maximizing its squared norm. This results in an eigenvalue problem of the Gram matrix and we can obtain the orthonormal basis vectors for the [Formula: see text]-dimensional subspace. By the orthogonal projection of the original time series on these basis vectors, we can obtain adaptive orthogonal decomposition of the time series without the redundancy of the original SSA decomposition.

scholarly journals Recognition of Emotional States using Multiscale Information Analysis of High Frequency EEG Oscillations

Entropy ◽  
2019 ◽  
Vol 21 (6) ◽  
pp. 609 ◽  
Author(s):  
Gao ◽  
Cui ◽  
Wan ◽  
Gu
Keyword(s):  
Emotion Recognition ◽  
High Frequency ◽  
Low Frequency ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Emotional States ◽  
Circumplex Model ◽  
Information Analysis ◽  
Eeg Signals ◽  
High Frequency Oscillations

Exploring the manifestation of emotion in electroencephalogram (EEG) signals is helpful for improving the accuracy of emotion recognition. This paper introduced the novel features based on the multiscale information analysis (MIA) of EEG signals for distinguishing emotional states in four dimensions based on Russell's circumplex model. The algorithms were applied to extract features on the DEAP database, which included multiscale EEG complexity index in the time domain, and ensemble empirical mode decomposition enhanced energy and fuzzy entropy in the frequency domain. The support vector machine and cross validation method were applied to assess classification accuracy. The classification performance of MIA methods (accuracy = 62.01%, precision = 62.03%, recall/sensitivity = 60.51%, and specificity = 82.80%) was much higher than classical methods (accuracy = 43.98%, precision = 43.81%, recall/sensitivity = 41.86%, and specificity = 70.50%), which extracted features contain similar energy based on a discrete wavelet transform, fractal dimension, and sample entropy. In this study, we found that emotion recognition is more associated with high frequency oscillations (51–100Hz) of EEG signals rather than low frequency oscillations (0.3–49Hz), and the significance of the frontal and temporal regions are higher than other regions. Such information has predictive power and may provide more insights into analyzing the multiscale information of high frequency oscillations in EEG signals.

Precipitation Forecast Based on WA-SVM Theory

2012 ◽  
Vol 518-523 ◽  
pp. 4039-4042
Author(s):  
Zhen Min Zhou
Keyword(s):  
Time Series ◽  
Support Vector Machine ◽  
Wavelet Analysis ◽  
Precipitation Forecast ◽  
Support Vector ◽  
Estimation Model ◽  
Original Time Series ◽  
Set Up ◽  
Original Time

In order to improve the precision of medium-long term rainfall forecast, the rainfall estimation model was set up based on wavelet analysis and support vector machine (WA-SVM). It decomposed the original rainfall series to different layers through wavelet analysis, forecasted each layer by means of SVM, and finally obtained the forecast results of the original time series by composition. The model was used to estimate the monthly rainfall sequence in the watershed. Comparing with other method which only uses support vector machine(SVM), it indicates that the estimated accuracy was improved obviously.

INTERPRETATION OF SINGULAR SPECTRUM ANALYSIS AS COMPLETE EIGENFILTER DECOMPOSITION

2012 ◽  
Vol 04 (04) ◽  
pp. 1250023 ◽  
Author(s):  
KENJI KUME
Keyword(s):  
Time Series ◽  
Spectrum Analysis ◽  
Singular Spectrum Analysis ◽  
Singular Value ◽  
Original Time Series ◽  
Singular Vectors ◽  
Singular Spectrum ◽  
Original Time ◽  
Value Decomposition ◽  
Insight Into

Singular spectrum analysis is a nonparametric and adaptive spectral decomposition of a time series. This method consists of the singular value decomposition for the trajectory matrix constructed from the original time series, followed with the subsequent reconstruction of the decomposed series. In the present paper, we show that these procedures can be viewed simply as complete eigenfilter decomposition of the time series. The eigenfilters are constructed from the singular vectors of the trajectory matrix and the completeness of the singular vectors ensure the completeness of the eigenfilters. The present interpretation gives new insight into the singular spectrum analysis.

Fault Detection and Classification in Smart Grids Using Wavelet Analysis

2020 ◽  
Author(s):  
Muhammad Ibrahim Munir ◽  
Sajid Hussain ◽  
Ali Al-Alili ◽  
Reem Al Ameri ◽  
Ehab El-Sadaany
Keyword(s):  
Wavelet Transform ◽  
High Frequency ◽  
Smart Grids ◽  
Short Circuit ◽  
Low Frequency ◽  
Evaluation Study ◽  
Open Circuit ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Transmission Grid

Abstract One of the core features of the smart grid deemed essential for smooth grid operation is the detection and diagnosis of system failures. For a utility transmission grid system, these failures could manifest in the form of short circuit faults and open circuit faults. Due to the advent of the digital age, the traditional grid has also undergone a massive transition to digital equipment and modern sensors which are capable of generating large volumes of data. The challenge is to preprocess this data such that it can be utilized for the detection of transients and grid failures. This paper presents the incorporation of artificial intelligence techniques such as Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) to detect and comprehensively classify the most common fault transients within a reasonable range of accuracy. For gauging the effectiveness of the proposed scheme, a thorough evaluation study is conducted on a modified IEEE-39 bus system. Bus voltage and line current measurements are taken for a range of fault scenarios which result in high-frequency transient signals. These signals are analyzed using continuous wavelet transform (CWT). The measured signals are afterward preprocessed using Discrete Wavelet Transform (DWT) employing Daubechies four (Db4) mother wavelet in order to decompose the high-frequency components of the faulty signals. DWT results in a range of high and low-frequency detail and approximate coefficients, from which a range of statistical features are extracted and used as inputs for training and testing the classification algorithms. The results demonstrate that the trained models can be successfully employed to detect and classify faults on the transmission system with acceptable accuracy.

Forecasting Crude Oil Demand Using a Hybrid SVR and Markov Approach

2010 ◽  
pp. 235-247 ◽  
Author(s):  
Wei Xu ◽  
Jue Wang ◽  
Jian Ma
Keyword(s):  
Time Series ◽  
Crude Oil ◽  
Energy Demand ◽  
General Trend ◽  
Random Fluctuation ◽  
Support Vector ◽  
Demand Prediction ◽  
Forecasting Method ◽  
Oil Demand ◽  
Original Time

In this chapter, a hybrid support vector regression (SVR) and Markov forecasting approach is proposed for energy demand prediction. The original time series of energy demand is firstly decomposed into the general trend series and the random fluctuation series. Then the SVR method is used to model the general trend series and the Markov forecasting method is used to model the random fluctuation series so that the tendencies of two series can be accurately predicted. The prediction results of two series are integrated to formulate an ensemble output for future energy demand. The proposed forecasting approach makes full use of the historical time series information so as to improve the forecasting precision of time series with large random fluctuation. To illustrate the applicability and capability of the proposed approach, it is used to analyze and forecast world crude oil demand. For verification, the proposed approach is compared with SVR method, Markov forecasting method and ARIMA. The results show that the hybrid SVR and Markov forecasting approach proposed in this chapter can be applied successfully and provide high accuracy and reliability for forecasting world crude oil demand.

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