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.

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 Natural variability and anthropogenic effects in a Central Mediterranean core

2012 ◽  
Vol 8 (2) ◽  
pp. 831-839 ◽  
Author(s):  
S. Alessio ◽  
G. Vivaldo ◽  
C. Taricco ◽  
M. Ghil
Keyword(s):  
Isotopic Ratio ◽  
Forecast Error ◽  
Singular Spectrum Analysis ◽  
Low Frequency ◽  
Natural Variability ◽  
Central Mediterranean ◽  
Spectral Content ◽  
Frequency Components ◽  
Oxygen Isotopic ◽  
Complete Series

Abstract. We evaluate the contribution of natural variability to the modern decrease in foraminiferal δ18O by relying on a 2200-yr-long, high-resolution record of oxygen isotopic ratio from a Central Mediterranean sediment core. Pre-industrial values are used to train and test two sets of algorithms that are able to forecast the natural variability in δ18O over the last 150 yr. These algorithms are based on autoregressive models and neural networks, respectively; they are applied separately to each of the δ18O series' significant variability components, rather than to the complete series. The separate components are extracted by singular-spectrum analysis and have narrow-band spectral content, which reduces the forecast error. By comparing the sum of the predicted low-frequency components to its actual values during the Industrial Era, we deduce that the natural contribution to these components of the modern δ18O variation decreased gradually, until it reached roughly 40%, as early as the end of the 1970s.

scholarly journals Triboelectric nanogenerator sensors for soft robotics aiming at digital twin applications

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Tao Jin ◽  
Zhongda Sun ◽  
Long Li ◽  
Quan Zhang ◽  
Minglu Zhu ◽  
...  
Keyword(s):  
Sensory Information ◽  
Tactile Sensor ◽  
Object Identification ◽  
Soft Robotics ◽  
Support Vector ◽  
Triboelectric Nanogenerator ◽  
Human Machine Interaction ◽  
Digital Twin ◽  
Time Operation ◽  
Real Time Operation

Abstract Designing efficient sensors for soft robotics aiming at human machine interaction remains a challenge. Here, we report a smart soft-robotic gripper system based on triboelectric nanogenerator sensors to capture the continuous motion and tactile information for soft gripper. With the special distributed electrodes, the tactile sensor can perceive the contact position and area of external stimuli. The gear-based length sensor with a stretchable strip allows the continuous detection of elongation via the sequential contact of each tooth. The triboelectric sensory information collected during the operation of soft gripper is further trained by support vector machine algorithm to identify diverse objects with an accuracy of 98.1%. Demonstration of digital twin applications, which show the object identification and duplicate robotic manipulation in virtual environment according to the real-time operation of the soft-robotic gripper system, is successfully created for virtual assembly lines and unmanned warehouse applications.

scholarly journals A Novel Changing Athlete Body Real-Time Visual Tracking Algorithm Based on Distractor-Aware SiamRPN and HOG-SVM

Electronics ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 378 ◽  
Author(s):  
Mingwei Sheng ◽  
Weizhe Wang ◽  
Hongde Qin ◽  
Lei Wan ◽  
Jun Li ◽  
...  
Keyword(s):  
Real Time ◽  
Large Data ◽  
Video Frame ◽  
Support Vector ◽  
Key Frame ◽  
Histograms Of Oriented Gradients ◽  
Time Operation ◽  
Haar Feature ◽  
Real Time Operation ◽  
Detection Window

Athlete detection in sports videos is a challenging task due to the dynamic and cluttered background. Distractor-aware SiamRPN (DaSiamRPN) has a simple network structure and can be utilized to perform long-term tracking of large data sets. However, similarly to the Siamese network, the tracking results heavily rely on the given position in the initial frame. Hence, there is a lack of solutions for some complex tracking scenarios, such as running and changing of bodies of athletes, especially in the stage from squatting to standing to running. The Haar feature-based cascade classifier is involved to catch the key frame, representing the video frame of the most dramatic changes of the athletes. DaSiamRPN is implemented as the tracking method. In each frame after the key frame, a detection window is given based on the bounding box generated by the DaSiamRPN tracker. In the new detection window, a fusion method (HOG-SVM) combining features of Histograms of Oriented Gradients (HOG) and a linear Support-Vector Machine (SVM) is proposed for detecting the athlete, and the tracking results are updated in real-time by fusing the tracking results of DaSiamRPN and HOG-SVM. Our proposed method has reached a stable and accurate tracking effect in testing on men’s 100 m video sequences and has realized real-time operation.

SVM-BASED PHONEME CLASSIFICATION AND LIP SHAPE REFINEMENT IN REAL-TIME LIP-SYNCH SYSTEM

2006 ◽  
Vol 20 (07) ◽  
pp. 1029-1051 ◽  
Author(s):  
HANSEOK KO ◽  
DAVID K. HAN
Keyword(s):  
Real Time ◽  
Processing Time ◽  
Support Vector ◽  
Mel Frequency Cepstral Coefficients ◽  
Phoneme Classification ◽  
Second Stage ◽  
Single Frame ◽  
Time Operation ◽  
Real Time Operation ◽  
Coarse To Fine

In this paper, we present a real time lip-synch system that activates 2-D avatar's lip motion in synch with incoming speech utterance. To achieve the real time operation of the system, the processing time was minimized by "merge and split" procedures resulting in coarse-to-fine phoneme classification. At each stage of phoneme classification, the support vector machine (SVM) method was applied to reduce the computational load while maintaining the desired accuracy. The coarse-to-fine phoneme classification, is accomplished via two_stages of feature extraction: in the first stage, each speech frame is acoustically analyzed for three classes of lip opening using Mel Frequency Cepstral Coefficients (MFCC) as a feature; in the second stage, each frame is further refined for detailed lip shape using formant information. The method was implemented in 2-D lip animation and it was demonstrated that the system was effective in accomplishing real-time lip-synch. This approach was tested on a PC using the Microsoft Visual Studio with an Intel Pentium IV 1.4 Giga Hz CPU and 384 MB RAM. It was observed that the methods of phoneme merging and SVM achieved about twice the speed in recognition than the method employing the Hidden Markov Model (HMM). A typical latency time per a single frame observed using the proposed method was in the order of 18.22 milliseconds while an HMM method under identical conditions resulted about 30.67 milliseconds.

scholarly journals Emotion Identification Using Extremely Low Frequency Components of Speech Feature Contours

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Chang-Hong Lin ◽  
Wei-Kai Liao ◽  
Wen-Chi Hsieh ◽  
Wei-Jiun Liao ◽  
Jia-Ching Wang
Keyword(s):  
Low Frequency ◽  
Principal Component ◽  
Support Vector ◽  
Emotional Speech ◽  
Emotion Identification ◽  
Identification Rate ◽  
Extremely Low Frequency ◽  
Frequency Components ◽  
Speech Feature ◽  
Speech Identification

The investigations of emotional speech identification can be divided into two main parts, features and classifiers. In this paper, how to extract an effective speech feature set for the emotional speech identification is addressed. In our speech feature set, we use not only statistical analysis of frame-based acoustical features, but also the approximated speech feature contours, which are obtained by extracting extremely low frequency components to speech feature contours. Furthermore, principal component analysis (PCA) is applied to the approximated speech feature contours so that an efficient representation of approximated contours can be derived. The proposed speech feature set is fed into support vector machines (SVMs) to perform multiclass emotion identification. The experimental results demonstrate the performance of the proposed system with 82.26% identification rate.

Autonomous soaring using a simplified MPC approach

2019 ◽  
Vol 123 (1268) ◽  
pp. 1666-1700
Author(s):  
G. Pogorzelski ◽  
F. J. Silvestre
Keyword(s):  
Linear Prediction ◽  
Thermal Environment ◽  
Flight Simulation ◽  
Guidance And Control ◽  
Prediction Horizon ◽  
Time Operation ◽  
Non Linear ◽  
Real Time Operation ◽  
And Control ◽  
Atmospheric Phenomena

ABSTRACTThe need for efficient propulsion systems allied to increasingly more challenging fixed-wing UAV mission requirements has led to recent research on the autonomous thermal soaring field with promising results. As part of that effort, the feasibility and advantages of model predictive control (MPC)-based guidance and control algorithms capable of extracting energy from natural occurring updrafts have already been demonstrated numerically. However, given the nature of the dominant atmospheric phenomena and the amplitude of the required manoeuvres, a non-linear optimal control problem results. Depending on the adopted prediction horizon length, it may be of large order, leading to implementation and real-time operation difficulties. Knowing that, an alternative MPC-based autonomous thermal soaring controller is presented herein. It is designed to yield a simple and small non-linear programming problem to be solved online. In order to accomplish that, linear prediction schemes are employed to impose the differential constraints, thus no extra variables are added to the problem and only linear bound restrictions result. For capturing the governing non-linear effects during the climb phase, a simplified representation of the aircraft kinematics with quasi-steady corrections is used by the controller internal model. Flight simulation results using a 3 degree-of-freedom model subjected to a randomly generated time varying thermal environment show that the aircraft is able to locate and exploit updrafts, suggesting that the proposed algorithm is a feasible MPC strategy to be employed in a practical application.

FACIAL BIOMETRICS USING NONTENSOR PRODUCT WAVELET AND 2D DISCRIMINANT TECHNIQUES

2009 ◽  
Vol 23 (03) ◽  
pp. 521-543 ◽  
Author(s):  
DAN ZHANG ◽  
XINGE YOU ◽  
PATRICK WANG ◽  
SVETLANA N. YANUSHKEVICH ◽  
YUAN YAN TANG
Keyword(s):  
Tensor Product ◽  
High Frequency ◽  
Low Frequency ◽  
Frequency Component ◽  
Feature Representation ◽  
Support Vector ◽  
Facial Features ◽  
Linear Discriminant ◽  
Illumination Changes ◽  
Frequency Components

A new facial biometric scheme is proposed in this paper. Three steps are included. First, a new nontensor product bivariate wavelet is utilized to get different facial frequency components. Then a modified 2D linear discriminant technique (M2DLD) is applied on these frequency components to enhance the discrimination of the facial features. Finally, support vector machine (SVM) is adopted for classification. Compared with the traditional tensor product wavelet, the new nontensor product wavelet can detect more singular facial features in the high-frequency components. Earlier studies show that the high-frequency components are sensitive to facial expression variations and minor occlusions, while the low-frequency component is sensitive to illumination changes. Therefore, there are two advantages of using the new nontensor product wavelet compared with the traditional tensor product one. First, the low-frequency component is more robust to the expression variations and minor occlusions, which indicates that it is more efficient in facial feature representation. Second, the corresponding high-frequency components are more robust to the illumination changes, subsequently it is more powerful for classification as well. The application of the M2DLD on these wavelet frequency components enhances the discrimination of the facial features while reducing the feature vectors dimension a lot. The experimental results on the AR database and the PIE database verified the efficiency of the proposed method.

scholarly journals Filter Characteristics in Image Decomposition with Singular Spectrum Analysis

2016 ◽  
Vol 08 (01) ◽  
pp. 1650002 ◽  
Author(s):  
Kenji Kume ◽  
Naoko Nose-Togawa
Keyword(s):  
Spectrum Analysis ◽  
Singular Spectrum Analysis ◽  
Low Frequency ◽  
Image Data ◽  
Original Data ◽  
Multidimensional Data ◽  
Singular Spectrum ◽  
Symmetry Properties ◽  
Frequency Components ◽  
Differential Type

Singular spectrum analysis is developed as a nonparametric spectral decomposition of a time series. It can be easily extended to the decomposition of multidimensional lattice-like data through the filtering interpretation. In this viewpoint, the singular spectrum analysis can be understood as the adaptive and optimal generation of the filters and their two-step point-symmetric operation to the original data. In this paper, we point out that, when applied to the multidimensional data, the adaptively generated filters exhibit symmetry properties resulting from the bisymmetric nature of the lag-covariance matrices. The eigenvectors of the lag-covariance matrix are either symmetric or antisymmetric, and for the 2D image data, these lead to the differential-type filters with even- or odd-order derivatives. The dominant filter is a smoothing filter, reflecting the dominance of low-frequency components of the photo images. The others are the edge-enhancement or the noise filters corresponding to the band-pass or the high-pass filters. The implication of the decomposition to the image denoising is briefly discussed.

scholarly journals An EEMD-Based Denoising Method for Seismic Signal of High Arch Dam Combining Wavelet with Singular Spectrum Analysis

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Bo Li ◽  
Lixin Zhang ◽  
Qiling Zhang ◽  
Shengmei Yang
Keyword(s):  
High Frequency ◽  
Singular Spectrum Analysis ◽  
Low Frequency ◽  
Arch Dam ◽  
Seismic Signals ◽  
High Arch Dam ◽  
Denoising Method ◽  
Arch Dams ◽  
Frequency Components ◽  
High Arch Dams

Due to complicated noise interference, seismic signals of high arch dam are of nonstationarity and a low signal-to-noise ratio (SNR) during acquisition process. The traditional denoising method may have filtered effective seismic signals of high arch dams. A self-adaptive denoising method based on ensemble empirical mode decomposition (EEMD) combining wavelet threshold with singular spectrum analysis (SSA) is proposed in this paper. Based on the EEMD result for seismic signals of high arch dams, a continuous mean square error criterion is used to distinguish high-frequency and low-frequency components of the intrinsic mode functions (IMFs). Denoised high-frequency IMF using wavelet threshold is reconstructed with low-frequency components, and SSA is implemented for the reconstructed signal. Simulation signal denoising analysis indicates that the proposed method can significantly reduce mean square error under low SNR condition, and the overall denoising effect is superior to EEMD and EEMD-Wavelet threshold denoising algorithms. Denoising analysis of measured seismic signals of high arch dams shows that the performance of denoised seismic signals using EEMD-Wavelet-SSA is obviously improved, and natural frequencies of the high arch dams can be effectively identified.

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