The Impact of Sampling on Flow Prediction using Support Vector Machines

2013 ◽  
Author(s):  
M. Cisty ◽  
J. Bezak
Keyword(s):  
Support Vector Machines ◽  
Support Vector ◽  
Flow Prediction ◽  
Vector Machines ◽  
The Impact

The impact of functional connectivity changes on support vector machines mapping of fMRI data

2008 ◽  
Vol 172 (1) ◽  
pp. 94-104 ◽  
Author(s):  
João Ricardo Sato ◽  
Janaina Mourão-Miranda ◽  
Maria da Graça Morais Martin ◽  
Edson Amaro ◽  
Pedro Alberto Morettin ◽  
...  
Keyword(s):  
Support Vector Machines ◽  
Functional Connectivity ◽  
Fmri Data ◽  
Support Vector ◽  
Vector Machines ◽  
The Impact

VISUAL SPEECH RECOGNITION USING OPTICAL FLOW AND SUPPORT VECTOR MACHINES

2011 ◽  
Vol 10 (02) ◽  
pp. 167-187 ◽  
Author(s):  
AYAZ A. SHAIKH ◽  
DINESH K. KUMAR ◽  
JAYAVARDHANA GUBBI
Keyword(s):  
Support Vector Machines ◽  
Optical Flow ◽  
Computer Control ◽  
Visual Speech ◽  
Support Vector ◽  
Temporal Segmentation ◽  
Reading Technique ◽  
Vector Machines ◽  
Lip Reading ◽  
The Impact

A lip-reading technique that identifies visemes from visual data only and without evaluating the corresponding acoustic signals is presented. The technique is based on vertical components of the optical flow (OF) analysis and these are classified using support vector machines (SVM). The OF is decomposed into multiple non-overlapping fixed scale blocks and statistical features of each block are computed for successive video frames of an utterance. This technique performs automatic temporal segmentation (i.e., determining the start and the end of an utterance) of the utterances, achieved by pair-wise pixel comparison method, which evaluates the differences in intensity of corresponding pixels in two successive frames. The experiments were conducted on a database of 14 visemes taken from seven subjects and the accuracy tested using five and ten fold cross validation for binary and multiclass SVM respectively to determine the impact of subject variations. Unlike other systems in the literature, the results indicate that the proposed method is more robust to inter-subject variations with high sensitivity and specificity for 12 out of 14 visemes. Potential applications of such a system include human computer interface (HCI) for mobility-impaired users, lip reading mobile phones, in-vehicle systems, and improvement of speech based computer control in noisy environment.

Intelligent Approach for Fault Diagnosis in Power Transmission Systems Using Support Vector Machines

2007 ◽  
Vol 8 (4) ◽  
Author(s):  
Ravikumar B. ◽  
Thukaram Dhadbanjan ◽  
H. P. Khincha
Keyword(s):  
Fault Diagnosis ◽  
Support Vector Machines ◽  
Power Systems ◽  
Power System ◽  
Transmission System ◽  
Fault Analysis ◽  
Support Vector ◽  
Transmission Systems ◽  
Vector Machines ◽  
The Impact

This paper presents an approach for identifying the faulted line section and fault location on transmission systems using support vector machines (SVMs) for diagnosis/post-fault analysis purpose. Power system disturbances are often caused by faults on transmission lines. When fault occurs on a transmission system, the protective relay detects the fault and initiates the tripping operation, which isolates the affected part from the rest of the power system. Based on the fault section identified, rapid and corrective restoration procedures can thus be taken to minimize the power interruption and limit the impact of outage on the system. The approach is particularly important for post-fault diagnosis of any mal-operation of relays following a disturbance in the neighboring line connected to the same substation. This may help in improving the fault monitoring/diagnosis process, thus assuring secure operation of the power systems. In this paper we compare SVMs with radial basis function neural networks (RBFNN) in data sets corresponding to different faults on a transmission system. Classification and regression accuracy is reported for both strategies. Studies on a practical 24-Bus equivalent EHV transmission system of the Indian Southern region is presented for indicating the improved generalization with the large margin classifiers in enhancing the efficacy of the chosen model.

scholarly journals The Impact of Different Kernel Functions on the Performance of Scintillation Detection Based on Support Vector Machines

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5219 ◽  
Author(s):  
Caner Savas ◽  
Fabio Dovis
Keyword(s):  
Support Vector Machines ◽  
Kernel Functions ◽  
Gaussian Kernel ◽  
Polynomial Kernel ◽  
Ionospheric Scintillation ◽  
Support Vector ◽  
Running Time ◽  
Scintillation Detection ◽  
Vector Machines ◽  
The Impact

Scintillation caused by the electron density irregularities in the ionospheric plasma leads to rapid fluctuations in the amplitude and phase of the Global Navigation Satellite Systems (GNSS) signals. Ionospheric scintillation severely degrades the performance of the GNSS receiver in the signal acquisition, tracking, and positioning. By utilizing the GNSS signals, detecting and monitoring the scintillation effects to decrease the effect of the disturbing signals have gained importance, and machine learning-based algorithms have been started to be applied for the detection. In this paper, the performance of Support Vector Machines (SVM) for scintillation detection is discussed. The effect of the different kernel functions, namely, linear, Gaussian, and polynomial, on the performance of the SVM algorithm is analyzed. Performance is statistically assessed in terms of probabilities of detection and false alarm of the scintillation event. Real GNSS signals that are affected by significant phase and amplitude scintillation effect, collected at the South African Antarctic research base SANAE IV and Hanoi, Vietnam have been used in this study. This paper questions how to select a suitable kernel function by analyzing the data preparation, cross-validation, and experimental test stages of the SVM-based process for scintillation detection. It has been observed that the overall accuracy of fine Gaussian SVM outperforms the linear, which has the lowest complexity and running time. Moreover, the third-order polynomial kernel provides improved performance compared to linear, coarse, and medium Gaussian kernel SVMs, but it comes with a cost of increased complexity and running time.

scholarly journals Locating Impact on Structural Plate Using Principal Component Analysis and Support Vector Machines

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Heming Fu ◽  
Qingsong Xu
Keyword(s):  
Principal Component Analysis ◽  
Support Vector Machines ◽  
Principal Component ◽  
Original Data ◽  
Component Analysis ◽  
Support Vector ◽  
Data Sets ◽  
Vector Machines ◽  
Pca Algorithm ◽  
The Impact

A new method which integrates principal component analysis (PCA) and support vector machines (SVM) is presented to predict the location of impact on a clamped aluminum plate structure. When the plate is knocked using an instrumented hammer, the induced time-varying strain signals are collected by four piezoelectric sensors which are mounted on the plate surface. The PCA algorithm is adopted for the dimension reduction of the large original data sets. Afterwards, a new two-layer SVM regression framework is proposed to improve the impact location accuracy. For a comparison study, the conventional backpropagation neural networks (BPNN) approach is implemented as well. Experimental results show that the proposed strategy achieves much better locating accuracy in comparison with the conventional approach.

Misfire-misfuel classification using support vector machines

2007 ◽  
Vol 221 (9) ◽  
pp. 1183-1195 ◽  
Author(s):  
E Gani ◽  
C Manzie
Keyword(s):  
Support Vector Machines ◽  
Oxygen Sensor ◽  
Control Unit ◽  
Support Vector ◽  
Exhaust Gas ◽  
Vector Machines ◽  
Recovery Strategies ◽  
Different Types ◽  
The Impact

This paper proposes the use of support vector machines to perform classification between different types of missed combustion event in a six-cylinder engine. On-board diagnostics regulations require the detection of missed combustion events, which is possible through interpretation of crankshaft speed information. However, current approaches provide no information on the actual cause of the event, in particular whether it was caused by a misfuel (absence of fuel) or a misfire (absence of spark) event. Whilst the impact on the environment and emission treatment systems due to misfuel is minimal, misfire events are detrimental to both. Consequently information regarding the causes of missing combustion events potentially allows the development of unique recovery strategies particular to the source of the problem. In this paper, an approach is proposed that will provide the potential for, firstly, detection of a missing combustion event and, secondly, real-time classification of the event into either misfuel or misfire events using feedback from a heated universal exhaust gas oxygen sensor. In order to evaluate the potential of such a system in an engine control unit, a computational complexity measure is also presented.

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