An online input force time history reconstruction algorithm using dynamic principal component analysis

2018 ◽  
Vol 99 ◽  
pp. 516-533 ◽  
Author(s):  
J. Prawin ◽  
A. Rama Mohan Rao
Keyword(s):  
Principal Component Analysis ◽  
Time History ◽  
Principal Component ◽  
Reconstruction Algorithm ◽  
Component Analysis ◽  
Input Force ◽  
Dynamic Principal Component Analysis ◽  
Force Time

scholarly journals Study of Stationary Load Increase of Computer-Network Traffic via Dynamic Principal-Component Analysis

2012 ◽  
Vol 2012 ◽  
pp. 1-10
Author(s):  
Shengkun Xie ◽  
Anna T. Lawniczak
Keyword(s):  
Principal Component Analysis ◽  
Network Traffic ◽  
Performance Indicator ◽  
Principal Component ◽  
Component Analysis ◽  
Switching Network ◽  
Traffic Data ◽  
Load Increase ◽  
Dynamic Principal Component Analysis ◽  
Detection Schemes

Many network monitoring applications and performance analysis tools are based on the study of an aggregate measure of network traffic, for example, number of packets in transit (NPT). The simulation modeling and analysis of this type of performance indicator enables a theoretical investigation of the underlying complex system through different combination of network setups such as routing algorithms, network source loads or network topologies. To detect stationary increase of network source load, we propose a dynamic principal component analysis (PCA) method, first to extract data features and then to detect a stationary load increase. The proposed detection schemes are based on either the major or the minor principal components of network traffic data. To demonstrate the applications of the proposed method, we first applied them to some synthetic data and then to network traffic data simulated from the packet switching network (PSN) model. The proposed detection schemes, based on dynamic PCA, show enhanced performance in detecting an increase of network load for the simulated network traffic data. These results show usefulness of a new feature extraction method based on dynamic PCA that creates additional feature variables for event detection in a univariate time series.

scholarly journals Dynamic Principal Component Analysis with Nonoverlapping Moving Window and Its Applications to Epileptic EEG Classification

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Shengkun Xie ◽  
Sridhar Krishnan
Keyword(s):  
Principal Component Analysis ◽  
Single Channel ◽  
Principal Component ◽  
Component Analysis ◽  
Classification Performance ◽  
Detection Methods ◽  
Moving Window ◽  
Feature Extraction Method ◽  
Epilepsy Diagnosis ◽  
Dynamic Principal Component Analysis

Classification of electroencephalography (EEG) is the most useful diagnostic and monitoring procedure for epilepsy study. A reliable algorithm that can be easily implemented is the key to this procedure. In this paper a novel signal feature extraction method based on dynamic principal component analysis and nonoverlapping moving window is proposed. Along with this new technique, two detection methods based on extracted sparse features are applied to deal with signal classification. The obtained results demonstrated that our proposed methodologies are able to differentiate EEGs from controls and interictal for epilepsy diagnosis and to separate EEGs from interictal and ictal for seizure detection. Our approach yields high classification accuracy for both single-channel short-term EEGs and multichannel long-term EEGs. The classification performance of the method is also compared with other state-of-the-art techniques on the same datasets and the effect of signal variability on the presented methods is also studied.

scholarly journals INTERPRETATION OF PROPULSIVE FORCE IN TETHERED SWIMMING THROUGH PRINCIPAL COMPONENT ANALYSIS

2018 ◽  
Vol 24 (3) ◽  
pp. 178-181 ◽  
Author(s):  
Rodrigo Maciel Andrade ◽  
Aylton José Figueira Júnior ◽  
Vanessa Metz ◽  
Alberto Carlos Amadio ◽  
Júlio Cerca Serrão
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Peak Force ◽  
Rate Of Force Development ◽  
Impulse Generation ◽  
Force Development ◽  
Time To Peak ◽  
Force Time ◽  
Minimum Force

ABSTRACT Introduction: Propulsive force in swimming, represented through impulse, is related to performance. However, since the as different biomechanical parameters contribute to impulse generation, coaches have a difficult task when seeking for performance improvement. Objective: Identify the main components involved in impulse generation in the front crawl stroke. Methods: Fourteen swimmers underwent a 10-second all-out fully tethered swimming test. The following parameters were obtained from the force-time curve: minimum force, peak force, mean force, time to peak force, rate of force development and stroke duration. This stage was followed by a principal component analysis. Results: The principal component analysis showed that component 1, predominantly kinetic, was composed of peak force, mean force and rate of force development, and accounted for 49.25% of total impulse variation, while component 2, predominantly temporal, composed of minimum force, stroke duration, and time to peak force, represented 26.43%. Conclusion: Kinetic parameters (peak force, mean force, and rate of force development) are more closely associated with impulse augmentation and, hypothetically, with non-tethered swimming performance. Level of Evidence II; Diagnostic studies - Investigating a diagnostic test.

scholarly journals A Multiobjective Evolutionary Approach for Solving Large-Scale Network Reconstruction Problems via Logistic Principal Component Analysis

2021 ◽  
Author(s):  
Chaolong Ying ◽  
Jing Liu ◽  
Kai Wu ◽  
Chao Wang
Keyword(s):  
Principal Component Analysis ◽  
Network Structure ◽  
Large Scale ◽  
Evolutionary Process ◽  
Principal Component ◽  
Reconstruction Algorithm ◽  
Component Analysis ◽  
Network Reconstruction ◽  
Reconstruction Error ◽  
Large Scale Networks

Currently, the problem of uncovering complex network structure and dynamics from time series is prominent in many fields. Despite the recent progress in this area, reconstructing large-scale networks from limited data remains a tough problem. Existing works treat connections of nodes as continuous values, leaving a challenge of setting a proper cut-off value to distinguish whether the connections exist or not. Besides, their performances on large-scale networks are far from satisfactory. Considering the reconstruction error and sparsity as two objectives, this paper proposes a subspace learning based evolutionary multiobjective network reconstruction algorithm, termed as SLEMO-NR, to solve the aforementioned problems. In the evolutionary process, we assume that binary-coded individuals obey the Bernoulli distribution and can use the probability and natural parameter as the alternative representations. Moreover, our approach utilizes the logistic principal component analysis (LPCA) to learn a subspace containing the features of network structure. The offspring solutions are generated in the learned subspace and then can be mapped back to the original space via LPCA. Benefitting from the alternative representations, a preference-based local search operator is proposed to concentrate on finding solutions approximate to the true sparsity. The experimental results on synthetic networks and six real-world networks demonstrate that, due to the well-learned network structure subspace and the preference-based strategy, our approach is effective in reconstructing large-scale networks compared to six existing methods.

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