Study of microarray time series data based on Forward-Backward Linear Prediction and Singular Value Decomposition

Communication is classified in terms of verbal and nonverbal information. We discuss an acquisition method of knowledge from nonverbal information. In particular, a gesture is an efficient form of nonverbal communication as well as in verbal ways, and we formulate here a method that measures similarity and estimation between gestures. A gesture includes human embodied knowledge, and therefore the visible bodily actions can communicate particular messages. However, we have infinite patterns for gesture, determined by personality. Recently, the singular spectrum analysis method is utilized as an attractive method. In this paper, we propose a new method for acquiring embodied knowledge from time-series data on gestures using singular value decomposition. The motion behavior is categorized into several clusters with similarity and estimation between interval time-series data. We discuss the usefulness of the proposed method using an example of gesture motion.

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Denoising with Singular Value Decomposition for Phase Identification in Power Distribution Systems

10.36227/techrxiv.15102072 ◽

2021 ◽

Author(s):

Nicholas Zaragoza ◽

Vittal Rao

Keyword(s):

Singular Value Decomposition ◽

Power Distribution ◽

Distribution System ◽

Distribution Systems ◽

Time Series Data ◽

Singular Value ◽

Identification Accuracy ◽

Series Data ◽

Phase Identification ◽

Value Decomposition

Phase identification is the problem of determining what phase(s) that a load is connected to in a power distribution<br>system. However, real world sensor measurements used for phase identification have some level of noise that can hamper the ability to identify phase connections using data driven methods. Knowing the phase connections is important to keep the distribution system balanced so that parts of the system aren’t overloaded which can lead to inefficient operations, accelerated component degradation, and system destruction at worst. We use Singular Value Decomposition (SVD) with the optimal Singular Value Hard Threshold (SVHT) as part of a feature engineering pipeline to denoise data matrices of voltage magnitude measurements. This approach results in a reduction in frobenius error and an increase in average phase identification accuracy over a year of time series data. K-medoids clustering is used on the denoised voltage magnitude measurements to perform phase identification.<br>

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Time-Series Data Analysis Using Sliding Window Based SVD for Motion Evaluation

Journal of Advanced Computational Intelligence and Intelligent Informatics ◽

10.20965/jaciii.2017.p1240 ◽

2017 ◽

Vol 21 (7) ◽

pp. 1240-1250

Author(s):

Yinlai Jiang ◽

Isao Hayashi ◽

Shuoyu Wang ◽

Kenji Ishida ◽

◽

...

Keyword(s):

Time Series ◽

Time Series Data ◽

Sliding Window ◽

Singular Values ◽

Singular Value ◽

Series Data ◽

Singular Vectors ◽

Walking Difficulty ◽

The Right ◽

Value Decomposition

A method based on singular value decomposition (SVD) is proposed for extracting features from motion time-series data observed with various sensing systems. Matrices consisting of the sliding window (SW) subsets of time-series data are decomposed, yielding singular vectors as the patterns of the motion, and the singular values as a scalar, by which the corresponding singular vectors describe the matrices.The sliding window based singular value decomposition was applied to analyze acceleration during walking. Three levels of walking difficulty were simulated by restricting the right knee joint in the measurement. The accelerations of the middles of the shanks and the back of the waist were measured and normalized before the SW-SVD was performed.The results showed that the first singular values inferred from the acceleration data of the restricted side (the right shank) significantly related to the increase of the restriction among all the subjects while there were no common trends in the singular values of the left shank and the waist. The SW-SVD was suggested to be a reliable method to evaluate walking disability. Furthermore, a 2D visualization tool is proposed to provide intuitive information about walking difficulty which can be used in walking rehabilitation to monitor recovery.

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Denoising with Singular Value Decomposition for Phase Identification in Power Distribution Systems

10.36227/techrxiv.15102072.v1 ◽

2021 ◽

Author(s):

Nicholas Zaragoza ◽

Vittal Rao

Keyword(s):

Singular Value Decomposition ◽

Power Distribution ◽

Distribution System ◽

Distribution Systems ◽

Time Series Data ◽

Singular Value ◽

Identification Accuracy ◽

Series Data ◽

Phase Identification ◽

Value Decomposition

Phase identification is the problem of determining what phase(s) that a load is connected to in a power distribution<br>system. However, real world sensor measurements used for phase identification have some level of noise that can hamper the ability to identify phase connections using data driven methods. Knowing the phase connections is important to keep the distribution system balanced so that parts of the system aren’t overloaded which can lead to inefficient operations, accelerated component degradation, and system destruction at worst. We use Singular Value Decomposition (SVD) with the optimal Singular Value Hard Threshold (SVHT) as part of a feature engineering pipeline to denoise data matrices of voltage magnitude measurements. This approach results in a reduction in frobenius error and an increase in average phase identification accuracy over a year of time series data. K-medoids clustering is used on the denoised voltage magnitude measurements to perform phase identification.<br>

Download Full-text