• Journal of KIISE
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• v.45 no.3
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• pp.280-287
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• 2018

Recently, the use of a recommendation system and tensor data analysis, which has high-dimensional data, is increasing, as they allow us to analyze the tensor and extract potential elements and patterns. However, due to the large size and complexity of the tensor, it needs to be decomposed in order to analyze the tensor data. While several tools are used for tensor decomposition such as rTensor, pyTensor, and MATLAB, since such tools run on a single machine, they are unable to handle large data. Also, while distributed tensor decomposition tools based on Hadoop can handle a scalable tensor, its computing speed is too slow. In this paper, we propose S-PARAFAC, which is a tensor decomposition tool based on Apache Spark, in distributed in-memory environments. We converted the PARAFAC algorithm into an Apache Spark version that enables rapid processing of tensor data. We also compared the performance of the Hadoop based tensor tool and S-PARAFAC. The result showed that S-PARAFAC is approximately 4~25 times faster than the Hadoop based tensor tool.

• Smart Structures and Systems
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• v.13 no.2
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• pp.257-280
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• 2014

In this paper, a novel PARAllel FACtor (PARAFAC) decomposition based Blind Source Separation (BSS) algorithm is proposed for modal identification of structures equipped with tuned mass dampers. Tuned mass dampers (TMDs) are extremely effective vibration absorbers in tall flexible structures, but prone to get de-tuned due to accidental changes in structural properties, alteration in operating conditions, and incorrect design forecasts. Presence of closely spaced modes in structures coupled with TMDs renders output-only modal identification difficult. Over the last decade, second-order BSS algorithms have shown significant promise in the area of ambient modal identification. These methods employ joint diagonalization of covariance matrices of measurements to estimate the mixing matrix (mode shape coefficients) and sources (modal responses). Recently, PARAFAC BSS model has evolved as a powerful multi-linear algebra tool for decomposing an $n^{th}$ order tensor into a number of rank-1 tensors. This method is utilized in the context of modal identification in the present study. Covariance matrices of measurements at several lags are used to form a $3^{rd}$ order tensor and then PARAFAC decomposition is employed to obtain the desired number of components, comprising of modal responses and the mixing matrix. The strong uniqueness properties of PARAFAC models enable direct source separation with fine spectral resolution even in cases where the number of sensor observations is less compared to the number of target modes, i.e., the underdetermined case. This capability is exploited to separate closely spaced modes of the TMDs using partial measurements, and subsequently to estimate modal parameters. The proposed method is validated using extensive numerical studies comprising of multi-degree-of-freedom simulation models equipped with TMDs, as well as with an experimental set-up.

• Journal of Korean Society on Water Environment
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• v.40 no.2
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• pp.67-78
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• 2024

The introduction of a significant amount of phosphorous into aquatic environments can lead to eutrophication, which can in turn result in algal blooms. For the effective management of watersheds and the prevention of water quality problems related to nonpoint organic matter (OM) sources, it is essential to pinpoint the predominant OM sources. Several potential OM sources were sampled from upper agricultural watersheds, such as fallen leaves, riparian reeds, riparian plants, paddy soil, field soil, riparian soil, cow manure, and swine manure. Stream samples were collected during two storm events, and the concentrations of dissolved organic carbon (DOC) and phosphorous (DOP) from these OM sources and stream samples were assessed. DOM indicators using fluorescence spectroscopy, including HIX, FI, BIX, and EEM-PARAFAC, were evaluated in terms of their relevance in discerning DOM sources during storm events. Representative DOM descriptors were chosen based on specific criteria, such as value ranges and pronounced differences between low and high-flow periods. Consequently, the spectral slope ratio (S_R) paired with fluorescence index (FI) using end-member mixing analysis (EMMA) proved to be suitable for estimating the contribution of organic carbon (OC). The contribution of each organic phosphorous (OP) in stream samples was determined using the phosphorous-to-carbon (P/C) ratio in conjunction with the OC contribution. Notably, OP derived from swine manure in stream samples was found to make the most dominant contribution, ranging from 61.3% to 94.2% (average 78.1% ± 12.7%). The results of this research offer valuable insights into the selection of suitable indicators to recognize various OM sources and highlight the main sources of OP in forested-agricultural watersheds.