• 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.

• Proceedings of the Korea Information Processing Society Conference
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• 2011.04a
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• pp.1253-1254
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• 2011

본 논문에서는 블로그 공간을 텐서로 표현하고, 이를 분석한다. 분석 결과에 따르면, PARAFAC 분해를 통하여 특정 주제를 나타내는 커뮤니티들을 올바르게 파악할 수 있었으며, 각 커뮤니티에서 영향력 있는 블로그들과 키워드들, 그리고 권위 있는 포스트들을 식별할 수 있었다.

• Journal of Korea Multimedia Society
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• v.22 no.4
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• pp.443-454
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• 2019

In recent years, there has been active research on a recommender system that considers three or more inputs in addition to users and goods, making it a multi-dimensional array, also known as a tensor. The main issue with using tensor is that there are a lot of missing values, making it sparse. In order to solve this, the tensor can be shrunk using the tensor decomposition algorithm into a lower dimensional array called a factor matrix. Then, the tensor is reconstructed by calculating factor matrices to fill original empty cells with predicted values. This is called tensor reconstruction. In this paper, we propose a user-based Top-K recommender system by normalized PARAFAC tensor reconstruction. This method involves factorization of a tensor into factor matrices and reconstructs the tensor again. Before decomposition, the original tensor is normalized based on each dimension to reduce overfitting. Using the real world dataset, this paper shows the processing of a large amount of data and implements a recommender system based on Apache Spark. In addition, this study has confirmed that the recommender performance is improved through normalization of the tensor.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.7
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• pp.2879-2890
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• 2020

In this paper, we study the estimation of angle, range and polarization parameters of a bistatic polarization sensitive frequency diverse array multiple-input multiple-output (PSFDA-MIMO) radar system. The application of polarization sensitive array in receiver is explored. A signal model of bistatic PSFDA-MIMO radar system is established. In order to utilize the multi-dimensional structure of array signals, the matched filtering radar data can be represented by a third-order tensor model. A joint estimation of the direction-of-departure (DOD), direction-of-arrival (DOA), range and polarization parameters based on parallel factor (PARAFAC) algorithm is proposed. The proposed algorithm does not need to search spectral peaks and singular value decomposition, and can obtain automatic pairing estimation. The method was compared with the existing methods, and the results show that the performance of the method is better. Therefore, the accuracy of the parameter estimation is further improved.

• 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 KIISE:Computing Practices and Letters
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• v.14 no.3
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• pp.296-300
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• 2008

Nonnegative tensor factorization(NTF) is a recent multiway(multilineal) extension of nonnegative matrix factorization(NMF), where nonnegativity constraints are imposed on the CANDECOMP/PARAFAC model. In this paper we consider the Tucker model with nonnegativity constraints and develop a new tensor factorization method, referred to as nonnegative Tucker decomposition (NTD). We derive multiplicative updating algorithms for various discrepancy measures: least square error function, I-divergence, and $\alpha$-divergence.

• Journal of Korean Society on Water Environment
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• v.38 no.1
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• pp.31-42
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• 2022

Dissolved organic matter (DOM), which changes according to various factors, is ubiquitously present from natural environments to engineered treatment systems. Only limited information is available regarding the environmental functions of DOM after bulk analyses are only applied for characterization. In this paper, latest DOM analytical techniques are briefly introduced, which include fluorescence excitation-emission matrix with parallel factor analysis (EEM-PARAFAC), size-exclusion chromatography with an organic carbon detector (SEC-OCD), carbon/nitrogen stable-isotope ratio, and Fourier transform-ion cyclotron resonance-mass spectroscopy (FT-ICR-MS). Recent examples of using advanced analyses to interpret the phenomena associated with DOM occurring in natural and engineered systems are presented here. Through EEM-PARAFAC, different components like protein-like, fulvic-like, and humic-like can be identified and tracked individually through the investigated systems. SEC-OCD allows researchers to quantify different size fractions. FT-ICR-MS provides thousands of molecular formulas present in bulk DOM samples. Lastly, carbon/nitrogen stable-isotope ratio offers reasonable tools for tracking the sources in environments. We also discuss the advantages and weakness of the above-mentioned characterizing tools. Specifically, they focus on single environmental factors (different sourced-DOM and interaction of sediment-pore water) or simple changes after individual treatment processes. Through collaboration with the advanced techniques later, they help the researchers to better understand environmental behaviors in aquatic systems and serve as essential tools for addressing various pending problems associated with DOM.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.12
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• pp.4967-4986
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• 2015

Multi-hop relaying communications have great potentials in improving transmission performance by deploying relay nodes. The benefit is critically dependent on the accuracy of the channel state information (CSI) of all the transmitting links. However, the CSI has to be estimated. In this paper, we investigate the channel estimation problem in one-way multi-hop MIMO amplify-and-forward (AF) relay system, where both the two-hop and three-hop communication link exist. Traditional point-to-point MIMO channel estimation methods will result in error propagation in estimating relay links, and separately tackling the channel estimation issue of each link will lose the gain as part of channel matrices involved in multiple communication links. In order to exploit all the available gains, we develop a novel channel estimation model by structuring different communication links using the PARAFAC and PARATUCK2 tensor analysis. Furthermore, a two-stage fitting algorithm is derived to estimate all the channel matrices involved in the communication process. In particular, essential uniqueness is further discussed. Simulation results demonstrate the advantage and effectiveness of the proposed channel estimator.

• Journal of Korean Society on Water Environment
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• v.34 no.4
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• pp.399-408
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• 2018

This research investigated the effects of weir (Ipobo) construction on the dynamics and the related spatial distributions of pollutants inflowing from tributaries (Yanghwacheon and Bokhacheon). Conductivity measurements and water sampling were conducted longitudinally, horizontally, and vertically in the waterbody upstream of the area located in Ipobo. Additionally, collected water samples were used for the dissolved organic carbon (DOC) analysis and fluorescence analysis which results in the SUVA, HIX, BIX, and FI calculation and parallel factor analysis (PARAFAC). Consequently, the results of the Conductivity, DOC, SUVA, and HIX showed that high concentration of pollutants that were flowing from the area of Bokhacheon which was mixed along the flow of the main river. The results of the BIX and FI did not show significant difference along the river flow which represented that allochthonous and terrestrial DOM, and for this reason was dominated in the whole waterbody rather than just the autochthonous DOM. The PARAFAC results showed that the two fluorescence components, humic-like and protein-like, constituted the fluorescence matrices of the water samples. The prevailing discipline notes that the two components were inflowing from the tributaries, however, a refractory component, humic-like substances, was relatively accumulated near the weir. From the results, the dynamics and spatial distributions of the DOM are dependent on the DOM characteristics, which induces the application of a specialized DOM analysis method to investigate the effects of a subsequent weir construction on the dynamics and spatial distributions of pollutants inflowing from the tributaries.

• 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.