• Journal of Korea Society of Digital Industry and Information Management
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• v.17 no.4
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• pp.53-62
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• 2021

Deep learning models such as convolutional neural networks and recurrent neual networks process a huge amounts of data, so they require a lot of storage and consume a lot of time and power due to memory access. Recently, research is being conducted to reduce memory usage and access by compressing data using the feature that many of deep learning data are highly sparse and localized. In this paper, we propose a compression-decompression method of storing only the non-zero data and the location information of the non-zero data excluding zero data. In order to make the location information of non-zero data, the matrix data is divided into sections uniformly. And whether there is non-zero data in the corresponding section is indicated. In this case, section division is not executed only once, but repeatedly executed, and location information is stored in each step. Therefore, it can be properly compressed according to the ratio and distribution of zero data. In addition, we propose a hardware structure that enables compression and decompression without complex operations. It was designed and verified with Verilog, and it was confirmed that it can be used in hardware deep learning accelerators.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.5
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• pp.2588-2609
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• 2019

Speech homomorphic encryption has become one of the key components in secure speech storing in the public cloud computing. The major problem of speech homomorphic encryption is the huge data expansion of speech cipher-text. To address the issue, this paper presents a speech homomorphic encryption scheme with less data expansion, which is a probabilistic statistics and addition homomorphic cryptosystem. In the proposed scheme, the original digital speech with some random numbers selected is firstly grouped to form a series of speech matrix. Then, a proposed matrix encryption method is employed to encrypt that speech matrix. After that, mutual information in sample speech cipher-texts is reduced to limit the data expansion. Performance analysis and experimental results show that the proposed scheme is addition homomorphic, and it not only resists statistical analysis attacks but also eliminates some signal characteristics of original speech. In addition, comparing with Paillier homomorphic cryptosystem, the proposed scheme has less data expansion and lower computational complexity. Furthermore, the time consumption of the proposed scheme is almost the same on the smartphone and the PC. Thus, the proposed scheme is extremely suitable for secure speech storing in public cloud computing.

• Journal of Information Processing Systems
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• v.17 no.3
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• pp.462-472
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• 2021

With an increase in the scale of recommender systems, users' rating data tend to be extremely sparse. Some methods have been utilized to alleviate this problem; nevertheless, it has not been satisfactorily solved yet. Therefore, we propose an effective pre-rating method based on users' dichotomous preferences and average ratings fusion. First, based on a user-item ratings matrix, a new user-item preference matrix was constructed to analyze and model user preferences. The items were then divided into two categories based on a parameterized dynamic threshold. The missing ratings for items that the user was not interested in were directly filled with the lowest user rating; otherwise, fusion ratings were utilized to fill the missing ratings. Further, an optimized parameter λ was introduced to adjust their weights. Finally, we verified our method on a standard dataset. The experimental results show that our method can effectively reduce the prediction error and improve the recommendation quality. As for its application, our method is effective, but not complicated.

• ETRI Journal
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• v.42 no.6
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• pp.922-931
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• 2020

A hierarchical iterative algorithm for the canonical polyadic decomposition (CPD) of tensors is proposed by improving the traditional conjugate gradient least squares (CGLS) method. Methods based on algebraic operations are investigated with the objective of estimating the direction of arrival (DoA) and polarization parameters of signals impinging on an array with electromagnetic (EM) vector-sensors. The proposed algorithm adopts a hierarchical iterative strategy, which enables the algorithm to obtain a fast recovery for the highly collinear factor matrix. Moreover, considering the same accuracy threshold, the proposed algorithm can achieve faster convergence compared with the alternating least squares (ALS) algorithm wherein the highly collinear factor matrix is absent. The results reveal that the proposed algorithm can achieve better performance under the condition of fewer snapshots, compared with the ALS-based algorithm and the algorithm based on generalized eigenvalue decomposition (GEVD). Furthermore, with regard to an array with a small number of sensors, the observed advantage in estimating the DoA and polarization parameters of the signal is notable.

• Journal of the Korean Institute of Telematics and Electronics A
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• v.29A no.11
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• pp.14-18
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• 1992

A gradient matrix method in conjunction with the transmission-reflection method to determine the complex permittivity and permeability of a microwave material is presented. This method does not incur the phase ambiguity due to an improper sample length, and is applicable to the measurement of low-loss materials of a half wavelength. A gradient matrix for a coaxial cable sample is derived, and the results are illustrated.

• Korean Journal of Mathematics
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• v.22 no.3
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• pp.443-454
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• 2014

In this paper, we give linearization of generalized Fi-bonacci sequences {$g_n$} and {$q_n$}, respectively, defined by Eq.(5) and Eq.(6) below and use this result to give the matrix form of the nth power of a companion matrix of {$g_n$} and {$q_n$}, respectively. Then we re-prove the Cassini's identity for {$g_n$} and {$q_n$}, respectively.

• Kyungpook Mathematical Journal
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• v.51 no.3
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• pp.261-281
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• 2011

In this paper, we will construct symmetric links by using the method adapted from the graph theory, and study a Seifert matrix of a symmetric link from the information of the Seifert matrix of the base link and the corresponding group action.

• 한국데이터정보과학회:학술대회논문집
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• 2003.05a
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• pp.89-95
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• 2003

Cumulative sum(CUSUM) control charts for monitoring dispersion matrix under multivariate normal process are proposed. Performances of the proposed CUSUM charts are measured in terms of average run length(ARL) by simulation. Numerical results show that small reference values of the proposed CUSUM chart is more efficient for small shifts in the production process.

• Proceedings of the IEEK Conference
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• 2009.05a
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• pp.323-325
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• 2009

In this paper, we present simulation results of Dynamic Matrix Control(DMC) to a boiler steam temperature. In order to control of steam temperature, we choose the input-output variables and generate the step response model by each input variable's step test. After that, the control structure executes on-line control with optimization using step response model. Proposed controller is applied to the APESS(Doosan company's boiler model simulator) and it is observed that the simulation results show satisfactory performance of proposed control.

• Honam Mathematical Journal
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• v.28 no.2
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• pp.205-212
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• 2006

The Lie algebra of type $F_4$ has the 26 dimensional representation. Its matrix realization can be obtained via 26 by 26 matrices and has a direct useful application to degenerate principal series for p-adic groups of type $F_4$.