• Journal of the Korea Society of Computer and Information
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• v.25 no.9
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• pp.53-62
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• 2020

Product recommendation services that have been researched recently are only recommended through the customer's product purchase history. In this paper, we propose the digital signage service through customers' behavior pattern analysis that is recommending through not only purchase history, but also behavior pattern that customers take when choosing products. This service analyzes customer behavior patterns and extracts interests about products that are of practical interest. The service is learning extracted interest rate and customers' purchase history through the Wide & Deep model. Based on this learning method, the sparse vector of other products is predicted through the MF(Matrix Factorization). After derive the ranking of predicted product interest rate, this service uses the indoor signage that can interact with customers to expose the suitable advertisements. Through this proposed service, not only online, but also in an offline environment, it would be possible to grasp customers' interest information. Also, it will create a satisfactory purchasing environment by providing suitable advertisements to customers, not advertisements that advertisers randomly expose.

• The KIPS Transactions:PartA
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• v.10A no.2
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• pp.137-140
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• 2003

Recently, CG (Conjugate Gradient) scheme for the optimization of the Rayleigh quotient has been proven a very attractive and promising technique for interior eigenvalues for the following eigenvalue problem, Ax＝λx (1) The given matrix A is assummed to be large and sparse, and symmetric. Also, the method is very amenable to parallel computations. A proper choice of the preconditioner significantly improves the convergence of the CG scheme. We compare the parallel preconditioners for the computation of the interior eigenvalues of a symmetric matrix by CG-type method. The considered preconditioners are Point-SSOR, ILU (0) in the multi-coloring order, and Multi-Color Block SSOR (Symmetric Succesive OverRelaxation). We conducted our experiments on the CRAY­T3E with 128 nodes. The MPI (Message Passing Interface) library was adopted for the interprocessor communications. The test matrices are up to $512{\times}512$ in dimensions and were created from the discretizations of the elliptic PDE. All things considered the MC-BSSOR seems to be most robust preconditioner.

• Journal of the Korean Institute of Telematics and Electronics
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• v.21 no.2
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• pp.71-82
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• 1984

A programme, called BIPOLE, for the numerical analysis of twotimensional n-p-n bipolar transistors was developed. It has included the SRH and Auger recolnbination processes, the mobility dependence on the impurity density and the electric field, and the band-gap narrowing effect. The finite difference equations of the fundamental semiconductor equations are formulated using Newton's method for Poisson's equation and the divergence theorem for the hole and electron continuity equations without physical restrictions. The matrix of the linearized equations is sparse, symmetric M-matrix. For the solution of the linearized equations ICCG method and Gummel's algorithm have been employed. The programme BIPOLE has been applied to various kinds of the steady-state problems of n-p-n transistors. For the examples of applications the variations of common emitter current gain, emitter and diffusion capacitances, and input and output characteristics are calculated. Three-dimensional representations of some D.C. physical quantities such as potential and charge carrier distributions were displayed. This programme will be used for the nome,rical analysis of the distortion phenom ana of two-dimensional n-p-n transistors. The BIPOLE programme is available for everyone.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.12
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• pp.3798-3814
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• 2022

Social recommendation algorithm can alleviate data sparsity and cold start problems in recommendation system by integrated social information. Among them, matrix-based decomposition algorithms are the most widely used and studied. Such algorithms use dot product operations to calculate the similarity between users and items, which ignores user's potential preferences, reduces algorithms' recommendation accuracy. This deficiency can be avoided by a metric learning-based social recommendation algorithm, which learns the distance between user embedding vectors and item embedding vectors instead of vector dot-product operations. However, previous works provide no theoretical explanation for its plausibility. Moreover, most works focus on the indirect impact of social friends on user's preferences, ignoring the direct impact on user's rating preferences, which is the influence of user rating preferences. To solve these problems, this study proposes a user bias drift social recommendation algorithm based on metric learning (BDML). The main work of this paper is as follows: (1) the process of introducing metric learning in the social recommendation scenario is introduced in the form of equations, and explained the reason why metric learning can replace the click operation; (2) a new user bias is constructed to simultaneously model the impact of social relationships on user's ratings preferences and user's preferences; Experimental results on two datasets show that the BDML algorithm proposed in this study has better recommendation accuracy compared with other comparison algorithms, and will be able to guarantee the recommendation effect in a more sparse dataset.

• Journal of the Korean Data and Information Science Society
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• v.24 no.5
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• pp.999-1011
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• 2013

In this research, we propose a document recommendation method which can find documents that are relatively important to a specific document based on citation information. The key idea is parameter tuning in the Neumann kernal which is an intermediate between a measure of importance (HITS) and of relatedness (co-citation). Our method properly selects the tuning parameter ${\gamma}$ in the Neumann kernal minimizing the prediction error in future citation. We also discuss some comutational issues needed for analysing large citation data. Finally, results of analyzing patents data from the US Patent Office are given.

• The Journal of the Acoustical Society of Korea
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• v.39 no.3
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• pp.191-199
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• 2020

Currently, studies on the estimation algorithm based on compressive sensing are actively underway, but to the best of our knowledge, no study on the performance of the Sparse Spectrum Fitting (SpSF) algorithm in nonuniform sensor arrays has been made. This paper deals with the derivation of the compressive sensing based covariance fitting algorithm extended to the frequency domain. In addition, it shows the performance of directon-of-arrival estimation of the frequency domain SpSF algorithm in non-uniform linear sensor array system and the sensor array failure situation.

• Journal of Mechanical Science and Technology
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• v.14 no.9
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• pp.985-996
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• 2000

The issue of quench is related to safety operation of large-scale superconducting magnet system fabricated by cable-in-conduit conductor. A numerical method is presented to simulate the thermal hydraulic quench characteristics in the superconducting Tokamak magnet system, One-dimensional fluid dynamic equations for supercritical helium and the equation of heat conduction for the conduit are used to describe the thermal hydraulic characteristics in the cable-in-conduit conductor. The high heat transfer approximation between supercritical helium and superconducting strands is taken into account due to strong heating induced flow of supercritical helium. The fully implicit time integration of upwind scheme for finite volume method is utilized to discretize the equations on the staggered mesh. The scheme of a new adaptive mesh is proposed for the moving boundary problem and the time term is discretized by the-implicit scheme. It remarkably reduces the CPU time by local linearization of coefficient and the compressible storage of the large sparse matrix of discretized equations. The discretized equations are solved by the IMSL. The numerical implement is discussed in detail. The validation of this method is demonstrated by comparison of the numerical results with those of the SARUMAN and the QUENCHER and experimental measurements.

• Journal of Korea Society of Industrial Information Systems
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• v.7 no.5
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• pp.59-66
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• 2002

Many important scientific and engineering problems require the computation of a small number of eigenvalues for large nonsymmetric matrices. The biorthogonal Lanczos method is one of the methods to solve that problem, but it faces serious breakdown problems. In this paper, we introduce a modified biorthogonal Lanczos method to find a few eigenvalues of a large sparse nonsymmetric matrix. The proposed method generates reduction matrices that are similar to those generated by the standard biorthogonal Lanczos method. We prove that the breakdown conditions of our method are less stringent than the standard method. We then implement the modified biorthogonal Lanczos method on the CRAY machine and discuss the decreased breakdown conditions.

• Journal of Microbiology and Biotechnology
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• v.8 no.6
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• pp.700-704
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• 1998

Each of the recombinant structural genes of Helicobacter pylori urease, ureA and ureB, was cloned and overexpressed as inclusion bodies. Solubilization and renaturation of the inclusion bodies were carried out, to accelerate the pairing of sulfhydryl groups and the incorporation of nickel ions, which would lead to the native structure with high enzyme activity. Rates of urea hydrolysis were monitored as an indication of in vitro activation of renatured ureases. The activation of the apoprotein using 1 mM nickel ion, 100 mM sodium bicarbonate and a 10:1 ratio of reducing power resulted in a weak urease activity (about 11% of the native urease activity encoded by pTZ 19R/ure-l). When a sparse matrix screen method originally discovered for the crystallization of proteins was used, the activity increased higher than that obtained using glutathione. The effect of polyethylene glycol (PEG) on the activity was noticeable, giving two-fold increase in the specific activity (about 11 U/mg of protein corresponding to 22% of the native urease activity encoded by pTZ19R/ure-1).

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2011.07a
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• pp.437-438
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• 2011

Spectral segmentation is a major trend in image segmentation. Specially, constrained spectral segmentation, inspired by the user-given inputs, remains its challenging task. Since it makes use of the spectrum of the affinity matrix of a given image, its overall quality depends mainly on how to design the graphical model. In this work, we propose a sparse, multi-layer graphical model, where the pixels and the over-segmented regions are the graph nodes. Here, the graph affinities are computed by using the must-link and cannot-link constraints as well as the likelihoods that each node has a specific label. They are then used to simultaneously cluster all pixels and regions into visually coherent groups across all layers in a single multi-layer framework of Normalized Cuts. Although we incorporate only the adjacent connections in the multi-layer graph, the foreground object can be efficiently extracted in the spectral framework. The experimental results demonstrate the relevance of our algorithm as compared to existing popular algorithms.