• Journal of the Korean Mathematical Society
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• v.56 no.4
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• pp.869-915
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• 2019

Let H be a Krull monoid with class group G such that every class contains a prime divisor. Then every nonunit $a{\in}H$ can be written as a finite product of irreducible elements. If $a=u_1{\cdot}\;{\ldots}\;{\cdot}u_k$ with irreducibles $u_1,{\ldots},u_k{\in}H$, then k is called the length of the factorization and the set L(a) of all possible k is the set of lengths of a. It is well-known that the system ${\mathcal{L}}(H)=\{{\mathcal{L}}(a){\mid}a{\in}H\}$ depends only on the class group G. We study the inverse question asking whether the system ${\mathcal{L}}(H)$ is characteristic for the class group. Let H' be a further Krull monoid with class group G' such that every class contains a prime divisor and suppose that ${\mathcal{L}}(H)={\mathcal{L}}(H^{\prime})$. We show that, if one of the groups G and G' is finite and has rank at most two, then G and G' are isomorphic (apart from two well-known exceptions).

• 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.

• The Korean Journal of Physiology and Pharmacology
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• v.23 no.4
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• pp.237-249
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• 2019

Confirming the direct link between neural circuit activity and animal behavior has been a principal aim of neuroscience. The genetically encoded calcium indicator (GECI), which binds to calcium ions and emits fluorescence visualizing intracellular calcium concentration, enables detection of in vivo neuronal firing activity. Various GECIs have been developed and can be chosen for diverse purposes. These GECI-based signals can be acquired by several tools including two-photon microscopy and microendoscopy for precise or wide imaging at cellular to synaptic levels. In addition, the images from GECI signals can be analyzed with open source codes including constrained non-negative matrix factorization for endoscopy data (CNMF_E) and miniscope 1-photon-based calcium imaging signal extraction pipeline (MIN1PIPE), and considering parameters of the imaged brain regions (e.g., diameter or shape of soma or the resolution of recorded images), the real-time activity of each cell can be acquired and linked with animal behaviors. As a result, GECI signal analysis can be a powerful tool for revealing the functions of neuronal circuits related to specific behaviors.

• Convergence Security Journal
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• v.21 no.3
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• pp.3-11
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• 2021

Most of the internet security protocols rely on classical algorithms based on the mathematical complexity of the integer factorization problem, which becomes vulnerable to a quantum computer. Recent progresses of quantum computing technologies have highlighted the need for applying quantum key distribution (QKD) on existing network protocols. We report the development and integration of a plug & play QKD device with a commercial IPSec device by replacing the session keys used in IPSec protocol with the quantum ones. We expect that this work paves the way for enhancing security of the star-type networks by implementing QKD with the end-to-end IP communication.

• Journal for History of Mathematics
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• v.33 no.6
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• pp.303-314
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• 2020

Extending the method of extractions of square and cube roots in Jiuzhang Suanshu, Jia Xian introduced zengcheng kaifangfa in the 11th century. The process of zengcheng kaifangfa is exactly the same with that in Ruffini-Horner method introduced in the 19th century. The latter is based on the synthetic divisions, but zengcheng kaifangfa uses the binomial expansions. Since zengcheng kaifangfa is based on binomial expansions, traditional mathematicians in East Asia could not relate the fact that solutions of polynomial equation p(x) = 0 are determined by the linear factorization of p(x). The purpose of this paper is to reveal the difference between the mathematical structures of zengcheng kaifangfa and Ruffini-Honer method. For this object, we first discuss the reasons for zengcheng kaifangfa having difficulties to connect solutions with linear factors. Furthermore, investigating multiple solutions of equations constructed by tianyuanshu, we show differences between two methods and the structure of word problems in the East Asian mathematics.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.19 no.3
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• pp.51-59
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• 2009

The CRT-RSA cryptosystem is very vulnerable to fault insertion attacks in which an attacker can extract the secret prime factors p, q of modulus N by inserting an error during the computational operation on the cryptographic chip. In this paper, after implementing the CRT-RSA cryptosystem, we try to extract the secret key embedded in commercial microcontroller using optical injection tools such as laser beam or camera flash. As a result, we make sure that the commercial microcontroller is very vulnerable to fault insertion attacks using laser beam and camera flash, and can apply the prime factorization attack on CRT-RSA Cryptosystem.

• The Journal of Information Systems
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• v.31 no.1
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• pp.271-287
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• 2022

Purpose This research aims to study the changes in recruitment needed for the growth and survival of companies in the rapidly changing industry. In particular, we built a real company's worklist accounting for the rapidly advancing data-driven digital transformation, and presented the capabilities and conditions required for work. Design/methodology/approach we selected 37 jobs based on NCS to develop the employment search requirements by analyzing the business characteristics and work capabilities of the industry and company. The business specification indicators were converted into a matrix through the TF-IDF process, and the NMF algorithm is used to extract the features of each document. Also, the cosine distance measurement method is utilized to determine the similarity of the job specification conditions. Findings Companies tended to prefer "IT competency," which is a specification related to computer use and certification, and "experience competency," which is a specification for experience and internship. In addition, 'foreign language competency' was additionally preferred depending on the job. This analysis and development of job requirements would not only help companies to find the talents but also be useful for the jobseekers to easily decide the priority of their specification activities.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.1
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• pp.153-160
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• 2022

A greedy algorithm is proposed to select a subset of nodes of a graph for bandlimited graph signals in which each signal value is generated with its weight. Since graph signals are weighted, we seek to minimize the weighted reconstruction error which is formulated by using the QR factorization and derive an analytic result to find iteratively the node minimizing the weighted reconstruction error, leading to a simplified iterative selection process. Experiments show that the proposed method achieves a significant performance gain for graph signals with weights on various graphs as compared with the previous novel selection techniques.

• Journal of Information Technology Applications and Management
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• v.29 no.3
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• pp.43-55
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• 2022

In this study, with the goal of developing a deep learning-based product recommendation model for effective matching of influencers and products, a deep learning model with a collaborative filtering model combined with generalized matrix decomposition(GMF), a collaborative filtering model based on multi-layer perceptron (MLP), and neural collaborative filtering and generalized matrix Factorization (NeuMF), a hybrid model combining GMP and MLP was developed and tested. In particular, we utilize one-class problem free boosting (OCF-B) method to solve the one-class problem that occurs when training is performed only on positive cases using implicit feedback in the deep learning-based collaborative filtering recommendation model. In relation to model selection based on overall experimental results, the MLP model showed highest performance with weighted average precision, weighted average recall, and f1 score were 0.85 in the model (n=3,000, term=15). This study is meaningful in practice as it attempted to commercialize a deep learning-based recommendation system where influencer's promotion data is being accumulated, pactical personalized recommendation service is not yet commercially applied yet.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.475-475
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• 2022

The aim of this study is to identify the significant pollutant sources from the tributaries that are affecting the water quality of the study site, the Geum River and provide a solution to enhance the water quality. Multivariate statistical analysis modles such as cluster analysis, Principal component analysis (PCA) and positive matrix factorization (PMF) were applied to identify and prioritize the major pollutant sources of the two major tributaries, Gab-cheon and Miho-cheon, of the Geum River. PCA identifies three major pollutant sources for Gab-cheon and Miho-cheon, respectively. For Gab-cheon, wastewater treatment plant (WWTP), urban, and agricultural pollutions are identified as major pollutant sources. For Miho-cheon, agricultural, urban, and forest land are identified as major pollutant sources. On the contrary, PMF identifies three pollutant sources in Gab-cheon, same as PCA result and two pollutant sources in Miho-cheon. Water quality control scenarios are formulated and improvement of water quality in the river locations are simulated and analyzed with the Environmental Fluid Dynamic Code (EFDC) model. Scenario results were evaluated using a water quality index developed by Canadian Council of Ministers of the Environment. PCA and PMF appears to be effective to identify water pollution sources for the Geum river and also its tributaries in detail and thus can be used for the development of water quality improvement alternative of the above water bodies.