• Journal of the Korean Data and Information Science Society
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• v.25 no.5
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• pp.1107-1116
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• 2014

In this paper, we review recent developments in network analysis using the graph theory, and introduce ongoing research area with relevant theoretical results. In specific, we introduce basic notations in graph, and conditional and marginal approach in constructing the adjacency matrix. Also, we introduce the Marcenko-Pastur law, the Tracy-Widom law, the white Wishart distribution, and the spiked distribution. Finally, we mention the relationship between degrees and eigenvalues for the detection of hubs in a network.

• Honam Mathematical Journal
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• v.27 no.1
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• pp.115-129
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• 2005

In this paper, we give a digital graph-theoretical approach of the study of digital images with relation to a simplicial complex. Thus, a digital graph $G_k$ with some k-adjacency in ${\mathbb{Z}}^n$ can be recognized by the simplicial complex spanned by $G_k$. Moreover, we demonstrate that a graphically $(k_0,\;k_1)$-continuous map $f:G_{k_0}{\subset}{\mathbb{Z}}^{n_0}{\rightarrow}G_{k_1}{\subset}{\mathbb{Z}}^{n_1}$ can be converted into the simplicial map $S(f):S(G_{k_0}){\rightarrow}S(G_{k_1})$ with relation to combinatorial topology. Finally, if $G_{k_0}$ is not $(k_0,\;3^{n_0}-1)$-homotopy equivalent to $SC^{n_0,4}_{3^{n_0}-1}$, a graphically $(k_0,\;k_1)$-continuous map (respectively a graphically $(k_0,\;k_1)$-isomorphisim) $f:G_{k_0}{\subset}{\mathbb{Z}}^{n_0}{\rightarrow}G_{k_1}{\subset}{\mathbb{Z}^{n_1}$ induces the group homomorphism (respectively the group isomorphisim) $S(f)_*:{\pi}_1(S(G_{k_0}),\;v_0){\rightarrow}{\pi}_1(S(G_{k_1}),\;f(v_0))$ in algebraic topology.

• Safety and Health at Work
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• v.12 no.1
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• pp.42-50
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• 2021

Introduction: Despite huge investments in new technology and transportation infrastructure, terrible accidents still remain a reality of traffic. Methods: Severe traffic accidents were analyzed from four prevailing modes of today's transportations: sea, air, railway, and road. Main root causes of all four accidents were defined with implementation of the approach, based on Flanagan's critical incident technique. In accordance with Molan's Availability Humanization model (AH model), possible preventive or humanization interventions were defined with the focus on technology, environment, organization, and human factors. Results: According to our analyses, there are significant similarities between accidents. Root causes of accidents, human behavioral patterns, and possible humanization measures were presented with rooted graphs. It is possible to create a generalized model graph, which is similar to rooted graphs, for identification of possible humanization measures, intended to prevent similar accidents in the future. Majority of proposed humanization interventions are focused on organization. Organizational interventions are effective in assurance of adequate and safe behavior. Conclusions: Formalization of root cause analysis with rooted graphs in a model offers possibility for implementation of presented methods in analysis of particular events. Implementation of proposed humanization measures in a particular analyzed situation is the basis for creation of safety culture.

• Journal of Information Processing Systems
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• v.14 no.3
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• pp.709-726
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• 2018

Database classification is an important preprocessing step for the multi-database mining (MDM). In fact, when a multi-branch company needs to explore its distributed data for decision making, it is imperative to classify these multiple databases into similar clusters before analyzing the data. To search for the best classification of a set of n databases, existing algorithms generate from 1 to ($n^2-n$)/2 candidate classifications. Although each candidate classification is included in the next one (i.e., clusters in the current classification are subsets of clusters in the next classification), existing algorithms generate each classification independently, that is, without taking into account the use of clusters from the previous classification. Consequently, existing algorithms are time consuming, especially when the number of candidate classifications increases. To overcome the latter problem, we propose in this paper an efficient approach that represents the problem of classifying the multiple databases as a problem of identifying the connected components of an undirected weighted graph. Theoretical analysis and experiments on public databases confirm the efficiency of our algorithm against existing works and that it overcomes the problem of increase in the execution time.

• The Journal of Korean Association of Computer Education
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• v.9 no.6
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• pp.41-51
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• 2006

Educators have recognized the usefulness of the e-BBS(electronic-Bulletin Board System) as an asynchronous communication medium for collaborative learning. However, theoretical outcomes to understand the structural analysis of thc e-BBS are not adequate enough. As a consequence, we are left with inadequate supportive tools to understand complex communication phenomena and to assist teachers to guide students to educational purposes. This can cause teachers to have a laissez-faire approach using BBS as a communication medium for education due to the lack of understanding the structural and overall situation. To address this issue, this paper presents a theoretical foundation to help understand the structure of discussion activities via the e-BBS and then presents a computational model, based on the structural analysis, for the behavioral analysis of participants. The formal representation of bi-directional discussion activities is realized by devising two graphs - the reply graph and the connection graph. Various measurements and statistics for the analysis are presented in the paper. To clarify the analysis, we classify the results produced through the analysis of discussion activities and participants' behaviors.

• Journal of Korea Multimedia Society
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• v.3 no.6
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• pp.674-684
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• 2000

One key obstacle which has been identified in achieving parallel processing is to communicate effectively between processors during execution. One approach to achieving an optimal delay time is to use expander graph. The networks and algorithms which are based on expander graphs are successfully exploited to yield fast parallel algorithms and efficient design. The AKS sorting algorithm in time O(logN) which is an important result is based on the use of expanders. The expander graph also can be applied to construct a concentrator and a superconcentrator. Since Margulis found a way to construct an explicit linear expander graph, several expander graphs have been developed. But the proof of existence of such graphs is in fact provided by a nonconstructive argument. We investigate the expander network on the hypercube network. We prove the expansion of a sin81e stage hypercube network and extend this from a single stage to multistage networks. The results in this paper provide a theoretical analysis of expansion in the hypercube network.

• Korean Chemical Engineering Research
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• v.55 no.4
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• pp.520-529
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• 2017

Viscosity data were measured at 298.15 K and 308.15 K for formamide + 1-propanol, 2-propanol, 1-butanol, 2-methyl-1-propanol or 2-methyl-2-propanol mixtures. For an equimolar mixture, deviation in viscosity follows the sequence: 2-methyl-2-propanol >2-methyl-1-propanol>1-butanol>2-propanol>1-propanol. The viscosity data were further analyzed in terms of graph theory. Free energy of activation was also calculated from experimental viscosity data along with previously reported excess volume data. The deviation in viscosity and free energy of activation were fitted to Redlich-Kister polynomial equation. The viscosity data were also correlated by correlations like Grunberg-Nissan, Tamura-Kurata, HindMcLaughlin-Ubbelohde, and Katti-Chaudhari relation. Various adjustable parameters, $G_{12}$, $T_{12}$, $H_{12}$, and $W_{vis}/RT$, of various correlations were used to predict viscosity deviation of binary mixtures. Positive value of $G_{12}$ indicates strong interaction in the studied systems. Grunberg-Nissan relation has lowest deviation among the four correlations for formamide + 1-propanol or 2-propanol mixtures; and for mixtures of formamide with 1-butanol or 2-methyl-1-propanol, TamuraKurata has lowest deviation. Grunberg-Nissan gives lowest deviation for formamide + 2-methyl-2-propanol mixtures.

• Korean Chemical Engineering Research
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• v.55 no.5
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• pp.668-678
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• 2017

Intermolecular interactions were studied for binary mixtures of 2-propanol + cyclohexane, n-hexane, benzene, toluene, o-, m- and p-xylenes by measuring ultrasonic speeds (u) over the entire range of composition at 298.15 K and 308.15 K. From these results the deviation in ultrasonic speed was calculated. These results were fitted to the Redlich-Kister equation to derive the binary coefficients along with standard deviations between the experimental and calculated data. Acoustic parameters such as excess isentropic compressibility ($K_s^E$), intermolecular free length ($L_f$) and available volume ($V_a$) were also derived from ultrasonic speed data and Jacobson's free length theory. The ultrasonic speed data were correlated by Nomoto's relation, Van Dael's mixing relation, impedance dependence relation, and Schaaff's collision factor theory. Van Dael's relation gives the best prediction of u in the binary mixtures containing aliphatic hydrocarbons. The ultrasonic speed data and isentropic compressibility were further analyzed in terms of Jacobson's free length theory.

• Journal of Internet Computing and Services
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• v.22 no.5
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• pp.27-33
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• 2021

With the broad adoption of the Internet of Things (IoT) in a variety of scenarios and application services, management and orchestration entities require upgrading the traditional architecture and develop intelligent models with ultra-reliable methods. In a heterogeneous network environment, mission-critical IoT applications are significant to consider. With erroneous priorities and high failure rates, catastrophic losses in terms of human lives, great business assets, and privacy leakage will occur in emergent scenarios. In this paper, an efficient resource slicing scheme for optimizing federated learning in software-defined IoT (SDIoT) is proposed. The decentralized support vector regression (SVR) based controllers predict the IoT slices via packet inspection data during peak hour central congestion to achieve a time-sensitive condition. In off-peak hour intervals, a centralized deep neural networks (DNN) model is used within computation-intensive aspects on fine-grained slicing and remodified decentralized controller outputs. With known slice and prioritization, federated learning communications iteratively process through the adjusted resources by virtual network functions forwarding graph (VNFFG) descriptor set up in software-defined networking (SDN) and network functions virtualization (NFV) enabled architecture. To demonstrate the theoretical approach, Mininet emulator was conducted to evaluate between reference and proposed schemes by capturing the key Quality of Service (QoS) performance metrics.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.67-86
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• 2020

Recommender Systems have been huge influence users and business more and more. Recently the importance of E-commerce has been reached rapid growth greatly in world-wide COVID-19 pandemic. Recommender system is the center of E-commerce lively. Top ranked E-commerce managers mentioned that recommender systems have a major influence on customer's purchase such as about 50% of Netflix, Amazon sales from their recommender systems. Most algorithms have been focused on improving accuracy of recommender system regardless of novelty, diversity, serendipity etc. Recommender systems with only high accuracy cannot satisfy business long-term profit because of generating sales polarization. In addition, customers do not experience enjoyment of shopping from only focusing accuracy recommender system because customer's preference is changed constantly. Therefore, recommender systems with various values need to be developed for user's high satisfaction. Reranking is the most useful methodology to realize diversity of recommender system. In this paper, diversity of recommender system is represented through constructing high similarity with users who have different preference using each user's purchased item's category algorithm. It is distinguished from past research approach which is changing the algorithm of recommender system without user's diversity preference level. We tried to discover user's diversity preference level and observed the results how the effect was different according to user's diversity preference level. In addition, graph-based recommender system was used to show diversity through user's network, not collaborative filtering. In this paper, Amazon Grocery and Gourmet Food data was used because the low-involvement product, such as habitual product, foods, low-priced goods etc., had high probability to show customer's diversity. First, a bipartite graph with users and items simultaneously is constructed to make graph-based recommender system. However, each users and items unipartite graph also need to be established to show diversity of recommender system. The weight of each unipartite graph has played crucial role changing Jaccard Distance of item's category. We can observe two important results from the user's unipartite network. First, the user's diversity preference level is observed from the network and second, dissimilar users can be discovered in the user's network. Through the research process, diversity of recommender system is presented highly with small accuracy loss and optimalization for higher accuracy is possible controlling diversity ratio. This paper has three important theoretical points. First, this research expands recommender system research for user's satisfaction with various values. Second, the graph-based recommender system is developed newly. Third, the evaluation indicator of diversity is made for diversity. In addition, recommender systems are useful for corporate profit practically and this paper has contribution on business closely. Above all, business long-term profit can be improved using recommender system with diversity and the recommender system can provide right service according to user's diversity level. Lastly, the corporate selling low-involvement products have great effect based on the results.