• Korean Journal of Agricultural Science
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• v.46 no.4
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• pp.955-969
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• 2019

Analyzing on-line consumer responses is directly related to the management performance of food companies. Therefore, this study collected and analyzed data from an on-line portal site created by consumers about food companies with issues and examined the relationships between the data and the management performance. Through this process, we identified consumers' awareness of these companies obtained from big data analysis and analyzed the relationship between the results and the sales and stock prices of the companies through a time-series graph and correlation analysis. The results of this study were as follows. First, the result of the text mining analysis suggests that consumers respond more sensitively to negative issues than to positive issues. Second, the emotional analysis showed that companies' ethics issues (Enterprise 3 and 4) have a higher level of emotional continuity than that of food safety issues. It can be interpreted that the problem of ethical management has great influence on consumers' purchasing behavior. Finally, In the case of all negative food issues, the number of word frequency and emotional scores showed opposite trends. As a result of the correlation analysis, there was a correlation between word frequency and stock price in the case of all negative food issues and also between emotional scores and stock price. Recently, studies using big data analytics have been conducted in various fields. Therefore, based on this research, it is expected that studies using big data analytics will be done in the agricultural field.

• The Korea Journal of Herbology
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• v.30 no.3
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• pp.63-68
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• 2015

Objectives : We applied the term weighting method used in the field of data search to quantify relevancy between symptoms and medicinal herbs, and, based on this, we aim to introduce a method of visualizing the characteristics of medicinal herbs. Methods ： We proposed HF-IFF, an adaptation of TF-IDF, which is a term weighting measurement method adapted in the field of data search. Using this method, we deduced relevancy between symptoms and medicinal herbs In Cheongkangeuigam that was published in 1984 by organizing the medical theory of Cheongkang, Kim Younghoon, and visualized this as a graph in order to compare the characteristics of medicinal herbs used for different symptoms. Results ： HF-IFF is the product of HF and IFF, where HF is the frequency of the relevant medicinal herb for a set of symptoms, and IFF is the inverse of the number of formulations (FF) containing that herb. A total of 251 types of medicinal herb are used in Cheongkangeuigam, and 1538 formulations are classified according to 67 types of symptom. The overall mean for HF-IFF was 0.491, with a maximum of 4.566 and a minimum of 0.013. Conclusions ： In spite of several limitations, we were able to use HF-IFF to measure relevancy between symptoms and medicinal herbs, with formulations as an intermediate. We were able to use the quantified results to visually express the characteristics of the herbs used for symptoms by bubble chart and word-cloud from HF-IFF.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.5
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• pp.179-186
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• 2014

Current networks such as social network, web page link, traffic network are big data which have the large numbers of nodes and edges. Many applications such as social network services and navigation systems use these networks. Since big networks are not fit into the memory, existing in-memory based analysis techniques cannot provide high performance. Frontier-Expansion-Merge (FEM) framework for graph search operations using three corresponding operators in the relational database (RDB) context. FEM exploits an index table that stores pre-computed partial paths for efficient shortest path discovery. However, the index table of FEM has low hit ratio because the indices are determined by distances of indices rather than the possibility of containing a shortest path. In this paper, we propose an method that construct index table using high degree nodes having high hit ratio for efficient shortest path discovery. We experimentally verify that our index technique can support shortest path discovery efficiently in real-world datasets.

• Journal of the Korea Society of Computer and Information
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• v.12 no.1 s.45
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• pp.17-24
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• 2007

Critical practicality problems are cause to search the presentation and contents according to user request and purpose in previous internet system. Recently, there are a lot of researches about dynamic adaptable ontology based system. We designed ontology based educational system which uses discrete probability and user profile. This system provided advanced usability of contents by ontology and dynamic adaptive model based on discrete probability distribution function and user profile in ontology educational systems. This models represents application domain to weighted direction graph of dynamic adaptive objects and modeling user actions using dynamically approach method structured on discrete probability function. Proposed probability analysis can use that presenting potential attribute to user actions that are tracing search actions of user in ontology structure. This approach methods can allocate dynamically appropriate profiles to user.

• The Journal of the Korea Contents Association
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• v.21 no.12
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• pp.1-12
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• 2021

An objective expert discrimination scheme is needed for finding researchers who have insight and knowledge about a particular field of research. There are two types of expert discrimination schemes such as a citation graph based method and a formula based method. In this paper, we propose an efficient expert discrimination scheme considering various characteristics that have not been considered in the existing formula based methods. In order to discriminate the expertise of researchers, we present six expertise indices such as quality, productivity, contributiveness, recentness, accuracy, and durability. We also consider the number of social citations to apply the characteristics of academic search sites. Finally, we conduct various experiments to prove the validity and feasibility of the proposed scheme.

• Journal of Korea Society of Industrial Information Systems
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• v.27 no.5
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• pp.1-11
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• 2022

With the development of deep learning technology, various research and development are underway to estimate preference rankings through learning, and it is used in various fields such as web search, gene classification, recommendation system, and image search. Approximation algorithms are used to estimate deep learning-based preference ranking, which builds more than k comparison sets on all comparison targets to ensure proper accuracy, and how to build comparison sets affects learning. In this paper, we propose a k-disjoint comparison set generation algorithm and a k-chain comparison set generation algorithm, a novel algorithm for generating paired comparison sets for crowd-sourcing-based deep learning affinity measurements. In particular, the experiment confirmed that the k-chaining algorithm, like the conventional circular generation algorithm, also has a random nature that can support stable preference evaluation while ensuring connectivity between data.

• Journal of Astronomy and Space Sciences
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• v.39 no.1
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• pp.1-10
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• 2022

The universe is thought to be filled with not only Standard Model (SM) matters but also dark matters. Dark matter is thought to play a major role in its construction. However, the identity of dark matter is as yet unknown, with various search methods from astrophysical observartion to particle collider experiments. Because of the cross-section that is a thousand times smaller than SM particles, dark matter research requires a large amount of data processing. Therefore, optimization and parallelization in High Performance Computing is required. Dark matter in hypothetical hidden sector is though to be connected to dark photons which carries forces similar to photons in electromagnetism. In the recent analysis, it was studied using the decays of a dark photon at collider experiments. Based on this, we studies double dark photon decays at lepton colliders. The signal channels are e⁺e^- → A'A' and e⁺e^- → A'A'γ where dark photon A' decays dimuon. These signal channels are based on the theory that dark photons only decay into heavily charged leptons, which can explain the muon magnetic momentum anomaly. We scanned the cross-section according to the dark photon mass in experiments. MadGraph5 was used to generate events based on a simplified model. Additionally, to get the maximum expected number of events for the double dark photon channel, the detector efficiency for several center of mass (CM) energy were studied using Delphes and MadAnalysis5 for performance comparison. The results of this study will contribute to the search for double dark photon channels at lepton colliders.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.2
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• pp.564-582
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• 2015

This paper proposes a novel swarm intelligence optimization method which integrates bacterial foraging optimization (BFO) with quantum computing, called quantum bacterial foraging optimization (QBFO) algorithm. In QBFO, a multi-qubit which can represent a linear superposition of states in search space probabilistically is used to represent a bacterium, so that the quantum bacteria representation has a better characteristic of population diversity. A quantum rotation gate is designed to simulate the chemotactic step for the sake of driving the bacteria toward better solutions. Several tests are conducted based on benchmark functions including multi-peak function to evaluate optimization performance of the proposed algorithm. Numerical results show that the proposed QBFO has more powerful properties in terms of convergence rate, stability and the ability of searching for the global optimal solution than the original BFO and quantum genetic algorithm. Furthermore, we examine the employment of our proposed QBFO for cognitive radio spectrum allocation. The results indicate that the proposed QBFO based spectrum allocation scheme achieves high efficiency of spectrum usage and improves the transmission performance of secondary users, as compared to color sensitive graph coloring algorithm and quantum genetic algorithm.

• Journal of the Korean Data and Information Science Society
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• v.22 no.1
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• pp.19-27
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• 2011

Recommendation systems are developed to overcome the problems of selection and to promote intention to use. In this study, we propose a recommendation system using adjacency data according to user's behavior over time. For this, the product adjacencies are identified from the adjacency matrix based on graph theory. This research finds that there is a trend in the users' behavior over time though product adjacency fluctuates over time. The system is tested on its usability. The tests show that implementing this recommendation system increases users' intention to purchase and reduces the search time.

• The Korean Journal of Food And Nutrition
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• v.13 no.5
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• pp.403-410
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• 2000

This study was carried out to understand the effect of addition of potato search on the frozen dough. The characteristics of frozen dough were measured by the farinogram, the extensogram and the amylogram. The results of these measurements show that the dough added with starch has higher stability than the control. The physical and chemical change of the dough were measured in accordance with the period of the frozen storage. The dough added with starch showed smaller physical and chemical change than control, which means that the starch prevents the frozen dough from the deterioration during the frozen storage. It is supposed from this result that the starch protects the activity of yeast and the structure of gluten matrices from frozen damage. It is understood from this study that addition of potato starch into frozen dough improve the stability of the frozen dough.