• 산경연구논집
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• 제9권3호
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• pp.31-43
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• 2018

Purpose - With the technology of the 4th industrial revolution, business models of agricultural sector are changing rapidly toward convergence and high value-added business models due to the 6th industrialization of agricultural. Our goals is to examine the 6th industrialization of agriculture, and then to apply the technology of the 4th industrial revolution to the 6th industrialization of agriculture, suggesting the possibility of future agriculture, and then linking the export competitiveness of agricultural products. Research design, data, and methodology - As the methodology, case studies and empirical analyzes were conducted as well as literature studies. The case analyses included tomatoes, pig breeding farms, and an empirical analysis was conducted using the AHP analysis by experts of the 6th industrialization. In addition, using 124 survey data, this study examined and analyzed the items of the 4th industrial revolution technology for the 6th industrialization of agriculture and the strengthening of export competitiveness. Results - Results showed that the technology of the 4th Industrial Revolution helped "6th industrialization of agriculture" and "the strengthening of export competitiveness" using two successful cases. The AHP analysis was also carried out, and it was found that the improvement of the technology in the 4th industrial revolution could contribute to the future industrialization as well as the 6th industrialization of agriculture. First of all, we looked many conditions were important and urgent. Among the technologies of the 4th industrial revolution, the mobile, big data were important. Moreover, it was recognized that linkage and convergence related efforts would greatly contribute to strengthening export competitiveness of agriculture such as price and quality competitiveness. Conclusions - The 4th industrial revolution such as hyper-connectivity, hyper-intelligence and hyper-predictability contribute greatly to the 6th industrialization of agriculture, and therefore it is essential to improve the competitiveness of the agricultural sector by using the technology of the 4th industrial revolution. In particular, based on analyses of the diamond model, the "demand conditions" was the most important factor for the activation of the 6th Industrialization, and then "related and supporting fields", "factor conditions" and "business context" were followed in order. The results of this study can be useful for policy, practical and academic sectors.

• Journal of Computing Science and Engineering
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• 제11권1호
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• pp.32-38
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• 2017

Determining the correct word sense among ambiguous senses is essential for semantic analysis. One of the models for word sense disambiguation is the word space model which is very simple in the structure and effective. However, when the context word vectors in the word space model are merged into sense vectors in a sense inventory, they become typically very large but still suffer from the lexical scarcity. In this paper, we propose a word sense disambiguation method using word embedding that makes the sense inventory vectors compact and efficient due to its additive compositionality. Results of experiments with a Korean sense-tagged corpus show that our method is very effective.

• 한국CDE학회논문집
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• 제18권6호
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• pp.407-416
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• 2013

Acting an important risk management tool for main contractors, pre-qualification has served a key marketing tool for subcontractors in various industries. Current industrial environment has required the time intensive pre-qualification ability to small and medium-size subcontractors as a matter of competitive business. In order to support the subcontractors, this paper proposes a database model for pre-qualification management system (PQMS) that automates the pre-qualification process by using information technologies. The modeling process consists of specifications for its requirements, functional modules, and a database model for the PQMS.

• Journal of information and communication convergence engineering
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• 제11권4호
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• pp.235-240
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• 2013

Cloud systems are popular computing environment because they can provide easy access to computing resources for users as well as efficient use of resources for companies. The resources of cloud computing are heterogeneous and jobs have various characteristics. One such issue is effective job scheduling. Scheduling in the cloud system may be defined as a multiple criteria decision model. To address this issue, this paper proposes a priority-based two-step job scheduling algorithm. On the first level, jobs are classified based on preference. Resources are dedicated to a job if a deadline failure would cause severe results or critical business losses. In case of only minor discomfort or slight functional impairment, the job is scheduled using a best effort approach. On the second level, jobs are allocated to adequate resources through their priorities that are calculated by the analytic hierarchic process model. We then analyze the proposed algorithm and make a scheduling example to confirm its efficiency.

• 대한산업공학회지
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• 제40권2호
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• pp.151-162
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• 2014

This study proposes an optimization model to plan the patient distribution and medical resource allocation considering the diverse characteristics of disaster. For reflecting the particularity of disaster response, we configured a few scenarios such as availability of emergency surgery of non-major medical staff and the change in number of patients estimated reflecting the uncertainty, urgency and convergence of disaster. And we finally tested the effects of the scenarios' combination on the objective function defined as maximum number of survival patients. Our experimental results are expected to highlight the significance of the proposed model as well as the applicability of scenarios under disaster response.

• International journal of advanced smart convergence
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• 제7권4호
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• pp.27-39
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• 2018

Since the global positioning system (GPS) has been included in mobile devices (e.g., for car navigation, in smartphones, and in smart watches), the impact of personal GPS log data on daily life has been unprecedented. For example, such log data have been used to solve public problems, such as mass transit traffic patterns, finding optimum travelers' routes, and determining prospective business zones. However, a real-time analysis technique for GPS log data has been unattainable due to theoretical limitations. We introduced a machine learning model in order to resolve the limitation. In this paper presents a new, three-stage real-time prediction model for a person's daily route activity. In the first stage, a machine learning-based clustering algorithm is adopted for place detection. The training data set was a personal GPS tracking history. In the second stage, prediction of a new person's transient mode is studied. In the third stage, to represent the person's activity on those daily routes, inference rules are applied.

• 산업융합연구
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• 제13권3호
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• pp.1-9
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• 2015

The purpose of the present is to investigate the relation of organizational cynicism to organizational justice and OCB(organizational citizenship behavior). Questionnaire data were collected from 265 employees. To test the hypotheses, structural equation model was employed. The model showed adequate fit to the data. Results showed that organizational justice(distributive justice and procedural justice) were negatively related to organizational cynicism. Also, results supported the hypothesized relationship between organizational cynicism and organizational citizenship behavior(OCBIs and OCBOs). And organizational justice indirectly influences OCB through organizational cynicism as expected. Implications are discussed and future research directions are outlined.

• 웰빙융합연구
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• 제7권1호
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• pp.9-16
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• 2024

Purpose: This study explores the factors affecting purchase intention of smartphones among millennials. The study incorporates factors of brand equity, specifically brand awareness, brand loyalty, perceived quality and their mediation effect in purchase intention. Research design, data, and methodology: This study evaluates the role of brand equity factors in influencing purchase intentions by using structural equation modeling to analyze 197 respondents. Results: The findings indicate that brand loyalty, followed by brand awareness, and perceived quality are significant factors in determining customer purchase intention. Further, brand loyalty mediates the relationship between perceived quality and purchase intention, as well as between brand awareness and purchase intention significantly. Additionally, perceived quality mediates the relationship between brand awareness and purchase intention significantly. Finally, the serial mediation of perceived quality and brand loyalty significantly affects the relationship between brand awareness and purchase intention. Conclusions: This research has provided valuable insights into the relationship between brand equity and purchase intention among millennials supporting the Aaker's Model. Useful theoretical and managerial implications also have been provided.

• 한국전자통신학회논문지
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• 제18권5호
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• pp.901-910
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• 2023

대학에서는 4차 산업혁명에 맞추어 디지털 융합시대를 이끌어갈 창의·융합 인재를 육성하기 위하여 프로그래밍 언어적 사고를 기반으로 SW교육을 비전공자 대상으로 운영하고 있다. 하지만 학습자들은 프로그래밍 언어의 문법과 생소한 프로그래밍 언어를 습득하는 과정에 난점을 겪고 있다. 본 연구에서는 SW 비전공자들에게 학습과정에서 고충을 해소하기 위해서 소프트웨어 교육 모형을 제안하는 데 목적을 두었다. 프로그래밍 언어사고를 기반으로 EZMKER kit 교구 교육모델 중심을 알고리즘 기술과 다이어그램 기술을 도입하여 프로그래밍 언어와 문법에 대한 학습부족을 극복하고 구조적 소프트웨어 교육모델을 Top-Down시스템 학습모델로 설계하여 구현하게 되었다.

• Journal of information and communication convergence engineering
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• 제22권1호
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• pp.44-55
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• 2024

High-utility itemset mining (HUIM) is a dominant technology that enables enterprises to make real-time decisions, including supply chain management, customer segmentation, and business analytics. However, classical support value-driven Apriori solutions are confined and unable to meet real-time enterprise demands, especially for large amounts of input data. This study introduces a groundbreaking model for top-N high utility itemset mining in real-time enterprise applications. Unlike traditional Apriori-based solutions, the proposed convolutional sequential embedding metrics-driven cosine-similarity-based multilayer perception learning model leverages global and contextual features, including semantic attributes, for enhanced top-N recommendations over sequential transactions. The MATLAB-based simulations of the model on diverse datasets, demonstrated an impressive precision (0.5632), mean absolute error (MAE) (0.7610), hit rate (HR)@K (0.5720), and normalized discounted cumulative gain (NDCG)@K (0.4268). The average MAE across different datasets and latent dimensions was 0.608. Additionally, the model achieved remarkable cumulative accuracy and precision of 97.94% and 97.04% in performance, respectively, surpassing existing state-of-the-art models. This affirms the robustness and effectiveness of the proposed model in real-time enterprise scenarios.