• Information Systems Review
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• v.23 no.3
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• pp.1-23
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• 2021

The national economic policy paradigm is constantly changing according to the global business environment. Among them, fostering SMEs is a core policy of many developed countries. The growth of SMEs contributes to the creation of jobs and the development of local communities in the era of employment-free growth. In particular, the growth of SMEs is the foundation for growth into mid-sized and large enterprises. Therefore, the growth of SMEs plays an important role in the national economy. Information and communication technology (ICT) became important much more with the emergence of the 4th industrial revolution. Among them, the growth of ICT SMEs is the nation's future asset. Therefore, this study examines and verifies the main factors affecting the performance of ICT SMEs from the view of their R&D resources. On the basis of 1,999 SMEs dataset, empirical analysis was performed to investigate the influence of R&D resources on their corporate performance. Its results are as follows. First, based on theresource-based theory, ICT SMEs' R&D investment, R&D manpower, and government support policies were found to have a positive effect on securing a company's competitive advantage. Second, it was found that the level of product has a positive effect on the company's performance. Finally, it was found that M&A and technology acquisition method strategies differ according to the growth stage of the company. Therefore, in order to achieve technological innovation and corporate performance of ICT SMEs, the government support policy and investment into internal R&D personnel play as main factors. In addition, it was found that technology acquisition strategies differ depending on the growth stage of the company.

• Journal of Internet of Things and Convergence
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• v.8 no.4
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• pp.85-96
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• 2022

The purpose of this study was to investigate how the management strategies and organizational culture required in the digital economy have an effect on business performance. It provided basic data on management strategies and organizational culture necessary to approach as a digital leading country. For data collection, a survey was conducted from March 1 to May 30, 2022 for companies located in J province and engaged in industries related to the digital economy. The survey was conducted online and non-face-to-face, and a total of 225 companies participated in the survey. For statistical analysis, frequency analysis, exploratory factor analysis and reliability analysis, cluster analysis, independent sample t-test, and multiple regression analysis were performed. The research results are as follows. First, organizational culture was classified into high and low groups according to preference in innovation oriented, relationship oriented, task oriented, and hierarchical oriented. Second, the 4 types of organizational culture showed differences in prospectors strategy, analyzers strategy, defenders strategy, differentiation strategy, cost leadership strategy, financial performance, and non-financial performance according to preference. Third, management strategies affecting financial performance were found to be analyzers strategy, differentiation strategy, prospectors strategy, and cost leadership strategy. Fourth, management strategies affecting non-financial performance were found to be differentiation strategy, defenders strategy, analysis strategy, offensive strategy, cost leadership strategy, and focus strategy. Fifth, organizational culture affecting financial performance was found to be task oriented. Sixth, organizational culture affecting non-financial performance was found to be innovation oriented and relationship oriented. Through these studies, it is expected that the economy will be revitalized in the domestic market and a growth ecosystem that can take a new leap forward is created in the global market.

• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.229-247
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• 2023

With the increasing emphasis on anomaly detection across various fields, diverse anomaly detection algorithms have been developed for various data types and anomaly patterns. However, the performance of anomaly detection algorithms is generally evaluated on publicly available datasets, and the specific performance of each algorithm on anomalies of particular types remains unexplored. Consequently, selecting an appropriate anomaly detection algorithm for specific analytical contexts poses challenges. Therefore, in this paper, we aim to investigate the types of anomalies and various attributes of data. Subsequently, we intend to propose approaches that can assist in the selection of appropriate anomaly detection algorithms based on this understanding. Specifically, this study compares the performance of anomaly detection algorithms for four types of anomalies: local, global, contextual, and clustered anomalies. Through further analysis, the impact of label availability, data quantity, and dimensionality on algorithm performance is examined. Experimental results demonstrate that the most effective algorithm varies depending on the type of anomaly, and certain algorithms exhibit stable performance even in the absence of anomaly-specific information. Furthermore, in some types of anomalies, the performance of unsupervised anomaly detection algorithms was observed to be lower than that of supervised and semi-supervised learning algorithms. Lastly, we found that the performance of most algorithms is more strongly influenced by the type of anomalies when the data quantity is relatively scarce or abundant. Additionally, in cases of higher dimensionality, it was noted that excellent performance was exhibited in detecting local and global anomalies, while lower performance was observed for clustered anomaly types.

• Journal of Korea Water Resources Association
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• v.45 no.2
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• pp.165-178
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• 2012

The objective of this study is to evaluate the applicability of Land Surface Model (LSM) for estimating the runoff on East Asia. Global geographical and weather data are used as input for the model and for the model verification, the simulated runoff results are compared with observed data from 34 global observation stations provided by Global Runoff Data Center (GRDC). K$\ddot{o}$ppen's climate zone is used to calculate the model parameter for ungaged basins. As a result, the simulated runoff shows good performance comparing with observed data in 17 basins assumed as ungaged basins. The Hydrologic components on East Asia area are estimated from the model and the continental water balance components are seasonally similar to each country. Also, it reveals that runoffs from southern China, Japan and Taiwan are much higher than those from mongolian and northern China.

• Journal of Digital Convergence
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• v.14 no.10
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• pp.431-438
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• 2016

This study focused on the Korean-wave content consumed in the global market and analyzed success/failure factors of services through business model analysis. It aims to offer an effective new media content platform model. ViKi, Drama Fever, Maaduu.com which are representative global OTT were researched on management strategy by case analysis. The success of the global OTT platform is organized into three factors, target customer coverage, revenue model and community activation. Clear and wide coverage of target customer is important to determine the value of the service. Also, revenue model based on the pay service and community for Korean-wave fandom are essential to make good performance in new media platform business.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.13 no.5
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• pp.377-388
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• 2020

In this paper, the autonomous mobile robot whit only LRF sensors proposes an algorithm for avoiding moving obstacles in an environment where a global map containing fixed obstacles. First of all, in oder to avoid moving obstacles, moving obstacles are extracted using LRF distance sensor data and a global map. An ellipse-shaped safety radius is created using the sum of relative vector components between the extracted moving obstacles and of the autonomuos mobile robot. Considering the created safety radius, the autonomous mobile robot can avoid moving obstacles and reach the destination. To verify the proposed algorithm, use quantitative analysis methods to compare and analyze with existing algorithms. The analysis method compares the length and run time of the proposed algorithm with the length of the path of the existing algorithm based on the absence of a moving obstacle. The proposed algorithm can be avoided by taking into account the relative speed and direction of the moving obstacle, so both the route and the driving time show higher performance than the existing algorithm.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.2
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• pp.289-307
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• 2022

The Global Cybersecurity Index (GCI) developed by the International Telecommunication Union (ITU) is used to diagnose a country's cybersecurity development level and to strengthen its cybersecurity capabilities. This paper analyzes GCI and tries to suggest a way to strengthen its effectiveness. In addition, we analyze the GCI version 1~GCI version 4 evaluation index in advance, and examine the development plan through SWOT analysis. Through this, basic principles for GCI improvement and utilization will be established, and new indicators related to the GCI version 5 questionnaire will be discovered and suggested. This paper is expected to be used as basic data for GCI performance analysis and improvement plan. In addition, it is intended to contribute to enhance the effectiveness of GCI and the nation's cybersecurity capabilities by proposing more advanced proactive and reactive indicators to be applied to the future GCI evaluations. This paper is an improvement and development for the research result of [1].

• Journal of the Korea Society of Computer and Information
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• v.29 no.7
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• pp.53-71
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• 2024

Generative Artificial Intelligence (AI) has become the focus of societal attention due to its wide range of applications and profound impact. This paper constructs a comprehensive theoretical model based on the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2), integrating variables such as Personal Innovativeness and Perceived Risk to study the key factors influencing enterprises' adoption of Generative AI. We employed Structural Equation Modeling (SEM) to verify the hypothesized paths and used the Bootstrapping method to test the mediating effect of Behavioral Intention. Additionally, we explored the moderating effect of Perceived Risk through Hierarchical Regression Analysis. The results indicate that Performance Expectancy, Effort Expectancy, Social Influence, Price Value, and Personal Innovativeness have significant positive impacts on Behavioral Intention. Behavioral Intention plays a significant mediating role between these factors and Use Behavior, while Perceived Risk negatively moderates the relationship between Behavioral Intention and Use Behavior. This study provides theoretical and empirical support for how enterprises can effectively adopt Generative AI, offering important practical implications.

• Journal of Hydrogen and New Energy
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• v.33 no.6
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• pp.660-673
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• 2022

In achieving carbon neutrality and the hydrogen economy, the estimation of H₂ cost is critical in terms of CCU technologies. This study analyzes LCOH of hydrogen produced by the carbon utilization unit with methane reforming and CO₂ from thermal power plant. LCOH for H₂ made with CO is estimated in three ways of Joint Cost Allocations with financial performance risk assessment. Regarding cost analysis, the zero value of LCOH is $6,003/ton. We found that the CCU technology has economic feasibility in terms of profitability. The sensitivity analysis result shows that the input ratio is more influential to the LCOH than other variables. Risk analysis presents the baseline price of zero value of LCOH - $8,408/ton, which is higher than the cost analysis - $6,003/ton. Mainly, the price variability of natural gas primarily affects the LCOH. The study has significant value in analyzing the financial performance risks as well as the cost of H₂ produced by a Plasma-based CCU system.

• Journal of the Korea Society of Computer and Information
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• v.26 no.3
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• pp.127-135
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• 2021

Raising large numbers of animals in very narrow environments such as laying hens house can be very damaged from small environmental change. Previously researched about laying hens sound analysis system has a problem for applying to the laying hens house because considering only the limited situation of laying hens house. In this paper, to solve the problem, we propose a new laying hens sound analysis model using MFCC feature vector. This model can detect 7 situations that occur in actual laying hens house through 9 kinds of laying hens sound analysis. As a result of the performance evaluation of the proposed laying hens sound analysis model, the average AUC was 0.93, which is about 43% higher than that of the frequency feature analysis method.