• Management & Information Systems Review
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• v.38 no.1
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• pp.225-239
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• 2019

In order for a company to grow through technological innovation, technological innovation capacity to support technological innovation is more important than anything else. In addition, the technology commercialization process can not be ignored in order to lead to the improvement of the business performance. In this context, this study analyzed the impact of firm's technological innovation capacity on business performance and tried to analyze whether technological innovation capacity has a moderating effect on technological innovation capacity. To analyze the purpose of this study, we collect data through questionnaires of small and medium venture companies located in the southeast region of korea. The results of multiple regression analysis based on 132 collected company survey data are summarized as follows. First, Technology innovation capacity has a positive effect on business performance. Specifically, companies with well-equipped R&D capabilities, technology accumulation capabilities, and technology innovation systems showed higher business performance(market competitiveness, business growth potential, and business profitability). Second, technology commercialization capacity has a positive effect on the effect of technological innovation capacity on business performance. This result implies that a company with a good technical commercialization capability increases the positive influence of technological innovation capacity on business performance. The results of this study suggest that it is important to systematically manage the technology commercialization capacity in order to generate business performance through technological innovation.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.2
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• pp.155-161
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• 2019

Currently, numerous companies and institutes provide services using the Internet, and establish and operate Information Systems to manage them efficiently and reliably. The Information System implies the possibility of losing the ability to provide normal services due to a disaster or disability. It is preparing for this by utilizing a disaster recovery system. However, existing disaster recovery systems cannot perform normal recovery if files for system recovery are corrupted. In this paper, we proposed a system that can verify the integrity of the system recovery file and proceed with recovery by utilizing hyper-ledger fabric blockchain. The PBFT consensus algorithm is used to generate the blocks and is performed by the leader node of the blockchain network. In the event of failure, verify the integrity of the recovery file by comparing the hash value of the recovery file with the hash value in the blockchain and proceed with recovery. For the evaluation of proposed techniques, a comparative analysis was conducted based on four items: existing system recovery techniques and data consistency, able to data retention, recovery file integrity, and using the proposed technique, the amount of traffic generated was analyzed to determine whether it was actually applicable.

• Management & Information Systems Review
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• v.37 no.4
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• pp.41-62
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• 2018

As the volume of unstructured data increases through various social media, Internet news articles, and blogs, the importance of text analysis and the studies are increasing. Since text analysis is mostly performed on a specific domain or topic, the importance of constructing and applying a domain-specific dictionary has been increased. The quality of dictionary has a direct impact on the results of the unstructured data analysis and it is much more important since it present a perspective of analysis. In the literature, most studies on text analysis has emphasized the importance of dictionaries to acquire clean and high quality results. However, unfortunately, a rigorous verification of the effects of dictionaries has not been studied, even if it is already known as the most essential factor of text analysis. In this paper, we generate three dictionaries in various ways from 39,800 news articles and analyze and verify the effect each dictionary on the accuracy of document classification by defining the concept of Intrinsic Rate. 1) A batch construction method which is building a dictionary based on the frequency of terms in the entire documents 2) A method of extracting the terms by category and integrating the terms 3) A method of extracting the features according to each category and integrating them. We compared accuracy of three artificial neural network-based document classifiers to evaluate the quality of dictionaries. As a result of the experiment, the accuracy tend to increase when the "Intrinsic Rate" is high and we found the possibility to improve accuracy of document classification by increasing the intrinsic rate of the dictionary.

• Korean Journal of Plant Resources
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• v.34 no.5
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• pp.478-490
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• 2021

We aimed to generate basic breeding data for melon (Cucumis melo L.). A total of 219 melon accessions conserved at the National Agrobiodiversity Center (NAC) in Rural Development Administration (RDA) were used in this study, of which 72 (33%) were collected from India. The majority of accessions showed orange (42%) and white (36%) flesh color. In addition to phenotypic evaluations, the accessions were genotyped using a molecular marker for the carotenoid biosynthesis gene CmOr. DNA fragments of the expected size were amplified in 205 out of 219 accessions. Digestion of the PCR products with HinfI restriction endonuclease showed 100% concordance between phenotype and genotype in green-fleshed accessions, but 98%, 97%, and 80% concordance in orange-, white-, and creamy-fleshed accessions, respectively. Sequence analysis revealed single nucleotide changes in the three positions of SNP1, SNP2 and SNP1int in the CmOr gene among accessions. These newly found alleles suggest that there are multiple mechanisms in determining fruit flesh color in melon. Also, the phenotype data of diverse accessions obtained in this study will be a valuable source for melon breeding.

• The Journal of the Korea Contents Association
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• v.21 no.10
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• pp.90-102
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• 2021

Technology evaluation is to assess the ability of technology commercialization entities to generate profits by using the subject technology, and domestic technology evaluation agencies have established and implemented their own evaluation systems. In particular, the recently developed technology evaluation model in the fields of marine and fishery does not sufficiently reflect the poor environment for technology development compared to other industries, so it does not pass the level of T4 rating, which is considered appropriate for investment. This is recognized as a challenge that occurs when the common evaluation indicators and evaluation scales used in other industries, and when the scoring system for T1 to T10 grading is similarly or identically utilized. Therefore, through this study, we intend to secure the appropriateness and reliability of the results of the comprehensive rating calculation by developing technology evaluation models and indicators that well explain the nine marine and fisheries industry classification systems. Based on KED and technology evaluation case data, AHP-based index weighting and Monte Carlo simulation-based rating system are applied and the results of case studies are verified. Through the proposed model, we aim to enhance the usability of R&D and commercialization support programs based on fast, convenient and objective evaluation results by applying to upcoming technology evaluation cases.

• Journal of Korean Society of Forest Science
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• v.100 no.1
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• pp.70-78
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• 2011

In this study, we develop a fire stochastic simulation model by season based on the historical fire data in Korea. The model is utilized to generate sequences of fire events that are consistent with Korean fire history. We employ a three-stage approach. First, a random draw from a Bernoulli distribution is used to determine if any fire occurs for each day of a simulated fire season. Second, if a fire does occur, a random draw from a geometric multiplicity distribution determines their number. Last, ignition times for each fire are randomly drawn from a Poisson distribution. This specific distributional forms are chosen after analysis of Korean historical fire data. Maximum Likelihood Estimation (MLE) is used to estimate the primary parameters of the stochastic models. Fire sequences generated with the model appear to follow historical patterns with respect to diurnal distribution and total number of fires per year. We expect that the results of this study will assist a fire manager for planning fire suppression policies and suppression resource allocations.

• The Journal of the Korea Contents Association
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• v.21 no.4
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• pp.26-37
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• 2021

This study conducted a big data analysis on news to identify the agenda of media literacy, which has been socially discussed, and on which relevant policy directions will be proposed. To this end 1,336 articles from January 1, 2019 to September 30, 2020 were collected and a topic modeling analysis was conducted according to four periods. Five topics for each period were derived through the analysis, and implications based on the results are as follows. First, the government should implement a nation-level systematic approach to media literacy education according to life cycle stages to generate economic and cultural value. Second, local communities and schools should provide systematic support and education guidance activities to ensure a sustainable ecosystem for media literacy and prevent an educational gap and loss in learning. Third, efforts should be made in various aspects to minimize the side effects resulting from constantly providing media literacy education; furthermore a culture of desirable media application should be established. Finally, a research environment for scientific research on media literacy, active exchange of experience and value obtained in the field, and long-term accumulation of research results should be encouraged to develop a robust knowledge exchange culture.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.6
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• pp.84-99
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• 2021

As the micro-mobility market grows, the demand for route guidance, that includes uphill information as well, is increasing. Since the climbing angle depends on the electric motor uesed, it is necessary to establish an uphill road DB according to the threshold standard. Although road alignment information is a very important element in the basic information of the roads, there is no information currently on the longitudinal slope in the road digital map. The High Definition(HD) map which is being built as a preparation for the era of autonomous vehicles has the altitude value, unlike the existing standard node link system. However, the HD map is very insufficient because it has the altitude value only for some sections of the road network. This paper, hence, intends to propose a method to generate the road longitudinal slope using currently available data. We developed a method of computing the longitudinal slope by combining the digital elevation model and the standard link system. After creating an altitude at the road link point divided by 4m based on the Seoul road network, we calculated individual slope per unit distance of the road. After designating a representative slope for each road link, we have extracted the very steep road that cannot be climbed with personal mobility and the slippery roads that cannot be used during heavy snowfall. We additionally described errors in the altitude values due to surrounding terrain and the issues related to the slope calculation method. In the future, we expect that the road longitudinal slope information will be used as basic data that can be used for various convergence analyses.

• Journal of Korea Entertainment Industry Association
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• v.15 no.8
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• pp.389-401
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• 2021

This study was performed to generate basic data to establish a health promotion plan for residents of Chungcheongbuk-do by identifying characteristics of discharged patients residing in the Chungcheongbuk-do area from an In-Depth Post-Discharge Injury Survey reported by the Korea Centers for Disease Control and Prevention(KCDCP). The Report provided data on demographic characteristics, medical institution use characteristics, medical use characteristics, and disease characteristics of patients discharged from medical institutions with 100 or more beds from 2013 to 2017. The total number of Chungcheongbuk-do residents who were admitted and discharged from 2013 to 2017 was estimated to be 1,656,590, and the discharge rate was 21,089, which was higher than the national average of 13,882 in 2016. The regions where the discharge rate increased during this period include Goesan, Yeongdong, Boeun, Okcheon, Jeungpyeong, and Eumseong-gun, which are mainly rural areas. Among the patients hospitalized and discharged from hospitals outside the Chungcheongbuk-do area, the discharge rate of patients who used hospitals in Incheon/Gyeonggi areas and Daejeon/Chungnam areas increased slightly. Among the malignant tumor patients, the number of lung cancer(included trachea & bronchial cancer) patients was the highest. In addition, the discharge rate was highest for patients with respiratory diseases. This study suggests that efforts need to be made to lower the discharge rate for infection, circulatory disease, genitourinary disease, and musculoskeletal disorder patients

• Journal of Intelligence and Information Systems
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• v.28 no.4
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• pp.347-364
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• 2022

Fraudulent companies or sellers strategically manipulate reviews to influence customers' purchase decisions; therefore, the reliability of reviews has become crucial for customer decision-making. Since customers increasingly rely on online reviews to search for more detailed information about products or services before purchasing, many researchers focus on detecting manipulated reviews. However, the main problem in detecting manipulated reviews is the difficulties with obtaining data with manipulated reviews to utilize machine learning techniques with sufficient data. Also, the number of manipulated reviews is insufficient compared with the number of non-manipulated reviews, so the class imbalance problem occurs. The class with fewer examples is under-represented and can hamper a model's accuracy, so machine learning methods suffer from the class imbalance problem and solving the class imbalance problem is important to build an accurate model for detecting manipulated reviews. Thus, we propose an OpenAI-based reviews generation model to solve the manipulated reviews imbalance problem, thereby enhancing the accuracy of manipulated reviews detection. In this research, we applied the novel autoregressive language model - GPT-3 to generate reviews based on manipulated reviews. Moreover, we found that applying GPT-3 model for oversampling manipulated reviews can recover a satisfactory portion of performance losses and shows better performance in classification (logit, decision tree, neural networks) than traditional oversampling models such as random oversampling and SMOTE.