• ETRI Journal
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• v.34 no.3
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• pp.439-449
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• 2012

In recent innovation trends, one notable feature is the merging and overlapping of technologies: in other words, technological convergence. A key technological convergence is the fusion of biotechnology (BT) and information technology (IT). Major IT advances have led to innovative devices that allow us to advance BT. However, the lack of data on IT-BT convergence is a major impediment: relatively little research has analyzed the inter-disciplinary relationship of different industries. We propose a systematic approach to analyzing the technological convergence of BT and IT. Patent analysis, including citation and co-classification analyses, was adopted as a main method to measure the convergence intensity and coverage, and two portfolio matrices were developed to manage the technological convergence. The contribution of this paper is that it provides practical evidences for IT-BT convergence, based on quantitative data and systematic processes. This has managerial implications for each sector of IT and BT.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1445-1456
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• 2003

The ASTER TIR (thermal infrared radiometer) sensor has 5 spectral bands over 8 to 12 ${\mu}$m region. Rock type classification using the ASTER TIR nighttime data was performed in the Erta Ale range of the Ethiopian Rift Valley. Erta Ale range is the most important axial volcanic chain of the Afar region. The petrographic diversity of lava erupted in this area is very important, ranging from magnesian transitional basalt to rhyolites. We tried to classify the rock types based on the spectral behavior of each volcanic rock types in thermal infrared range and estimated SiO$_{2}$ content with emission data by the ASTER TIR.

• Proceedings of the SAREK Conference
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• 2006.06a
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• pp.454-459
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• 2006

There are two main streams for pattern classification technology One is the method using PCA (Principal Component Analysis) and the other is the method using Neural network. Both of them have merits and demerits. In general, using PCA is so simple while using neural network can improve algorithm continually. Algorithm using neural network needs so many calculations rendering very slow response. In this work, an attempt is made to develop algorithms adopting both PCA and neural network merits for simpler, but faster and smarter.

• Journal of Information Technology Services
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• v.21 no.5
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• pp.65-79
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• 2022

The smart factory has spread to small and mid-size enterprises (SMEs) under the leadership of the government. Smart factory consists of a work area, an operation management area, and an industrial control system (ICS) area. However, each site is combined with the IT system for reasons such as the convenience of work. As a result, various breaches could occur due to the weakness of the IT system. This study seeks to discover the items and vulnerabilities that SMEs who have difficulties in information security due to technology limitations, human resources, and budget should first diagnose and check. First, to compare the existing domestic and foreign smart factory vulnerability classification systems and improve the current classification system, the latest smart factory vulnerability information is collected from NVD, CISA, and OWASP. Then, significant keywords are extracted from pre-processing, co-occurrence network analysis is performed, and the relationship between each keyword and vulnerability is discovered. Finally, the improvement points of the classification system are derived by mapping it to the existing classification system. Therefore, configuration and maintenance, communication and network, and software development were the items to be diagnosed and checked first, and vulnerabilities were denial of service (DoS), lack of integrity checking for communications, inadequate authentication, privileges, and access control in software in descending order of importance.

• Proceedings of the Korean Statistical Society Conference
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• 2001.11a
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• pp.195-197
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• 2001

Data Mining is used to discover patterns and relationships in huge amounts of data. Researchers in many different fields have shown great interest in data mining analysis. Using the classification technique of data mining analysis, the available model for Receiver Operating Characteristic(ROC) method is presented. We present that this may help analyze result of data mining techniques.

• Journal of Korean Society of Rural Planning
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• v.14 no.4
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• pp.121-128
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• 2008

The purpose of this study is to identify the trend of the contents of the plan that was implemented through contents analysis on the master plan project of rural village development projects that began in 2004. Contents analysis is based on the classification of business in the detailed enforcement regulations of rural village development project. Analysis on the project contents was conducted for 30 days from June 20 to July 20 for the 132 regions established in the master plan during the period from 2004 to 2007. The results of the analysis showed the following. First, scenery facilities, rural tourism, cultural welfare and income basis projects accounted for 76.5% of the total projects. Second, with regard to investment costs depending on the contents of the projects, cultural welfare, rural tourism and income basis projects accounted for 66.3% of the total investment costs. Third, it was found out that, with regard to the trend of change in the project contents by year, income basis projects were sharply reduced whereas cultural welfare and scenery facilities projects were increased. Finally, with regard to the analysis on the projects by region, it was found that Gangwon, Gyeonggi and Chungnam gave high weight on rural tourism, whereas Gyeongbuk, Jeonnam and Jeonbuk gave low weight on rural tourism. Particularly, Gyeongnam was found to have given low weight on income basis project.

• Korean Journal of Cognitive Science
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• v.35 no.1
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• pp.23-48
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• 2024

Building human-aligned artificial intelligence (AI) for social support remains challenging despite the advancement of Large Language Models. We present a novel method, the Chain of Empathy (CoE) prompting, that utilizes insights from psychotherapy to induce LLMs to reason about human emotional states. This method is inspired by various psychotherapy approaches-Cognitive-Behavioral Therapy (CBT), Dialectical Behavior Therapy (DBT), Person-Centered Therapy (PCT), and Reality Therapy (RT)-each leading to different patterns of interpreting clients' mental states. LLMs without CoE reasoning generated predominantly exploratory responses. However, when LLMs used CoE reasoning, we found a more comprehensive range of empathic responses aligned with each psychotherapy model's different reasoning patterns. For empathic expression classification, the CBT-based CoE resulted in the most balanced classification of empathic expression labels and the text generation of empathic responses. However, regarding emotion reasoning, other approaches like DBT and PCT showed higher performance in emotion reaction classification. We further conducted qualitative analysis and alignment scoring of each prompt-generated output. The findings underscore the importance of understanding the emotional context and how it affects human-AI communication. Our research contributes to understanding how psychotherapy models can be incorporated into LLMs, facilitating the development of context-aware, safe, and empathically responsive AI.

• Korean Journal of Plant Resources
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• v.19 no.6
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• pp.716-720
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• 2006

The genetic diversity among the genus Viola was evaluated using the random amplified polymorphic DNA (RAPD) method. A total of 142 distinct amplification fragments by 18 random primers were scored to perform the cluster analysis with UPGMA. Viola species from the subsection Patellares were clustered into group I to IV. The groups from I to IV were consistent with its morphological taxonomy, series Pinnatae, Chinensis, Variegatae, and Patellares in the subsection Patellares, respectively. Even though V. albida and V. albida var. takahasii were classified in Chinensis, they were assigned into group I. The cluster analysis separated other subsections from Patellares in the section Nomimium. Interestingly, V. verecunda and V. grypoceras in subsections Biobatae and Trigonocarpae, respectively, were clustered into group C with a high similarity coefficient. Therefore, RAPD analysis can be used for providing an alternative classification system to identify genotypes and morphological characters of Viola species.

• Journal of the Korea Convergence Society
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• v.12 no.4
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• pp.9-21
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• 2021

The study aims to identify convergence and trends in technology-based patent data for the biohealth sector in IP5 countries (KR, EP, JP, US, CN) and present the direction of development in that industry. We used patent co-classification analysis-based network analysis and TF-IDF-based text mining as the principal methodology to understand the current state of technology convergence. As a result, the technology convergence cluster in the biohealth industry was derived in three forms: (A) Medical device for treatment, (B) Medical data processing, and (C) Medical device for biometrics. Besides, as a result of trend analysis based on technology convergence results, it is analyzed that Korea is likely to dominate the market with patents with high commercial value in the future as it is derived as a market leader in (B) medical data processing. In particular, the field is expected to require technology convergence activation policies and R&D support strategies for the technology as the possibility of medical data utilization by domestic bio-health companies expands, along with the policy conversion of the "Data 3 Act" passed by the National Assembly in January 2019.

• Journal of Environmental Impact Assessment
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• v.18 no.6
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• pp.359-366
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• 2009

CCS (Carbon Capture and Storage) is considered as the most promising interim solution to deal with the greenhouse gas such as $CO_2$ responsible for global warming. Even though carefully chosen geologic formations are known to contain stored gas for a long time period, there are potential risks of leakage. Up to now, applicable risk assessment procedures for the leakage of $CO_2$ are not available. This study presents a basis for risk analysis applicable to a complex geologic storage system. It starts with the classification of potential leakage pathways. Receptors and the leakage effect on them are identified and quantified. Then, a fault tree is constructed, which yields the minimum cut set (i.e., the most vulnerable leakage pathway) and quantifies the probability of the leakage risk through the cut set. The methodology will provide a tool for risk assessment in a CCS project. The outcomes of the assessment will not only ensure the safety of the CCS system but also offer a reliable and efficient monitoring plan.