• KIPS Transactions on Software and Data Engineering
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• v.9 no.11
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• pp.339-344
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• 2020

During COVID-19, education industry is organizing the concept of 'Edutech', which has evolved one step further from the existing e-Learning, by introducing various intelligent information technologues based on the core technology of the 4th industrial revolution and spreading it through diverse contents. Meanwhile, each industries are creating new industries by applying new technology to existing businesses and ask for needs of cultivating human resources who understand the existing traditional ICT technology and industrial business which can solve a newly rising problems. However, it is difficult to build contents for cultivating such human resources with the existing e-learning of transferring knowledge by one-way or some two-way commnication system which has established some interactive conversational system. Accordingly, this study conducted a research on a cooperative e-learning system that enables educators to communicate with learners in real time and allows problem-solving education based on the existing two-way communication system. As a result, frame for contents and prototype was developedp and artially applied to the actual class and conducted an efficiency analysis, which resulted in the validation of being applied to the actual class as a simulation-based cooperative content.

• Journal of Intelligence and Information Systems
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• v.11 no.2
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• pp.123-149
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• 2005

What the structure of knowledge management capability (KMC) to improve the organizational performance is an important issue for researchers and practitioners with growing interest in recent years. In this paper, we begin with a deep thinking about the resource-based view and knowledge-based view of the firm applying to knowledge management issues. By exploring the two underlying theories of knowledge management, together with an intensive review and interpretation of existing literatures, we obtain six major dimensions of KMC. We then propose an integrated conceptual model of KMC and its relationship with organizational performance. A PLS analysis of the gathered data from organizations in Korea which already have enterprise-wide knowledge management systems is conducted to validate the proposed model. We discuss several meaningful implications and draw several insightful conclusions surrounding the KMC.

• Korean Journal of Metals and Materials
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• v.56 no.11
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• pp.813-821
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• 2018

The basic oxygen furnace (BOF) steelmaking process in the steel industry is highly complicated, and subject to variations in raw material composition. During the BOF steelmaking process, it is essential to maintain the carbon content and the endpoint temperature at their set points in the liquid steel. This paper presents intelligent models used to estimate the endpoint temperature in the basic oxygen furnace (BOF) steelmaking process. An artificial neural network (ANN) model and a least-squares support vector machine (LSSVM) model are proposed and their estimation performance compared. The classical partial least-squares (PLS) method was also compared with the others. Results of the estimations using the ANN, LSSVM and PLS models were compared with the operation data, and the root-mean square error (RMSE) for each model was calculated to evaluate estimation performance. The RMSE of the LSSVM model 15.91, which turned out to be the best estimation. RMSE values for the ANN and PLS models were 17.24 and 21.31, respectively, indicating their relative estimation performance. The essential input parameters used in the models can be selected by sensitivity analysis. The RMSE for each model was calculated again after a sequential input selection process was used to remove insignificant input parameters. The RMSE of the LSSVM was then 13.21, which is better than the previous RMSE with all 16 parameters. The results show that LSSVM model using 13 input parameters can be utilized to calculate the required values for oxygen volume and coolant needed to optimally adjust the steel target temperature.

• Journal of the Korean Society for Library and Information Science
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• v.53 no.4
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• pp.341-359
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• 2019

To improve research productivity, it is important to acquire up-to-date information quickly. To this end, researchers seek information resources through various channels and methods and utilize them in their research and development processes. The Korea Institute of Science and Technology Information (KISTI) has developed a platform for integrated scientific and scientific information service called ScienceOn to provide specialized information, infrastructure resources, industry and technology analysis resources in one place to support the research and information ecosystem. Through this process, accessibility and usability are enhanced through the connection and convergence of various information and services. In this study, we look at recent R&D trends in scientific technology academic information integration services and recommended packaging services that can be utilized in batches according to the purpose of use. We look forward to improving national R&D productivity by strengthening the linkage and convergence of scientific and technological information.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.4
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• pp.526-532
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• 2022

With the development of IoT technology, image processing is being utilized in various fields such as image analysis, image recognition, medical industry, and factory automation. Noise is generated in image data from causes such as defect in transmission line. Image noise must be removed because it damages the performance of the image processing application program. Salt and Pepper noise is a representative type of image noise, and various studies have been conducted to remove Salt and Pepper noise. Widely known methods include A-TMF, AFMF, and SDWF. However, as the noise density increases, the performance deteriorates. Thus, this paper proposes an algorithm that performs filtering using a fuzzy logic weight mask only in case of noise after noise determination. In order to prove the noise removal performance of the proposed algorithm, an experiment was performed on images with 10% to 90% noise added and the PSNR was compared.

• Korean Journal of Clinical Pharmacy
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• v.31 no.4
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• pp.301-310
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• 2021

Background: Trust is a key component for the good relationship between patients and healthcare professionals but trust for community pharmacists has not been studied much. Objectives: This study aimed to measure public trust in community pharmacists and to investigate variables that affect trust level in South Korea. Methods: A total of 25 questions, including 13-items for three dimensions of trust (pharmacists' behavior/attitude, technical competence, communication skills) and 1-item for overall trust were developed. The survey was conducted online and the data from 416 respondents were analyzed with a t-test, an ANOVA and a multiple linear regression analysis. Results: The average scores (mean ± standard deviation) for the three dimensions of trust in community pharmacists were 3.47±1.05 (out of 5 points) for pharmacists' behavior/attitude, 3.67 ± 0.99 for technical competence, and 3.66±0.99 for communication skills. The average of the 13 items incorporating all parameters was 3.56±1.02 and the overall trust level was 7.16±1.62 (out of 10 points). The total sum of the 13 items differed significantly by age group (p=0.02) and frequency of pharmacy visits (p=0.04). Each dimension had an independent impact on the trust level, and pharmacists' behavior/attitude had the greatest impact on trust levels. Conclusions: This study showed that pharmacists' behavior/attitude had the most significant impact on the trust level. However, the level of trust in pharmacists' behavior/attitude is not yet sufficiently satisfactory, and further improvements are required to increase trust in community pharmacists.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.3
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• pp.555-564
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• 2022

Recently, cyberattacks are using hacking techniques utilizing intelligent and advanced malicious codes for non-face-to-face environments such as telecommuting, telemedicine, and automatic industrial facilities, and the damage is increasing. Traditional information protection systems, such as anti-virus, are a method of detecting known malicious URLs based on signature patterns, so unknown malicious URLs cannot be detected. In addition, the conventional static analysis-based malicious URL detection method is vulnerable to dynamic loading and cryptographic attacks. This study proposes a technique for efficiently detecting malicious URLs by dynamically learning malicious URL data. In the proposed detection technique, malicious codes are classified using machine learning-based feature selection algorithms, and the accuracy is improved by removing obfuscation elements after preprocessing using Weighted Euclidean Distance(WED). According to the experimental results, the proposed machine learning-based malicious URL detection technique shows an accuracy of 89.17%, which is improved by 2.82% compared to the conventional method.

• Convergence Security Journal
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• v.22 no.2
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• pp.27-42
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• 2022

In the era of the Fourth Industrial Revolution, digital transformation to increase the effectiveness of industry is becoming more important to achieving the goal of industrial innovation. The digital new deal and smart defense are required for digital transformation and utilize artificial intelligence, big data analysis technology, and the Internet of Things. These changes can innovate the industrial fields of national defense, society, and health with new intelligent services by continuously expanding cyberspace. As a result, work productivity, efficiency, convenience, and industrial safety will be strengthened. However, the threat of cyber-attack will also continue to increase due to expansion of the new domain of digital transformation. This paper presents the risk scenarios of cyber-attack threats in the Fourth Industrial Revolution. Further, we propose a preemptive intrusion response model to bolster the complex security environment of the future, which is one of the fundamental alternatives to solving problems relating to cyber-attack. The proposed model can be used as prior research on cyber security strategy and technology development for preemptive response to cyber threats in the future society.

• Journal of Intelligence and Information Systems
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• v.29 no.4
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• pp.287-308
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• 2023

After launching its service in November 2022, ChatGPT has rapidly increased the number of users and is having a significant impact on all aspects of society, bringing a major turning point in the history of artificial intelligence. In particular, the inference ability of large language models such as ChatGPT is improving at a rapid pace through prompt engineering techniques. This reasoning ability can be considered as an important factor for companies that want to adopt artificial intelligence into their workflows or for individuals looking to utilize it. In this paper, we begin with an understanding of in-context learning that enables inference in large language models, explain the concept of prompt engineering, inference with in-context learning, and benchmark data. Moreover, we investigate the prompt engineering techniques that have rapidly improved the inference performance of large language models, and the relationship between the techniques.

• Journal of the Korea Society of Computer and Information
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• v.29 no.3
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• pp.75-81
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• 2024

In this paper, we proposes a method for real-time processing of inter-floor noise problems by embedding TinyML, which includes a deep learning model, into ultra-low-power systems. The reason this method is feasible is because of lightweight deep learning model technology, which allows even systems with small computing resources to perform inference autonomously. The conventional method proposed to solve inter-floor noise problems was to send data collected from sensors to a server for analysis and processing. However, this centralized processing method has issues with high costs, complexity, and difficulty in real-time processing. In this paper, we address these limitations by employing On-Sensor AI using TinyML. The method presented in this paper is simple to install, cost-effective, and capable of processing problems in real-time.