• Journal of the Korea Convergence Society
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• v.12 no.12
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• pp.93-106
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• 2021

Blenderbot 2.0 is a dialogue model representing open domain chatbots by reflecting real-time information and remembering user information for a long time through an internet search module and multi-session. Nevertheless, the model still has many improvements. Therefore, this paper analyzes the limitations and errors of BlenderBot 2.0 from three perspectives: model, data, and dialogue. From the data point of view, we point out errors that the guidelines provided to workers during the crowdsourcing process were not clear, and the process of refining hate speech in the collected data and verifying the accuracy of internet-based information was lacking. Finally, from the viewpoint of dialogue, nine types of problems found during conversation and their causes are thoroughly analyzed. Furthermore, practical improvement methods are proposed for each point of view, and we discuss several potential future research directions.

• Land and Housing Review
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• v.12 no.2
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• pp.121-139
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• 2021

Technical documents in the construction field are changing from paper documents to electronic ones. As a result, the industry witnesses a trend of using portable electronic devices in searching or retrieving necessary information such as relevant regulations. Despite the improvement in the accessibility to general technical documents, a limitation is still found in accessing the electronic documents on the regulations. We see the barrier for field engineers to enhance their technical knowledge. One of major barriers is that videos, animations, and virtual reality information to enhance the visual understanding of technical content related to regulations are not linked. It is the interactive electronic technical manual (IETM) that can address such issues. The IETM is an electronic document system that enables real-time information acquisition while operating in the form of conversations with users by linking multimedia functions to document types such as specifications and guidelines. This study establishes a model of the IETM that can be operated in the planning stage of a construction project. The study also verifies its usability with a hypothetical case study. This study aims to improve the usability of the IETM in the construction project by analyzing the application effect of the IETM using the AHP technique.

• Korean Chemical Engineering Research
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• v.59 no.2
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• pp.209-218
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• 2021

First principle-based modeling studies have been performed to improve the heat exchange efficiency of ORV and optimize operation, but the heat transfer coefficient of ORV is an irregular system according to time and location, and it undergoes a complex modeling process. In this study, FNN, LSTM, and AutoML-based modeling were performed to confirm the effectiveness of data-based modeling for complex systems. The prediction accuracy indicated high performance in the order of LSTM > AutoML > FNN in MSE. The performance of AutoML, an automatic design method for machine learning models, was superior to developed FNN, and the total time required for model development was 1/15 compared to LSTM, showing the possibility of using AutoML. The prediction of NG and seawater discharged temperatures using LSTM and AutoML showed an error of less than 0.5K. Using the predictive model, real-time optimization of the amount of LNG vaporized that can be processed using ORV in winter is performed, confirming that up to 23.5% of LNG can be additionally processed, and an ORV optimal operation guideline based on the developed dynamic prediction model was presented.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.12
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• pp.731-741
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• 2020

University campus plans include urban and spatial values and identities that they emphasize. The purpose of this study is to analyze sustainable campus policy, space, and the application to architectural planning, which UBC in Canada pursues independently from a Green Campus Certificate System. Sustainable directions and architectural components are deduced. The results are as follows: 1) the correspondence between the campus plan's goal and architectural implementation is the most important. Thus, the university must build a system for the plan's goal and a strategy to make a sustainable campus. 2) A guideline and system are requested to make many experts in various fields and stakeholders participate in the initial stage through "Design Charrette." 3) A system of virtuous circulation must be built so that feedback can be applied through the real-time comparison and verification of building energy consumption. Another goal of this study is emphasizing the necessity of campus policy and plans based on the "Living Laboratory" concept to make a sustainable city. This study could be meaningful because it supports a basis for triggering the establishment of goals for a sustainable plan and implementation in Korean universities.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.34 no.1
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• pp.209-238
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• 2023

This study aims to propose the main learning contents, functions and activation plans for building an e-learning platform for libraries through a literature review, case analysis and expert survey. Through the literature review, it was found that libraries must play a role in providing high-quality online education for users in the e-learning ecosystem. Based on the previous studies, a learning function analysis tool was developed for the analysis of the library's e-learning platform. Based on this, the learning contents, learning functions and characteristics of library e-learning platforms were analyzed, and expert surveys and interviews were conducted. As a results, the construction of a platform for effectively applying learning processes and technology is essential for the library's sustainable e-learning services. The contents that should be provided for characteristics of library education, reading guidance, information literacy instruction, library usage instruction, and the latest IT technologies. And The main learning functions include the ability to conduct video lectures and real-time classes among learning types, and learning activity support functions, a cloud platform support function and a personalized environment support function. Additionally, suggested re-education for library staff to improve their technical skills and the formation of an e-learning team.

• The Journal of Society for e-Business Studies
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• v.26 no.4
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• pp.133-154
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• 2021

Today smart phone is the most common media with a vehicle called 'application'. In order to understand how media users select applications and build their repertoire, this study conducted two-step approach using big data from smart phone log for 4 weeks in November 2019, and finally classified 8 media repertoire groups. Each of the eight media repertoire groups showed differences in time spent of mobile application category compared to other groups, and also showed differences between groups in demographic distribution. In addition to the academic contribution of identifying the mobile application repertoire with large scale behavioral data, this study also has significance in proposing a two-step approach that overcomes 'outlier issue' in behavioral data by extracting prototype vectors using SOM (Sefl-Organized Map) and applying it to k-means clustering for optimization of the classification. The study is also meaningful in that it categorizes customers using e-commerce services, identifies customer structure based on behavioral data, and provides practical guides to e-commerce communities that execute appropriate services or marketing decisions for each customer group.

• Smart Media Journal
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• v.11 no.10
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• pp.9-21
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• 2022

The recent expansion of the use of social media has served as an opportunity to express users' opinions in real time in various fields such as society, economy, politics, and culture, and brought many platforms that provide various information about companies. Among them, Glassdoor.com which started 2008 in US provides users with evaluations of the current and the former employees of their companies and also provides a outlooks for the company's growth Such a platform has the utility of providing necessary information to whom want to find a job or change jobs. In addition to this, variable studies have shown that the company information provided through these platforms is useful for investors as well. In this study, it was tested whether the corporate growth prospects of employees provided by Jobplanet, a platform with a typical function similar to Glassdoor.com in Korea, have predictive power to predict actual corporate growth. The forecast provided by Jobplanet and the company's financial indicator data received from FnGuide were collected and composed of panel data and analyzed using fixed effect model regression analysis. As a result, it was found that companies with positive prospects had higher employment growth than companies with negative prospects. When the outlook was neutral, the employment growth rate was higher than that of companies with a negative outlook.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.1
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• pp.31-40
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• 2022

The importance of disaster management and response is emerging as various disasters such as COVID-19, torrential rains, and fires occur one after another. In addition, in order to respond efficiently throughout disaster response activities, it is necessary to quickly collect disaster site information and quickly check the site situation through photo and video information so that rapid disaster response can be achieved. In this study, essential information required for decision-making was derived by analyzing the essential activities of each disaster response stage, analyzing the crisis management standard manual and related laws for each disaster type, and daily comprehensive report. In addition, a list of information necessary to grasp the situation of the disaster site and grasp the status of real-time damage was derived to establish guidelines for collecting volatile disaster site information, and disaster situation information can be efficiently displayed through a spatial information-based display system. By presenting essential disaster management information to be collected first, the person in charge of collecting information can efficiently collect information, and the situation room in charge of disaster response decision-making is expected to enable more efficient disaster situation management by receiving only the necessary information.

• Convergence Security Journal
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• v.22 no.4
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• pp.161-168
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• 2022

After the corona virus, as non-face-to-face services are activated, domain services that guarantee integrity by embedding sensing information of the Internet of Things (IoT) with block chain technology are expanding. For example, in areas such as safety and security using CCTV, a process is required to safely update firmware in real time and to confirm that there is no malicious intrusion. In the existing safe security processing procedures, in many cases, the person in charge performing official duties carried a USB device and directly updated the firmware. However, when private blockchain technology such as Hyperledger is used, the convenience and work efficiency of the Internet of Things environment can be expected to increase. This article describes scenarios in how to prevent vulnerabilities in the operating environment of various customers such as firmware updates and device changes in a non-face-to-face environment. In particular, we introduced the optimal blockchain technique for the Internet of Things (IoT), which is easily exposed to malicious security risks such as hacking and information leakage. In this article, we tried to present the necessity and implications of security management that guarantees integrity through operation applying block chain technology in the increasingly expanding Internet of Things environment. If this is used, it is expected to gain insight into how to apply the blockchain technique to guidelines for strengthening the security of the IoT environment in the future.

• Journal of Korea Society of Industrial Information Systems
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• v.29 no.1
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• pp.135-144
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• 2024

In this study, we propose an automatic classification model for quantitative multidimensional analysis based on facet theory to understand public opinions and demands on major issues through big data analysis. Civil complaints, as a form of public feedback, are generated by various individuals on multiple topics repeatedly and continuously in real-time, which can be challenging for officials to read and analyze efficiently. Specifically, our research introduces a new classification framework that utilizes facet theory and political analysis models to analyze the characteristics of citizen complaints and apply them to the policy-making process. Furthermore, to reduce administrative tasks related to complaint analysis and processing and to facilitate citizen policy participation, we employ deep learning to automatically extract and classify attributes based on the facet analysis framework. The results of this study are expected to provide important insights into understanding and analyzing the characteristics of big data related to citizen complaints, which can pave the way for future research in various fields beyond the public sector, such as education, industry, and healthcare, for quantifying unstructured data and utilizing multidimensional analysis. In practical terms, improving the processing system for large-scale electronic complaints and automation through deep learning can enhance the efficiency and responsiveness of complaint handling, and this approach can also be applied to text data processing in other fields.