• Journal of Service Research and Studies
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• v.10 no.4
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• pp.89-100
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• 2020

The smart factory is an important axis of the 4th industrial revolution. Smart factory is a system that induces the maximum efficiency and effectiveness of production using the IoT and intelligent sensing systems. The product lifecycle management technique is a method that can actively reflect the consumer's requirements in the smart factory and manage the entire process from the consumer to the post management. There have been many studies on product lifecycle management, but studies on how to organize product lifecycle management knowledge domains in preparation for the era of the 4th industrial revolution were insufficient. This study analyzed the opinions of a group of experts preparing for the 4th industrial revolution in terms of product lifecycle management. The impact of the 4th industrial revolution on the detailed knowledge areas of product lifecycle management was investigated. The changes in product lifecycle management were summarized using a qualitative data analysis technique for a group of experts. Based on the opinions of experts, the product lifecycle management, which consists of a total of 30 detailed knowledge areas, was prepared to supplement or prepare for the 4th industrial revolution. This study investigates changes in product lifecycle management in preparation for the 4th industrial revolution in the knowledge domain of the existing defined product life cycle management. In future research, it is necessary to redefine the knowledge domain of product life cycle management suitable for the era of the 4th industrial revolution and investigate the perception of experts. Considering the social culture and technological change factors of the 4th industrial revolution, the scope and scope of product life cycle management can be newly defined.

• Journal of the Korean Association of Geographic Information Studies
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• v.26 no.3
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• pp.85-96
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• 2023

This study developed and evaluated a safe route guidance service for electric personal assistive mobility device used mainly by the mobility impaired people to improve their mobility. Thirteen underlying factors affecting the mobility of electric personal assistive mobility device have been derived through a survey with the mobility impaired people and employees in related organizations in Busan Metropolitan City. After assigning safety scores to individual factors and identifying the relevant factors along routes of interest with an object detection AI model, the safe route for electric personal assistive mobility device was provided through an optimal path-finding algorithm. As a result of comparing the general route of T-map and the recommended route of this study for the identical routes, the latter had relatively fewer obstacles and the gentler slope than the former, implicating that the recommended route is safer than the general one. As future works, it is necessary to enhance the function of a route guidance service based on the real-time location of users and to conduct spot investigations to evaluate and verify its social acceptability.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.255-258
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• 2022

As environmental pollution continues, attention on renewable energy is on the constant rise in recent days. Although various kinds of renewable energy such as solar, wind power and biomass energy have been generated in Jeju, opening and analyzing cases on related data seem insufficient. Therefore, this study is being conducted to deduce the variables which have high relation with solar panel&s output and to understand machine learning methods that can be applied to solar power generation data by utilizing Kaggle platform, which is actively used by a number of scientists. Then, it is planned to propose a form of solar power generation dataset by researching machine learning methods that could be applied to the data. To be specific, analyzing solar power generation data with the Kaggle platform, this study will provide complements on gathering solar power data in Jeju. This study is anticipated to be utilized on data analysis for developing the solar power industry in Jeju. That is, this study is expected to reveal the room for improvement inherent in existing open datasets in Jeju, so that they could be constructed in a suitable form for machine learning for AI analytics. Through this process, a method to increase efficiency of solar power generation is anticipated to be prepared.

• Journal of Service Research and Studies
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• v.13 no.1
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• pp.127-143
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• 2023

Currently, the domestic traditional market has not escaped the swamp of stagnation that began in the early 2000s despite various projects promoted by many related players such as the central government and local governments. In order to overcome the crisis faced by the traditional market, various R&Ds have recently been conducted on how to build a smart traditional market that combines information and communication technologies such as big data analysis, artificial intelligence, and the Internet of Things. This study analyzes various previous studies, users of traditional markets, and application cases of ICT technology in foreign traditional markets since 2012 and proposes a model to build a smart traditional market using ICT technology based on the analysis. The model proposed in this study includes building a traditional market metaverse that can interact with visitors, certifying visits to traditional markets through digital signage with NFC technology, improving accuracy of fire detection functions using IoT and AI technology, developing smartphone apps for market launch information and event notification, and an e-commerce system. If a smart traditional market platform is implemented and operated based on the smart traditional market platform model presented in this study, it will not only draw interest in the traditional market to MZ generation and foreigners, but also contribute to revitalizing the traditional market in the future.

• Journal of the Korean Institute of Landscape Architecture
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• v.51 no.5
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• pp.44-56
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• 2023

This research explores the enhancement of park operation and management by analyzing the changing demands of park users. While traditional methods depended on surveys, there has been a recent shift towards utilizing social media data to understand park usage trends. Notably, most research has focused on text data from social media, overlooking the valuable insights from image data. Addressing this gap, our study introduces a novel method of assessing park usage using social media image data and then applies it to actual city park evaluations. A unique image analysis tool, built on Visual Question Answering (VQA) deep learning technology, was developed. This tool revealed specific city park details such as user demographics, behaviors, and locations. Our findings highlight three main points: (1) The VQA-based image analysis tool's validity was proven by matching its results with traditional text analysis outcomes. (2) VQA deep learning technology offers insights like gender, age, and usage time, which aren't accessible from text analysis alone. (3) Using VQA, we derived operational and management strategies for city parks. In conclusion, our VQA-based method offers significant methodological advancements for future park usage studies.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.6
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• pp.975-987
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• 2023

With the advancement of Artificial Intelligence (AI) technologies, the provision of data-driven AI services that enable automation and intelligence is increasing across industries, raising concerns about the AI security risks that may arise from the use of AI. Accordingly, Foreign countries recognize the need and importance of AI regulation and are focusing on developing related policies and regulations. This movement is also happening in Korea, and AI regulations have not been specified, so it is necessary to compare and analyze existing policy proposals or guidelines to derive common factors and identify complementary points, and discuss the direction of domestic AI regulation. In this paper, we investigate AI security risks that may arise in the AI life cycle and derive six points to be considered in establishing domestic AI regulations through analysis of each risk. Based on this, we analyze AI policy proposals and recommendations in Korea and validate additional issues. In addition, based on a review of the main content of AI laws in the US and EU and the analysis of this paper, we propose measures to improve domestic guidelines and policies in the field of AI.

• Journal of Internet Computing and Services
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• v.24 no.6
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• pp.119-125
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• 2023

Through the Ukraine-Russia war, the military importance of drones is being reassessed, and North Korea has completed actual verification through a drone provocation towards South Korea at 2022. Furthermore, North Korea is actively integrating artificial intelligence (AI) technology into drones, highlighting the increasing threat posed by drones. In response, the Republic of Korea military has established Drone Operations Command(DOC) and implemented various drone defense systems. However, there is a concern that the efforts to enhance capabilities are disproportionately focused on striking systems, making it challenging to effectively counter swarm drone attacks. Particularly, Air Force bases located adjacent to urban areas face significant limitations in the use of traditional air defense weapons due to concerns about civilian casualties. Therefore, this study proposes a new passive air defense method that aims at disrupting the object detection capabilities of AI models to enhance the survivability of friendly aircraft against the threat posed by AI based swarm drones. Using laser-based adversarial examples, the study seeks to degrade the recognition accuracy of object recognition AI installed on enemy drones. Experimental results using synthetic images and precision-reduced models confirmed that the proposed method decreased the recognition accuracy of object recognition AI, which was initially approximately 95%, to around 0-15% after the application of the proposed method, thereby validating the effectiveness of the proposed method.

• Information Systems Review
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• v.25 no.4
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• pp.183-204
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• 2023

Due to the COVID-19 pandemic, lots of occasions need to be held in online environment. This is the reason why "Metaverse" gets lots of attention in 2021. A number of companies made announcements on Metaverse, and this situation also boomed stock market. This paper investigates the relationship between Metaverse initiatives and business value of the firm (i.e., stock prices). We examine this relationship by using event study method with Lexis-Nexis News data from 2019 to 2021. The results indicate that Metaverse initiatives significantly impact positive influence on firm's value. In the technological perspective, technical factors affect more positive market returns, including Metaverse enablers (e.g., NFT, VR devices, digital twin) and common infrastructure (e.g., semiconductor, AI, cloud), and especially virtual environment was emphasized. Additionally, in the strategical perspective, radical innovation (e.g., pivoting, acquisition) impact more positive market return rather than incremental innovation (e.g., partnership, investment). Also, firms from non-service industries can achieve benefits from Metaverse initiatives rather than service industry in some degree.

• Information Systems Review
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• v.25 no.1
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• pp.129-143
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• 2023

In the era of big data, the field of artificial intelligence is showing remarkable growth, and in particular, the image classification learning methods by deep learning are becoming an important area. Various studies have been actively conducted to further improve the performance of CNNs, which have been widely used in image classification, among which a representative method is the Convolutional Recurrent Neural Network (CRNN) algorithm. The CRNN algorithm consists of a combination of CNN for image classification and RNNs for recognizing time series elements. However, since the inputs used in the RNN area of CRNN are the flatten values extracted by applying the convolution and pooling technique to the image, pixel values in the same phase in the image appear in different order. And this makes it difficult to properly learn the sequence of arrangements in the image intended by the RNN. Therefore, this study aims to improve image classification performance by proposing a novel hybrid method of CNN and RNN applying the concepts of encoder and decoder. In this study, the effectiveness of the new hybrid method was verified through various experiments. This study has academic implications in that it broadens the applicability of encoder and decoder concepts, and the proposed method has advantages in terms of model learning time and infrastructure construction costs as it does not significantly increase complexity compared to conventional hybrid methods. In addition, this study has practical implications in that it presents the possibility of improving the quality of services provided in various fields that require accurate image classification.

• Information Systems Review
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• v.23 no.1
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• pp.23-43
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• 2021

Recently, many studies have been conducted to increase the accuracy of stock price prediction by analyzing candlestick charts using artificial intelligence techniques. However, these studies failed to consider the time-series characteristics of candlestick charts and to take into account the emotional state of market participants in data learning for stock price prediction. In order to overcome these limitations, this study produced input data by combining volatility index and candlestick charts to consider the emotional state of market participants, and used the data as input for a new method proposed on the basis of combining variantion autoencoder (VAE) and attention mechanisms for considering the time-series characteristics of candlestick chart. Fifty firms were randomly selected from the S&P 500 index and their stock prices were predicted to evaluate the performance of the method compared with existing ones such as convolutional neural network (CNN) or long-short term memory (LSTM). The results indicated the method proposed in this study showed superior performance compared to the existing ones. This study implied that the accuracy of stock price prediction could be improved by considering the emotional state of market participants and the time-series characteristics of the candlestick chart.