• Journal of Internet Computing and Services
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• v.23 no.5
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• pp.87-101
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• 2022

Cyber-attacks occur in the blink of an eye in cyberspace, and the damage is increasing all over the world. Therefore, it is necessary to develop a cyber common operational picture that can grasp the various assets belonging to the 3rd layer of cyberspace from various perspectives. By applying the method for grasping battlefield information used by the military, it is possible to achieve optimal cyberspace situational awareness. Therefore, in this study, the visualization screens necessary for the cyber common operational picture are identified and the criteria (response speed, user interface, object symbol, object size) are investigated. After that, the framework is designed by applying the identified and investigated items, and the visualization screens are implemented accordingly. Finally, among the criteria investigated by the visualization screen, an experiment is conducted on the response speed that cannot be recognized by a photograph. As a result, all the implemented visualization screens met the standard for response speed. Such research helps commanders and security officers to build a cyber common operational picture to prepare for cyber-attacks.

• Journal of the Korea Society of Computer and Information
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• v.27 no.9
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• pp.49-57
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• 2022

Currently, the problem of food shortage is emerging in our society due to climate problems and an increase population in the world. As a solution to this problem, we propose a multi-remote control smart farm that combines artificial intelligence (AI) and information and communication technology (ICT) technologies. The proposed smart farm integrates ICT technology to remotely control and manage crops without restrictions in space and time, and to multi-control the growing environment of crops. In addition, using Arduino and deep-learning technology, a smart farm capable of multiple control through a smart-phone application (APP) was proposed, and Ai technology with various data securing and diagnosis functions while observing crop growth in real-time was included. Various sensors in the smart farm are controlled by using the Arduino, and the data values of the sensors are stored in the built database, so that the user can check the stored data with the APP. For multiple control for multiple crops, each LED, COOLING FAN, and WATER PUMP for two or more growing environments were applied so that the user could control it conveniently. And by implementing an APP that diagnoses the growth stage through the Tensor-Flow framework using deep-learning technology, we developed an application that helps users to easily diagnose the growth status of the current crop.

• Korean Journal of Remote Sensing
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• v.38 no.5_2
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• pp.725-735
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• 2022

Currently, disasters occurring in Korea are characterized by unpredictability and complexity. Due to these features, property damage and human casualties are increasing. Since the initial response process of these disasters is directly related to the scale and the spread of damage, optimal decision-making is essential, and information of the site must be obtained through timely applicable sensors. However, it is difficult to make appropriate decisions because indiscriminate information is collected rather than necessary information in the currently operated Disaster and Safety Situation Office. In order to improve the current situation, this study proposed a framework that quickly collects various disaster image information, extracts information required to support decision-making, and utilizes it. To this end, a web-based display system and a smartphone application were proposed. Data were collected close to real time, and various analysis results were shared. Moreover, the capability of supporting decision-making was reviewed based on images of actual disaster sites acquired through CCTV, smartphones, and UAVs. In addition to the reviewed capability, it is expected that effective disaster management can be contributed if institutional mitigation of the acquisition and sharing of disaster-related data can be achieved together.

• Korean small business review
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• v.42 no.2
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• pp.117-137
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• 2020

We examine the current status of smart factory deployment and diffusion programs in Korea, and seek to promote manufacturing innovation from the perspective of SMEs. The main conclusions of this paper are as follows. First, without additional market creation and supply chain improvement, smart factories are unlikely to raise profitability leading to overinvestment. Second, new business models need to connect "manufacturing process efficiency" with "R&D" and "marketing" in value chain in smart factories. Third, when introducing smart factories, we need to focus on the areas where process-embedded technology is directly linked to corporate competitiveness. Based on the modularity-maturity matrix (Pisano and Shih, 2012) and the examples of U.S. Manufacturing Innovation Institute (MII), we establish the new smart factory deployment policy measures as follows. First, we shift our smart factory strategy from quantitative expansion to qualitative upgrading. Second, we promote by each sector the formation of industrial commons that help SMEs to jointly develop R&D, exchange standardized data and practices, and facilitate supplier-led procurement system. Third, to implement new technology and business models, we encourage partnerships, collaborations, and M&As between conventional SMEs and start-ups and business ventures. Fourth, the whole deployment process of smart factories is indexed in detail to identify the problems and provide appropriate solutions.

• Korean Association of Arts Management
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• no.58
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• pp.121-152
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• 2021

This study attempted to analyze the effect of the color series of artworks installed as environmental stimuli in commercial spaces on the satisfaction of visitors and the moderating effect of the relationship. To this end, based on the SOR model of Stimulate-Organism-Response applied to burial environment research in the field of environmental psychology, and the preceding research using the SOR model, artwork color series(S)-mood and spaace amenity(O)-A research framework for satisfaction(R) was developed. In the experiment, an online questionnaire was conducted for domestic college students and graduate students by producing images with two conditions depending on the case where warm colors and cold colors were installed for the color series of artworks. As a result of verifying the difference in satisfaction of respondents corresponding to the two conditions through regression analysis, it was found that the warm color(vs. cold color) of the artwork color series induces higher visitor satisfaction. In addition, as a result of verifying the controlling factors of mood and space amenity variables in this relationship of influence, a significant moderating effect was found when the positive mood of warm colors(vs. cold colors) in the artwork color series was felt higher than the average. And, of the four types of space amenity, it was found that a significant moderating effect appeared when only comfort and aesthetics were measured as moderating variables. The result of this study proves that the warm color series of artworks that stimulate the physical environment of commercial spaces has a more positive effect on the satisfaction of visitors than the cold color series, and this is reinforced by positive mood, comfort, and aesthetics. It adds understanding and provides useful implications for marketing strategies for building an effective spatial image.

• Knowledge Management Research
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• v.23 no.2
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• pp.277-299
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• 2022

The massive card transaction data generated in the tourism industry has become an important resource that implies tourist consumption behaviors and patterns. Based on the transaction data, developing a smart service system becomes one of major goals in both tourism businesses and knowledge management system developer communities. However, the lack of rating scores, which is the basis of traditional recommendation techniques, makes it hard for system designers to evaluate a learning process. In addition, other auxiliary factors such as temporal, spatial, and demographic information are needed to increase the performance of a recommendation system; but, gathering those are not easy in the card transaction context. In this paper, we introduce CTDDTR, a novel approach using card transaction data to recommend tourism services. It consists of two main components: i) Temporal preference Embedding (TE) represents tourist groups and services into vectors through Doc2Vec. And ii) Deep tourism Recommendation (DR) integrates the vectors and the auxiliary factors from a tourism RDF (resource description framework) through MLP (multi-layer perceptron) to provide services to tourist groups. In addition, we adopt RFM analysis from the field of knowledge management to generate explicit feedback (i.e., rating scores) used in the DR part. To evaluate CTDDTR, the card transactions data that happened over eight years on Jeju island is used. Experimental results demonstrate that the proposed method is more positive in effectiveness and efficacies.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.397-412
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• 2023

In order for social robots to take root in human life, it is important to consider the technical implementation of social robots and human psychology toward social robots. This study aimed to derive potential social robot clusters based on the emotions consumers feel about social robot appearance design, and to identify and compare important design characteristics and emotional differences of each cluster. In our study, we established a social robot emotion framework to measure and evaluate the emotions consumers feel about social robots, and evaluated the emotions of social robot designs based on the semantic differential method, an kansei engineering approach. We classified 30 social robots into 4 clusters by conducting a multidimensional scaling method and K-means cluster analysis based on the emotion evaluation results, confirmed the characteristics of design elements for each cluster, and conducted a comparative analysis on consumer emotions. We proposed a strategic direction for successful social robot design and development from a human-centered perspective based on the design characteristics and emotional differences derived for each cluster.

• Economic Analysis
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• v.27 no.2
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• pp.71-123
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• 2021

In this study, we present a methodology and model to estimate land prices and the value of land assets in North Korea in the absence of any data about land characteristics from North Korean authorities. Using this framework, we experimentally make market price-based estimates for land assets across the entire urban area of North Korea. First, we estimate the determinants of land prices in South Korea using data on market prices of land from the late 1970s, when it was estimated that the income level gap between South Korea and North Korea wasn't relatively large, and from the early 1980s, when urbanization levels in both of them were similar. Second, we calculate land prices and their relative ratios for each city and urban area in North Korea around 2015 by substituting proxy variables of determinants of land prices derived through a geographic information analysis of North Korea into the function of land prices that we have already estimated. Finally, we estimate the value of land assets in urban areas across North Korea by combining the ratio of housing transaction prices surveyed in several cities in North Korea with the relative prices estimated in this research. As a result, land prices in urban areas in North Korea, looking at the relative ratio of price by city, are estimated to be the highest, at 100.00, in Tongdaewon district of Pyongyang, and to be the lowest, at 1.70, in Phungso county, Ryanggang Province. Meanwhile, the value of land assets in urbanized areas was estimated at $21.6 billion in 2015, which was 1.2 to 1.3 times the GDP of North Korea that year. This ratio is similar to South Korea's in the 1978-1980 period, when the South Korean economy grew at an average rate of 6%. Considering North Korea's growth rate of about 1% in the 2013-2014 period, its ratio of land assets to GDP appears very high.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.3
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• pp.88-103
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• 2023

With the recent advancement of 4th Industrial Revolution technology, transportation systems are generating large amounts of mobility data related to the individual movement trajectories of vehicles and people. There are many constraints on utilizing mobility data containing personal information. Thus, in South Korea, the processing and generation of pseudonymized information and the analysis and utilization of this information have been managed in a dual manner by applying separate agencies and technologies through the revision of the Data 3 Act and the enactment of the Data Basic Act. However, this dual approach fails to securely support the entire data lifecycle and suffers from inefficiencies in terms of processing time and cost. Therefore, to compensate for the problems of the existing Expert Data Combination System and Data Safety Zone, this study proposes an Integrated Data Safety Zone Framework that integrates and unifies the process of generating, processing, analyzing, and utilizing mobility data. The integrated process for data processing was redesigned, and common requirements and core technologies were derived. The result is an architecture for a next-generation Integrated Data Safety Zone system that can manage and utilize the entire life cycle of mobility data at one stop.

• Journal of Internet of Things and Convergence
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• v.10 no.1
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• pp.69-75
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• 2024

This paper proposes an innovative cognitive IoT framework specifically designed for fog computing (FC) in the context of industrial Internet of Things (IIoT). The discourse in this paper is centered on the intricate design and functional architecture of the Cognitive IoT platform. A crucial feature of this platform is the integration of machine learning (ML) and artificial intelligence (AI), which enhances its operational flexibility and compatibility with a wide range of industrial applications. An exemplary application of this platform is highlighted through the Predictive Maintenance-as-a-Service (PdM-as-a-Service) model, which focuses on real-time monitoring of machine conditions. This model transcends traditional maintenance approaches by leveraging real-time data analytics for maintenance and management operations. Empirical results substantiate the platform's effectiveness within a fog computing milieu, thereby illustrating its transformative potential in the domain of industrial IoT applications. Furthermore, the paper delineates the inherent challenges and prospective research trajectories in the spheres of Cognitive IoT and Fog Computing within the ambit of Industrial Internet of Things (IIoT).