• Journal of Digital Convergence
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• v.15 no.7
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• pp.147-157
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• 2017

Recently, ICT Retail Convergence(IRC) has been rapidly increasing to improve consumer satisfaction and consumer experience. In this paper, we aim to diagnose IRC from consumers' point of view by reviewing the present status and value of IRC according to consumer purchase decision making process. Based on the previous studies in retail industry, we classified IRC into 4 types: Experience-specific tech(Virtual Reality and Augmented Reality); Information-specific tech(Artificial Intelligence and Big Data); Location-based tech(Radio Frequency Identification and Beacon); Payment-related tech(Fin-tech and Biometrics). Next, we found that there is a difference in value provided to consumers according to the type of technology, analysing the value by consumer purchase decision making process. This study can be useful to introduce IRC for improving consumer satisfaction as well as ICT and Retail. Also, it can be basic data for future technology studies with a consumer perspective.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.225-227
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• 2022

Now In Seoul, about 75,000 CCTVs are installed in 25 district offices. Each ward office in Seoul has built a control center for CCTV control and is building information such as people, vehicle types, license plate recognition and color classification into big data through 24-hour artificial intelligence intelligent image analysis. Seoul Metropolitan Government has signed MOUs with the Ministry of Land, Infrastructure and Transport, the National Police Agency, the Fire Service, the Ministry of Justice, and the military base to enable rapid response to emergency/emergency situations. In other words, we are building a smart city that is safe and can prevent disasters by providing CCTV images of each ward office. In this paper, the CCTV image is designed to extract the characteristics of the vehicle and personnel when an incident occurs through artificial intelligence, and based on this, predict the escape route and enable continuous tracking. It is designed so that the AI automatically selects and displays the CCTV image of the route. It is designed to expand the smart city integration platform by providing image information and extracted information to the adjacent ward office when the escape route of a person or vehicle related to an incident is expected to an area other than the relevant jurisdiction. This paper will contribute as basic data to the development of smart city integrated platform research.

• Journal of Korean Tunnelling and Underground Space Association
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• v.19 no.6
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• pp.915-936
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• 2017

In this study, current status of Korean hazard mitigation guideline for tunnel operation is summarized. It shows that requirement for CCTV installation has been gradually stricted and needs for tunnel incident detection system in conjunction with the CCTV in tunnels have been highly increased. Despite of this, it is noticed that mathematical algorithm based incident detection system, which are commonly applied in current tunnel operation, show very low detectable rates by less than 50%. The putative major reasons seem to be (1) very weak intensity of illumination (2) dust in tunnel (3) low installation height of CCTV to about 3.5 m, etc. Therefore, an attempt in this study is made to develop an deep-learning based tunnel incident detection system, which is relatively insensitive to very poor visibility conditions. Its theoretical background is given and validating investigation are undertaken focused on the moving vehicles and person out of vehicle in tunnel, which are the official major objects to be detected. Two scenarios are set up: (1) training and prediction in the same tunnel (2) training in a tunnel and prediction in the other tunnel. From the both cases, targeted object detection in prediction mode are achieved to detectable rate to higher than 80% in case of similar time period between training and prediction but it shows a bit low detectable rate to 40% when the prediction times are far from the training time without further training taking place. However, it is believed that the AI based system would be enhanced in its predictability automatically as further training are followed with accumulated CCTV BigData without any revision or calibration of the incident detection system.

• The Journal of the Korea Contents Association
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• v.20 no.11
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• pp.656-668
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• 2020

The Sejong City was launched in July 2012 and was initially focused on the relocation of central administrative agencies, but it has been changing from an administrative city to a fourth industrial city since 2019 to a smart city and the implementation of Korea's New Deal in 2020. Identity design needs to be reevaluated accordingly. In particular, the web environment is also calling for an optimized identity design due to rapid changes in information technology such as various wearables and the Internet of Things. As the number of responsive web sites where information and communication technologies can be developed and optimized screens can be viewed increased, identity was intuitively communicated to users and designs were applied to make them more distinct and empathetic to other cities. Prior to the study, we looked at prior studies on the changing times in the web environment and the reactive web, and analyzed the identity design of the reactive web and applied minimalism characteristics step by step. Based on this, we surveyed experts and non-experts on the proposed survey by applying minimalist characteristics (simple, repeatability, and spatiality) of reactive identity and found that it was easily and intuitively recognizable in a small web environment such as mobile. Therefore, we hope that Sejong City's identity will continue to be studied in various ways and efficient management so that identity can be established in accordance with the changes of the times.

• Journal of Aerospace System Engineering
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• v.14 no.1
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• pp.44-53
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• 2020

In conjunction with the Fourth Industrial Revolution, the unmanned aerial vehicle industry is being developed to a new paradigm by combining advanced technologies such as AI, Big Data and the IoT. Aeronautical developed countries such as the U.S. are focusing their efforts on the development of the safer unmanned aerial vehicles. The Korea Aerospace Research Institute, as part of the national R&D project in 2011, had succeeded in developing the first vertical takeoff and landing (VTOL) UAS, called Smart-UAV. However, although the development technology of the VTOL UAS is possessed, developing and operating of the VTOL UAS for commercial or military use are limited. The type certification procedure of the VTOL UAS developed by domestic technology is stipulated in the Korean Aviation Safety Act, but the Korean VTOL UAS airworthiness standards (KAS) hsve not been established. Thus, this study investigated the development trends of the VTOL UAS in Korea and abroad and national certification systems and procedures, and benchmarked the special conditions for the VTOL aircraft, announced by the EASA on July 2, 2019, to establish standards for type certificate of the VTOL UAS in Korea.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.16 no.6
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• pp.67-78
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• 2017

Traffic congestion cost is increasing annually. Specifically congestion caused by the CDB traffic contains more than a half of the total congestion cost. Recent advancement in the field of Big Data, AI paved the way to industry revolution 4.0. And, these new technologies creates tremendous changes in the traffic information dissemination. Eventually, accurate and timely traffic information will give a positive impact on decreasing traffic congestion cost. This study, therefore, focused on developing both recurrent and non-recurrent congestion prediction models on urban roads by adopting Recurrent Neural Network(RNN), a tribe in machine learning. Two hidden layers with scaled conjugate gradient backpropagation algorithm were selected, and tested. Result of the analysis driven the authors to 25 meaningful links out of 33 total links that have appropriate mean square errors. Authors concluded that RNN model is a feasible model to predict congestion.

• Strategy21
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• s.44
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• pp.5-27
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• 2018

The purpose of this paper is to look at USN's efforts to rebuild its combat power in the face of a reemergence of great powers competition, and to propose some recommendations for the ROKN. In addition to the plan to augment its fleet towards a 355-ships capacity, the USN is pursuing to improve exponentially combat lethality(quality) of its existing fleet by means of innovative science and technology. In other words, the USN is putting its utmost efforts to improve readiness of current forces, to modernize maintenance facilities such as naval shipyards, and simultaneously to invest in innovative weapons system R&D for the future. After all, the USN seems to pursue innovations in advanced military Science & Technology as the best way to ensure continued supremacy in the coming strategic competition between great powers. However, it is to be seen whether the USN can smoothly continue these efforts to rebuild combat strength vis-a-vis its new competition peers, namely China and Russian navy, due to the stringent fiscal constraints, originating, among others, from the 2011 Budget Control Act effective yet. Then, it seems to be China's unilateral and assertive behaviors to expand its maritime jurisdiction in the South China Sea that drives the USN's rebuild-up efforts of the future. Now, some changes began to be perceived in the basic framework of the hitherto regional maritime security, in the name of declining sea control of the USN as well as withering maritime order based on international law and norms. However, the ROK-US alliance system is the most excellent security mechanism upon which the ROK, as a trading power, depends for its survival and prosperity. In addition, as denuclearization of North Korea seems to take significant time and efforts to accomplish in the years to come, nuclear umbrella and extended deterrence by the US is still noting but indispensible for the security of the ROK. In this connection, the naval cooperation between ROKN and USN should be seen and strengthened as the most important deterrents to North Korean nuclear and missile threats, as well as to potential maritime provocation by neighboring countries. Based on these observations, this paper argues that the ROK Navy should try to expand its own deterrent capability by pursuing selective technological innovation in order to prevent this country's destiny from being dictated by other powers. In doing so, however, it may be too risky for the ROK to pursue the emerging, disruptive innovative technologies such as rail gun, hypersonic weapon... etc., due to enormous budget, time, and very thin chance of success. This paper recommends, therefore, to carefully select and extensively invest on the most cost-effective technological innovations, suitable in the operational environments of the ROK. In particular, this paper stresses the following six areas as most potential naval innovations for the ROK Navy: long range precision strike; air and missile defense at sea; ASW with various unmanned maritime system (UMS) such as USV, UUV based on advanced hydraulic acoustic sensor (Sonar) technology; network; digitalization for the use of AI and big data; and nuclear-powered attack submarines as a strategic deterrent.

• Journal of Navigation and Port Research
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• v.42 no.6
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• pp.539-545
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• 2018

In recent years, research on shipping market forecasting with the employment of non-linear AI models has attracted significant interest. In previous studies, input variables were selected with reference to past papers or by relying on the intuitions of the researchers. This paper attempts to address this issue by applying the stepwise regression model and the random forest model to the Cape-size bulk carrier market. The Cape market was selected due to the simplicity of its supply and demand structure. The preliminary selection of the determinants resulted in 16 variables. In the next stage, 8 features from the stepwise regression model and 10 features from the random forest model were screened as important determinants. The chosen variables were used to test both models. Based on the analysis of the models, it was observed that the random forest model outperforms the stepwise regression model. This research is significant because it provides a scientific basis which can be used to find the determinants in shipping market forecasting, and utilize a machine-learning model in the process. The results of this research can be used to enhance the decisions of chartering desks by offering a guideline for market analysis.

• Journal of Internet Computing and Services
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• v.20 no.1
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• pp.17-24
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• 2019

Since the information of $4^{th}$ Industrial Revolution is introduced in WEF (World Economic Forum) in 2016, IoT, AI, Big Data, 5G, Cloud Computing, 3D/4DPrinting, Robotics, Nano Technology, and Bio Engineering have been rapidly developed as business applications as well as technologies themselves. Among the diverse business applications for IoT, wearable devices are recognized as the leading application devices for final customers. This longitudinal study is compared to the results of the 1st study conducted to identify customer needs of activity trackers, and links the identified users' needs with the well-known marketing frame of marketing mix. For this longitudinal study, a survey was applied to university students in June, 2018, and ANOVA were applied for major variables on usable features. Further, potential customer needs were identified and visualized by Word Cloud Technique. According to the analysis results, different from other high tech IT devices, activity trackers have diverse and unique potential needs. The results of this longitudinal study contribute primarily to understand usable features and their changes according to product maturity. It would provide some valuable implications in dynamic manner to activity tracker designers as well as researchers in this arena.

• Journal of Korea Society of Digital Industry and Information Management
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• v.17 no.3
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• pp.63-83
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• 2021

The appearance of education is also rapidly changing in social changes represented by social networks. And the development of information and communication technology is also having a widespread effect on the education field. In the era of untact caused by Covid-19, education through smart learning is having a greater effect on students as well as adult learners more quickly and broadly. In addition, smart learning is not just limited to learning content, but is developing into personalized, convergence, and intelligent. The purpose of this study is to identify the factors of ARCS motivation theory that can determine the learning motivation of smart learning users, and to empirically study the casual relationship between these factors on education achievement through practical value and hedonic value. Specifically, I would like to examine how the independent variables ARCS motivation factors (attention, relevance, confidence, and satisfaction) affect learners' education achievement through the parameters of practical value and hedonic value. To this end, a research model was presented that applied the main variables of attention, relevance, confidence, and satisfaction, which are four elements of ARCS motivation theory, a specific and systematic motivational strategy to induce and maintain learners' motivation. In order to empirically verify the research model of this study, a survey was carried out on learners with experience using smart learning. As a result of the study, first attention was found to have a positive effect on the hedonic value. Second, relevance was found to have a positive effect on the hedonic value. Third, it was found that confidence did not have a positive effect on the practical value and the hedonic value. Forth, satisfaction was found to have a positive effect on the practical value and the hedonic value. Fifth, practical value was found to have a positive effect on the education achievement. Sixth, hedonic value was found to have a positive effect on the education achievement. Through this, it can be seen that the intrinsic motivation of learners using smart learning affects the education achievement of users through intrinsic and extrinsic value. A variety of smart learning that combines advanced IT technologies such as AI and big data can contribute to improving learners' education achievement more effectively and efficiently. Furthermore, it can contribute a lot to social development.