• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.3
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• pp.459-464
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• 2023

The mobile house of this article is provided with stand-alone power system that uses photovoltaic energy and enables sensing and environmental monitoring. Excess power generated is stored in lithium batteries, which enable smooth operation of the mobile house even in environment in which solar energy cannot be used. The house has been designed that its systems can be operated continuously by diesel power generation even when photovoltaic energy cannot be generated due to long rainy season or heavy snow. BMS (batter management system) has been constructed for photovoltaic and power management, and monitors the charge/discharge and usage amount of photovoltaic energy. Various sensing data are recorded and transmitted automatically, and the design allows for wireless monitoring by means of computer and smartphone app. The container house proposed in this study enables efficient energy management by performing optimal energy operation in remote areas, parks, event venues, and construction sites where there is no system power source.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.33 no.1
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• pp.139-166
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• 2022

Ethical Issues increase when people engage in smart technological systems such as 5G, IoT, Cloud computing services and AI applications. Range of this research is comparison of various literacy concepts and its ethical issues in considering of 5G features and UX. 86 research papers and reports which have been published within the recent 5 years (2017-2022), relating the research subject, are investigated and analyzed. Two results show that various literacies can be grouped into four areas and that some of common issues among those areas as well as unique issues of each area are identified. Based on the literature analysis, an Operational Definition of Ethics-Literacy is presented and the model of ethics-literacy curriculum supporting ethical behavior of 5G information professionals is developed and suggested.

• Smart Structures and Systems
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• v.29 no.5
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• pp.653-660
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• 2022

Gravel scattering that is generated during operation of high-speed railway vehicle is cause to damage of vehicle such as windows, axle protector and so on. Especially, those are frequently occurred in winter season when snow ice is generated easily. Above all, damage of vehicle windows has not only caused maintenance cost but also increased psychological anxiety of passengers. Various methods such as heating system using copper wire, heating jacket and heating air are applied to remove snow ice generated on the under-body of vehicle. However, the methods require much run-time and man power which can be low effectiveness of work. Therefore, this paper shows that large-area heating system was developed based on heating coat in order to fundamentally prevent snow ice damage on high-speed railway vehicle in the winter season. This system gives users high convenience because that can remotely control the heating system using IoT-based wireless communication. For evaluating the applicability to railroad sites, a field test on an actual high-speed railroad operation was conducted by applying these techniques to the brake cylinder of a high-speed railroad vehicle. From the results, it evaluated how input voltage and electric power per unit area of the heating specimen influences exothermic performance to draw the permit power condition for icing. In the future, if the system developed in the study is applied at the railroad site, it may be used as a technique for preventing all types of damages occurring due to snow ice in winter.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.272-274
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• 2021

Recently, due to COVID-19, the use of non-face-to-face authentication and fever detection systems is increasing. As the number of confirmed cases increases, the government is making it mandatory to authenticate and install a fever detector. It is used for entering and leaving not only general restaurants but also all stores. However, in most cases, the heat detector and the authentication device are separately configured and used, which is very inconvenient. Therefore, this study was conducted to develop an access control system that can simultaneously perform these functions. A smart access control system was developed by combining IOT technology as well as a fever detection function and smart personal recognition function. It is expected to further develop K-Quarantine by distributing it to public facilities and nursing facilities in the future.

• Smart Structures and Systems
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• v.31 no.1
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• pp.1-11
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• 2023

Assessment of the compressive strength of concrete plays a major role during formwork removal and in the prestressing process. In concrete, temperature changes occur due to hydration which is an influencing factor that decides the compressive strength of concrete. Many methods are available to find the compressive strength of concrete, but the maturity method has the advantage of prognosticating strength without destruction. The temperature-time factor is found using a LM35 temperature sensor through the IoT technique. An experimental investigation was carried out with 56 concrete cubes, where 35 cubes were for obtaining the compressive strength of concrete using a universal testing machine while 21 concrete cubes monitored concrete's temperature by embedding a temperature sensor in each grade of M25, M30, M35, and M40 concrete. The mathematical prediction model equation was developed based on the temperature-time factor during the early age compressive strength on the 1^st, 2^nd, 3^rd and 7^th days in the M25, M30, M35, and M40 grades of concrete with their temperature. The 14^th, 21^st and 28^th day's compressive strength was predicted with the mathematical predicted equation and compared with conventional results which fall within a 2% difference. The compressive strength of concrete at any desired age (day) before reaching 28 days results in the discovery of the prediction coefficient. Comparative analysis of the results found by the predicted mathematical model show that, it was very close to the results of the conventional method.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.3
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• pp.186-197
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• 2023

In this study, we focus on the improvement of data quality transmitted from a weather buoy that guides a route of ships. The buoy has an Internet-of-Thing (IoT) including sensors to collect meteorological data and the buoy's status, and it also has a wireless communication device to send them to the central database in a ground control center and ships nearby. The time interval of data collected by the sensor is irregular, and fault data is often detected. Therefore, this study provides a framework to improve data quality using machine learning models. The normal data pattern is trained by machine learning models, and the trained models detect the fault data from the collected data set of the sensor and adjust them. For determining fault data, interquartile range (IQR) removes the value outside the outlier, and an NGBoost algorithm removes the data above the upper bound and below the lower bound. The removed data is interpolated using NGBoost or long-short term memory (LSTM) algorithm. The performance of the suggested process is evaluated by actual weather buoy data from Korea to improve the quality of 'AIR_TEMPERATURE' data by using other data from the same buoy. The performance of our proposed framework has been validated through computational experiments based on real-world data, confirming its suitability for practical applications in real-world scenarios.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.34 no.4
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• pp.553-568
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• 2024

Due to the rapid development of IoT technology and the increase in home activities after the COVID-19 pandemic, users' demand for smart homes has increased significantly. As the size of the smart home market increases every year and the number of users increases, the importance of personal information protection and various security issues is also growing. It often grants temporary users smart home owner rights and gives them access to the system. However, this can easily allow access to third parties because the authorities granted are not properly managed. In addition, it is necessary to prevent the possibility of secondary damage using personal information collected through smart home devices and sensors. Therefore, in this paper, to prevent indiscriminate access to smart home systems without reducing user convenience, access rights are subdivided and designed according to the functions and types of smart home devices, and a token-based user access control technique using personal devices is proposed.

• Journal of Technology Innovation
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• v.23 no.3
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• pp.169-199
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• 2015

Information and communication technologies (ICT) have been combined with products from other industries to provide new functionality, as recently shown in the cases of Internet of Things (IoT). Modularity assumes a crucial role in such technological convergence, and has impacts on the relationship between organizations as well as competition within an industry. Interface standards, which ensure the connectivity between modules, serve as a critical factor in the process by which modularity affects organization systems and industry structure. To understand the aforementioned phenomenon, we studied modularity and interface standards with a focus on the interaction between technology and organization systems and subsequent changes in industrial dynamics. This paper examines previous literature on modularity and interface standards in the aspects of product architecture, organization systems, and institutional factors. With this analytical framework, we conducted a comparative analysis of electronic and automotive industries to derive implications for standardization policy. This research has shown the significance of external open interface standards in shaping an industrial landscape where a variety of module producers horizontally compete. It also advises that policymakers take into account product characteristics, engagement of leading firms in an industry, and institutional factors such as WTO law in the design of standardization policy.

• Journal of Navigation and Port Research
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• v.42 no.6
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• pp.437-445
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• 2018

IMO (International Maritime Organization) has been strengthening the regulations of ship emission gas such as sulfur oxides (SOX), nitrogen oxides (NOX) and carbon dioxides (CO2) to protect the marine environment. Especially, ECA (Emission Control Area) has been set and operated in the USA and US. As a countermeasure against these environmental regulations, the demand for environmentally, friendly and highly efficient vessels has led to a growing interest in technology related research with respect to electric propulsion systems capable of reducing exhaust gas. Container ships were excluded from the application coverage of the electric propulsion systems for reasons of operation at economical speed. However, in the future, the need for electric propulsion system is expected to rise, because it is easy to monitor and control so that it can be an applicate to smart ship which are represented by fourth industrial revolution technology. In this study, research was carried out to design a generator and battery capacity through the load analysis of the 6,800TEU container ship to apply the electric propulsion system of the container ship. A capacity design based on the load analysis has an advantage that the generator can be operated in a high efficiency section through the load distribution control using the battery.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.2
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• pp.67-72
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• 2022

Recently, various intelligent application services using artificial intelligence are being actively developed. In particular, research on artificial intelligence-based real-time prediction services is being actively conducted in the manufacturing industry, and the demand for artificial intelligence services that can detect and predict fire and odors is very high. However, most of the existing detection and prediction systems do not predict the occurrence of fires and odors, but rather provide detection services after occurrence. This is because AI-based prediction service technology is not applied in existing systems. In addition, fire prediction, odor detection and odor level prediction services are services with ultra-low delay characteristics. Therefore, in order to provide ultra-low-latency prediction service, edge computing technology is combined with artificial intelligence models, so that faster inference results can be applied to the field faster than the cloud is being developed. Therefore, in this paper, we propose an LSTM algorithm-based learning model that can be used for fire prediction and odor detection/prediction, which are most required in the manufacturing industry. In addition, the proposed learning model is designed to be implemented in edge devices, and it is proposed to receive real-time sensor data from the IoT terminal and apply this data to the inference model to predict fire and odor conditions in real time. The proposed model evaluated the prediction accuracy of the learning model through three performance indicators, and the evaluation result showed an average performance of over 90%.