• 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%.

• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.7
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• pp.1098-1105
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• 2021

The International Maritime Organization has steadily strengthened environmental regulations on nitrogen oxides and carbon dioxide emitted from marine vessels. Consequently, the demand for electric propulsion vessels based on eco-friendly elements has increased. To this end, research and development has been steadily conducted for various vessels. In electric propulsion systems, a redundancy configuration is typically adopted to increase reliability and facilitate the onboard arrangement. Furthermore, studies have been actively conducted to ensure the safety of electric propulsion systems through the combination with digital twin technology. A digital twin can be used to predict outcomes in advance by implementing real-world equipment or space in a virtual world like twins, integrating real-world information and data with the virtual world, and performing computer simulations of situations that can occur in a real environment. In this study, we perform failure modes and effects analysis (FMEA) to validate the electric power management system (PMS) redundancy scheme for the digital twin technology development of electric propulsion vessels. Then, we propose the role and algorithm of PMS as a compensation function for preventing primary and secondary damages caused by a single equipment failure of the PMS and preventing additional damages by analyzing the impact on the entire system under real vessel operating conditions based on the redundancy FMEA suggested for the ship classification and certification. We verified the improvement in propulsion conservation through tests.

• Journal of Service Research and Studies
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• v.12 no.3
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• pp.1-25
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• 2022

This study was conducted for technology development and industrial and enterprise system design for the sustainable life of mankind. Human society is facing a crisis. As the power of mankind has increased due to the development of nuclear weapons and information and communication technologies, the risk of human society has greatly increased. The value of growth and freedom is increasing due to capitalism and democratic systems, so technological innovation is accelerating, and industries and companies are growing significantly. New technologies and industries can greatly develop human society and put it at risk. This study was conducted with the aim of redesigning technology, industry, and enterprise systems so that humans who live on Earth can be more sustainable for a longer time. It presented a practical alternative for a long-term sustainable human society. It suggested alternatives for what philosophy and methodology should be developed for the whole of humanity and in each individual national society, for developing technologies, fostering industries, and operating corporate systems. First of all, the problems of the technology development system, industrial system, and enterprise system of human society were analyzed. The characteristics and problems were analyzed in terms of sustainability of human society. The necessary and sufficient conditions for an alternative system to solve the raised problems were derived. A system that satisfies these conditions was designed and presented. The alternative system was named as a servicism system as a system based on the service philosophy. The structure, operation model, and implementation plan of the new technology industry enterprise system were presented. In the future, follow-up studies are needed to be concreted at the level of individual countries and human society as a whole.

• Journal of Korea Water Resources Association
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• v.56 no.7
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• pp.439-449
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• 2023

Recent advancements in data measuring technology have facilitated the installation of various sensors, such as pressure meters and flow meters, to effectively assess the real-time conditions of water distribution systems (WDSs). However, as cities expand extensively, the factors that impact the reliability of measurements have become increasingly diverse. In particular, demand data, one of the most significant hydraulic variable in WDS, is challenging to be measured directly and is prone to missing values, making the development of accurate data generation models more important. Therefore, this paper proposes an adversarially trained autoencoder (ATAE) model based on generative deep learning techniques to accurately estimate demand data in WDSs. The proposed model utilizes two neural networks: a generative network and a discriminative network. The generative network generates demand data using the information provided from the measured pressure data, while the discriminative network evaluates the generated demand outputs and provides feedback to the generator to learn the distinctive features of the data. To validate its performance, the ATAE model is applied to a real distribution system in Austin, Texas, USA. The study analyzes the impact of data uncertainty by calculating the accuracy of ATAE's prediction results for varying levels of uncertainty in the demand and the pressure time series data. Additionally, the model's performance is evaluated by comparing the results for different data collection periods (low, average, and high demand hours) to assess its ability to generate demand data based on water consumption levels.

• Journal of the Korean Association of Geographic Information Studies
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• v.14 no.4
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• pp.63-76
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• 2011

This study purposes to develop a model to analyze soil loss in estimating prior disaster influence. The model of analyzing soil loss develops the soil loss analysis system on the basis of Internet by introducing cloud computing system, and also develops a standalone type in connection with HyGIS. The soil loss analysis system is developed to draw a distribution chart without requiring a S/W license as well as without preparing basic data such as DEM, soil map and land cover map. Besides, it can help users to draw a soil loss distribution chart by applying various factors like direct rain factors. The tools of Soil Loss Anaysis Model in connection with HyGiS are developed as add-on type of GMMap2009 in GEOMania, and also are developed to draw Soil Loss Hazard Map suggested by OECD. As a result of using both models, they are developed very conveniently to analyze soil loss. Hereafter, these models will be able to be improved continuously through researches to analyze sediment a watershed outlet and to calculate R value using data of many rain stations.

• Korean Journal of Remote Sensing
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• v.32 no.3
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• pp.323-330
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• 2016

Communication, Ocean and Meteorological Satellite (COMS) was launched in 2010 with three payloads that include Ka-band communication payload developed by Ministry of Science, ICT and Future Planning (MSIP) and Electronics and Telecommunications Research Institute (ETRI). This study introduces a real-time remote vessel monitoring system built in the Socheongcho Ocean Research Station using the Ka-band communication satellite. The system is composed of three steps; real-time data collection, transmission, and processing/visualization. We describe hardware (H/W) and software systems (S/W) installed to perform each step and the whole procedure that made the raw data become vessel information for a real-time ocean surveillance. In addition, we address functional requirements of H/W and S/W and the important considerations for successful operation of the system. The system is now successfully providing, in near real-time, ship information over a VHF range using AIS data collected in the station. The system is expected to support a rapid and effective surveillance over a huge oceanic area. We hope that the concept of the system can be fully used for real-time maritime surveillance using communication satellite in future.

• Journal of Internet Computing and Services
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• v.19 no.1
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• pp.87-95
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• 2018

Due to global warming and increased energy costs around the world, interests of energy saving and efficiency have been increased. In particular, forging factories need methods to save energy and increase productivity because of needing amounts of energy uses. To solve the problem, we propose a system, which includes collection, monitoring, and analysis process, to monitor energy uses each facility in realtime based on the IoT devices. This system insists of worksheets management, facility/energy management, realtime monitoring, history search, data analysis through connecting with existed ERP/MES Systems in manufacturing factories. The energy monitoring process is to present used energy collected from IoT devices connected with installed gasmeter and wattmeter each facility. This system provide the change of energy uses, usage fee, energy conversion, and green gas information in realtime on Web and mobile devices. This system will be enhanced with energy saving technology by analyzing constructed big data of energy uses. We can also propose a method to increase productivity by integrating this system with functions of digitalized worksheets and optimized models for production process.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.4
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• pp.74-84
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• 2016

Open Source Software (OSS) is a new paradigm for software development. The system is based on the notion of giving software (including sources) away for free, and making money on services, customizing and maintenance. For these reasons, many software companies have considered adopting and using OSS in Software R&D. A variety of factors may influence the use of decision making of OSS. The objective of this study was to explore the significant factors affecting the use decision of OSS and the job performance of OSS usage in software R&D. A research model was suggested based on the TOE Framework and Information Systems Success Model. These findings show that technical benefits of OSS have significant effects on OSS use. The technical benefits of OSS, and organization context, in turn, have significant effects on the use of OSS. On the other hand, the technical risks of OSS and the environment context have no effects on OSS use. In addition, OSS use and user satisfaction have significant effects on the individual job performance. This research contributes towards advancing the theoretical understanding of the OSS Benefits and Performance in Software Development.

• Journal of Digital Convergence
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• v.14 no.8
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• pp.37-46
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• 2016

Patent application as achievements from R&D institutions in public sector have quantitatively increased by expanding R&D investment for enhancing competitiveness but there have been few tangible outputs from the investment. From this reason, TLO(Technology Transfer&Licensing Organization) and its operation becomes more important to implement technology transfer and commercialization and to bring success in the related business. To get work done more efficiently and to improve utilizing products of the R&D in the TLO, this research is to draw domains and activities of TLO and establish its task systems by prioritizing activities. From literature reviews and expert interviews, we generated 6 work domains and 21 task items. Applying AHP analysis, we discriminated the relative importance from task items and analyzed its priority. The finding of this research can provide implications for TLO to increase work efficiency and improve its performance.

• The Journal of the Convergence on Culture Technology
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• v.6 no.2
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• pp.437-447
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• 2020

The purpose of this study is to research the countermeasures and expected effects through the use of drones in the field of disaster prevention as a drone-based smart quarantine performance method. The environmental, market, and technological approaches to the review of the current quarantine performance task and its countermeasures are as follows. First, in terms of the environment, the effectiveness of the quarantine performance business using drone-based control is to broaden the utilization of forest, bird flu, livestock, facility areas, mosquito larvae, pests, and to simplify and provide various effective prevention systems such as AI and cholera. Second, in terms of market, the standardization of livestock and livestock quarantine laws and regulations according to the use of disinfection and quarantine missions using domestic standardized drones through the introduction of new technologies in the quarantine method, shared growth of related industries and discovery of new markets, and animal disease prevention It brings about the effect of annual budget savings. Third, the technical aspects are (1) on-site application of disinfection and prevention using multi-drone, a new form of animal disease prevention, (2) innovation in the drone industry software field, and (3) diversification of the industry with an integrated drone control / control system applicable to various markets. (4) Big data drone moving path 3D spatial information analysis precise drone traffic information ensures high flight safety, (5) Multiple drones can simultaneously auto-operate and fly, enabling low-cost, high-efficiency system deployment, (6) High precision that this was considered due to the increase in drone users by sector due to the necessity of airplane technology. This study was prepared based on literature surveys and expert opinions, and the future research field needs to prove its effectiveness based on empirical data on drone-based services. The expected effect of this study is to contribute to the active use of drones for disaster prevention work and to establish policies related to them.