• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.5
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• pp.7-15
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• 2022

Since the direct and indirect damage caused by the fire in the underground utility tunnel will cause great damage to society as a whole, it is necessary to make efforts to prevent and control it in advance. The most of the fires that occur in cables are caused by short circuits, earth leakage, ignition due to over-current, overheating of conductor connections, and ignition due to sparks caused by breakdown of insulators. In order to find the cause of fire at an early stage due to the characteristics of the underground utility tunnel and to prevent disasters and safety accidents, we are constantly managing it with a detection system using image analysis and making efforts. Among them, a case of developing a fire detection system using CCTV-based deep learning image analysis technology has been reported. However, CCTV needs to be supplemented because there are blind spots. Therefore, we would like to develop a high-performance acoustic-based deep learning model that can prevent fire by detecting the spark sound before spark occurs. In this study, we propose a method that can collect sound in underground utility tunnel environments using microphone sensor through development and experiment of prototype module. After arranging an acoustic sensor in the underground utility tunnel with a lot of condensation, it verifies whether data can be collected in real time without malfunction.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.15 no.6
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• pp.506-514
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• 2022

This paper is about developing an intravenous injection management system that can provide nurses with information related to intravenous injection in real-time to compensate for possible instability factors during intravenous injection. The intravenous injection management system consists of an app-based user S/W and a web-based administrator S/W. User S/W is implemented to provide users with the ability to identify patients who need intravenous injection through smartphones, tablet PCs, and nursing PDAs, recognize information codes given to patients, and enter and share treatment contents and treatment items after intravenous injection. As a result of intravenous injection treatment uploaded through the user app, the manager S/W can check the records of intravenous injection treatment items, perform user management functions, emergency notification registration and management functions, and data upload functions. The implemented system has not yet been tested on the EMR system used in the actual hospital. Therefore, through further research, S/W will be optimized and actual environmental application tests will be conducted through cooperation with hospitals.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.15 no.4
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• pp.283-290
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• 2022

In this study, an IoT inspection system that can be linked with a server was developed using a gas timer and ESP-01 Wi-Fi module installed on a gas valve in the home. The server environment of the gas leak IoT inspection system was installed with APM (Apache, PHP, MySQL) to collect gas pressure data by generation so that leakage checks could be performed. In order to control the gas leak IoT inspection system, the app inventory was used to manage the gas leak check value in real time. In addition, user convenience has been enhanced so that membership management, WiFi settings, and leakage check values can be checked through mobile apps. In order to manage subscribers by region, the user list was checked by logging in in in the administrator mode so that the information on whether or not the leak test was conducted and the results could be provided. In addition, when the user presses the gas leak check button, the pressure is automatically checked, and the measured value is stored in the server, and when a gas leak occurs, the leakage check is performed after alarm and repair so that it can be used if normal. In addition, in order to prevent overlapping membership, membership management can be performed based on MAC addresses.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.3
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• pp.18-29
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• 2022

Recently, cooperative intelligent transport systems (C-ITS) testbeds have been deployed in great numbers, and advanced autonomous driving research using V2X communication technology has been conducted actively worldwide. In particular, the broadcasting services in their beginning days, giving warning messages, basic safety messages, traffic information, etc., gradually developed into advanced network services, such as platooning, remote driving, and sensor sharing, that need to perform real-time. In addition, technologies improving these advanced network services' throughput and latency are being developed on many fronts to support these services. Notably, this research analyzed the network latency requirements of the advanced network services to develop a remote driving service for the droid type low-speed robot based on the 3GPP C-V2X communication technology. Subsequently, this remote driving service's performance was evaluated using system modeling (that included the operator behavior) and simulation. This evaluation showed that a respective core and access network latency of less than 30 ms was required to meet more than 90 % of the remote driving service's performance requirements under the given test conditions.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.2
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• pp.169-174
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• 2023

Currently, overall power usage is also increasing as power demand such as homes, offices, and factories increases. The increase in power use also raised interest in standby power as a change in awareness of energy saving appeared. Home and office devices are consuming power even in standby conditions. Accordingly, there is a growing need to reduce standby power, and it aims to have standby power of 1W or less. An intelligent outlet uses a near-field wireless network to connect to a home network and cut or reduce standby power of a lamp or appliance connected to an outlet. This research aims to develop a monitoring system and an intelligent outlet that can remotely monitor the amount of electricity used in a lighting lamp or a home appliance connected to an outlet using a short-range wireless network (Zigbee). Also, The intelligent outlet and monitoring system developed makes it possible for a user to easily cut off standby power by using a portable device. Intelligent outlets will not only reduce standby power but also be applicable to fire prevention systems. Devices that cut off standby power include intelligent outlets and standby power cutoff switches, so they will prevent short circuits and fires.

• Journal of Service Research and Studies
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• v.12 no.3
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• pp.144-157
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• 2022

This study began by pointing out the problem of domestic media reporting on 'fake news' regulations that frequently appear through the French 'Les proposals de loi control de l'information'case, while still approaching with different standards and perspectives on where to see fake news. In the age of 'social network services', the answer to what the media is, what the news is, and who the reporter is increasingly difficult. While reviewing the long history and background of the spread of fake news examined in this study, it was confirmed that could not determine the concept and scope of fake news, punished, regulated, controlled, or judged simply by one standard. From the perspective of 'freedom of expression' set by the law, we have the authority to express our opinions freely. In addition, 'online' space is a place where fake news is generated and spread, but at the same time, there is plenty of room to act as an antidote. In the end, the only alternative to the damage of long-term fake news will be to create a media environment that allows more high-quality "real news" to pour out, allowing us to develop our ability to judge reliable information through balanced competition among various news in the free market of ideas.

• KIPS Transactions on Computer and Communication Systems
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• v.12 no.3
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• pp.103-110
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• 2023

With the spread of the Internet, anonymous communities emerged along with the activation of communities for communication between people, and many users are doing harm to others, such as posting aggressive posts and leaving comments using anonymity. In the past, administrators directly checked posts and comments, then deleted and blocked them, but as the number of community users increased, they reached a level that managers could not continue to monitor. Initially, word filtering techniques were used to prevent malicious writing from being posted in a form that could not post or comment if a specific word was included, but they avoided filtering in a bypassed form, such as using similar words. As a way to solve this problem, deep learning was used to monitor posts posted by users in real-time, but recently, the community uses words that can only be understood by the community or from a human perspective, not from a general Korean word. There are various types and forms of characters, making it difficult to learn everything in the artificial intelligence model. Therefore, in this paper, we proposes a preprocessing technique in which each character of a sentence is imaged using a CNN model that learns the consonants, vowel and spacing images of Korean word and converts characters that can only be understood from a human perspective into characters predicted by the CNN model. As a result of the experiment, it was confirmed that the performance of the LSTM, BiLSTM and CNN-BiLSTM models increased by 3.2%, 3.3%, and 4.88%, respectively, through the proposed preprocessing technique.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.6
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• pp.385-391
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• 2023

As the use of electric vehicles has increased to minimize carbon emissions, the analyzing the state and performance of lithium-ion batteries that is instrumental in electric vehicles have been important. Comprehensive analysis using not only the voltage, current and temperature of the battery pack, which can affect the condition and performance of the battery, but also the driving data and charging pattern data of the electric vehicle is required. Therefore, a thorough analysis is imperative, utilizing electric vehicle operation data, charging pattern data, as well as battery pack voltage, current, and temperature data, which collectively influence the condition and performance of the battery. Therefore, collection and preprocessing of battery data collected from electric vehicles, collection and preprocessing of data on driver driving habits in addition to simple battery data, detailed design and modification of artificial intelligence algorithm based on the analyzed influencing factors, and A battery analysis and evaluation model was designed. In this paper, we gathered operational data and battery data from real-time electric buses. These data sets were then utilized to train a Random Forest algorithm. Furthermore, a comprehensive assessment of battery status, operation, and charging patterns was conducted using the explainable Artificial Intelligence (XAI) algorithm. The study identified crucial influencing factors on battery status, including rapid acceleration, rapid deceleration, sudden stops in driving patterns, the number of drives per day in the charging and discharging pattern, daily accumulated Depth of Discharge (DOD), cell voltage differences during discharge, maximum cell temperature, and minimum cell temperature. These factors were confirmed to significantly impact the battery condition. Based on the identified influencing factors, a battery analysis and evaluation model was designed and assessed using the Random Forest algorithm. The results contribute to the understanding of battery health and lay the foundation for effective battery management in electric vehicles.

• Journal of Advanced Navigation Technology
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• v.28 no.1
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• pp.149-154
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• 2024

Research on particulate matter is advancing in real-time, and various methods are being studied to improve the accuracy of prediction models. Furthermore, studies that take into account various factors to understand the precise causes and impacts of particulate matter are actively being pursued. This paper trains an LSTM model using seasonal data and another LSTM model using concentration-based data. It compares and analyzes the PM_2.5 prediction performance of the two models. To train the model, weather data and air pollutant data were collected. The collected data was then used to confirm the correlation with PM_2.5. Based on the results of the correlation analysis, the data was structured for training and evaluation. The seasonal prediction model and the concentration-specific prediction model were designed using the LSTM algorithm. The performance of the prediction model was evaluated using accuracy, RMSE, and MAPE. As a result of the performance evaluation, the prediction model learned by concentration had an accuracy of 91.02% in the "bad" range of AQI. And overall, it performed better than the prediction model trained by season.

• Information Systems Review
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• v.18 no.3
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• pp.51-71
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• 2016

Tourism products are intangible goods. Given this nature, tourist experience should be recorded and visualized through media, such as pictures, videos, and souvenir. Online platforms played the role of media given the growth of information and communication technology. Tourists post their travels for real-time documentation of their experiences, but they also tend to reminisce about past experiences that they posted on social media. Social media is not only a channel of self-presentation or a means of communication with other people, but it also serves as an archive of electronic records to bring back memories. Given this finding, we investigated the impact of social media on the autobiographical memory (recollection and vividness) of tourists and their intention to revisit a certain destination. The results showed social media interface and the impact of display quality on the recollection and vivid memory. The predictor of memory recollection of tourists is intention to revisit a destination. Social media is considered an archive of travel memory that indulges people to reminisce. Theoretical and practical implications were provided based on these results.