• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.14 no.6
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• pp.93-98
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• 2014

Bicycle accidents increase as the number of people riding bicycles increases following the trend try to enhance health and looking for alternative energy sources in the era of high oil price. In bicycle accident cases, physical risk is higher because the impact of the accident has a direct effect on the body of the rider. Therefore, the bicycle rider in an accident might unable to report the accident by themselves, thus, unable to quickly respond to the accident situation. This study developed a system for informing bicycle accidents upon bicycle accident by reporting and texting the accident location using a smart phone application after identifying the accident location using a GPS equipment based on the signal that senses the accident through the system installed in the bicycle for the purpose to improve bicycle riders' safety. This study confirmed the effectiveness of the system developed to quickly respond to the accident to prevent secondary damage through an experiment.

• The Bulletin of The Korean Astronomical Society
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• v.42 no.2
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• pp.61.1-61.1
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• 2017

As the application of deep-learning methods has been succeeded in various fields, they have a high potential to be applied to space weather forecasting. Convolutional neural network, one of deep learning methods, is specialized in image recognition. In this study, we apply the AlexNet architecture, which is a winner of Imagenet Large Scale Virtual Recognition Challenge (ILSVRC) 2012, to the forecast of daily solar flare occurrence using the MatConvNet software of MATLAB. Our input images are SOHO/MDI, EIT $195{\AA}$, and $304{\AA}$ from January 1996 to December 2010, and output ones are yes or no of flare occurrence. We consider other input images which consist of last two images and their difference image. We select training dataset from Jan 1996 to Dec 2000 and from Jan 2003 to Dec 2008. Testing dataset is chosen from Jan 2001 to Dec 2002 and from Jan 2009 to Dec 2010 in order to consider the solar cycle effect. In training dataset, we randomly select one fifth of training data for validation dataset to avoid the over-fitting problem. Our model successfully forecasts the flare occurrence with about 0.90 probability of detection (POD) for common flares (C-, M-, and X-class). While POD of major flares (M- and X-class) forecasting is 0.96, false alarm rate (FAR) also scores relatively high(0.60). We also present several statistical parameters such as critical success index (CSI) and true skill statistics (TSS). All statistical parameters do not strongly depend on the number of input data sets. Our model can immediately be applied to automatic forecasting service when image data are available.

• Journal of Korea Multimedia Society
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• v.16 no.9
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• pp.1089-1097
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• 2013

The applications for considering their position and daily patterns of dementia patients have been developed in an early stage even if the older patients who have weaker or serious symptoms has increased in various forms. In this paper, we develop an android-based application which displays positions and pathways of patients on maps and provide messages in the cases of dangerous situations. Guardians need to register schedules including safe areas and personal information of their patients. This system registers behavior status categorized as normal or abnormal each position which is sent to a database. In particular, the deviation status is assigned in case the patients are not within the safe areas that their guardians registered on their schedule. The wandering status is assigned in case the patients are repeatedly passed by their pathways. This smart application contains the modules such as patient position sending, guardian and patient information, patient schedule and safe zone registration, position and behavior status registration, pathway display and message sending, and rescue request. This system sends the notification and alarm service providing normal and abnormal behavior with deviation and wandering status of patients respectively.

• Journal of the Korea Convergence Society
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• v.8 no.4
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• pp.19-24
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• 2017

The growing number of electronic financial transactions (e-banking) has entailed the rapid increase in security threats such as extortion and falsification of financial transaction data. Against such background, rigid security and countermeasures to hedge against such problems have risen as urgent tasks. Thus, this study aims to implement an improved case model by applying the Fraud Detection System (hereinafter, FDS) in a financial corporation 'A' using big data technique (e.g. the function to collect/store various types of typical/atypical financial transaction event data in real time regarding the external intrusion, outflow of internal data, and fraud financial transactions). As a result, There was reduction effect in terms of previous scenario detection target by minimizing false alarm via advanced scenario analysis. And further suggest the future direction of the enhanced FDS.

• Journal of Internet of Things and Convergence
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• v.8 no.6
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• pp.85-92
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• 2022

Demand for big data analysis and AI developers is increasing, but there is a lack of an education base to supply them. In this paper, by developing a cloud-based artificial intelligence education platform, the goal was to establish an environment in which practical practical training can be efficiently learned at low cost at educational institutions and IT companies. The development of the education platform was carried out by planning scenarios for each user, architecture design, screen design, implementation of development functions, and hardware construction. This training platform consists of a containerized workload, service management platform, lecture and development platform for instructors and students, and secured cloud stability through real-time alarm system and age test, CI/CD development environment, and reliability through docker image distribution. The development of this education platform is expected to expand opportunities to enter new businesses in the education field and contribute to fostering working-level human resources in the AI and big data fields.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.8
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• pp.1248-1254
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• 2022

Efforts are being made to prevent traffic accidents in the school zone in advance. However, traffic accidents in school zones continue to occur. If the driver can know the situation information in the child protection area in advance, accidents can be reduced. In this paper, we design a camera that eliminates blind spots in school zones and a number recognition camera system that can collect pre-traffic information. It is designed by improving the LIDAR system that recognizes vehicle speed and pedestrians. It collects and processes pedestrian and vehicle image information recognized by cameras and LIDAR, and applies artificial intelligence time series analysis and artificial intelligence algorithms. The artificial intelligence traffic accident prevention system learned by deep learning proposed in this paper provides a forced push service that delivers school zone information to the driver to the mobile device in the vehicle before entering the school zone. In addition, school zone traffic information is provided as an alarm on the LED signboard.

• Atmosphere
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• v.31 no.5
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• pp.525-537
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• 2021

Black ices on road surfaces in winter tend to cause severe and terrible accidents. It is very difficult to detect black ice events in advance due to their localities as well as sensitivities to surface and upper meteorological variables. This study develops a methodology to detect the road sections vulnerable to black ice with the use of road surface temperature data obtained from a mobile road weather observation vehicle. The 7 experiments were conducted on the route from Nam-Wonju IC to Nam-Andong IC (132.5 km) on the Jungang Expressway during the period from December 2020 to February 2021. Firstly, temporal road surface temperature data were converted to the spatial data with a 50 m resolution. Then, the spatial road surface temperature was normalized with zero mean and one standard deviation using a simple normalization, a linear de-trend and normalization, and a low-pass filter and normalization. The resulting road thermal map was calculated in terms of road surface temperature differences. A road ice index was suggested using the normalized road temperatures and their horizontal differences. Road sections vulnerable to black ice were derived from road ice indices and verified with respect to road geometry and sky view, etc. It was found that black ice could occur not only over bridges, but also roads with a low sky view factor. These results are expected to be applicable to the alarm service for black ice to drivers.

• International Journal of Computer Science & Network Security
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• v.22 no.6
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• pp.39-44
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• 2022

The advent, course, and possible consequences of the COVID-19 pandemic are now the focus of global attention. From whichever side the geopolitical centers of influence might view it, the problem of the coronavirus concerns all world leaders and the representatives of all branches of science, especially physicians, economists, and politicians - virtually the entire population of the planet. The uniqueness of the COVID-19 phenomenon lies in the uncertainty of the problem itself, the peculiarities and specifics of the course of the biological processes in modern conditions, as well as the sharp confrontation of the main political players on the world stage. Based on an analysis of scientific research, the article describes the profile of the emotional concept of "anxiety" in Russian linguoculture. Through monitoring the headlines of Russian media reports in the "COVID-19" section of Google News and Mail News news aggregators dated August 4-6, 2021, the study establishes the quantitative and qualitative characteristics of the alarm-generating news products on coronavirus in the Russian segment of the Internet and interprets the specifics of media information about COVID-19. The level of mass media criticism in Russia is determined through a phone survey. It is concluded that coronavirus reports in online media conceptualize anxiety about the SARS virus and the COVID-19 disease as a complex cognitive structure. The media abuse the trick of "magic numbers" and emotionally expressive words in news headlines, which are perceived by mass information consumers first and typically uncritically.

• Journal of the Institute of Convergence Signal Processing
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• v.23 no.3
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• pp.150-159
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• 2022

This paper presents an implementation of detecting whether a helmet is worn and there is a fall accident through individual image analysis in real-time from extracting the image objects of several workers active in the industrial field. In order to detect image objects of workers, YOLO, a deep learning-based computer vision model, was used, and for whether a helmet is worn or not, the extracted images with 5,000 different helmet learning data images were applied. For whether a fall accident occurred, the position of the head was checked using the Pose real-time body tracking algorithm of Mediapipe, and the movement speed was calculated to determine whether the person fell. In addition, to give reliability to the result of a falling accident, a method to infer the posture of an object by obtaining the size of YOLO's bounding box was proposed and implemented. Finally, Telegram API Bot and Firebase DB server were implemented for notification service to administrators.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.260-262
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• 2021

In recent years, many studies are being conducted to reduce the damage to humans in the event of a fire. In case of fire in large cities, evacuation route guidance services are provided using Mobile GIS (geographic information system). However, among the algorithms used in the existing evacuation route system, Dijkstra Algorithm has a problem that when the cost is negative, it cannot obtain an infinite loop or an accurate result value, and does not help to select an appropriate shortest route by searching all routes. For this reason, in this paper, we propose the shortest route guidance system based on A^* Algorithm. In case of fire, the shortest route is searched and the shortest route is visualized and provided using a map service on a mobile device using mobile GIS.