• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.326-328
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• 2021

The use of shared electric kickboards that can move quickly within a short distance at a relatively low price is increasing significantly. In this paper, we propose a system for recognizing incorrect parking of an abandoned shared kickboard by applying deep learning-based object recognition technology. In this paper, a model similar to CNN was created separately considering the characteristics of the experimental data, and it was shown that a recognition rate of 60% was obtained through the experiment.

• Journal of the Korea Convergence Society
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• v.10 no.12
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• pp.93-99
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• 2019

Small electric vehicles, such as electric bicycles or electric kickboards, operate with the power charged in a battery mounted in the vehicle, and some of these users use emergency power sockets installed in apartments or public facilities without getting permission. For this reason, the necessity for a simple method to approve the use of power with instant payment system rises for the building managers and small vehicle users as well. In this paper, we propose a technique to charge batteries for small electric vehicles with less than 1 [kW] through a power supply control device installed on the existing 15 [A]. sockets on the common residential properties or public buildings. It also describes the power user authorization algorithm and how to charge fees for the power used. As a result of this research, this paper shows how the user authentication power supply system with the effect of preventing power theft can be realized by creating an environment in which a battery in a small electric vehicle can be easily charged.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.1
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• pp.189-194
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• 2021

With the recent increase in the proportion of single-person households, starting in 2016, the domestic shared personnel mobility market such as electric kickboards and electric wheels has grown rapidly. Personal transportation means such as electric kickboards are power devices using electricity and are eco-friendly, lightweight, and do not occupy a separate parking space. Above all, it has the advantage of being convenient to travel short and medium distances, so it has been able to obtain a lot of demand from younger users who pursue reasonable consumption, and accordingly, the related market has grown rapidly. However, as absence of the charging station for electric kickboards, electric kickboards are left everywhere on the road, and are emerging as a threat to safety as well as aesthetics. Therefore, this paper aims to research and propose a new IoT management system for efficient recovery of shared electric kickboards. Through this system, it is expected that the high recovery rate of the electric kickboard can be maintained, and in conclusion, the safety of the user and the surrounding environment can be improved.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.11a
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• pp.263-265
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• 2022

공유 모빌리티 시장의 발전으로 인해 공유 전동킥보드 사용자의 수가 증가하고 있다. 하지만 무분별한 공유 전동킥보드 주차와 방치 문제가 있어 안전한 도시 환경에 위협이 되고 있다. 이를 해결하기 위해 본 연구는 블록체인 기반의 시민 참여형 공유 전동킥보드 관리 모델을 제안한다. 공유 전동 킥보드 회사들은 스마트 컨트랙트를 통해 사용자의 공유 전동킥보드 반납 내역을 기반으로 신뢰 점수를 관리하고 공유한다. 사용한 공유 전동킥보드를 올바르게 주차 및 반납을 하여 높은 신뢰 점수를 갖는 사용자에게 인센티브를 지급하여 사용자가 스스로 전동킥보드를 잘 주차하도록 유도한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.05a
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• pp.260-261
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• 2023

공유 전동킥보드의 사용이 증가함에 따라, 불법 주차와 같은 문제점이 발생하고 있다. 시민들의 안전을 위협하는 문제를 해결하기 위해 CNN 모델 기반의 공유 전동킥보드 주차 판단 시스템을 구현하였다. 공유 전동킥보드에 탑재된 카메라, 기울기 센서를 통해 주차 상태를 판단하고, solidity와 python의 web3.py를 이용하여 컨소시엄 블록체인을 설계하였다. 주차 판단 기준이 되는 요소를 추가하고 가중치를 부여함으로써 신뢰 점수 식을 개선하였다. 본 논문에서 제안하는 모델을 통해 이용자의 자발적인 반납과 회사들의 효율적인 관리를 유도할 수 있다.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.986-987
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• 2023

본 논문은 신경근육질환 환자의 이동 문제를 해결하고, 기존 전동킥보드 시스템의 한계와 문제점도 해결하는 '신경근육질환 환자를 위한 전동킥보드 안전 환경 시스템'을 제안한다. 주요 특징은 다음과 같다. 첫째, 헬멧 착용 검사를 통과해야만전동킥보드를 이용할 수 있다. 휴대폰 전면 카메라를 통해 사용자의 모습을 촬영하면 딥러닝 모델을 통해 헬멧 착용 여부를 판단한다. 둘째, 주행 금지구역에서는 이용자 추적 모드를 활성화하여 OpenCV를 통해 이용자를 검출및 추적하고이에 따라 모터 PWM을 조절해서 방향 및 속력을 조절함으로써 이용자를 추적한다. 셋째, 헬멧 내 자이로 센서와 쇼크 센서를 통해 주행 사고를 감지하고 SMS를 이용해 해당 보호자에게 자동으로 사고 정보를 전달한다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.3
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• pp.486-489
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• 2021

Recently, the demand for the use of personal mobility vehicles, such as an electric kickboard, is increasing explosively because of its high portability and usability. However, the number of traffic accidents caused by personal mobility vehicles has also increased rapidly in recent years. To address the issues regarding the driver's safety, we propose a novel approach that can monitor context information around personal mobility vehicles using deep learning-based object detection and smartphone captured videos. In the proposed framework, a smartphone is attached to a personal mobility device and a front or rear view is recorded to detect an approaching object that may affect the driver's safety. Through the detection results using YOLOv5 model, we report the preliminary results and validated the feasibility of the proposed approach.

• IEMEK Journal of Embedded Systems and Applications
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• v.17 no.3
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• pp.167-175
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• 2022

With the recent expansion of electric kickboards and bicycle sharing services, more and more people use them. In addition, the rapid growth of the delivery business due to the COVID-19 has significantly increased the use of two-wheeled vehicles and personal mobility. As the accident rate increases, the rule related to the two-wheeled vehicles is changed to 'mandatory helmets for kickboards and single-person transportation' and was revised to prevent boarding itself without driver's license. In this paper, we propose a wearable smart safety equipment, called SafetyHelmet, that can keep helmet-wearing duty and lower the accident rate with the communication between helmets and mobile devices. To make this function available, we propose a safe driving assistance function by notifying the driver when an object that interferes with driving such as persons or other vehicles are detected by applying the YOLO v5 object detection algorithm. Therefore it is intended to provide a safer driving assistance by reducing the failure rate to identify dangers while driving single-person transportation.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.41 no.5
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• pp.543-550
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• 2021

Recently, there has been an increase of accidents related to the rise in the use of Personal Mobility Vehicle (PMV). To solve this problem, the National Assembly announced an amendment that restricted the use of PMV to bicycle roads and to prohibited for people under 13 years old to use PMV, but there is no detailed information about travel speed and safety. In this study, a survey was conducted by dividing the group into minors and adults based on the age of obtaining a driver's license to find out the direction of improvement of laws and systems about PMV. Our results showed that adults considered PMV as more dangerous (Adults 5.50, Minors 4.94) and the suggested age for PMV use was lower from minors than adults (Adults 15.70, Minors 13.85). We found that proper travel speed on bicycle roads differed according to the presence of a driveway (Driveway 26.21 km/h, Non-Driveway 23.55 km/h) and minors had higher a travel speed than adults on all types of bicycle road. Also, Helmets for PMV were seen as the most important safety equipment on all types of bicycle road. and the importance of other safety equipment differed according to the presence of a driveway (Driveway Front-Lighting, Non-Driveway Car Horn). Through this study, It proposes that we have to make new regulations about the use of front lights and horns, as well as enforcement measures that differentiate the speed on each bicycle road type as a way to improve the laws and systems for PMV.