• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.6
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• pp.153-172
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• 2018

Overloading of Commercial vehicles have been an important area of transportation as one of the main causes of pavement damage, bridge collapse, severe traffic accident, etc. In this study, we analyzed the effects of overweight prevention by analyzing overweight driving patterns and using weight sensors. First, we analyzed relevant literatures of overweight and surveyed the commercial weight sensors. Then we chose the typical type of overweight vehicles based of overweight enforcement data analysis. MEMs inclinometer weight sensor were installed to 10 test vehicles and data was collected by weight sensors and gps in real time. As a result of gross vehicle weight and axle weight analysis, it was found weight sensor could decrease overweight rate. However, since the number of samples of test vehicles is insufficient to represent the whole commercial vehicle, further studies are deemed possible through the extension test.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.6
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• pp.1323-1328
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• 2021

In this paper, we present a medical electric scooter sharing platform for the transportation vulnerable who are experiencing difficulties and inconveniences in moving. The proposed medical electric scooter sharing platform for the transportation vulnerable includes basic mobile rental, return, and functions that incorporate the IOT technology of the currently operating personal mobility sharing platform. The safety function has been strengthened. The medical electric scooter sharing platform for the transportation vulnerable stores driving data on the server in real time through GPS, and strengthens the alarm and call function in advance of an accident to enable rapid SOS processing. By making the quick contact and responding to the situation, people with disabilities can drive safely and comfortably.

• Journal of The Korean Society of Integrative Medicine
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• v.9 no.3
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• pp.193-201
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• 2021

Purpose : The purpose of this study was to compare the effect of pelvic exercise on the CVA and spinal curve in adults with forward head posture compared to the group using only neck exercise when pelvic exercise was performed in parallel with conventional neck exercise. Methods : GPS 400 and Formetric were used to identify craniovertebral angle (CVA), thoracic kyphosis, lumbar lordosis, and pelvic torsion and were measured by an experienced research manager. Forward head posture (FHP) was selected for people whose angle between the line connecting the ear ball and the seventh cervical spine and the horizontal line is 50 degrees or less. The 30 selected students were randomly divided into 15 experimental groups and 15 control groups. Mackenzie exercise and sling exercise were performed for neck exercise in both the experimental group and the control group, and pelvic exercise using a Swiss ball was additionally performed in the experimental group. All data collected in this study were analyzed using SPSS statistics 21.0. Cervical vetebral angle (CVA), thoracic kyphosis, lumbar lordosis, and pelvic torsion were compared at 0 weeks, 3 weeks, and 6 weeks for each measurement factor using ANOVA with repeated measures. For the statistical significance test, the significance level of α was set to .05. Results : The changes of pelvic torsion, thoracic kyphosis and CVA due to pelvic exercise using Swiss Ball and neck exercise were changed over time, but lumbar lordosis were not changed. And there were no significant differences among the groups. Conclusion : In conclusion, the pelvic correction exercise is considered to be an effective exercise for correcting the FHP and requires regular pelvic correction exercises. We expect the results to be used in clinical trials.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.1
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• pp.177-182
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• 2023

The Act on the Promotion of Mobility for the Transportation Vulnerable stipulates that the transportation vulnerable should be able to enjoy convenience when using public transportation. However, this law is not being implemented properly enough to bring up a petition saying, "Please allow the visually impaired to take a bus." Even if you try to use a call taxi for the disabled instead of public transportation, you have to apply and wait two to three hours. Therefore, this paper aims to design and implement systems for the vulnerable and their guardians, such as increasing the opening time of the ticket gate more than usual if the cane rings a notification on the bus and subway station designated using Bluetooth. Accordingly, it is expected to bring about effects such as the availability of public transportation, economic savings, safety guarantees, and prevention of missing children for the vulnerable.

• KIPS Transactions on Computer and Communication Systems
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• v.12 no.3
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• pp.93-102
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• 2023

Data processing through the cloud causes many problems, such as latency and increased communication costs in the communication process. Therefore, many researchers study edge computing in the IoT, and autonomous driving is a representative application. In indoor self-driving, unlike outdoor, GPS and traffic information cannot be used, so the surrounding environment must be recognized using sensors. An efficient autonomous driving system is required because it is a mobile environment with resource constraints. This paper proposes a machine-learning method using neural networks for autonomous driving in an indoor environment. The neural network model predicts the most appropriate driving command for the current location based on the distance data measured by the LiDAR sensor. We designed six learning models to evaluate according to the number of input data of the proposed neural networks. In addition, we made an autonomous vehicle based on Raspberry Pi for driving and learning and an indoor driving track produced for collecting data and evaluation. Finally, we compared six neural network models in terms of accuracy, response time, and battery consumption, and the effect of the number of input data on performance was confirmed.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.5
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• pp.1339-1355
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• 2023

Localization is a hot research spot for many areas, especially in the mobile robot field. Due to the weak signal of the global positioning system (GPS), the alternative schemes in an indoor environment include wireless signal transmitting and receiving solutions, laser rangefinder to build a map followed by a re-localization stage and visual positioning methods, etc. Among all wireless signal positioning techniques, Wi-Fi is the most common one. Wi-Fi access points are installed in most indoor areas of human activities, and smart devices equipped with Wi-Fi modules can be seen everywhere. However, the localization of a mobile robot using a Wi-Fi scheme usually lacks orientation information. Besides, the distance error is large because of indoor signal interference. Another research direction that mainly refers to laser sensors is to actively detect the environment and achieve positioning. An occupancy grid map is built by using the simultaneous localization and mapping (SLAM) method when the mobile robot enters the indoor environment for the first time. When the robot enters the environment again, it can localize itself according to the known map. Nevertheless, this scheme only works effectively based on the prerequisite that those areas have salient geometrical features. If the areas have similar scanning structures, such as a long corridor or similar rooms, the traditional methods always fail. To address the weakness of the above two methods, this work proposes a coarse-to-fine paradigm and an improved localization algorithm that utilizes Wi-Fi to assist the robot localization in a geometrically similar environment. Firstly, a grid map is built by using laser SLAM. Secondly, a fingerprint database is built in the offline phase. Then, the RSSI values are achieved in the localization stage to get a coarse localization. Finally, an improved particle filter method based on the Wi-Fi signal values is proposed to realize a fine localization. Experimental results show that our approach is effective and robust for both global localization and the kidnapped robot problem. The localization success rate reaches 97.33%, while the traditional method always fails.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.440-440
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• 2022

1970년대부터 집중 건설 된 우리나라의 다목적댐, 홍수조절댐, 용수전용댐 등의 대형 국가 수자원시설물들의 '고령화'가 급속히 진행되어 수리구조물에 대한 안정성을 주기적으로 파악할 수 있는 정밀안전모니터링 체계 구축이 시급한 시점이다. 주기적인 정밀안전모니터링 방법들 중에는 위성 등을 활용한 원격관측 기술들이 최근 시도되고 있다. 위성 영상레이더(SAR; Synthetic Aperture Radar)는 마이크로파 대역의 전자기파를 송·수신하는 능동센서로 날씨 및 주·야간에 영향을 받지 않고 지표면 관측이 가능한 장점이 있다. 특히, 고정산란체 영상레이더 간섭(PSInSAR; Permanent Scatterer Interferometry SAR)기법은 영상레이더 영상에서 긴밀도(coherence)가 상대적으로 높은 수자원시설물과 같은 고정산란체의 위상(phase) 정보를 이용하여 mm급의 측정민감도로 시계열 변위 분석이 가능하다. 또한, 여러 장의 InSAR 영상을 생성하였기 때문에 DEM 오차, 위성궤도 오차, 대기 성분에 의한 지연 오차 등을 보다 정밀하게 제거할 수 있는 장점이 있다 본 연구에서는 국내 중대형 수자원시설물의 정밀안전모니터링을 위하여 고정산란체 영상레이더 간섭 기법을 영암금호방조제, 영주댐, 소양강댐 등에 적용하여 시계열 변위 분석을 수행하였다. 2014년 11월부터 2022년 3월(현재)까지 획득된 Sentinel-1 SLC(Single Look Complex) 위성자료의 상승(Ascending) 궤도 126장 및 하강(Descending)궤도 187장을 각각 활용하였다. 두 위성궤도를 모두 활용하여 수직, 수평 변위 등 3차원 분석을 수행하였으며, 특히 소양강댐 GPS 관측 자료와 정확도 검증에서 연평균 2mm의 RMSE를 보였다. 이를 통해 위성 원격탐사 기술로도 댐, 보, 방조제와 같은 수자원시설물에 대한 시계열 변위 분석을 통한 댐 안전관리가 가능함을 보여주고 있다. 2025년 발사될 국내 C-밴드 SAR 탑재 수자원위성 개발을 통해 한반도 재방문주기를 단축시킴으로써, 한반도 전역의 수자원시설물 정밀안전진단체계 구축이 가능할 것으로 기대된다.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.267-267
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• 2022

댐 운영에 있어서 필요한 수문자료는 강수량, 수위, 유량, 저수량 자료 등이 있다. 이중 저수량은 주로 댐수위-저수용량 곡선식을 이용하여 계산한다. 댐수위-저수용량 곡선식은 댐 부근에서 계측 되는 한 개의 수위자료를 이용하여 저수용량을 산정하며, 이는 큰 저수지 면적과 저수지 수면이 일정하지 않다는 것을 고려할 때 큰 오차가 발생할 수 있다. 본 연구에서는 음향 도플러 유속계 ADCP(Acoustic Doppler Current Profiler) 이용하여 보성강댐 저수지 수면경사를 실측하고, 동시에 실시간 이동측위시스템인 RTK-GPS(Real Time Kinematic)를 이용하여 이를 검증하였다. ADCP는 유수의 흐름을 방해하지 않으면서 수중에 발사된 음파의 도플러 효과를 이용하여 유속, 유량 및 측량이 가능한 장비이며, RTK-GPS의 경우 정밀한 위치정보를 가지고 있는 기준국의 위상에 대한 보정치를 실시간으로 이용하여 오차가 ±0.03m 이하인 것으로 알려졌다. 보성강댐의 하류에서 ADCP와 RTK-GPS를 장착한 보트를 저수지 종방향으로 처음부터 끝까지 이동하여 약 7.5km 종단측량을 실시하였고 저수지 지형적 특성을 고려하여 약 700m마다 횡단측량을 실시하여 종방향뿐만 아니라 횡방향 수면차도 조사하였다. 그 결과 보성강댐의 상류로 갈수록 수면경사가 전체적으로 상승하는 경향을 보였지만 일부구간에서 수위가 하강하는 경우도 발생하였다. 이는 미약하지만 저수지 내에 흐름이 발생하고 이 흐름에 따른 통제가 변화되는 것과 중간에 유입되는 지류의 영향 등으로 구간별로 수면경사 차이가 발생하는 것으로 추정된다. 횡방향 수면차는 지류가 유입되는 일부구간에서 다소 차이를 보였지만 큰 영향을 없는 것으로 판단된다. 보성강댐 저수지 수면을 종방향 및 횡방향으로 실측한 결과 구간별로 차이를 보였으며 최대 EL. 126.60m, 최소 EL. 126.33m 나타났다. 댐 상류 부근의 수면높이 EL. 126.50m와 비교하면 +0.10m, -0.17m 차이를 보였으며 이는 저수량 산정에 큰 오차를 발생시킨다. 효과적인 댐 운영을 위해서는 유입량 및 유출량을 정확하게 산정하는 것도 필요하지만 저수량을 정확하게 파악하는 것 역시 필요하다. 저수량을 정확하게 산정하려면 수킬로미터가 넘는 저수지 크기를 고려하여 수면경사를 실시간으로 계측하는 등의 노력이 필요한 것으로 판단된다.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.344-352
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• 2022

Accurate indoor localization of construction workers and mobile assets is essential in safety management. Existing positioning methods based on GPS, wireless, vision, or sensor based RTLS are erroneous or expensive in large-scale indoor environments. Tightly coupled sensor fusion mitigates these limitations. This research paper proposes a state-of-the-art positioning methodology, addressing the existing limitations, by integrating Stereo Visual Inertial Odometry (SVIO) with fiducial landmarks called AprilTags. SVIO determines the relative position of the moving assets or workers from the initial starting point. This relative position is transformed to an absolute position when AprilTag placed at various entry points is decoded. The proposed solution is tested on the NVIDIA ISAAC SIM virtual environment, where the trajectory of the indoor moving forklift is estimated. The results show accurate localization of the moving asset within any indoor or underground environment. The system can be utilized in various use cases to increase productivity and improve safety at construction sites, contributing towards 1) indoor monitoring of man machinery coactivity for collision avoidance and 2) precise real-time knowledge of who is doing what and where.

• Annual Conference of KIPS
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• 2008.11a
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• pp.882-884
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• 2008

통상 하드웨어 캐시의 크기보다 수십에서 수백배 큰 크기의 데이타를 랜덤하게 접근하는 경우 낮은 메모리 접근 지역성(locality)에 기인하여 캐시 메모리 성능이 급격히 저하되는 문제를 야기한다. 예를 들면, 현재 보편적으로 사용되고 있는 차량용 General Positioning System (GPS) 프로그램의 경우 최대 32개의 위성으로부터 데이터를 받아 수신단의 위치를 계산하는 부분이 핵심 모듈중의 하나 이며, 이는 전체 성능의 50% 이상을 차지한다. 이러한 모듈에서는 위성 신호를 실시간으로 받아 버퍼 메모리에 저장하며, 이때 필요한 데이터가 순차적으로 저장되지 못하기 때문에 랜덤하게 데이터를 읽어 사용하게 된다. 결과적으로 낮은 지역성에 기인하여 실시간 (realtime)안에 데이터 처리를 하기 어려운 문제에 직면하게 된다. 통상의 통신 응용의 알고리즘 상에 내재된(inherited) 낮은 메모리 접근 지역성을 개선하는 것은 알고리즘 상에서의 접근을 요구한다. 이는 높은 비용이 필요함으로 본 연구에서는 사용되는 데이터 구조를 변환하여 지역성을 높이는 방향으로 접근하였다. 결과적으로 핵심 모듈에서 2배, 전체 시스템 성능에서 14%를 개선할 수 있었다.