• Journal of the Korea Society of Computer and Information
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• v.27 no.12
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• pp.161-169
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• 2022

With the development of mobile devices and global positioning systems, various location-based services can be utilized, which collects user's location information and provides services based on it. In this process, there is a risk of personal sensitive information being exposed to the outside, and thus Geo-indistinguishability (Geo-Ind), which protect location privacy of LBS users by perturbing their true location, is widely used. However, owing to the data perturbation mechanism of Geo-Ind, it is hard to accurately obtain the density distribution of LBS users from the collection of perturbed location data. Thus, in this paper, we aim to develop a novel method which enables to effectively compute the user density distribution from perturbed location dataset collected under Geo-Ind. In particular, the proposed method leverages Expectation-Maximization(EM) algorithm to precisely estimate the density disribution of LBS users from perturbed location dataset. Experimental results on real world datasets show that our proposed method achieves significantly better performance than a baseline approach.

• Journal of The Korean Society of Integrative Medicine
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• v.10 no.4
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• pp.199-207
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• 2022

Purpose : The purpose of this study is to investigate the effect of shoulder stabilization exercise accompanied by application of a pelvic compression belt on the muscle activity, pain and function of the muscles around the shoulder in subjects with round shoulders. Methods : For the study method, 28 students who were enrolled in K University with a distance of 1 cm or more between the clavicle of the peak and the outer ear path were selected through GPS 400 global postural analysis system measurement. The subjects were randomly assigned to 14 participants in the group wearing a pelvic compression belt and 14 patients in the group not wearing a pelvic compression belt. In all subjects, the muscle activities of the middle trapezius, lower trapezius, and serratus anterior muscles and the shoulder pain disorder index (SPADI) were measured. The intervention was performed 3 times a week for 4 weeks, and the applied intervention was push-up plus and modified prone cobra exercise. The muscle activities of the middle trapezius, lower trapezius, and serratus anterior muscles and SPADI score were compared using dependent t test before and after intervention. Results : In this study, both groups showed that the muscle activity of the middle trapezius, lower trapezius, and serratus anterior significantly increased after the intervention compared to before the intervention. On the other hand, SPADI showed no significant difference. Conclusion : The results of this study showed that muscle activity in the peri-shoulder joint was increased after push-up plus and modified prone cobra exercise in both groups, regardless of whether pelvic compression was applied or not. Therefore, it was found that shoulder stabilization exercise using the pelvic compression belt also contributed to the enhancement of muscle activity in the joints around the shoulder.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.86-86
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• 2023

최근에 들어 환경보전과 지속가능한 하천관리의 중요성이 대두되고 있으며, 통합물관리에 있어 수리량과 수질을 연계한 통합 모니터링의 필요성이 커지고 있다. 수리량과 수질 분야에 대한 모니터링 기술은 지속적인 연구가 이루어져 왔으나, 각 분야의 개별적 연구로 인해 수리량과 수질을 통합하여 모니터링 하는 기술 개발은 미흡한 수준이다. 또한 수질 측정은 수질오염공정시험기준에 있는 채수 기준에 따라 채수하여 측정하고 있으며, 채수 지점은 하천의 수심별로 달리하여 정해진다. 수리 측정은 현장계측을 통한 2차원적 계측으로 진행하고 있어 수질 측정 시 채수지점과 수리 측정지점은 일치하지 않는다. 동일 지점에서의 수질과 수리량을 동시에 고려하고 있지 못한 모니터링은 본류와 지류의 혼합거동이 많은 국내 하천 특성을 반영하지 못한다. 또한 현재의 수질·수리 모니터링은 ADCP나 다항목수질측정기 같은 고가의 장비를 운영하며, 홍수기와 같은 고위험 계측 조건에서 인력을 통해 측정하고 있기에 고비용의 장비운영비와 인명 피해를 야기시키고 있다. 따라서 무인 원격 기술을 적용한 하천 모니터링 기술과 수질과 수리량의 데이터 연계를 통한 3차원 모니터링 기술의 확보는 하천관리에 있어 매우 필수적이다. 본 연구에서는 수중 무인 원격이동체인 ROV와 무인 원격이동체(USV)를 활용한 3차원 수질·수리 모니터링 기술 개발에 관한 연구를 수행하였다. 국내 하천 특성을 고려한 혼합거동을 분석하기 위해 ROV에 수중 GPS 장비와 수질센서를 부착시켜 수중 내 2차원으로 측정되는 수리량과 동일한 좌표를 가지는 수질자료를 계측하여 하천의 연직 분포와 수평적 분포를 통해 화학적 수리적 거동을 분석하여 하천의 3차원 혼합거동 양상을 판단할 수 있었다. 이와 같은 무인 원격이동체를 통한 3차원 수질·수리 모니터링 기술은 하천의 3차원 분석에서 수질·수리량 보간 자료로 활용 가능하며, 효율적인 모니터링을 통하여 하천 전반 및 통합물관리에 있어 크게 기여할 것이라 사료된다.

• 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.

• Journal of Auto-vehicle Safety Association
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• v.14 no.1
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• pp.6-13
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• 2022

The Ministry of Land, Infrastructure and Transport (MoLIT) and the Korean Automobile Testing and Research Institute (KATRI) are supporting the development of Lv.3 automated vehicle (hereinafter, AV) technology by constructing an automated driving pilot city (as known as K-City) equipped with total 5 evaluation environments (urban, motorway, suburban, community road, and autonomous parking facility) which is a test bed exclusively for AV (2017~2018). An upgrade project is in a progress to materialize harsh environments such as bad weather (rain, fog, etc.) and reproduction of communication jamming (GPS blocking, etc.) with the purpose of supporting the development of Lv.4 connected & automated vehicle (hereinafter, CAV) technology (2019~2022). We intend to proactively establish a national level standard for CAV test-bed and test road requirements, test method, etc. for establishment of a road map for the construction of the test bed which is being promoted step by step and analyze and, when required, benchmark the case of China that has announced and is utilizing it. Through this, we plan to define standardized requirements (evaluation facility, evaluation system, etc.) on the test bed for the development of Lv.4/4+ CAV technology and utilize the same for the design and construction of a test bed, establishment of a road map for the construction of a real car-based test environment related to the support for autonomous driving service substantiation, etc. through provision of an evaluation environment utilizing K-City, and the establishment of a K-City upgrade strategies, etc.

• 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 Information Processing Society Conference
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• 2021.11a
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• pp.371-374
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• 2021

자율주행 기술이 급속도로 발전하고 있지만, 자율주행 레이싱 기술과 관련 산업은 전세계적으로 아직 걸음마 수준이다. 본 연구팀은 국내 자율주행차 대표기업인 (주)언맨드솔루션에서 지원하는 플랫폼을 사용하여 자율주행 레이싱 제어 모델 프레임워크를 설계하고 기초 실험을 진행하였다. 제안된 자율주행 레이싱 제어 모델은 GPS 신호처리부, LiDAR 신호처리부, 영상처리부, 차량제어부, 추월/회피 제어부, 컨트롤러 통신부 등으로 구성된다. 실험을 통해 각 구성요소에 대한 기본 성능을 검증하였고, 레이싱에 최적화된 인공지능(AI) 기반 추월/회피 제어 알고리즘 개발을 위한 중요한 토대를 마련하였다. 본 연구를 바탕으로 2021년 11월에 국내 최초로 개최되는 세계 AI 로보카레이스 대회에 출전하여 제안된 자율주행 레이싱 제어 모델 프레임워크의 성능을 검증할 계획이다.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.3D
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• pp.307-315
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• 2008

The aim of this study is to develop travel time estimation model by using Self-Organized Neural network(in brief, SON) algorithm. Travel time data based on vehicles equipped with GPS and number-plate matching collected from National road number 3 (between Jangji-IC and Gonjiam-IC), which is pilot section of National Highway Traffic Management System were employed. We found that the accuracies of travel time are related to location of detector, the length of road section and land-use properties. In this paper, we try to develop travel time estimation using SON to remedy defects of existing neural network method, which could not additional learning and efficient structure modification. Furthermore, we knew that the estimation accuracy of travel time is superior to optimum located detectors than based on existing located detectors. We can expect the results of this study will make use of location allocation of detectors in highway.

• 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.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.11a
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• pp.622-624
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• 2022

최근 편리성과 경제성 등의 이유로 개인형 이동장치인 전동 킥보드의 사용이 증가하고 있다. 사용자들은 앱으로 주변의 전동 킥보드 위치를 확인한 뒤, 가까운 기기를 찾아 이용한다. 하지만 전동 킥보드의 위치는 GPS로 표시되기 때문에 10 m 이상의 오차가 날 수 있다. 이를 보완하기 위해 (주)올룰로의 킥고잉은 사용자가 전동 킥보드 반납 시 촬영한 전동 킥보드 사진을 GPS 위치 정보와 함께 제공한다. 이 사진을 통해 다음 사용자는 더욱 정확히 전동 킥보드를 찾을 수 있다. 하지만 일부 사용자들은 전동 킥보드가 존재하지 않는 사진을 올리기도 하며, 따라서 사용자들이 촬영한 사진 중 실제 전동 킥보드가 존재하는 사진들만 제공하는 것은 매우 중요하다. 따라서 본 논문은 사용자들이 촬영한 사진 중 실제 전동 킥보드가 존재하는 사진들만 정확히 인식하는 YOLO 기반 시스템을 개발한다. 제안 방법은 (1) 전동 킥보드를 부분별로 탐지하는 기법과 (2) 전동 킥보드를 촬영된 각도에 따라 세분화하여 인식하는 기법을 사용한다. 실제 사용자들이 촬영한 사진을 사용한 실험 결과, 제안 방법은 기존 방법에 비해 더욱 정확히 전동 킥보드 사진을 인식하는 것을 확인하였다.