Eunseong Pak

Mr. Eunseong Pak

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Research Focus & Metrics

Academic Career Length

3Years (2024-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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28 November 2024 in Preprints.org
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EPEunseong Pak
Eunseong Pak
BKBYEONGHUN KIM
BYEONGHUN KIM
KLKil-Soo Lee
Kil-Soo Lee
YCYong-Chul Cha
Yong-Chul Cha
HKHwa Young Kim
Hwa Young Kim
In this paper, the development and performance evaluation of a 4WD robot system designed to follow near-distance moving objects using a 2D LiDAR sensor are presented. The study incorporates identifier (ID) classification and a distance-based dynamic angle of perception model to enhance the tracking capabilities of the 2D LiDAR sensor. A particle filter algorithm was utilized to verify the accuracy of object tracking. Furthermore, a Proportional-Derivative (PD) controller was designed and implemented to ensure the stability of the robot during operation. Experimental results demonstrate the potential applicability of these approaches in various industrial applications.

31 December 2024 in Applied Sciences
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Journal Article
Open access
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EPEunseong Pak
Eunseong Pak
BKBYEONGHUN KIM
BYEONGHUN KIM
KLKil-Soo Lee
Kil-Soo Lee
YCYong-Chul Cha
Yong-Chul Cha
HKHwa Young Kim
Hwa Young Kim
In this paper, the development and performance evaluation of a 4WD robot system designed to follow near-distance moving objects using a 2D LiDAR sensor are presented. The study incorporates identifier (ID) classification and a distance-based dynamic angle of perception model to enhance the tracking capabilities of the 2D LiDAR sensor. A particle filter algorithm was utilized to verify the accuracy of object tracking. Furthermore, a proportional–derivative (PD) controller was designed and implemented to ensure the stability of the robot during operation. The experimental results demonstrate the potential applicability of these approaches in various industrial applications.

3 July 2026 in Applied Sciences
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Journal Article
Open access
PDF file
EPEunseong Pak
Eunseong Pak
BYBok-Joong Yoon
Bok-Joong Yoon
KLKil-Soo Lee
Kil-Soo Lee
YCYong-Chul Cha
Yong-Chul Cha
HKHwa Young Kim
Hwa Young Kim
Outdoor industrial sites, including logistics terminals, construction yards, and civil infrastructure worksites, increasingly require supervisory systems for monitoring autonomous mobile units under variable wireless and operational conditions. This study presents an edge-cloud telemetry platform that connects V2X on-board and roadside units to a normalized data pipeline and an operator dashboard. The architecture assigns frame reception and data validation to the edge layer, while cloud services perform stream ingestion, storage, querying, and visualization using a Kafka-Elasticsearch-Grafana stack. A fixed supervisory schema was defined for position, heading, speed, mission state, battery level, and error flags so that virtual fields used in early validation can later be replaced by measured signals without changing downstream interfaces. Physical field validation was conducted using a single test vehicle in a construction-site emulation environment to evaluate communication continuity and dashboard refresh behavior. Multi-unit applicability was examined at the architecture and schema levels, and a preliminary payload-level capacity estimate was derived using the telemetry frequency and payload-length assumptions. Under the tested site conditions, the system maintained continuous reception and visualization over an approximately 700 m distance from the RSU-side reference location. The measured end-to-end display delay averaged 0.78 s, with a standard deviation of 0.059 s and a maximum of 0.96 s. Under a 10 Hz status-message condition, the estimated pure-payload traffic was approximately 23 kbps per mobile unit. These results indicate that V2X-based edge-cloud telemetry can provide a practical baseline for supervisory monitoring in outdoor industrial sites, while simultaneous multi-vehicle validation, detailed network-load evaluation, and long-term field testing remain necessary future work.