Jun Hong Park
Hanyang University · 工学
研究室紹介
Professor Jun Hong Park's research lab specializes in advanced materials and intelligent systems, focusing on the development of flexible and wearable electronic devices, structural health monitoring using deep learning, and environmental monitoring of vehicle emissions. The lab integrates nanofabrication techniques with AI-driven diagnostics to enable non-invasive medical screening and real-time condition monitoring of mechanical and structural systems. Key research directions include inkjet-printed conductive films for flexible electronics, AI-based analysis of physiological sounds (e.g., cough for pneumonia detection), and emission control technologies for sustainable transportation.
Research Overview
Research Output Trend
Figures are computed from collected data and may differ slightly.
Selected Papers
15With the rapid progress in the deep learning technology, it is being used for vibration-based structural health monitoring. When the vibration is used for extracting features for system diagnosis, it is important to correlate the measured signal to the current status of the structure. The measured vibration responses show large deviation in spectral and transient characteristics for systems to be monitored. Consequently, the diagnosis using vibration requires complete understanding of the extrac
Despite the strengthening of vehicle emissions standards and test methods, nitrogen oxide (NOx) emissions from on-road mobile sources are not being notably reduced. The introduction of real driving emission (RDE) regulations is expected to reduce the discrepancy between emission regulations and actual air pollution. To analyze the effects of RDE regulations on heavy-duty diesel vehicles, pollutants emitted while driving were measured using a portable emission measurement system (PEMS) for Euro 5
Pneumonia is a serious disease often accompanied by complications, sometimes leading to death. Unfortunately, diagnosis of pneumonia is frequently delayed until physical and radiologic examinations are performed. Diagnosing pneumonia with cough sounds would be advantageous as a non-invasive test that could be performed outside a hospital. We aimed to develop an artificial intelligence (AI)-based pneumonia diagnostic algorithm. We collected cough sounds from thirty adult patients with pneumonia o
Sound radiation from electric motor‐driven vehicles is negligibly small compared to sound radiation from internal combustion engine automobiles. When running on a local road, an artificial sound is required as a warning signal for the safety of pedestrians. In this study, an engine sound was synthesized by combining artificial mechanical and combustion sounds. The mechanical sounds were made by summing harmonic components representing sounds from rotating engine cranks. The harmonic components,