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시험을 통한 한국형 고속전철 차량의 속도에 따른 실내소음 수준 분석
박찬경(Chankyoung Park),박춘수(Chunsu Park),김기환(Kihwan Kim),이억재(Ukjae Lee),나희승(Heeseong Na) 한국철도학회 2004 한국철도학회 학술발표대회논문집 Vol.- No.-
Korean High Speed Train (KHST) designed to operate at 350㎞/h has been tested on KyungBu high speed line since it was developed in 2002. The specification of the interior noise level for high speed train in Korea has been adopted through the contract between KHRC and Korea TGV consortium, not a national specification. But it can not be adopted to KHST designed at 350㎞/h because this has involved up to 300Km/h. Therefor, in this paper, the interior noise level at 350㎞/h are predicted in passenger car using the results at 300Km/h and these results show that the KHST"s interior noise levels are good up to 300Km/h but need to be reduced at 350Km/h in the view point of limit value at 300Km/h of the contract between KHRC and Korea TGV consortium. Also it proposed to make a national specification for the interior noise level to evaluate it in detail at 350Km/h.
김상수(Sang-Soo KIM),박춘수(Chunsu PARK),목진용(Jinyong MOK),한영재(Youngjae HAN),임용찬(Yongchan IM) 대한기계학회 2007 대한기계학회 춘추학술대회 Vol.2007 No.10
Korean Train Express (KTX) has opened to commercial traffic since 2004 at maximum speed 300㎞/h. To secure the safety and reliability of high speed railway, it is necessary to inspect and maintain the rail regularly. Korea High-speed Railway (HSR 350x) was developed and is being tested. There are 4 data acquisition modules at HSR 350x and over 400 channels are monitored during test run. Track irregularity inspection module for HSR 350x was designed and constructed over 300㎞/h. In this paper, we introduce structure and interface with track irregularity inspection system and the measurement system of HSR 350. Test run to verify the reliability of the system is performed and the result shows good performance track inspection measuring system.
이시열,김선호,이동언,박춘수,김민우,SiYeoul Lee,Seonho Kim,Dongeon Lee,ChunSu Park,MinWoo Kim 대한의용생체공학회 2023 의공학회지 Vol.44 No.4
Clinical ultrasound (US) is a widely used imaging modality with various clinical applications. However, capturing a large field of view often requires specialized transducers which have limitations for specific clinical scenarios. Panoramic imaging offers an alternative approach by sequentially aligning image sections acquired from freehand sweeps using a standard transducer. To reconstruct a 3D volume from these 2D sections, an external device can be employed to track the transducer's motion accurately. However, the presence of optical or electrical interferences in a clinical setting often leads to incorrect measurements from such sensors. In this paper, we propose a deep learning (DL) framework that enables the prediction of scan trajectories using only US data, eliminating the need for an external tracking device. Our approach incorporates diverse data types, including correlation volume, optical flow, B-mode images, and rawer data (IQ data). We develop a DL network capable of effectively handling these data types and introduce an attention technique to emphasize crucial local areas for precise trajectory prediction. Through extensive experimentation, we demonstrate the superiority of our proposed method over other DL-based approaches in terms of long trajectory prediction performance. Our findings highlight the potential of employing DL techniques for trajectory estimation in clinical ultrasound, offering a promising alternative for panoramic imaging.