Amir Tjolleng

Dr. Amir Tjolleng

Industrial Engineering Department, Binus University, Jakarta, Indonesia
orcid

Research Focus & Metrics

Academic Career Length

14Years (2013-Present)

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

Representative Publications

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1 March 2017 in Applied Ergonomics
Verified
Journal Article
Closed access
ATAmir Tjolleng
Amir Tjolleng
KJKihyo Jung
Kihyo Jung
WHWongi Hong
Wongi Hong
WLWonsup Lee
Wonsup Lee
BLBaekhee Lee
Baekhee Lee
SPSeikwon Park
Seikwon Park


22 August 2017 in Maritime Economics & Logistics
Verified
Journal Article
Closed access
PDF file
PEPayman Eslami
Payman Eslami
KJKihyo Jung
Kihyo Jung
DLDaewon Lee
Daewon Lee
ATAmir Tjolleng
Amir Tjolleng

1 October 2022 in Applied Ergonomics
Verified
Journal Article
Closed access
ATAmir Tjolleng
Amir Tjolleng
JCJoonho Chang
Joonho Chang
Jangwoon ParkJangwoon Park
Jangwoon Park
WLWonsup Lee
Wonsup Lee
MCMyeongseop Cha
Myeongseop Cha
KJKihyo Jung
Kihyo Jung

13 February 2020 in Applied Sciences
Verified
Journal Article
Open access
PDF file
PSPark Sunghyun
Park Sunghyun
ATAmir Tjolleng
Amir Tjolleng
JCJoonho Chang
Joonho Chang
MCMyeongseop Cha
Myeongseop Cha
JPJongcheol Park
Jongcheol Park
KJKihyo Jung
Kihyo Jung
Detection and localization of the dents on a vehicle body that occurs during manufacturing is critical to achieve the appearance quality of a new vehicle. This study proposes a region-based convolutional neural network (R-CNN) to detect and localize dents for a vehicle body inspection. For a better feature extraction, this study employed a lighting system, which can highlight dents on an image by projecting the Mach bands (bright-dark stripes). The R-CNN was trained using the highlighted images by the Mach bands, and heat-maps were prepared with the classification scores estimated from the R-CNN to localize dents. This study applied the proposed R-CNN to the inspection of dents on the surface of a car body and quantitatively analyzed its performances. The detection accuracy of the dents was 98.5% for the testing data set, and mean absolute error between the actual dents and estimated dents were 13.7 pixels, which were close to one another. The proposed R-CNN could be applied to detect and localize surface dents during the manufacture of vehicle bodies in the automobile industry.

19 December 2022 in Applied Sciences
Verified
Journal Article
Open access
PDF file
ATAmir Tjolleng
Amir Tjolleng
JYJaesik Yang
Jaesik Yang
KJKihyo Jung
Kihyo Jung
The accelerator pedal in a vehicle can be pressed by either of two (natural and rotated) foot postures, according to driver preference. Since the rotated foot posture requires only foot motion to switch between the brake and accelerator pedals, most drivers prefer it over the natural foot posture, which requires both leg and foot motions to switch between the pedals. However, the rotated foot posture may require more leg-muscle efforts and an awkward foot posture while operating the accelerator pedal, due to an inappropriate contact between the sole of the foot and the top surface of the accelerator pedal. This study examined the effects of foot postures on leg-muscle activities and foot angles while pressing the accelerator pedal for 10 male drivers. The experimental data revealed that %MVC (maximum voluntary contraction) of leg muscles for the rotated posture (6.39%) was significantly higher than that of the natural posture (3.86%). Both foot postures showed shared patterns in foot angles while pressing the accelerator pedal; however, a slight awkward foot inversion was observed in the rotated posture while pressing the accelerator pedal. The findings of this study suggest that a new design of accelerator pedal is needed for a better user experience while pressing the accelerator pedal with the rotated foot posture.