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Physics-informed neural networks for learning fluid flows with symmetry
Kim Younghyeon,Kwak Hyungyeol,Nam Jaewook 한국화학공학회 2023 Korean Journal of Chemical Engineering Vol.40 No.9
We suggest symmetric variational physics-informed neural networks (symmetric VPINN) to learn the symmetric fluid flow and physical properties of fluids from a limited set of data. Symmetric VPINN is based on the VPINN framework and guarantees the symmetry of the solutions by modifying the network architecture. The effectiveness of the symmetric VPINN is demonstrated by predicting the velocity profiles and power-law fluid properties in the Poiseuille flow of a parallel channel. Symmetric VPINN models robustly and accurately learn power-law fluid flow in both forward and inverse problems. We demonstrate that the symmetric VPINN can be particularly useful when the power-law index is small and the data are extremely limited. The modified network architecture in the symmetric VPINN guides the neural network towards an exact solution by reinforcing symmetry. We show that symmetric VPINN is effective in obtaining unknown physical properties in practical experiments where data are scarce, suggesting the possibility of introducing known conditions of the system directly into the network structure to improve the accuracy of the network.
김영현(Younghyeon Kim),유상석(Sangseok Yu) 한국자동차공학회 2022 한국자동차공학회 지부 학술대회 논문집 Vol.2022 No.5
Various studies are being conducted on hydrogen fuel cell vehicles, and in particular, research on EMS strategies is being actively conducted. A forklift is operated by the power to lift objects and the power to move the vehicle, and because it is not used as a simple means of transportation, an EMS strategy different from that of conventional vehicles is required. Therefore, in this study, the EMS strategy of forklifts was analyzed by developing a fuel cell model and a battery model that can simulate the behavior of a fuel cell in a driving environment. First, PEMFC model and a battery model were designed and verified using experimental values. Sensitivity study of Energy Management system strategy was also carried out to find the optimal power ratio of the stack and the battery. Using a dedicated forklift cycle called the VDI cycle, we analyzed what kind of power distribution per weight load is efficient.
초목을 포함한 도로 환경에서의 필터링 기반 주행 가능 영역 검출 방법 및 하드웨어 구조
김영현 ( Younghyeon Kim ),하지석 ( Jiseok Ha ),최철호 ( Cheol-ho Choi ),문병인 ( Byungin Moon ) 한국정보처리학회 2021 한국정보처리학회 학술대회논문집 Vol.28 No.1
초목을 포함한 도로 환경에서, 초목 영역은 도로의 특성과 매우 유사하므로 주행 가능 영역으로 판단될 수 있다. 또한, 도로 검출을 위한 대부분의 U-V 시차 기반 하드웨어 시스템에서는 한 프레임의 시차가 모두 입력되기 전까지 다음 단계의 연산을 수행할 수 없는 문제가 있다. 이에 본 논문에서는 간단한 필터링 기법를 적용하여 초목을 포함한 도로 환경에서 즉각적으로 주행 가능 영역을 검출하는 방법 및 그 하드웨어 구조를 제안한다. 제안하는 방법은 93.08%의 정확도를 보인다. 또한, 제안하는 하드웨어 구조는 기존 방법보다 Slice LUTs 기준 60.26% 및 Slice Registers 기준 53.62% 적은 하드웨어 자원을 사용한다.
초목을 포함한 도로 환경에서 주행 가능 영역 검출을 위한 필터링 기반 방법 및 하드웨어 구조
김영현 ( Younghyeon Kim ),하지석 ( Jiseok Ha ),최철호 ( Cheol-ho Choi ),문병인 ( Byungin Moon ) 한국정보처리학회 2022 정보처리학회논문지. 소프트웨어 및 데이터 공학 Vol.11 No.1
Drivable area detection, one of the main functions of advanced driver assistance systems, means detecting an area where a vehicle can safely drive. The drivable area detection is closely related to the safety of the driver and it requires high accuracy with real-time operation. To satisfy these conditions, V-disparity-based method is widely used to detect a drivable area by calculating the road disparity value in each row of an image. However, the V-disparity-based method can falsely detect a non-road area as a road when the disparity value is not accurate or the disparity value of the object is equal to the disparity value of the road. In a road environment including vegetation, such as a highway and a country road, the vegetation area may be falsely detected as the drivable area because the disparity characteristics of the vegetation are similar to those of the road. Therefore, this paper proposes a drivable area detection method and hardware architecture with a high accuracy in road environments including vegetation areas by reducing the number of false detections caused by V-disparity characteristic. When 289 images provided by KITTI road dataset are used to evaluate the road detection performance of the proposed method, it shows an accuracy of 90.12% and a recall of 97.96%. In addition, when the proposed hardware architecture is implemented on the FPGA platform, it uses 8925 slice registers and 7066 slice LUTs.