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박진욱(Jinuk Park),이우람(Uram Lee),이재성(Jaesung Lee) 한국자동차공학회 2022 한국자동차공학회 부문종합 학술대회 Vol.2022 No.6
In a fuel cell system, air containing moisture. This air can freeze inside the Air-cut off valve. This makes it difficult to start the system in cold areas. In this paper, we propose a method of crushing ice using vibration.
인공 신경망 모형을 이용한 한국프로야구 관중 수요 예측
박진욱 ( Jinuk Park ),박상현 ( Sanghyun Park ) 한국정보처리학회 2017 한국정보처리학회 학술대회논문집 Vol.24 No.1
본 연구는 기존의 수요 예측 등의 시계열 분석에서 주로 사용되는 ARIMA 모형의 어려움을 극복하고자 인공신경망(Artificial Neural Network) 모형을 이용하여 한국 프로 야구 관중 수를 예측하였다. 인공신경망의 가장 기본적인 종류인 전방향 신경망(Feedforward Neural Network)의 초모수(Hyperparameter) 선정에 그리드 탐색(Grid Search)을 적용하여 최적의 모형을 찾고자 하였다. 훈련 자료로는 2015년 3월부터 8월까지의 일별 KBO 관중 수 자료를 대상으로 하였고, 예측력 검증을 위해 2015년 9월 관중 수를 예측하여 실제 관측값과 비교하였다. 그 결과, 그리드 탐색법에서 최적 모형이라고 판단한 모형의 예측력은, 평균 절대 백분율 오차(MAPE) 기준으로 평균 27.14% 였다. 또한, 앙상블 기법에서 착안하여 오차율이 낮은 모형 5개의 예측값 평균의 MAPE는 평균 28.58% 였다. 이는 다중회귀와 비교해보았을 때, 평균적으로 각각 14%, 13.6% 높은 예측력을 보이고 있다.
젖은 노면에서 타이어마모를 고려한 ESC 강인성 분석에 관한 연구
박진욱(Jinuk Park),조태근(Taekeun Cho),권재준(Jaejoon Kwon),홍태욱(Teawook Hong),박기홍(Kihong Park) 한국자동차공학회 2011 한국자동차공학회 학술대회 및 전시회 Vol.2011 No.11
In most research and development level for vehicle stability improvement, ESC performance evaluation has been conducted assuming a steady road friction. However, under a wet road condition, the tire performance differs greatly depending on the tread wear, vehicle velocity and water depth. For that reason, this research developed a tire force gain based on tread wear under a wet road condition. A vehicle stability test has been conducted on a HILS system, which includes a CarSim vehicle model with the tire force gain developed in the research and a commercial ESC. Through the HILS, a comparative analysis of existing constant road friction coefficient and the newly developed tire force gain has been carried out, focused on their different influences on the ESC performance.
An Improved LDPC ECC based on System Level Reprogramming for MLC NAND Flash
Jinuk Kim,Jihun Jung,Sungju Park 대한전자공학회 2020 Journal of semiconductor technology and science Vol.20 No.1
With the ever-increasing requirement for higher performance of system and memory with a huge storage capacity, Multi-Level Cell (MLC) flash memory holds a dominant position in flash memory. However, more errors are observed in flash memory as the storage size increases. Thus, various error correction techniques (e.g., Hamming, BCH, LDPC) have been adopted to maintain the reliability of data stored in flash memory. Retention error, which occurs in data that is not accessed for a long period after stored, is the major contributor of flash memory error. The incidence of retention error is highly related to p-e (program-erase) cycle of flash memory. However, in system level, flash memory should be erased before modifying data even if ECC (Error correction code) decoding is completed. This erase operation increases the p-e cycle of flash memory, resulting in high incidence of retention error. This paper introduces a safe in-place reprogramming scheme at system level to optimally correct errors without erasing. Additionally, LDPC decoding scheme uses information about the uncorrected errors to improve the performance of the LDPC decoder. Through an experiment that simulates real NAND flash memory chips, safe reprogramming scheme at system level and proposed LDPC decoding method are explicitly demonstrated. As a result of proposed in-place reprogramming, reliable retention time of data is extended. Also, proposed LDPC decoding along with safe reprogramming scheme reduced the number of average iterations up to 33% while maintaining a robust correction ability.