조달청의 조직 개선방안 : 조직 분위기를 중심으로

오광석,김석주,이형수,정병걸 ( Kwang Seok Oh,Seok Ju Kim,Hyang Soo Lee,Byung Kul Jung ) 한국행정연구원 1998 韓國行政硏究 Vol.7 No.3

굴삭기를 위한 레이저 스캐너 기반 확률 및 예견 작업 위험도 평가 알고리즘 개발

오광석,박성렬,서자호,이근호,이경수,Oh, Kwang Seok,Park, Sung Youl,Seo, Ja Ho,Lee, Geun Ho,Yi, Kyong Su 유공압건설기계학회 2016 드라이브·컨트롤 Vol.7 No.4

This paper presents a stochastic and predictive working-risk-assessment algorithm for excavators based on a one-layer laser scanner. The one-layer laser scanner is employed to detect objects and to estimate an object's dynamic behaviors such as the position, velocity, heading angle, and heading rate. To estimate the state variables, extended and linear Kalman filters are applied in consideration of laser-scanner information as the measurements. The excavator's working area is derived based on a kinematic analysis of the excavator's working parts. With the estimated dynamic behaviors and the kinematic analysis of the excavator's working parts, an object's behavior and the excavator's working area such as the maximum, actual, and predicted areas are computed for a working risk assessment. The four working-risk levels are defined using the predicted behavior and the working area, and the intersection-area-based quantitative-risk level has been computed. An actual test-data-based performance evaluation of the designed stochastic and predictive risk-assessment algorithm is conducted using a typical working scenario. The results show that the algorithm can evaluate the working-risk levels of the excavator during its operation.

Volume Transition Behaviors of NIPA / Alkylacrylamide - Derivatives Copolymer Gels by Photon Correlation Spectroscopy

오광석,이영돈,배영찬 ( K . S . Oh,Y . D . Lee,Y . C . Bae ) 한국공업화학회 1996 한국공업화학회 연구논문 초록집 Vol.1994 No.1

회전관성의 순환최소자승 추정을 이용한 모델 예견 기반 굴삭기의 충돌회피 알고리즘 개발

오광석,서자호,이근호,Oh, Kwang Seok,Seo, Jaho,Lee, Geun Ho 유공압건설기계학회 2016 드라이브·컨트롤 Vol.7 No.4

This paper proposes a model-prediction-based collision-avoidance algorithm for excavators for which the recursive-least-squares (RLS) estimation of the excavator's rotational inertia is used. To estimate the rotational inertia of the excavator, the RLS estimation with multiple forgetting and two updating rules for the nominal parameter and the forgetting factors was conducted based on the excavator-swing dynamics. The average value of the estimated rotational inertia that is for the minimizing effects of the estimation error was computed using the recursive-average method with forgetting. Based on the swing dynamics, the computed average of the rotational inertia, the damping coefficient for braking, and the excavator's braking angle were predicted, and the predicted braking angle was compared with the detected-object angle for a safety evaluation. The safety level defined in this study consists of the three levels safe, warning, and emergency braking. The analytical rotational-inertia-based performance evaluation of the designed estimation algorithm was conducted using a typical working scenario. The results of the safety evaluation show that the predictive safety-evaluation algorithm of the proposed model can evaluate the safety level of the excavator during its operation.

차량 시뮬레이터 접목을 위한 실시간 인체거동 해석기법

오광석,손권,최경현,Oh, Kwangseok,Son, Kwon,Choi, Kyunghyun 대한기계학회 2002 大韓機械學會論文集A Vol.26 No.5

Visual effects are important cues for providing occupants with virtual reality in a vehicle simulator which imitates real driving. The viewpoint of an occupant is sensitively dependent upon the occupant's posture, therefore, the total human body motion must be considered in a graphic simulator. A real-time simulation is required for the dynamic analysis of complex human body motion. This study attempts to apply a neural network to the motion analysis in various driving situations. A full car of medium-sized vehicles was selected and modeled, and then analyzed using ADAMS in such driving conditions as bump-pass and lane-change for acquiring the accelerations of chassis of the vehicle model. A hybrid III 50%ile adult male dummy model was selected and modeled in an ellipsoid model. Multibody system analysis software, MADYMO, was used in the motion analysis of an occupant model in the seated position under the acceleration field of the vehicle model. Acceleration data of the head were collected as inputs to the viewpoint movement. Based on these data, a back-propagation neural network was composed to perform the real-time analysis of occupant motions under specified driving conditions and validated output of the composed neural network with MADYMO result in arbitrary driving scenario.

휠로더 가상 성능평가를 위한 V상차 작업 운전자 모델

오광석(Kwangseok Oh),김학구(Hakgu Kim),고경은(Kyungeun Ko),김판영(Panyoung Kim),이경수(Kyongsu Yi) 대한기계학회 2014 大韓機械學會論文集A Vol.38 No.11

본 논문에서는 가상의 V상차 작업을 위한 이벤트 기반의 휠로더 운전자 모델을 개발하였다. 운전자 모델 개발의 목적은 휠로더의 일반적인 작업인 V상차 작업 시 동역학적 해석과 작업성능을 가상의 시뮬레이션 모델과 운전자 입력을 이용해 예측 및 평가하는 것이다. V상차 작업은 4단계로 이루어져 있으며, 총 8 개의 이벤트로 인해 순차적으로 작업이 진행된다. 개발된 3D 휠로더 시뮬레이션 모델은 Matlab/Simulink 환경에서 구성 되었으며, 시뮬레이션 결과는 V상차 작업의 실차 데이터와 비교 되었다. 본 연구에서 개발된 운전자 모델로 향후 가상의 V상차 작업에 대한 작업성능 및 동역학적 해석이 가능할 것으로 본다. This paper presents the development of an event-based operator model of a wheel loader for virtual V-pattern working. The objective of this study is to analyze the performance and dynamic behavior of the wheel loader for a typical V-pattern. The proposed typical V-pattern working is divided into four stages. The developed operator model is based on eight events, and the operator"s inputs are occurred sequentially by event. A 3D dynamic simulation model of the wheel loader is developed and used to analyze the dynamic behavior during working, and the simulation results are compared with the experimental data of V-pattern working. The proposed 3D dynamic simulation model and operator model are constructed using MATLAB/Simulink. The proposed operator model for V-pattern working is expected to enable evaluation of the working performance and dynamic behavior of the wheel loader.

초임계 수를 이용한 리튬코발트 옥사이드 나노입자 합성

오광석 ( Kwang Seok Oh ),김재덕 ( Jae Duck Kim ),임종성 ( Jong Sung Lim ),이윤우 ( Youn Woo Lee ) 한국화학공학회 2007 화학공학의이론과응용 Vol.10 No.2

주행 및 작업성능 통합 시뮬레이션을 위한 휠로더 동역학 모델의 개발

오광석(Kwangseok Oh),김학구(Hakgoo Kim),윤승재(Seungjae Yun),고경은(Kyungeun Ko),김판영(Panyoung Kim),이경수(Kyongsu Yi) 한국자동차공학회 2012 한국자동차공학회 학술대회 및 전시회 Vol.2012 No.11

This paper presents a 3D dynamic simulation model of a wheel loader. The objective of development of the wheel loader dynamic simulation model is to test the performance of the Wheel loader under both working and driving conditions by simulation. The wheel loader dynamic simulation model consists of 3 parts: Vehicle powertrain module, Hydraulic module for working and steering, and Vehicle dynamic module. Vehicle powertrain module consists of engine, torque converter and transmission, and Hydraulic module consists of pump, valve, cylinder and attachments. 3D dynamic simulation module is based on vehicle dynamics and multi-body (front and rear bodies) dynamic analysis method. Front and rear bodies are connected by pin in the center of steering system. Action/reaction forces and moments applied to the pin are calculated by solving front/rear dynamic simultaneous equations. Forces and moments at the pin are considered to analyze how they have an effect on the dynamics of wheel loader. The wheel loader dynamic simulation model is verified through real measurement, and it is shown that proposed dynamic simulation model can represent actual dynamics of wheel loader.

슬라이딩 모드 관측기를 이용한 기구학 모델 기반 자율주행 자동차의 예견 고장진단 알고리즘

오광석(Kwang Seok Oh),이경수(Kyong Su Yi) 대한기계학회 2017 大韓機械學會論文集A Vol.41 No.10

본 논문은 슬라이딩 모드 관측기를 이용한 기구학 모델 기반 자율주행 자동차의 예견 고장진단 알고리즘에 관한 연구이다. 자율주행 자동차는 안전한 주행을 위해 신뢰성이 확보된 주행환경 정보와 차량의 동적상태 정보가 필요하다. 센서 정보의 신뢰성 판단을 위해 본 연구에서는 종방향 기구학 모델기반 슬라이딩모드 관측기를 이용하여 종방향 환경정보와 차량 가속도 정보를 실시간으로 상호 보완적 고장진단이 가능한 예견 알고리즘을 제안하였다. 적용된 슬라이딩 모드 관측기는 종방향 환경정보의 고장신호에도 강건한 입력신호 재건성능을 보이면서 알고리즘의 신뢰성을 확보할 수 있었다. 예견 고장진단 알고리즘의 합리적 성능평가를 위해 네 가지 조건에 대한 실제 주행 데이터 기반 선행차량 추종시나리오를 적용하였다. 성능평가 결과 본 연구에서 제안된 예견 고장진단 알고리즘은 모든 평가조건과 주행 시나리오에 대해 합리적인 고장진단 성능을 보여주었다. This paper describes a predictive fault diagnosis algorithm for autonomous vehicles based on a kinematic model that uses a sliding mode observer. To ensure the safety of autonomous vehicles, reliable information about the environment and vehicle dynamic states is required. A predictive algorithm that can interactively diagnose longitudinal environment and vehicle acceleration information is proposed in this paper to evaluate the reliability of sensors. To design the diagnosis algorithm, a longitudinal kinematic model is used based on a sliding mode observer. The reliability of the fault diagnosis algorithm can be ensured because the sliding mode observer utilized can reconstruct the relative acceleration despite faulty signals in the longitudinal environment information. Actual data based performance evaluations are conducted with various fault conditions for a reasonable performance evaluation of the predictive fault diagnosis algorithm presented in this paper. The evaluation results show that the proposed diagnosis algorithm can reasonably diagnose the faults in the longitudinal environment and acceleration information for all fault conditions.

다중 슬라이딩 모드 관측기를 이용한 모델 예측 기반 종방향 자율주행 센서 고장 탐지 알고리즘

오광석(Kwang Seok Oh),박성렬(Sung Youl Park),이경수(Kyong Su Yi) 대한기계학회 2019 大韓機械學會論文集A Vol.43 No.3

본 연구는 다중 슬라이딩 모드 관측기를 이용한 모델 예측 기반 종방향 자율주행 센서 고장 탐지알고리즘 개발에 관한 것이다. 자율주행 자동차의 선행차량과 함께 주행하는 조건에서 종방향 제어를 위해 사용되는 레이더 기반 상대속도 및 거리 그리고 자차량 가속도 정보의 고장탐지를 위해 다중 슬라이딩 모드 관측기와 모델 예측 알고리즘이 적용되었다. 선행차량과 함께 주행하는 조건에서 슬라이딩 모드 관측기는 상대가속도를 재건하고, 모델 예측 알고리즘 기반 상대거리 및 상대속도를 예측한다. 예측된 상태정보는 일정 시간 동안 실시간 저장되고, 저장된 상태정보들 중 현재 상태를 나타내는 값들을 이용하여 다중 슬라이딩 모드 관측기 기반 상대가속도를 재건한다. 예측되어 저장된 상태정보와 재건된 상대가속도 값들을 이용하여 레이더 및 가속도 센서 기반 획득된 정보의 고장을 탐지한다. 실 주행 데이터와 3차원 차량 동역학 모델을 이용하여 모델 예측 기반 고장탐지 알고리즘의 성능을 평가하였다. This paper describes the model prediction-based fault detection algorithm of a sensor for longitudinal autonomous driving using a multi-sliding mode observer. In order to detect the faults in radar and acceleration sensors used for longitudinal control of autonomous vehicles, a sliding mode observer and model predictive algorithm was used. In an actual driving situation where the subject vehicle drives with the preceding vehicle, the sliding model observer was used to reconstruct the relative acceleration while the model predictive algorithm was used to predict relative values such as relative displacement and velocity. The predicted states were saved in finite time, and relative accelerations were reconstructed based on the multi-sliding mode observer using the predicted states that represent the current state. Based on the predicted states and reconstructed accelerations, the faults in the sensors can be detected. The performance evaluation of the proposed model predictive algorithm was conducted using actual driving data and a 3D vehicle dynamics model.