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제품 사용데이터를 활용한 제품 열화상태 평가 방안에 대한 연구
Shin, Jongho,Jun, Hongbae,Cattaneo, Cedric,Kiritsis, Dimitris,Xirouchakis, Paul 한국CDE학회 2013 한국CDE학회 논문집 Vol.18 No.1
In general, the product is used under several circumstances including environmental and usage conditions. According to the circumstances, the product has various performance degradation processes. In order to optimize the lifecycle of product usage, it is important to observe the degradation process and make suitable decisions on product operations. However, there are not much research works in evaluating the degree of product degradation based on product usage data. Recently, due to emerging ICT (Information and Communication Technology) technologies, it becomes possible to get the product usage data. Based on the gathered data, it is possible to analyze the degree of product degradation. The analysis of product usage data can improve product use and product design with advanced decisions. To this end, this study addresses one approach based on FMEA/FMECA method, called PDMCA (Performance, Degradation Modes and Criticality Analysis) for evaluating product degradation status and making suitable decisions.
JongHo Shin,Yun Kyung Shin,Yong Se Kim (사)한국CDE학회 2010 한국CAD/CAM학회 국제학술발표 논문집 Vol.2010 No.8
As an effort to provide students the opportunity in enhancing design creativity in a personalized adaptive manner, an exercise program that address cognitive elements of creativity has been devised so that personalized needs in specific elements could be addressed. We conducted an experiment with students in interdisciplinary, integrated design where the exercise program for cognitive creativity elements with self-reporting of affective states was assigned between two simple conceptual design tasks. The experiment result supports that the exercise program helps in enhancing design creativity. Employing the experiment results, we are using data mining approaches in understanding the relations among various characteristics of students and their learning experiences in this creativity enhancement exercise. Findings in the experiments as well as data mining results will be presented together with implications in design creativity education.
Nonlinear Model Predictive Formation Flight
Jongho Shin,Kim, H.J. IEEE 2009 IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS P Vol.39 No.5
<P>This correspondence paper presents the validation of a formation flight control technique with obstacle avoidance capability based on nonlinear model predictive algorithms. Control architectures for multi-agent systems employed in this correspondence paper can be categorized as centralized, sequential decentralized, and fully decentralized methods. Centralized methods generally have better performance than decentralized methods. However, it is well known that the performance of the centralized methods for formation flight degrades when there exists communication failure among the vehicles, and they require more computation time than the decentralized method. This correspondence paper evaluates the control performance and the computation time reduction of the sequential decentralized and fully decentralized methods in comparison with the centralized method and shows that the fully decentralized method can be made effective against short term communication failure. The control inputs for formation flight are computed by nonlinear model predictive control (NMPC). The control input saturation and state constraints are incorporated as inequality constraints using Karush Kuhn Tucker conditions in the NMPC framework, and the collision avoidance can be considered in real time. The proposed schemes are validated by numerical simulations, which include the process and measurement noise for more realistic situations.</P>
Adaptive Path-Following Control for an Unmanned Surface Vessel Using an Identified Dynamic Model
Shin, Jongho,Kwak, Dong Jun,Lee, Young-il IEEE 2017 IEEE/ASME transactions on mechatronics Vol.22 No.3
<P>This paper proposes a path-following control method for an unmanned surface vessel (USV) based on an identified dynamic model. To handle the USV dynamic-model effectively, a three degree-of-freedom model is employed instead of a full nonlinear dynamic model and linearized at specific equilibrium condition. The linearized model is identified with real data from several experiments by utilizing a particle swarm optimization method. Then, based on the identified model, an adaptive control algorithm is proposed to follow several waypoints and velocity command. The proposed control method utilizes virtual control input, dynamic surface control method, and adaptive terms to handle matched and unmatched uncertainties simultaneously. The overall closed-loop stability is analyzed by introducing deadzone errors composed of tracking error and saturation function. Finally, some experiment with a remodeled commercial fishing boat are conducted and analyzed to validate the performance of the proposed methods.</P>
Autonomous Flight of the Rotorcraft-Based UAV Using RISE Feedback and NN Feedforward Terms
Jongho Shin,Kim, H. Jin,Youdan Kim,Dixon, Warren E. IEEE 2012 IEEE transactions on control systems technology Vol.20 No.5
<P>A position tracking control system is developed for a rotorcraft-based unmanned aerial vehicle (RUAV) using robust integral of the signum of the error (RISE) feedback and neural network (NN) feedforward terms. While the typical NN-based adaptive controller guarantees uniformly ultimately bounded stability, the proposed NN-based adaptive control system guarantees semi-global asymptotic tracking of the RUAV using the RISE feedback control. The developed control system consists of an inner-loop and outer-loop. The inner-loop control system determines the attitude of the RUAV based on an adaptive NN-based linear dynamic model inversion (LDI) method with the RISE feedback. The outer-loop control system generates the attitude reference corresponding to the given position, velocity, and heading references, and controls the altitude of the RUAV by the LDI method with the RISE feedback. The linear model for the LDI is obtained by a linearization of the nonlinear RUAV dynamics during hover flight. Asymptotic tracking of the attitude and altitude states is proven by a Lyapunov-based stability analysis, and a numerical simulation is performed on the nonlinear RUAV model to validate the effectiveness of the controller.</P>
Autonomous flight of the rotorcraft-based UAV using RISE feedback and NN feedforward terms
Jongho Shin,H. Jin Kim,Youdan Kim,Warren E. Dixon 한국산업응용수학회 2013 한국산업응용수학회 학술대회 논문집 Vol.8 No.1
A position tracking control system is developed for a rotorcraft-based unmanned aerial vehicle (RUAV) using robust integral of the signum of the error (RISE) feedback and neural network (NN) feedforward terms. While the typical NN-based adaptive controller guarantees uniformly ultimately bounded stability, the proposed NN-based adaptive control system guarantees semiglobal asymptotic tracking of the RUAV using the RISE feedback control. The developed control system consists of an inner-loop and outer-loop. The inner-loop control system determines the attitude of the RUAV based on an adaptive NN-based linear dynamic model inversion (LDI) method with the RISE feedback. The outer-loop control system generates the attitude reference corresponding to the given position, velocity, and heading references, and controls the altitude of the RUAV by the LDI method with the RISE feedback. The linear model for the LDI is obtained by a linearization of the nonlinear RUAV dynamics during hover flight. Asymptotic tracking of the attitude and altitude states is proven by a Lyapunov-based stability analysis, and a numerical simulation is performed on the nonlinear RUAV model to validate the effectiveness of the controller.
Model predictive flight control using adaptive support vector regression
Jongho Shin,H. Jin Kim,Sewook Park,Youdan Kim 한국산업응용수학회 2009 한국산업응용수학회 학술대회 논문집 Vol.2009 No.5
This paper explores an application of support vector regression (SVR) to model predictive control (MPC). SVR is employed to identify a dynamic system from input-output data, and the identified model is used for predicting the future states in the MPC framework. In order to deal with time-dependent perturbations, an online adaptation algorithm is proposed for compensating the error between the actual dynamics and identified model. The convergence property of the adaptation rule is discussed using discrete-time Lyapunov stability analysis. Finally, the proposed approach is applied to identification and flight control of a fixed-wing unmanned aircraft.