http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
- 中文 을 입력하시려면 zhongwen을 입력하시고 space를누르시면됩니다.
- 北京 을 입력하시려면 beijing을 입력하시고 space를 누르시면 됩니다.
RISS 인기검색어
- 검색키워드
- 저자 : Richard D. Braatz (검색결과 4 건)
- 무료
- 기관 내 무료
- 유료
-
-
이재형,Hong Jang,Richard D. Braatz 한국화학공학회 2016 Korean Journal of Chemical Engineering Vol.33 No.1
This paper proposes a maximum likelihood estimation (MLE) method for estimating time varying local concentration of the target molecule proximate to the sensor from the time profile of monomolecular adsorption and desorption on the surface of the sensor at nanoscale. Recently, several carbon nanotube sensors have been developed that can selectively detect target molecules at a trace concentration level. These sensors use light intensity changes mediated by adsorption or desorption phenomena on their surfaces. The molecular events occurring at trace concentration levels are inherently stochastic, posing a challenge for optimal estimation. The stochastic behavior is modeled by the chemical master equation (CME), composed of a set of ordinary differential equations describing the time evolution of probabilities for the possible adsorption states. Given the significant stochastic nature of the underlying phenomena, rigorous stochastic estimation based on the CME should lead to an improved accuracy over than deterministic estimation formulated based on the continuum model. Motivated by this expectation, we formulate the MLE based on an analytical solution of the relevant CME, both for the constant and the time-varying local concentrations, with the objective of estimating the analyte concentration field in real time from the adsorption readings of the sensor array. The performances of the MLE and the deterministic least squares are compared using data generated by kinetic Monte Carlo (KMC) simulations of the stochastic process. Some future challenges are described for estimating and controlling the concentration field in a distributed domain using the sensor technology.
-
State Estimation for a Carbon Nanotube-based Sensor Array System
Hong Jang,Jay H. Lee,Richard D. Braatz 제어로봇시스템학회 2015 제어로봇시스템학회 국제학술대회 논문집 Vol.2015 No.10
This paper proposes state estimation methods for tracking a time-varying local concentration of target molecules from the carbon-nanotube (CNT)-based sensing system. The signals triggered by adsorption/desorption events of a trace of the proximate target molecules on the sensors show strongly stochastic behavior. The stochastic nature can be modelled by the chemical master equation (CME) describing the time evolution of the probabilities for all the possible numbers of adsorbed molecules. The adsorption rate on each sensor is proportional to the local concentration, thus connecting the sensor signal to it. At the macroscopic level, the concentration, which is the state, evolves according to the continuum equation represented by ordinary differential equations (ODEs). Various state estimation methods including the Kalman filter (KF), particle filter (PF), and moving horizon estimator (MHE) are designed for the system with highly stochastic, non-Gaussian measurements. Their performances are compared for single sensor as well as multiple sensors in the neighborhood that shares same concentration dynamics.
-
Fast moving horizon estimation for a distributed parameter system
Hong Jang,Kwang-Ki K. Kim,Jay H. Lee,Richard D. Braatz 제어로봇시스템학회 2012 제어로봇시스템학회 국제학술대회 논문집 Vol.2012 No.10
Partial differential equations (PDEs) pose a challenge for control engineers, both in terms of theory and computational requirements. PDEs are usually approximated by ordinary differential or partial difference equations via the finite difference method, resulting in a high-dimensional state-space system. The obtained system matrix is often symmetric, which allows this high-dimensional system to be decoupled into a set of single-dimensional systems using the state coordinate transformation defined by a singular value decomposition. Any linear constraints in the original control problem can also be simplified by replacement by an ellipsoidal constraint. This reformulated moving horizon estimation (MHE) problem can be solved in orders of magnitude lower computation time than the original MHE problem, by employing an analytical solution obtained by moving the ellipsoidal constraint to the objective function as a penalty weighted by a decreasing penalty parameter. The proposed MHE algorithm is demonstrated for a one-dimensional diffusion in which the concentration field is estimated using distributed sensors.
연관 검색어 추천
이 검색어로 많이 본 자료
활용도 높은 자료
