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Preparation of Photocatalytic Active Inorganic Nanofibers Using Electrospinning
노선영,홍주형,김형섭,Noh, Sun-Young,Hong, Joo-Hyung,Kim, Hyung-Sup The Korean Fiber Society 2011 한국섬유공학회지 Vol.48 No.2
To obtain $TiO_2$ inorganic nanofibers, TiP/PVAc solutions were electrospun and calcined. Their microscopic structures and morphologies were characterized using FT-IR, WAXD, and SCM. The effects of the spinning and the calcination conditions on crystal formation and structure were then studied. Also, the specific surface area and the photocatalytic activity of the inorganic fibers wcre examined. The Study revealed that fibers with anatase structure showed higher specific sur/ace area and better photocatalytic activity than fibers with a rutile structure.
IMM Method Using Kalman Filter with Fuzzy Gain
노선영,주영훈,박진배,Noh, Sun-Young,Joo, Young-Hoon,Park, Jin-Bae Korean Institute of Intelligent Systems 2006 한국지능시스템학회논문지 Vol.16 No.2
In this paper, we propose an interacting multiple model (IMM) method using intelligent tracking filter with fuzzy gain to reduce tracking errors for maneuvering targets. In the proposed filter, the unknown acceleration input for each sub-model is determined by mismatches between the modelled target dynamics and the actual target dynamics. After a acceleration input is detected, the state estimates for each sub-filter are modified. To modify the accurate estimation, we propose the fuzzy gain based on the relation between the filter residual and its variation. To optimize each fuzzy system, we utilize the genetic algorithm (GA). The tracking performance of the proposed method is compared with those of the adaptive interacting multiple model(AIMM) method and input estimation (IE) method through computer simulations.
Robust Kalman filtering for the TS Fuzzy State Estimation
Sun Young Noh(노선영),Young Hoon Joo(주영훈),Jin Bae Park(박진배) 대한전기학회 2006 대한전기학회 학술대회 논문집 Vol.2006 No.7
In this paper, the Takagi-Sugeno (TS) fuzzy state estimation scheme, which is suggested for a steady state estimator using standard Kalman filter theory with uncertainties. In that case, the steady state with uncertain can be represented by the TS fuzzy model structure, which is further rearranged to give a set of uncertain linear model using standard Kalman filter theory. And then the unknown uncertainty is regarded as an additive process noise. To optimize fuzzy system, we utilize the genetic algorithm. The steady state solutions can be found for proposed linear model then the linear combination is used to derive a global model. The proposed state estimator is demonstrated on a truck-trailer.
초기치료단계 고형암 환자들의 간호요구와 간호사의 간호수행 비교연구
노선영(Noh Sun Young),태영숙(Tae Young Sook) 고신대학교 전인간호과학연구소 2015 전인간호과학학술지 Vol.8 No.-
Purpose: This study was conducted to provide basic information in order to develop and promote nursing practice for solid cancer patients by comparing their nursing needs with the nursing practice provided to them by nurses. Methods: The subjects of this study were 102 solid cancer patients and 97 nurse in K university at B city. To measure the nursing needs of solid cancer patients, the instrument modified by Seo Jung Sook(2008), which was used to measure the hame care needs of cancer patients by Gwon In Su & Eun Young(1999), was utilized in this study. Result: In the sub-factors of information needs about the disease and managing illness, the score of nursing needs was relatively high compared to the score of nursing practice. Conclusion: Thus, it is suggested to develop nursing intervention programs to improve nursing practice of nurses, considering the importance of providing information about disease and illness management
Variance-Constrained Fuzzy Filtering of Nonlinear Systems
노선영(Sun Young Noh),박진배(Jin Bae Park),주영훈(Young-Hoon Joo) 한국지능시스템학회 2011 한국지능시스템학회 학술발표 논문집 Vol.21 No.1
This paper is concerned with the filtering problem for nonlinear uncertain discrete-time stochastic system. The nonlinear plant is represented by a Takagi-Sugeno(T-S) fuzzy model. The parameter uncertainties are allowed to be norm-bounded and enter into the state matrix. The purpose of this problem is to design a linear filter such that, the error state of the filtering process is mean square bounded, and the steady-state variance of the estimation error of state is not more than the individual prescribed upper bound. The developed theoretical results are in the form of Kalman filter. Finally, an illustrative example is provided to show the effectiveness of the proposed approach.
Optimal Fuzzy Filter for Nonlinear Systems with Variance Constraints
Sun Young Noh(노선영),Jin Bae Park(박진배),Young Hoon Joo(주영훈) 한국지능시스템학회 2012 한국지능시스템학회논문지 Vol.22 No.5
본 논문에서는 추정 분산 제약을 갖는 비선형 이산시간에 대한 최적의 퍼지 필터에 대한 내용을 다루고자 한다. 필터를 설계할 때, 추정오차의 분산값은 필터의 성능이 결정하는 변수중 하나다. 이런 분산값에 더욱 강인한 필터를 설계하고자, 분산 제약 조건을 주어 필터를 설계하고자 한다. 먼저, 비선형 모델을 Tagaki-Sugeno 퍼지 모델을 이용하여 선형 모델로 변형한 후, 이 모델을 기반으로 선형 필터를 디자인한다. 이때 필터설계 과정 중 필터의 각 파라미터값을 구하기 위해 상태 추정오차 값은 평균제곱에 제한되며, 상태오차의 정상상태 분산값은 각각의 미리 정한 상한 제한 값 보다 작은 조건에서 필터를 설계하여 선형행렬부등식과 대수 이차 행렬부등식을 이용하여 파라미터값을 구한다. 이렇게 설계된 퍼지 필터는 트럭트레일러 시뮬레이션을 통해 설계 과정과 성능을 보여준다. In this paper, we consider the optimal fuzzy filter of nonlinear discrete-time with estimation error variance constraint. First, the Takagi and Sugeno(T-S) fuzzy model is employed to approximate the nonlinear system. Next, the error state is mean square bounded, and the steady state variance of the estimation error of each state is not more than the individual predefined value. It is shown that, the addressed problem can be carried out by solving linear matrix inequality(LMI) and some algebraic quadratic matrix inequalities. Finally, some examples are provided to illustrate the design procedure and expected performance through simulations.
Design of Target Tracking System Using a New Intelligent Algorithm
Sun Young Noh(노선영),Young Hoon Joo(주영훈),Jin Bae Park(박진배) 한국지능시스템학회 2005 한국지능시스템학회논문지 Vol.15 No.6
When the maneuver occurs, the performance of the standard Kalman filter has been degraded because mismatches between the modeled target dynamics and the actual target dynamics. To solve this problem, the unknown acceleration is determined by using the fuzzy logic based on genetic algorithm(GA) method. This algorithm is the method to estimate the increment of acceleration by a fuzzy system using th relation between maneuver fitler residual and non-maneuvering one. To optimize this system, a GA is utilized. And then, the modified filter is corrected by the new update equation method which is a fuzzy system using the relation between the filter residual and its variation. To shows the feasibility of the suggested method with only one filter, the computer simulations system are provided, this method is compared with multiple model method.