• Title/Summary/Keyword: analytic uncertainty model

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Adaptive Control based on a ParametricAffine Model for tail-control led Missiles (매개변수화 어파인 모델에 기반한 꼬리날개 제어유도탄의 적응제어)

  • 최진영;좌동경
    • 제어로봇시스템학회:학술대회논문집
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    • 2000.10a
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    • pp.2-2
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    • 2000
  • This paper presents an adaptive control against uncertainties in tail-controlled STT (skid-to-Turn) missiles. First, we derive an analytic uncertainty model from a parametricaffine missile model developed by the authors. Based on this analytic model, an adaptive feedbacklinearizing control law accompanied by a sliding model control law is proposed. We provide analyses of stability and output tracking performance of the overall adaptive missile system. The performance and validity of the proposed adaptive control scheme is demonstrated by simulation.

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An Improved Analytic Model for Power System Fault Diagnosis and its Optimal Solution Calculation

  • Wang, Shoupeng;Zhao, Dongmei
    • Journal of Electrical Engineering and Technology
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    • v.13 no.1
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    • pp.89-96
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    • 2018
  • When a fault occurs in a power system, the existing analytic models for the power system fault diagnosis could generate multiple solutions under the condition of one or more protective relays (PRs) and/or circuit breakers (CBs) malfunctioning, and/or an alarm or alarms of these PRs and/or CBs failing. Therefore, this paper presents an improved analytic model addressing the above problem. It takes into account the interaction between the uncertainty involved with PR operation and CB tripping and the uncertainty of the alarm reception, which makes the analytic model more reasonable. In addition, the existing analytic models apply the penalty function method to deal with constraints, which is influenced by the artificial setting of the penalty factor. In order to avoid the penalty factor's effects, this paper transforms constraints into an objective function, and then puts forward an improved immune clonal multi-objective optimization algorithm to solve the optimal solution. Finally, the cases of the power system fault diagnosis are served for demonstrating the feasibility and efficiency of the proposed model and method.

Adaptive Control Based on a Parametric Affine Model for Tail-Controlled Missiles (매개변수화 어파인 모델에 기반한 꼬리날개제어 유도탄의 적응제어)

  • 최진영;좌동경;송찬호
    • Journal of Institute of Control, Robotics and Systems
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    • v.9 no.7
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    • pp.547-555
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    • 2003
  • This paper presents an adaptive control against uncertainties in tail-controlled STT (Skid-to-Turn) missiles. We derive an analytic uncertainty model from a parametric affine missile model developed by the authors. Based on this analytic model, an adaptive feedback linearizing control law accompanied by a sliding mode control law is proposed. We provide analyses of stability and output tracking performance of the overall adaptive missile system. The performance and validity of the proposed adaptive control scheme are demonstrated by simulation.

UNCERTAINTY ANALYSIS OF DATA-BASED MODELS FOR ESTIMATING COLLAPSE MOMENTS OF WALL-THINNED PIPE BENDS AND ELBOWS

  • Kim, Dong-Su;Kim, Ju-Hyun;Na, Man-Gyun;Kim, Jin-Weon
    • Nuclear Engineering and Technology
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    • v.44 no.3
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    • pp.323-330
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    • 2012
  • The development of data-based models requires uncertainty analysis to explain the accuracy of their predictions. In this paper, an uncertainty analysis of the support vector regression (SVR) model, which is a data-based model, was performed because previous research showed that the SVR method accurately estimates the collapse moments of wall-thinned pipe bends and elbows. The uncertainty analysis method used in this study was an analytic uncertainty analysis method, and estimates with a 95% confidence interval were obtained for 370 test data points. From the results, the prediction interval (PI) was very narrow, which means that the predicted values are quite accurate. Therefore, the proposed SVR method can be used effectively to assess and validate the integrity of the wall-thinned pipe bends and elbows.

Uncertainty Analysis for Subsonic Wind Tunnel Testing (아음속풍동 시험에서의 불확도 해석)

  • Kwon, Ki-Jung;Sung, Bong-Zoo
    • Journal of the Korean Society for Aeronautical & Space Sciences
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    • v.30 no.4
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    • pp.123-130
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    • 2002
  • Aerodynamic results for an airplane model produced in a subsonic wind tunnel testing inadvertently include errors which are added during model preparation, data acquisition, manipulation, and/or describing the results. Predicting or analyzing uncertainty for the final results is necessary for trusting them and for applying them to real airplane. This paper describes the analytic and systematic method for calculating and describing the uncertainties of aerodynamic coefficients.

VALIDATION OF ON-LINE MONITORING TECHNIQUES TO NUCLEAR PLANT DATA

  • Garvey, Jamie;Garvey, Dustin;Seibert, Rebecca;Hines, J. Wesley
    • Nuclear Engineering and Technology
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    • v.39 no.2
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    • pp.133-142
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    • 2007
  • The Electric Power Research Institute (EPRI) demonstrated a method for monitoring the performance of instrument channels in Topical Report (TR) 104965, 'On-Line Monitoring of Instrument Channel Performance.' This paper presents the results of several models originally developed by EPRI to monitor three nuclear plant sensor sets: Pressurizer Level, Reactor Protection System (RPS) Loop A, and Reactor Coolant System (RCS) Loop A Steam Generator (SG) Level. The sensor sets investigated include one redundant sensor model and two non-redundant sensor models. Each model employs an Auto-Associative Kernel Regression (AAKR) model architecture to predict correct sensor behavior. Performance of each of the developed models is evaluated using four metrics: accuracy, auto-sensitivity, cross-sensitivity, and newly developed Error Uncertainty Limit Monitoring (EULM) detectability. The uncertainty estimate for each model is also calculated through two methods: analytic formulas and Monte Carlo estimation. The uncertainty estimates are verified by calculating confidence interval coverages to assure that 95% of the measured data fall within the confidence intervals. The model performance evaluation identified the Pressurizer Level model as acceptable for on-line monitoring (OLM) implementation. The other two models, RPS Loop A and RCS Loop A SG Level, highlight two common problems that occur in model development and evaluation, namely faulty data and poor signal selection

The Effect of Inaccurate Quality Signaling under Information Asymmetry

  • Seung Huh
    • Asia-Pacific Journal of Business
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    • v.14 no.1
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    • pp.231-246
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    • 2023
  • Purpose - This study attempts to provide a new theoretical perspective on the quality signaling and its impact on a market under information asymmetry, focusing on how the accuracy and the cost of quality signaling affect sellers' and buyers' profit, suggesting appropriate designs of quality signaling methods which mitigates information asymmetry. Design/methodology/approach - In order to examine the effect of quality signaling on strategic interactions within the market, we establish an analytic model where market outcomes are determined by seller's quality claim and price, and buyers are risk-neutral. By investigating this analytic model through relevant game trees, we find the subgame perfect Nash equilibria of the market and predict related market outcomes based on sellers' quality signaling strategy. Findings - Our analytic model shows counterintuitive results that seller profit will be the lowest with inaccurate quality signaling and the highest with no quality signaling, mostly due to the certification cost. Consequently, sellers should proceed with caution if the quality signaling is less than accurate, as it may backfire. We believe that this is due to the fact that the inaccuracy of quality signaling causes some confusion and uncertainty in both sellers and buyers' decision to maximize profit, making it hard for sellers to predict buyers' behavior. Research implications or Originality - Although the sources and types of quality signaling errors have been investigated in the literature, there has not been satisfactory understanding regarding how inaccuracy of quality certification affects specific market outcomes. We expect that our theoretical model would provide important implications on how to utilize quality signaling to solve adverse selection issues in markets under information asymmetry.

Multiobjective R&D Investment Planning under Uncertainty (불확실한 상황하에서의 다복적 R & D 투자계획수립에 관한 연구-최적화 기법과 계층화 분석과정의 통합접 접근방안을 중심으로-)

  • 이영찬;민재형
    • Journal of the Korean Operations Research and Management Science Society
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    • v.20 no.2
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    • pp.39-60
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    • 1995
  • In this paper, an integration of stochastic dynamic programming (SDP), integer goal programming (IGP) and analytic hierarchy process (AHP) is proposed to handle multiobjective-multicriteria sequential decision making problems under uncertainty inherent in R & D investment planning. SDP has its capability to handle problems which are sequential and stochastic. In the SDP model, the probabilities of the funding levels in any time period are generated using a subjective model which employs functional relationships among interrelated parameters, scenarios of future budget availability and subjective inputs elicited from a group of decision makers. The SDP model primarily yields an optimal investment planning policy considering the possibility that actual funding received may be less than anticipated one and thus the projects being selected under the anticipated budget would be interrupted. IGP is used to handle the multiobjective issues such as tradoff between economic benefit and technology accumulation level. Other managerial concerns related to the determination of the optimal project portifolio within each stage of the SDP model. including project selection, project scheduling and annual budget allocation are also determined by the IGP. AHP is proposed for generating scenario-based transformation probabilities under budgetary uncertainty and for quantifying the environmental risk to be considered.

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A SURVEY ON AMERICAN OPTIONS: OLD APPROACHES AND NEW TRENDS

  • Ahn, Se-Ryoong;Bae, Hyeong-Ohk;Koo, Hyeng-Keun;Lee, Ki-Jung
    • Bulletin of the Korean Mathematical Society
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    • v.48 no.4
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    • pp.791-812
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    • 2011
  • This is a survey on American options. An American option allows its owner the privilege of early exercise, whereas a European option can be exercised only at expiration. Because of this early exercise privilege American option pricing involves an optimal stopping problem; the price of an American option is given as a free boundary value problem associated with a Black-Scholes type partial differential equation. Up until now there is no simple closed-form solution to the problem, but there have been a variety of approaches which contribute to the understanding of the properties of the price and the early exercise boundary. These approaches typically provide numerical or approximate analytic methods to find the price and the boundary. Topics included in this survey are early approaches(trees, finite difference schemes, and quasi-analytic methods), an analytic method of lines and randomization, a homotopy method, analytic approximation of early exercise boundaries, Monte Carlo methods, and relatively recent topics such as model uncertainty, backward stochastic differential equations, and real options. We also provide open problems whose answers are expected to contribute to American option pricing.

Evidential Analytic Hierarchy Process Dependence Assessment Methodology in Human Reliability Analysis

  • Chen, Luyuan;Zhou, Xinyi;Xiao, Fuyuan;Deng, Yong;Mahadevan, Sankaran
    • Nuclear Engineering and Technology
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    • v.49 no.1
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    • pp.113-123
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    • 2017
  • In human reliability analysis, dependence assessment is an important issue in risky large complex systems, such as operation of a nuclear power plant. Many existing methods depend on an expert's judgment, which contributes to the subjectivity and restrictions of results. Recently, a computational method, based on the Dempster-Shafer evidence theory and analytic hierarchy process, has been proposed to handle the dependence in human reliability analysis. The model can deal with uncertainty in an analyst's judgment and reduce the subjectivity in the evaluation process. However, the computation is heavy and complicated to some degree. The most important issue is that the existing method is in a positive aspect, which may cause an underestimation of the risk. In this study, a new evidential analytic hierarchy process dependence assessment methodology, based on the improvement of existing methods, has been proposed, which is expected to be easier and more effective.