Model Updating Nonlinear System Identification Toolbox Project
National Aeronautics and Space Administration — ZONA Technology (ZONA) proposes to develop an enhanced model updating nonlinear system identification (MUNSID) methodology that utilizes flight data with...
Model Updating Nonlinear System Identification Toolbox Project
National Aeronautics and Space Administration — ZONA Technology proposes to develop an enhanced model updating nonlinear system identification (MUNSID) methodology by adopting the flight data with state-of-the-art...
Structural system identification: Structural dynamics model validation
Energy Technology Data Exchange (ETDEWEB)
Red-Horse, J.R.
1997-04-01
Structural system identification is concerned with the development of systematic procedures and tools for developing predictive analytical models based on a physical structure`s dynamic response characteristics. It is a multidisciplinary process that involves the ability (1) to define high fidelity physics-based analysis models, (2) to acquire accurate test-derived information for physical specimens using diagnostic experiments, (3) to validate the numerical simulation model by reconciling differences that inevitably exist between the analysis model and the experimental data, and (4) to quantify uncertainties in the final system models and subsequent numerical simulations. The goal of this project was to develop structural system identification techniques and software suitable for both research and production applications in code and model validation.
System identification application using Hammerstein model
Indian Academy of Sciences (India)
Saban Ozer
. 20(1): 1175–1188. [4] Hizir N B, Phan M Q, Betti R and Longman R W 2012. Identification of discrete-time bilinear systems through equivalent linear models. Nonlinear Dyn. 69(4): 2065–2078. [5] Hong X, Mitchell R J, Chen S, Harris C J, Li K ...
Identification models of the nervous system.
Zipser, D
1992-01-01
It has been widely observed that when artificial neural networks are trained by supervised learning to do computations that also occur in the nervous system, the behavior of the model neurons often closely resembles that of the real neurons involved in the task. It is not immediately clear why this should be the case or what use can be made of models generated by supervised learning. Here, recent developments are reviewed and analysed in an attempt to clarify these issues. This analysis is facilitated by treating supervised learning models of the brain as a special case of system identification, a general and well-studied modeling paradigm. The neural systems identification paradigm provides a systematic way to generate realistic models starting with a high-level description of a hypothesized computation and some architectural and physiological constraints about the area being modeled. There is no inherent limitation to the realism that can be incorporated into identification models. This approach eliminates the need to find neural implementation algorithms by ad hoc means and provides neuroscientists with a convenient way to build models that account for observed data.
Model Identification and Validation for a Heating System using MATLAB System Identification Toolbox
International Nuclear Information System (INIS)
Rabbani, Muhammad Junaid; Hussain, Kashan; Khan, Asim-ur-Rehman; Ali, Abdullah
2013-01-01
This paper proposed a systematic approach to select a mathematical model for an industrial heating system by adopting system identification techniques with the aim of fulfilling the design requirement for the controller. The model identification process will begin by collecting real measurement data samples with the aid of MATLAB system identification toolbox. The criteria for selecting the model that could validate model output with actual data will based upon: parametric identification technique, picking the best model structure with low order among ARX, ARMAX and BJ, and then applying model estimation and validation tests. Simulated results have shown that the BJ model has been best in providing good estimation and validation based upon performance criteria such as: final prediction error, loss function, best percentage of model fit, and co-relation analysis of residual for output
Model Updating Nonlinear System Identification Toolbox, Phase II
National Aeronautics and Space Administration — ZONA Technology (ZONA) proposes to develop an enhanced model updating nonlinear system identification (MUNSID) methodology that utilizes flight data with...
System Identification, Environmental Modelling, and Control System Design
Garnier, Hugues
2012-01-01
System Identification, Environmetric Modelling, and Control Systems Design is dedicated to Professor Peter Young on the occasion of his seventieth birthday. Professor Young has been a pioneer in systems and control, and over the past 45 years he has influenced many developments in this field. This volume is comprised of a collection of contributions by leading experts in system identification, time-series analysis, environmetric modelling and control system design – modern research in topics that reflect important areas of interest in Professor Young’s research career. Recent theoretical developments in and relevant applications of these areas are explored treating the various subjects broadly and in depth. The authoritative and up-to-date research presented here will be of interest to academic researcher in control and disciplines related to environmental research, particularly those to with water systems. The tutorial style in which many of the contributions are composed also makes the book suitable as ...
A system identification model for adaptive nonlinear control
Linse, Dennis J.; Stengel, Robert F.
1991-01-01
A system identification model that combines generalized-spline function approximation with a nonlinear control system is described. The complete control system contains three main elements: a nonlinear-inverse-dynamic control law that depends on a comprehensive model of the plant, a state estimator whose outputs drive the control law, and a function approximation scheme that models the system dynamics. The system-identification task, which combines an extended Kalman filter with a function approximator modeled as an artificial neural network, is considered. The results of an application of the identification techniques to a nonlinear transport aircraft model are presented.
Practical Modeling and Comprehensive System Identification of a BLDC Motor
Directory of Open Access Journals (Sweden)
Changle Xiang
2015-01-01
Full Text Available The aim of this paper is to outline all the steps in a rigorous and simple procedure for system identification of BLDC motor. A practical mathematical model for identification is derived. Frequency domain identification techniques and time domain estimation method are combined to obtain the unknown parameters. The methods in time domain are founded on the least squares approximation method and a disturbance observer. Only the availability of experimental data for rotor speed and armature current are required for identification. The proposed identification method is systematically investigated, and the final identified model is validated by experimental results performed on a typical BLDC motor in UAV.
System identification application using Hammerstein model
Indian Academy of Sciences (India)
Saban Ozer
because of its advanced theoretical background [3–5, 10]. However, many systems in real life have nonlinear beha- ... To describe a polynomial non-linear system with memory, the Volterra series expansion has been the ... suppression and adaptive noise suppression [19]. 2.3 Hammerstein model. Many systems can be ...
Modeling of Biometric Identification System Using the Colored Petri Nets
Petrosyan, G. R.; Ter-Vardanyan, L. A.; Gaboutchian, A. V.
2015-05-01
In this paper we present a model of biometric identification system transformed into Petri Nets. Petri Nets, as a graphical and mathematical tool, provide a uniform environment for modelling, formal analysis, and design of discrete event systems. The main objective of this paper is to introduce the fundamental concepts of Petri Nets to the researchers and practitioners, both from identification systems, who are involved in the work in the areas of modelling and analysis of biometric identification types of systems, as well as those who may potentially be involved in these areas. In addition, the paper introduces high-level Petri Nets, as Colored Petri Nets (CPN). In this paper the model of Colored Petri Net describes the identification process much simpler.
Model Updating Nonlinear System Identification Toolbox, Phase I
National Aeronautics and Space Administration — ZONA Technology proposes to develop an enhanced model updating nonlinear system identification (MUNSID) methodology by adopting the flight data with state-of-the-art...
Bionic models for identification of biological systems
Gerget, O. M.
2017-01-01
This article proposes a clinical decision support system that processes biomedical data. For this purpose a bionic model has been designed based on neural networks, genetic algorithms and immune systems. The developed system has been tested on data from pregnant women. The paper focuses on the approach to enable selection of control actions that can minimize the risk of adverse outcome. The control actions (hyperparameters of a new type) are further used as an additional input signal. Its values are defined by a hyperparameter optimization method. A software developed with Python is briefly described.
Advances in Modelling, System Identification and Parameter ...
Indian Academy of Sciences (India)
models determined from flight test data by using parameter estimation methods find extensive use in design/modification of flight control systems, high fidelity flight simulators and evaluation of handling qualitites of aircraft and rotorcraft. R K Mehra et al present new algorithms and results for flutter tests and adaptive notching ...
Modeling emotional content of music using system identification.
Korhonen, Mark D; Clausi, David A; Jernigan, M Ed
2006-06-01
Research was conducted to develop a methodology to model the emotional content of music as a function of time and musical features. Emotion is quantified using the dimensions valence and arousal, and system-identification techniques are used to create the models. Results demonstrate that system identification provides a means to generalize the emotional content for a genre of music. The average R2 statistic of a valid linear model structure is 21.9% for valence and 78.4% for arousal. The proposed method of constructing models of emotional content generalizes previous time-series models and removes ambiguity from classifiers of emotion.
IDENTIFICATION OF SYSTEMS IN TERMS OF THE WIENER MODEL
The report presents briefly a nonlinear model originally proposed by the late Norbert Wiener for the characterization of general systems. Three...procedures are then offered for the identification of any given system in terms of the Wiener model. Finally, this report presents the results of a digital
Vortex Tube Modeling Using the System Identification Method
Energy Technology Data Exchange (ETDEWEB)
Han, Jaeyoung; Jeong, Jiwoong; Yu, Sangseok [Chungnam Nat’l Univ., Daejeon (Korea, Republic of); Im, Seokyeon [Tongmyong Univ., Busan (Korea, Republic of)
2017-05-15
In this study, vortex tube system model is developed to predict the temperature of the hot and the cold sides. The vortex tube model is developed based on the system identification method, and the model utilized in this work to design the vortex tube is ARX type (Auto-Regressive with eXtra inputs). The derived polynomial model is validated against experimental data to verify the overall model accuracy. It is also shown that the derived model passes the stability test. It is confirmed that the derived model closely mimics the physical behavior of the vortex tube from both the static and dynamic numerical experiments by changing the angles of the low-temperature side throttle valve, clearly showing temperature separation. These results imply that the system identification based modeling can be a promising approach for the prediction of complex physical systems, including the vortex tube.
System Identification Theory Approach to Cohesive Sediment Transport Modelling
CHEN, HUIXIN
1997-01-01
Two aspects of the modelling sediment transport are investigated. One is the univariate time series modelling the current velocity dynamics. The other is the multivariate time series modelling the suspended sediment concentration dynamics. Cohesive sediment dynamics and numerical sediment transport model are reviewed and investigated. The system identification theory and time series analysis method are developed and applied to set up the time series model for current velocity a...
Neural networks for nonlinear dynamic system modelling and identification
Chen, S.; Billings, S. A.
1992-01-01
Many real-world systems exhibit complex non-linear characteristics and cannot be treated satisfactorily using linear systems theory. A neural network which has the ability to learn sophisticated non-linear relationships provides an ideal means of modelling complicated non-linear systems. This paper addresses the issues related to the identification of non-linear discrete-time dynamic systems using neural networks..........
Identification of linear error-models with projected dynamical systems
Czech Academy of Sciences Publication Activity Database
Krejčí, Pavel; Kuhnen, K.
2004-01-01
Roč. 10, č. 1 (2004), s. 59-91 ISSN 1387-3954 Keywords : identification * error models * projected dynamical systems Subject RIV: BA - General Mathematics Impact factor: 0.292, year: 2004 http://www.informaworld.com/smpp/content~db=all~content=a713682517
Reduced Complexity Volterra Models for Nonlinear System Identification
Directory of Open Access Journals (Sweden)
Hacıoğlu Rıfat
2001-01-01
Full Text Available A broad class of nonlinear systems and filters can be modeled by the Volterra series representation. However, its practical use in nonlinear system identification is sometimes limited due to the large number of parameters associated with the Volterra filter′s structure. The parametric complexity also complicates design procedures based upon such a model. This limitation for system identification is addressed in this paper using a Fixed Pole Expansion Technique (FPET within the Volterra model structure. The FPET approach employs orthonormal basis functions derived from fixed (real or complex pole locations to expand the Volterra kernels and reduce the number of estimated parameters. That the performance of FPET can considerably reduce the number of estimated parameters is demonstrated by a digital satellite channel example in which we use the proposed method to identify the channel dynamics. Furthermore, a gradient-descent procedure that adaptively selects the pole locations in the FPET structure is developed in the paper.
Thruster Modelling for Underwater Vehicle Using System Identification Method
Directory of Open Access Journals (Sweden)
Mohd Shahrieel Mohd Aras
2013-05-01
Full Text Available Abstract This paper describes a study of thruster modelling for a remotely operated underwater vehicle (ROV by system identification using Microbox 2000/2000C. Microbox 2000/2000C is an XPC target machine device to interface between an ROV thruster with the MATLAB 2009 software. In this project, a model of the thruster will be developed first so that the system identification toolbox in MATLAB can be used. This project also presents a comparison of mathematical and empirical modelling. The experiments were carried out by using a mini compressor as a dummy depth pressure applied to a pressure sensor. The thruster model will thrust and submerge until it reaches a set point and maintain the set point depth. The depth was based on pressure sensor measurement. A conventional proportional controller was used in this project and the results gathered justified its selection.
TLM modeling and system identification of optimized antenna structures
Directory of Open Access Journals (Sweden)
N. Fichtner
2008-05-01
Full Text Available The transmission line matrix (TLM method in conjunction with the genetic algorithm (GA is presented for the bandwidth optimization of a low profile patch antenna. The optimization routine is supplemented by a system identification (SI procedure. By the SI the model parameters of the structure are estimated which is used for a reduction of the total TLM simulation time. The SI utilizes a new stability criterion of the physical poles for the parameter extraction.
Nonlinear State Space Modeling and System Identification for Electrohydraulic Control
Directory of Open Access Journals (Sweden)
Jun Yan
2013-01-01
Full Text Available The paper deals with nonlinear modeling and identification of an electrohydraulic control system for improving its tracking performance. We build the nonlinear state space model for analyzing the highly nonlinear system and then develop a Hammerstein-Wiener (H-W model which consists of a static input nonlinear block with two-segment polynomial nonlinearities, a linear time-invariant dynamic block, and a static output nonlinear block with single polynomial nonlinearity to describe it. We simplify the H-W model into a linear-in-parameters structure by using the key term separation principle and then use a modified recursive least square method with iterative estimation of internal variables to identify all the unknown parameters simultaneously. It is found that the proposed H-W model approximates the actual system better than the independent Hammerstein, Wiener, and ARX models. The prediction error of the H-W model is about 13%, 54%, and 58% less than the Hammerstein, Wiener, and ARX models, respectively.
Data-Driven Photovoltaic System Modeling Based on Nonlinear System Identification
Directory of Open Access Journals (Sweden)
Ayedh Alqahtani
2016-01-01
Full Text Available Solar photovoltaic (PV energy sources are rapidly gaining potential growth and popularity compared to conventional fossil fuel sources. As the merging of PV systems with existing power sources increases, reliable and accurate PV system identification is essential, to address the highly nonlinear change in PV system dynamic and operational characteristics. This paper deals with the identification of a PV system characteristic with a switch-mode power converter. Measured input-output data are collected from a real PV panel to be used for the identification. The data are divided into estimation and validation sets. The identification methodology is discussed. A Hammerstein-Wiener model is identified and selected due to its suitability to best capture the PV system dynamics, and results and discussion are provided to demonstrate the accuracy of the selected model structure.
Optimization of Experimental Model Parameter Identification for Energy Storage Systems
Directory of Open Access Journals (Sweden)
Rosario Morello
2013-09-01
Full Text Available The smart grid approach is envisioned to take advantage of all available modern technologies in transforming the current power system to provide benefits to all stakeholders in the fields of efficient energy utilisation and of wide integration of renewable sources. Energy storage systems could help to solve some issues that stem from renewable energy usage in terms of stabilizing the intermittent energy production, power quality and power peak mitigation. With the integration of energy storage systems into the smart grids, their accurate modeling becomes a necessity, in order to gain robust real-time control on the network, in terms of stability and energy supply forecasting. In this framework, this paper proposes a procedure to identify the values of the battery model parameters in order to best fit experimental data and integrate it, along with models of energy sources and electrical loads, in a complete framework which represents a real time smart grid management system. The proposed method is based on a hybrid optimisation technique, which makes combined use of a stochastic and a deterministic algorithm, with low computational burden and can therefore be repeated over time in order to account for parameter variations due to the battery’s age and usage.
On Early Conflict Identification by Requirements Modeling of Energy System Control Structures
DEFF Research Database (Denmark)
Heussen, Kai; Gehrke, Oliver; Niemann, Hans Henrik
2015-01-01
Control systems are purposeful systems involving goal-oriented information processing (cyber) and technical (physical) structures. Requirements modeling formalizes fundamental concepts and relations of a system architecture at a high-level design stage and can be used to identify potential design...... issues early. For requirements formulation of control structures, cyber and physical aspects need to be jointly represented to express interdependencies, check for consistency and discover potentially conflicting requirements. Early identification of potential conflicts may prevent larger problems...... modeling for early requirements checking using a suitable modeling language, and illustrates how this approach enables the identification of several classes of controller conflict....
Modelling of Biometric Identification System with Given Parameters Using Colored Petri Nets
Petrosyan, G.; Ter-Vardanyan, L.; Gaboutchian, A.
2017-05-01
Biometric identification systems use given parameters and function on the basis of Colored Petri Nets as a modelling language developed for systems in which communication, synchronization and distributed resources play an important role. Colored Petri Nets combine the strengths of Classical Petri Nets with the power of a high-level programming language. Coloured Petri Nets have both, formal intuitive and graphical presentations. Graphical CPN model consists of a set of interacting modules which include a network of places, transitions and arcs. Mathematical representation has a well-defined syntax and semantics, as well as defines system behavioural properties. One of the best known features used in biometric is the human finger print pattern. During the last decade other human features have become of interest, such as iris-based or face recognition. The objective of this paper is to introduce the fundamental concepts of Petri Nets in relation to tooth shape analysis. Biometric identification systems functioning has two phases: data enrollment phase and identification phase. During the data enrollment phase images of teeth are added to database. This record contains enrollment data as a noisy version of the biometrical data corresponding to the individual. During the identification phase an unknown individual is observed again and is compared to the enrollment data in the database and then system estimates the individual. The purpose of modeling biometric identification system by means of Petri Nets is to reveal the following aspects of the functioning model: the efficiency of the model, behavior of the model, mistakes and accidents in the model, feasibility of the model simplification or substitution of its separate components for more effective components without interfering system functioning. The results of biometric identification system modeling and evaluating are presented and discussed.
Nonlinear FOPDT Model Identification for the Superheat Dynamic in a Refrigeration System
DEFF Research Database (Denmark)
Yang, Zhenyu; Sun, Zhen; Andersen, Casper
2011-01-01
An on-line nonlinear FOPDT system identification method is proposed and applied to model the superheat dynamic in a supermarket refrigeration system. The considered nonlinear FOPDT model is an extension of the standard FOPDT model by means that its parameters are time dependent. After...... the considered system is discretized, the nonlinear FOPDT identification problem is formulated as a Mixed Integer Non-Linear Programming problem, and then an identification algorithm is proposed by combining the Branch-and-Bound method and Least Square technique, in order to on-line identify these time......-dependent parameters. The proposed method is firstly tested through a number of numerical examples, and then applied to model the superheat dynamic in a supermarket refrigeration system based on experimental data. As shown in these studies, the proposed method is quite promising in terms of reasonable accuracy, large...
Nonlinear System Identification via Basis Functions Based Time Domain Volterra Model
Directory of Open Access Journals (Sweden)
Yazid Edwar
2014-07-01
Full Text Available This paper proposes basis functions based time domain Volterra model for nonlinear system identification. The Volterra kernels are expanded by using complex exponential basis functions and estimated via genetic algorithm (GA. The accuracy and practicability of the proposed method are then assessed experimentally from a scaled 1:100 model of a prototype truss spar platform. Identification results in time and frequency domain are presented and coherent functions are performed to check the quality of the identification results. It is shown that results between experimental data and proposed method are in good agreement.
Two models for identification and predicting behaviour of an induction motor system
Kuo, Chien-Hsun
2018-01-01
System identification or modelling is the process of building mathematical models of dynamical systems based on the available input and output data from the systems. This paper introduces system identification by using ARX (Auto Regressive with eXogeneous input) and ARMAX (Auto Regressive Moving Average with eXogeneous input) models. Through the identified system model, the predicted output could be compared with the measured one to help prevent the motor faults from developing into a catastrophic machine failure and avoid unnecessary costs and delays caused by the need to carry out unscheduled repairs. The induction motor system is illustrated as an example. Numerical and experimental results are shown for the identified induction motor system.
System Identification of a Heaving Point Absorber: Design of Experiment and Device Modeling
Directory of Open Access Journals (Sweden)
Giorgio Bacelli
2017-04-01
Full Text Available Empirically based modeling is an essential aspect of design for a wave energy converter. Empirically based models are used in structural, mechanical and control design processes, as well as for performance prediction. Both the design of experiments and methods used in system identification have a strong impact on the quality of the resulting model. This study considers the system identification and model validation process based on data collected from a wave tank test of a model-scale wave energy converter. Experimental design and data processing techniques based on general system identification procedures are discussed and compared with the practices often followed for wave tank testing. The general system identification processes are shown to have a number of advantages, including an increased signal-to-noise ratio, reduced experimental time and higher frequency resolution. The experimental wave tank data is used to produce multiple models using different formulations to represent the dynamics of the wave energy converter. These models are validated and their performance is compared against one another. While most models of wave energy converters use a formulation with surface elevation as an input, this study shows that a model using a hull pressure measurement to incorporate the wave excitation phenomenon has better accuracy.
Chaotic System Identification Based on a Fuzzy Wiener Model with Particle Swarm Optimization
International Nuclear Information System (INIS)
Yong, Li; Ying-Gan, Tang
2010-01-01
A fuzzy Wiener model is proposed to identify chaotic systems. The proposed fuzzy Wiener model consists of two parts, one is a linear dynamic subsystem and the other is a static nonlinear part, which is represented by the Takagi–Sugeno fuzzy model. Identification of chaotic systems is converted to find optimal parameters of the fuzzy Wiener model by minimizing the state error between the original chaotic system and the fuzzy Wiener model. Particle swarm optimization algorithm, a global optimizer, is used to search the optimal parameter of the fuzzy Wiener model. The proposed method can identify the parameters of the linear part and nonlinear part simultaneously. Numerical simulations for Henón and Lozi chaotic system identification show the effectiveness of the proposed method
Common Rail System for GDI Engines Modelling, Identification, and Control
Fiengo, Giovanni; Palladino, Angelo; Giglio, Veniero
2013-01-01
Progressive reductions in vehicle emission requirements have forced the automotive industry to invest in research and development of alternative control strategies. Continual control action exerted by a dedicated electronic control unit ensures that best performance in terms of pollutant emissions and power density is married with driveability and diagnostics. Gasoline direct injection (GDI) engine technology is a way to attain these goals. This brief describes the functioning of a GDI engine equipped with a common rail (CR) system, and the devices necessary to run test-bench experiments in detail. The text should prove instructive to researchers in engine control and students are recommended to this brief as their first approach to this technology. Later chapters of the brief relate an innovative strategy designed to assist with the engine management system; injection pressure regulation for fuel pressure stabilization in the CR fuel line is proposed and validated by experiment. The resulting control scheme ...
Boutalis, Yiannis; Kottas, Theodore; Christodoulou, Manolis A
2014-01-01
Presenting current trends in the development and applications of intelligent systems in engineering, this monograph focuses on recent research results in system identification and control. The recurrent neurofuzzy and the fuzzy cognitive network (FCN) models are presented. Both models are suitable for partially-known or unknown complex time-varying systems. Neurofuzzy Adaptive Control contains rigorous proofs of its statements which result in concrete conclusions for the selection of the design parameters of the algorithms presented. The neurofuzzy model combines concepts from fuzzy systems and recurrent high-order neural networks to produce powerful system approximations that are used for adaptive control. The FCN model stems from fuzzy cognitive maps and uses the notion of “concepts” and their causal relationships to capture the behavior of complex systems. The book shows how, with the benefit of proper training algorithms, these models are potent system emulators suitable for use in engineering s...
Numerical study on identification of transfer functions in a feedback system and model reduction
International Nuclear Information System (INIS)
Kishida, Kuniharu
1997-01-01
Identification of transfer function matrices in a feedback system is discussed by using the singular value decomposition of Hankel matrix from the viewpoint of inverse problems. A method of model reduction is considered, and selection criteria are proposed for identification of them. Transformation formula between open loop and closed loop transfer function matrices are determined from the feedback loop structure, and they are needed for identification of open loop transfer function matrices under such a condition where the feedback system is in a minimum phase. Though the identifiability of open loop transfer function matrices can be examined in the framework of innovation model equivalent to the feedback system, there are pole-zero cancellations in the identification of them. The method to reduce a model order of an open loop transfer function is discussed by using the singular value decomposition of a gramian given by the open loop transfer function with higher degree. To check reliability of the present algorithm, a simulation study is performed for an example. (author)
System Identification Based Proxy Model of a Reservoir under Water Injection
Directory of Open Access Journals (Sweden)
Berihun M. Negash
2017-01-01
Full Text Available Simulation of numerical reservoir models with thousands and millions of grid blocks may consume a significant amount of time and effort, even when high performance processors are used. In cases where the simulation runs are required for sensitivity analysis, dynamic control, and optimization, the act needs to be repeated several times by continuously changing parameters. This makes it even more time-consuming. Currently, proxy models that are based on response surface are being used to lessen the time required for running simulations during sensitivity analysis and optimization. Proxy models are lighter mathematical models that run faster and perform in place of heavier models that require large computations. Nevertheless, to acquire data for modeling and validation and develop the proxy model itself, hundreds of simulation runs are required. In this paper, a system identification based proxy model that requires only a single simulation run and a properly designed excitation signal was proposed and evaluated using a benchmark case study. The results show that, with proper design of excitation signal and proper selection of model structure, system identification based proxy models are found to be practical and efficient alternatives for mimicking the performance of numerical reservoir models. The resulting proxy models have potential applications for dynamic well control and optimization.
Metodology of identification parameters of models control objects of automatic trailing system
Directory of Open Access Journals (Sweden)
I.V. Zimchuk
2017-04-01
Full Text Available The determining factor for the successful solution of the problem of synthesis of optimal control systems of different processes are adequacy of mathematical model of control object. In practice, the options can differ from the objects taken priori, causing a need to clarification of them. In this context, the article presents the results of the development and application of methods parameters identification of mathematical models of control object of automatic trailing system. The stated problem in the article is solved provided that control object is fully controlled and observed, and a differential equation of control object is known a priori. The coefficients of this equation to be determined. Identifying quality criterion is to minimize the integral value of squared error of identification. The method is based on a description of the dynamics of the object in space state. Equation of identification synthesized using the vector-matrix representation of model. This equation describes the interconnection of coefficients of matrix state and control with inputs and outputs of object. The initial data for calculation are the results of experimental investigation of the reaction of phase coordinates of control object at a typical input signal. The process of calculating the model parameters is reduced to solving the system of equations of the first order each. Application the above approach is illustrated in the example identification of coefficients transfer function of control object first order. Results of digital simulation are presented, they are confirming the justice of set out mathematical calculations. The approach enables to do the identification of models of one-dimensional and multidimensional objects and does not require a large amount of calculation for its implementation. The order of identified model is limited capabilities of measurement phase coordinates of corresponding control object. The practical significance of the work is
Directory of Open Access Journals (Sweden)
Mosbeh R. Kaloop
2017-01-01
Full Text Available This study evaluates the performance of passively controlled steel frame building under dynamic loads using time series analysis. A novel application is utilized for the time and frequency domains evaluation to analyze the behavior of controlling systems. In addition, the autoregressive moving average (ARMA neural networks are employed to identify the performance of the controller system. Three passive vibration control devices are utilized in this study, namely, tuned mass damper (TMD, tuned liquid damper (TLD, and tuned liquid column damper (TLCD. The results show that the TMD control system is a more reliable controller than TLD and TLCD systems in terms of vibration mitigation. The probabilistic evaluation and identification model showed that the probability analysis and ARMA neural network model are suitable to evaluate and predict the response of coupled building-controller systems.
Identification for automotive systems
Hjalmarsson, Håkan; Re, Luigi
2012-01-01
Increasing complexity and performance and reliability expectations make modeling of automotive system both more difficult and more urgent. Automotive control has slowly evolved from an add-on to classical engine and vehicle design to a key technology to enforce consumption, pollution and safety limits. Modeling, however, is still mainly based on classical methods, even though much progress has been done in the identification community to speed it up and improve it. This book, the product of a workshop of representatives of different communities, offers an insight on how to close the gap and exploit this progress for the next generations of vehicles.
Mixed Lp Estimators Variety for Model Order Reduction in Control Oriented System Identification
Directory of Open Access Journals (Sweden)
Christophe Corbier
2015-01-01
Full Text Available A new family of MLE type Lp estimators for model order reduction in dynamical systems identification is presented in this paper. A family of Lp distributions proposed in this work combines Lp2 (1
Efficient Parameterization for Grey-box Model Identification of Complex Physical Systems
DEFF Research Database (Denmark)
Blanke, Mogens; Knudsen, Morten Haack
2006-01-01
Grey box model identification preserves known physical structures in a model but with limits to the possible excitation, all parameters are rarely identifiable, and different parametrizations give significantly different model quality. Convenient methods to show which parameterizations are the be......Grey box model identification preserves known physical structures in a model but with limits to the possible excitation, all parameters are rarely identifiable, and different parametrizations give significantly different model quality. Convenient methods to show which parameterizations...
Integration of system identification and finite element modelling of nonlinear vibrating structures
Cooper, Samson B.; DiMaio, Dario; Ewins, David J.
2018-03-01
The Finite Element Method (FEM), Experimental modal analysis (EMA) and other linear analysis techniques have been established as reliable tools for the dynamic analysis of engineering structures. They are often used to provide solutions to small and large structures and other variety of cases in structural dynamics, even those exhibiting a certain degree of nonlinearity. Unfortunately, when the nonlinear effects are substantial or the accuracy of the predicted response is of vital importance, a linear finite element model will generally prove to be unsatisfactory. As a result, the validated linear FE model requires further enhancement so that it can represent and predict the nonlinear behaviour exhibited by the structure. In this paper, a pragmatic approach to integrating test-based system identification and FE modelling of a nonlinear structure is presented. This integration is based on three different phases: the first phase involves the derivation of an Underlying Linear Model (ULM) of the structure, the second phase includes experiment-based nonlinear identification using measured time series and the third phase covers augmenting the linear FE model and experimental validation of the nonlinear FE model. The proposed case study is demonstrated on a twin cantilever beam assembly coupled with a flexible arch shaped beam. In this case, polynomial-type nonlinearities are identified and validated with force-controlled stepped-sine test data at several excitation levels.
Neuro-fuzzy models for systems identification applied to the operation of nuclear power plants
International Nuclear Information System (INIS)
Alves, Antonio Carlos Pinto Dias
2000-09-01
A nuclear power plant has a myriad of complex system and sub-systems that, working cooperatively, make the control of the whole plant. Nevertheless their operation be automatic most of the time, the integral understanding of their internal- logic can be away of the comprehension of even experienced operators because of the poor interpretability those controls offer. This difficulty does not happens only in nuclear power plants but in almost every a little more complex control system. Neuro-fuzzy models have been used for the last years in a attempt of suppress these difficulties because of their ability of modelling in linguist form even a system which behavior is extremely complex. This is a very intuitive human form of interpretation and neuro-fuzzy model are gathering increasing acceptance. Unfortunately, neuro-fuzzy models can grow up to become of hard interpretation because of the complexity of the systems under modelling. In general, that growing occurs in function of redundant rules or rules that cover a very little domain of the problem. This work presents an identification method for neuro-fuzzy models that not only allows models grow in function of the existent complexity but that beforehand they try to self-adapt to avoid the inclusion of new rules. This form of construction allowed to arrive to highly interpretative neuro-fuzzy models even of very complex systems. The use of this kind of technique in modelling the control of the pressurizer of a PWR nuclear power plant allowed verify its validity and how neuro-fuzzy models so built can be useful in understanding the automatic operation of a nuclear power plant. (author)
Physics-based mathematical models for quantum devices via experimental system identification
Energy Technology Data Exchange (ETDEWEB)
Schirmer, S G; Oi, D K L; Devitt, S J [Department of Applied Maths and Theoretical Physics, University of Cambridge, Wilberforce Rd, Cambridge, CB3 0WA (United Kingdom); SUPA, Department of Physics, University of Strathclyde, Glasgow G4 0NG (United Kingdom); National Institute of Informatics, 2-1-2 Hitotsubashi, Chiyoda-ku, Tokyo 101-8430 (Japan)], E-mail: sgs29@cam.ac.uk
2008-03-15
We consider the task of intrinsic control system identification for quantum devices. The problem of experimental determination of subspace confinement is considered, and simple general strategies for full Hamiltonian identification and decoherence characterization of a controlled two-level system are presented.
Prediction of Hemodynamic Response to Epinephrine via Model-Based System Identification.
Bighamian, Ramin; Soleymani, Sadaf; Reisner, Andrew T; Seri, Istvan; Hahn, Jin-Oh
2016-01-01
In this study, we present a system identification approach to the mathematical modeling of hemodynamic responses to vasopressor-inotrope agents. We developed a hybrid model called the latency-dose-response-cardiovascular (LDC) model that incorporated 1) a low-order lumped latency model to reproduce the delay associated with the transport of vasopressor-inotrope agent and the onset of physiological effect, 2) phenomenological dose-response models to dictate the steady-state inotropic, chronotropic, and vasoactive responses as a function of vasopressor-inotrope dose, and 3) a physiological cardiovascular model to translate the agent's actions into the ultimate response of blood pressure. We assessed the validity of the LDC model to fit vasopressor-inotrope dose-response data using data collected from five piglet subjects during variable epinephrine infusion rates. The results suggested that the LDC model was viable in modeling the subjects' dynamic responses: After tuning the model to each subject, the r (2) values for measured versus model-predicted mean arterial pressure were consistently higher than 0.73. The results also suggested that intersubject variability in the dose-response models, rather than the latency models, had significantly more impact on the model's predictive capability: Fixing the latency model to population-averaged parameter values resulted in r(2) values higher than 0.57 between measured versus model-predicted mean arterial pressure, while fixing the dose-response model to population-averaged parameter values yielded nonphysiological predictions of mean arterial pressure. We conclude that the dose-response relationship must be individualized, whereas a population-averaged latency-model may be acceptable with minimal loss of model fidelity.
Directory of Open Access Journals (Sweden)
Jeng-Wen Lin
2009-01-01
Full Text Available This paper proposes a statistical confidence interval based nonlinear model parameter refinement approach for the health monitoring of structural systems subjected to seismic excitations. The developed model refinement approach uses the 95% confidence interval of the estimated structural parameters to determine their statistical significance in a least-squares regression setting. When the parameters' confidence interval covers the zero value, it is statistically sustainable to truncate such parameters. The remaining parameters will repetitively undergo such parameter sifting process for model refinement until all the parameters' statistical significance cannot be further improved. This newly developed model refinement approach is implemented for the series models of multivariable polynomial expansions: the linear, the Taylor series, and the power series model, leading to a more accurate identification as well as a more controllable design for system vibration control. Because the statistical regression based model refinement approach is intrinsically used to process a “batch” of data and obtain an ensemble average estimation such as the structural stiffness, the Kalman filter and one of its extended versions is introduced to the refined power series model for structural health monitoring.
System Identification and 6-DOF Hovering Controller Design of Unmanned Model Helicopter
Kim, Byeongil; Chang, Yushin; Lee, Man Hyung
For a maneuvering unmanned autonomous helicopter, it is necessary to design a proper controller for each flight mode. In this paper, the overall helicopter dynamics is derived and a hovering model is linearized and transformed into a state-space form. However, since it is difficult to obtain parameters for stability derivatives in the state-space directly, a linear control model is derived by a time-domain parametric system identification method with real flight data of a model helicopter. Then, two different controllers (a linear feedback controller with the proportional gain and a robust controller) are designed and their performances are compared. The simulation results show outstanding performance. The validated controllers can be utilized to enable autonomous flight of a RUAV (Rotorcraft-based Unmanned Aerial Vehicle).
The Development Of Mathematical Model For Automated Fingerprint Identification Systems Analysis
International Nuclear Information System (INIS)
Ardisasmita, M. Syamsa
2001-01-01
Fingerprint has a strong oriented and periodic structure composed of dark lines of raised skin (ridges) and clear lines of lowered skin (furrows)that twist to form a distinct pattern. Although the manner in which the ridges flow is distinctive, other characteristics of the fingerprint called m inutiae a re what are most unique to the individual. These features are particular patterns consisting of terminations or bifurcations of the ridges. To assert if two fingerprints are from the same finger or not, experts detect those minutiae. AFIS (Automated Fingerprint Identification Systems) extract and compare these features for determining a match. The classic methods of fingerprints recognition are not suitable for direct implementation in form of computer algorithms. The creation of a finger's model was however the necessity of development of new, better algorithms of analysis. This paper presents a new numerical methods of fingerprints' simulation based on mathematical model of arrangement of dermatoglyphics and creation of minutiae. This paper describes also the design and implementation of an automated fingerprint identification systems which operates in two stages: minutiae extraction and minutiae matching
Wagner, A. Ben
2009-01-01
Many efforts are currently underway to disambiguate author names and assign unique identification numbers so that publications by a given scholar can be reliably grouped together. This paper reviews a number of operational and in-development services. Some systems like ResearcherId.Com depend on self-registration and self-identification of a…
Identification and Modeling of Electrohydraulic Force Control of the Material Test System (MTS)
International Nuclear Information System (INIS)
Ruan, J; Pei, X; Zhu, F M
2006-01-01
In the heavy-duty material test device, an electrohydraulic force servo system is usually utilized to load the tested samples. The signal from the pressure sensor is compared with the instruction and the difference between them is then fed to a digital servo valve to form a closed loop control to the target force. The performance of the electrohydraulic force servo system is not only closely related to how accurate to feed the flow rate to the hydraulic cylinder, but also the stiffness of the system which is dominated by the compressibility of oil. Thus the clarification of the characteristic parameters becomes the key of the solution to optimal force control. To identify the electrohydraulic force servo system various step signals are input to excite the dynamic response of the system. From the relationship between the step magnitude and the force response, the system model and the key control parameters are determined. The electrohydraulic force servo system is identified as a first order system with time constant varied with the pressure. Based on the identification of the system optimal control parameters are finally obtained and force rate error is reduced to 0.2% from original 3%
Computational model for supporting SHM systems design: Damage identification via numerical analyses
Sartorato, Murilo; de Medeiros, Ricardo; Vandepitte, Dirk; Tita, Volnei
2017-02-01
This work presents a computational model to simulate thin structures monitored by piezoelectric sensors in order to support the design of SHM systems, which use vibration based methods. Thus, a new shell finite element model was proposed and implemented via a User ELement subroutine (UEL) into the commercial package ABAQUS™. This model was based on a modified First Order Shear Theory (FOST) for piezoelectric composite laminates. After that, damaged cantilever beams with two piezoelectric sensors in different positions were investigated by using experimental analyses and the proposed computational model. A maximum difference in the magnitude of the FRFs between numerical and experimental analyses of 7.45% was found near the resonance regions. For damage identification, different levels of damage severity were evaluated by seven damage metrics, including one proposed by the present authors. Numerical and experimental damage metrics values were compared, showing a good correlation in terms of tendency. Finally, based on comparisons of numerical and experimental results, it is shown a discussion about the potentials and limitations of the proposed computational model to be used for supporting SHM systems design.
Identification of physical models
DEFF Research Database (Denmark)
Melgaard, Henrik
1994-01-01
The problem of identification of physical models is considered within the frame of stochastic differential equations. Methods for estimation of parameters of these continuous time models based on descrete time measurements are discussed. The important algorithms of a computer program for ML or MAP...... design of experiments, which is for instance the design of an input signal that are optimal according to a criterion based on the information provided by the experiment. Also model validation is discussed. An important verification of a physical model is to compare the physical characteristics...... of the model with the available prior knowledge. The methods for identification of physical models have been applied in two different case studies. One case is the identification of thermal dynamics of building components. The work is related to a CEC research project called PASSYS (Passive Solar Components...
Jafari, Zohreh; Edrisi, Mehdi; Marateb, Hamid Reza
2014-01-01
The purpose of this study was to estimate the torque from high-density surface electromyography signals of biceps brachii, brachioradialis, and the medial and lateral heads of triceps brachii muscles during moderate-to-high isometric elbow flexion-extension. The elbow torque was estimated in two following steps: First, surface electromyography (EMG) amplitudes were estimated using principal component analysis, and then a fuzzy model was proposed to illustrate the relationship between the EMG amplitudes and the measured torque signal. A neuro-fuzzy method, with which the optimum number of rules could be estimated, was used to identify the model with suitable complexity. Utilizing the proposed neuro-fuzzy model, the clinical interpretability was introduced; contrary to the previous linear and nonlinear black-box system identification models. It also reduced the estimation error compared with that of the most recent and accurate nonlinear dynamic model introduced in the literature. The optimum number of the rules for all trials was 4 ± 1, that might be related to motor control strategies and the % variance accounted for criterion was 96.40 ± 3.38 which in fact showed considerable improvement compared with the previous methods. The proposed method is thus a promising new tool for EMG-Torque modeling in clinical applications. PMID:25426427
Robust stator resistance identification of an IM drive using model reference adaptive system
International Nuclear Information System (INIS)
Madadi Kojabadi, Hossein; Abarzadeh, Mostafa; Aghaei Farouji, Said
2013-01-01
Highlights: ► We estimate the stator resistance and rotor speed of the IM. ► We proposed a new quantity to estimate the speed and stator resistance of IM. ► The proposed algorithm is robust to rotor resistance variations. ► We estimate the IM speed and stator resistance simultaneously to avoid speed error. - Abstract: Model reference adaptive system (MRAS) based robust stator resistance estimator for sensorless induction motor (IM) drive is proposed. The MRAS is formed with a semi-active power quantity. The proposed identification method can be achieved with on-line tuning of the stator resistance with robustness against rotor resistance variations. Stable and efficient estimation of IM speed at low region will be guaranteed by simultaneous identification of IM speed and stator resistance. The stability of proposed stator resistance estimator is checked through Popov’s hyperstability theorem. Simulation and experimental results are given to highlight the feasibility, the simplicity, and the robustness of the proposed method.
Directory of Open Access Journals (Sweden)
Kyu-Sik Park
2015-01-01
Full Text Available Hanger cables in suspension bridges are partly constrained by horizontal clamps. So, existing tension estimation methods based on a single cable model are prone to higher errors as the cable gets shorter, making it more sensitive to flexural rigidity. Therefore, inverse analysis and system identification methods based on finite element models are suggested recently. In this paper, the applicability of system identification methods is investigated using the hanger cables of Gwang-An bridge. The test results show that the inverse analysis and systemic identification methods based on finite element models are more reliable than the existing string theory and linear regression method for calculating the tension in terms of natural frequency errors. However, the estimation error of tension can be varied according to the accuracy of finite element model in model based methods. In particular, the boundary conditions affect the results more profoundly when the cable gets shorter. Therefore, it is important to identify the boundary conditions through experiment if it is possible. The FE model-based tension estimation method using system identification method can take various boundary conditions into account. Also, since it is not sensitive to the number of natural frequency inputs, the availability of this system is high.
Nonlinear system identification based on Takagi-Sugeno fuzzy modeling and unscented Kalman filter.
Vafamand, Navid; Arefi, Mohammad Mehdi; Khayatian, Alireza
2018-03-01
This paper proposes two novel Kalman-based learning algorithms for an online Takagi-Sugeno (TS) fuzzy model identification. The proposed approaches are designed based on the unscented Kalman filter (UKF) and the concept of dual estimation. Contrary to the extended Kalman filter (EKF) which utilizes derivatives of nonlinear functions, the UKF employs the unscented transformation. Consequently, non-differentiable membership functions can be considered in the structure of the TS models. This makes the proposed algorithms to be applicable for the online parameter calculation of wider classes of TS models compared to the recently published papers concerning the same issue. Furthermore, because of the great capability of the UKF in handling severe nonlinear dynamics, the proposed approaches can effectively approximate the nonlinear systems. Finally, numerical and practical examples are provided to show the advantages of the proposed approaches. Simulation results reveal the effectiveness of the proposed methods and performance improvement based on the root mean square (RMS) of the estimation error compared to the existing results. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.
Automated fingerprint identification system
International Nuclear Information System (INIS)
Bukhari, U.A.; Sheikh, N.M.; Khan, U.I.; Mahmood, N.; Aslam, M.
2002-01-01
In this paper we present selected stages of an automated fingerprint identification system. The software for the system is developed employing algorithm for two-tone conversion, thinning, feature extraction and matching. Keeping FBI standards into account, it has been assured that no details of the image are lost in the comparison process. We have deployed a general parallel thinning algorithm for specialized images like fingerprints and modified the original algorithm after a series of experimentation selecting the one giving the best results. We also proposed an application-based approach for designing automated fingerprint identification systems keeping in view systems requirements. We will show that by using our system, the precision and efficiency of current fingerprint matching techniques are increased. (author)
Performance Modelling of Automatic Identification System with Extended Field of View
DEFF Research Database (Denmark)
Lauersen, Troels; Mortensen, Hans Peter; Pedersen, Nikolaj Bisgaard
2010-01-01
This paper deals with AIS (Automatic Identification System) behavior, to investigate the severity of packet collisions in an extended field of view (FOV). This is an important issue for satellite-based AIS, and the main goal is a feasibility study to find out to what extent an increased FOV...
Egli Anthonioz, N M; Champod, C
2014-02-01
In the context of the investigation of the use of automated fingerprint identification systems (AFIS) for the evaluation of fingerprint evidence, the current study presents investigations into the variability of scores from an AFIS system when fingermarks from a known donor are compared to fingerprints that are not from the same source. The ultimate goal is to propose a model, based on likelihood ratios, which allows the evaluation of mark-to-print comparisons. In particular, this model, through its use of AFIS technology, benefits from the possibility of using a large amount of data, as well as from an already built-in proximity measure, the AFIS score. More precisely, the numerator of the LR is obtained from scores issued from comparisons between impressions from the same source and showing the same minutia configuration. The denominator of the LR is obtained by extracting scores from comparisons of the questioned mark with a database of non-matching sources. This paper focuses solely on the assignment of the denominator of the LR. We refer to it by the generic term of between-finger variability. The issues addressed in this paper in relation to between-finger variability are the required sample size, the influence of the finger number and general pattern, as well as that of the number of minutiae included and their configuration on a given finger. Results show that reliable estimation of between-finger variability is feasible with 10,000 scores. These scores should come from the appropriate finger number/general pattern combination as defined by the mark. Furthermore, strategies of obtaining between-finger variability when these elements cannot be conclusively seen on the mark (and its position with respect to other marks for finger number) have been presented. These results immediately allow case-by-case estimation of the between-finger variability in an operational setting. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.
Model-based identification and use of task complexity factors of human integrated systems
International Nuclear Information System (INIS)
Ham, Dong-Han; Park, Jinkyun; Jung, Wondea
2012-01-01
Task complexity is one of the conceptual constructs that are critical to explain and predict human performance in human integrated systems. A basic approach to evaluating the complexity of tasks is to identify task complexity factors and measure them. Although a great deal of task complexity factors have been studied, there is still a lack of conceptual frameworks for identifying and organizing them analytically, which can be generally used irrespective of the types of domains and tasks. This study proposes a model-based approach to identifying and using task complexity factors, which has two facets—the design aspects of a task and complexity dimensions. Three levels of design abstraction, which are functional, behavioral, and structural aspects of a task, characterize the design aspect of a task. The behavioral aspect is further classified into five cognitive processing activity types. The complexity dimensions explain a task complexity from different perspectives, which are size, variety, and order/organization. Twenty-one task complexity factors are identified by the combination of the attributes of each facet. Identification and evaluation of task complexity factors based on this model is believed to give insights for improving the design quality of tasks. This model for complexity factors can also be used as a referential framework for allocating tasks and designing information aids. The proposed approach is applied to procedure-based tasks of nuclear power plants (NPPs) as a case study to demonstrate its use. Last, we compare the proposed approach with other studies and then suggest some future research directions.
Mastering system identification in 100 exercises
Schoukens, J; Rolain, Yves
2012-01-01
"This book enables readers to understand system identification and linear system modeling through 100 practical exercises without requiring complex theoretical knowledge. The contents encompass state-of-the-art system identification methods, with both time and frequency domain system identification methods covered, including the pros and cons of each. Each chapter features MATLAB exercises, discussions of the exercises, accompanying MATLAB downloads, and larger projects that serve as potential assignments in this learn-by-doing resource"--
Walters, Everton St. Patrick
This work was motivated by the need to model a network of natural gas pipelines and its corresponding demand pipeline, in order to make predictions of the pressures at critical junctions in the network Development of such a model amounts to a system identification problem with limited information. In order to solve this problem, we developed a demand model that would provide estimates of the gas usage for the communities serviced by the pipeline network. The parameters of the demand model were estimated using an adaptive genetic algorithm. This new algorithm was first developed and compared with existing genetic algorithms. A discussion of the role played by crossover and mutation operators in the genetic algorithm was also presented. Based on the theory of gas dynamics and the known pipeline network topology, a resistor-capacitor network analog to the pipeline network was developed. The parameters of the resistor-capacitor model were estimated using ordinary least squares techniques. We first studied and developed a number principles and guidelines for a class of system identification problems. One of the main areas studied was the development of a generalized framework for least squares "parameter" identification of continuous-time systems from discrete-time measurements of the states of the continuous-time system. Subsequently, we extended our generalized framework to the least squares parameter identification of a class of resistor-capacitor networks. We also studied the effects on the estimated results of the integration scheme used in the process and the noise levels in the measured data. A demonstration of the benefits of the incorporation of the maximum available structural information of the system being modeled was also presented. Finally, we developed a set of guidelines for the required input signal frequencies and sampling frequencies to provide acceptable identification results for both the plant-model-match and reduced-order modeling problems
Embedded System for Biometric Identification
Rosli, Ahmad Nasir Che
2010-01-01
This chapter describes the design and implementation of an Embedded System for Biometric Identification from hardware and software perspectives. The first part of the chapter describes the idea of biometric identification. This includes the definition of
Identification and Damage Detection on Structural Systems
DEFF Research Database (Denmark)
Brincker, Rune; Kirkegaard, Poul Henning; Andersen, Palle
1994-01-01
A short introduction is given to system identification and damage assessment in civil engineering structures. The most commonly used FFT-based techniques for system identification are mentioned, and the Random decrement technique and parametric methods based on ARMA models are introduced. Speed...
System Identification of Civil Engineering Structures using State Space and ARMAV Models
DEFF Research Database (Denmark)
Andersen, P.; Kirkegaard, Poul Henning; Brincker, Rune
In this paper the relations between an ambient excited structural system, represented by an innovation state space system, and the Auto-Regressive Moving Average Vector (ARMAV) model are considered. It is shown how to obtain a multivariate estimate of the ARMAV model from output measurements, usi...
Directory of Open Access Journals (Sweden)
Mattia Zanon
2013-01-01
Full Text Available Continuous glucose monitoring (CGM by suitable portable sensors plays a central role in the treatment of diabetes, a disease currently affecting more than 350 million people worldwide. Noninvasive CGM (NI-CGM, in particular, is appealing for reasons related to patient comfort (no needles are used but challenging. NI-CGM prototypes exploiting multisensor approaches have been recently proposed to deal with physiological and environmental disturbances. In these prototypes, signals measured noninvasively (e.g., skin impedance, temperature, optical skin properties, etc. are combined through a static multivariate linear model for estimating glucose levels. In this work, by exploiting a dataset of 45 experimental sessions acquired in diabetic subjects, we show that regularisation-based techniques for the identification of the model, such as the least absolute shrinkage and selection operator (better known as LASSO, Ridge regression, and Elastic-Net regression, improve the accuracy of glucose estimates with respect to techniques, such as partial least squares regression, previously used in the literature. More specifically, the Elastic-Net model (i.e., the model identified using a combination of and norms has the best results, according to the metrics widely accepted in the diabetes community. This model represents an important incremental step toward the development of NI-CGM devices effectively usable by patients.
International Nuclear Information System (INIS)
Chetoui, Manel; Malti, Rachid; Thomassin, Magalie; Aoun, Mohamed; Najar, Slah; Abdelkrim, Naceur
2011-01-01
This paper deals with continuous-time system identification using fractional models in a noisy input/output context. The third-order cumulants based least squares method (tocls) is extended here to fractional models. The derivatives of the third-order cumulants are computed using a new fractional state variable filter. A numerical example is used to demonstrate the performance of the proposed method called ftocls (fractional third-order cumulants based least squares). The effect of the signal-to-noise ratio and the hyperparameter is studied.
Őri, Zsolt P
2017-05-01
A mathematical model has been developed to facilitate indirect measurements of difficult to measure variables of the human energy metabolism on a daily basis. The model performs recursive system identification of the parameters of the metabolic model of the human energy metabolism using the law of conservation of energy and principle of indirect calorimetry. Self-adaptive models of the utilized energy intake prediction, macronutrient oxidation rates, and daily body composition changes were created utilizing Kalman filter and the nominal trajectory methods. The accuracy of the models was tested in a simulation study utilizing data from the Minnesota starvation and overfeeding study. With biweekly macronutrient intake measurements, the average prediction error of the utilized carbohydrate intake was -23.2 ± 53.8 kcal/day, fat intake was 11.0 ± 72.3 kcal/day, and protein was 3.7 ± 16.3 kcal/day. The fat and fat-free mass changes were estimated with an error of 0.44 ± 1.16 g/day for fat and -2.6 ± 64.98 g/day for fat-free mass. The daily metabolized macronutrient energy intake and/or daily macronutrient oxidation rate and the daily body composition change from directly measured serial data are optimally predicted with a self-adaptive model with Kalman filter that uses recursive system identification.
Laguerre-Volterra model and architecture for MIMO system identification and output prediction.
Li, Will X Y; Xin, Yao; Chan, Rosa H M; Song, Dong; Berger, Theodore W; Cheung, Ray C C
2014-01-01
A generalized mathematical model is proposed for behaviors prediction of biological causal systems with multiple inputs and multiple outputs (MIMO). The system properties are represented by a set of model parameters, which can be derived with random input stimuli probing it. The system calculates predicted outputs based on the estimated parameters and its novel inputs. An efficient hardware architecture is established for this mathematical model and its circuitry has been implemented using the field-programmable gate arrays (FPGAs). This architecture is scalable and its functionality has been validated by using experimental data gathered from real-world measurement.
Meimon, Serge; Petit, Cyril; Fusco, Thierry; Kulcsar, Caroline
2010-11-01
Adaptive optics (AO) systems have to correct tip-tilt (TT) disturbances down to a fraction of the diffraction-limited spot. This becomes a key issue for very or extremely large telescopes affected by mechanical vibration peaks or wind shake effects. Linear quadratic Gaussian (LQG) control achieves optimal TT correction when provided with the temporal model of the disturbance. We propose a nonsupervised identification procedure that does not require any auxiliary system or loop opening and validate it on synthetic profile as well as on experimental data.
DEFF Research Database (Denmark)
Costanzo, Giuseppe Tommaso; Sossan, Fabrizio; Marinelli, Mattia
2013-01-01
This paper presents the grey-box modeling of a vapor-compression refrigeration system for residential applications based on maximum likelihood estimation of parameters in stochastic differential equations. Models obtained are useful in the view of controlling refrigerators as flexible consumption......, such as heat pumps for space heating, in order to smooth the load factor during peak hours, enhance reliability and efficiency in power networks and reduce operational costs.......This paper presents the grey-box modeling of a vapor-compression refrigeration system for residential applications based on maximum likelihood estimation of parameters in stochastic differential equations. Models obtained are useful in the view of controlling refrigerators as flexible consumption...
Model Identification for Control of Display Units in Supermarket Refrigeration Systems
DEFF Research Database (Denmark)
O'Connell, Niamh; Madsen, Henrik; Andersen, Philip Hvidthøft Delff
In this paper we propose a method for identifying and validating a model of the heat dynamics of a supermarket refrigeration display case for the purpose of advanced control. The model is established to facilitate the development of novel model-based control techniques for individual display units......, and the performance of candidate models is evaluated through cross-validation.The model developed in this work uses operational data from a small Danish supermarket. A three-state model is determined to be most appropriate for describing the dynamics of this system. Advanced local control employing the identified...... model can contribute to the extension of the control capabilities of the entire supermarket refrigeration system....
Even as stakeholder engagement in systems dynamic modeling efforts is increasingly promoted, the mechanisms for identifying which stakeholders should be included are rarely documented. Accordingly, for an Environmental Protection Agency’s Triple Value Simulation (3VS) mode...
DEFF Research Database (Denmark)
Ursem, Rasmus Kjær
of handling problems with non-linear constraints, multiple objectives, and dynamic components – properties that frequently appear in real-world problems. This thesis presents research in three fundamental areas of EC; fitness function design, methods for parameter control, and techniques for multimodal...... optimization. In addition to general investigations in these areas, I introduce a number of algorithms and demonstrate their potential on real-world problems in system identification and control. Furthermore, I investigate dynamic optimization problems in the context of the three fundamental areas as well...... as control, which is a field where real-world dynamic problems appear. Regarding fitness function design, smoothness of the fitness landscape is of primary concern, because a too rugged landscape may disrupt the search and lead to premature convergence at local optima. Rugged fitness landscapes typically...
On System Identification of Wind Turbines
DEFF Research Database (Denmark)
Kirkegaard, Poul Henning; Perisic, Nevena; Pedersen, B.J.
Recently several methods have been proposed for the system identification of wind turbines which can be considered as a linear time-varying system due to the operating conditions. For the identification of linear wind turbine models, either black-box or grey-box identification can be used....... The operational model analysis (OMA) methodology can provide accurate estimates of the natural frequencies, damping ratios and mode shapes of the systems as long as the measurements have a low noise to signal ratio. However, in order to take information about the wind turbine into account a grey...
Identification of hidden failures in control systems: a functional modelling approach
International Nuclear Information System (INIS)
Jalashgar, A.; Modarres, M.
1996-01-01
This paper presents a model which encompasses knowledge about a process control system's functionalities in a function-oriented failure analysis task. The technique called Hybrid MFM-GTST, mainly utilizes two different function - oriented methods (MFM and GTST) to identify all functions of the system components, and hence possible sources of hidden failures in process control systems. Hidden failures are referred to incipient failures within the system that in long term may lead to loss of major functions. The features of the method are described and demonstrated by using an example of a process control system
Directory of Open Access Journals (Sweden)
Stankevych Iryna V.
2017-03-01
Full Text Available The aim of the article is to ensure effectiveness of implementation of management objectives of an educational organization by identifying business processes of the quality management system of the educational organization and justifying the choice of language and diagrams of their modeling. The author emphasizes that the approach to identifying business processes of the quality management system of an educational organization should be based not on the types but on the results of the organization’s activity in higher education. It is determined that an effective approach to identifying business processes of the quality management system of an educational organization should be based on the life cycle of the educational service (quality loop, the requirements of the ISO 9001: 2015 standard for building the processes of the quality management system and the standardized list of business processes International Benchmarking Clearinghouse. For further implementation of the quality management system, the author developed a contextual diagram of the system, which is decomposed into processes of level “0” and level “1”. The paper discusses the advantages and disadvantages of various modeling languages applicable to business process virtualization, in particular the unified UML modeling language, which uses a number of diagrams to virtualize business processes of complex organizations. The practical recommendations developed for the identification and modeling of business processes in the quality management system will facilitate an effective implementation of such systems in activities of educational organizations, which will further ensure satisfaction of customer requirements and implementation of management objectives.
Downey, Brandon; Schmitt, John; Beller, Justin; Russell, Brian; Quach, Anthony; Hermann, Elizabeth; Lyon, David; Breit, Jeffrey
2017-11-01
As the biopharmaceutical industry evolves to include more diverse protein formats and processes, more robust control of Critical Quality Attributes (CQAs) is needed to maintain processing flexibility without compromising quality. Active control of CQAs has been demonstrated using model predictive control techniques, which allow development of processes which are robust against disturbances associated with raw material variability and other potentially flexible operating conditions. Wide adoption of model predictive control in biopharmaceutical cell culture processes has been hampered, however, in part due to the large amount of data and expertise required to make a predictive model of controlled CQAs, a requirement for model predictive control. Here we developed a highly automated, perfusion apparatus to systematically and efficiently generate predictive models using application of system identification approaches. We successfully created a predictive model of %galactosylation using data obtained by manipulating galactose concentration in the perfusion apparatus in serialized step change experiments. We then demonstrated the use of the model in a model predictive controller in a simulated control scenario to successfully achieve a %galactosylation set point in a simulated fed-batch culture. The automated model identification approach demonstrated here can potentially be generalized to many CQAs, and could be a more efficient, faster, and highly automated alternative to batch experiments for developing predictive models in cell culture processes, and allow the wider adoption of model predictive control in biopharmaceutical processes. © 2017 The Authors Biotechnology Progress published by Wiley Periodicals, Inc. on behalf of American Institute of Chemical Engineers Biotechnol. Prog., 33:1647-1661, 2017. © 2017 The Authors Biotechnology Progress published by Wiley Periodicals, Inc. on behalf of American Institute of Chemical Engineers.
Computer system for identification of tool wear model in hot forging
Directory of Open Access Journals (Sweden)
Wilkus Marek
2016-01-01
Full Text Available The aim of the research was to create a methodology that will enable effective and reliable prediction of the tool wear. The idea of the hybrid model, which accounts for various mechanisms of tool material deterioration, is proposed in the paper. The mechanisms, which were considered, include abrasive wear, adhesive wear, thermal fatigue, mechanical fatigue, oxidation and plastic deformation. Individual models of various complexity were used for separate phenomena and strategy of combination of these models in one hybrid system was developed to account for the synergy of various mechanisms. The complex hybrid model was built on the basis of these individual models for various wear mechanisms. The individual models expanded from phenomenological ones for abrasive wear to multi-scale methods for modelling micro cracks initiation and propagation utilizing virtual representations of granular microstructures. The latter have been intensively developed recently and they form potentially a powerful tool that allows modelling of thermal and mechanical fatigue, accounting explicitly for the tool material microstructure.
Freeman, Chris
2016-01-01
This book presents a comprehensive framework for model-based electrical stimulation (ES) controller design, covering the whole process needed to develop a system for helping people with physical impairments perform functional upper limb tasks such as eating, grasping and manipulating objects. The book first demonstrates procedures for modelling and identifying biomechanical models of the response of ES, covering a wide variety of aspects including mechanical support structures, kinematics, electrode placement, tasks, and sensor locations. It then goes on to demonstrate how complex functional activities of daily living can be captured in the form of optimisation problems, and extends ES control design to address this case. It then lays out a design methodology, stability conditions, and robust performance criteria that enable control schemes to be developed systematically and transparently, ensuring that they can operate effectively in the presence of realistic modelling uncertainty, physiological variation an...
Pourrezaei Khaligh, Sepehr
Model-based control design of small-scale helicopters involves considerable challenges due to their nonlinear and underactuated dynamics with strong couplings between the different degrees-of-freedom (DOFs). Most nonlinear model-based multi-input multi-output (MIMO) control approaches require the dynamic model of the system to be affine-in-control and fully actuated. Since the existing formulations for helicopter nonlinear dynamic model do not meet these requirements, these MIMO approaches cannot be applied for control of helicopters and control designs in the literature mostly use the linearized model of the helicopter dynamics around different trim conditions instead of directly using the nonlinear model. The purpose of this thesis is to derive the 6-DOF nonlinear model of the helicopter in an affine-in-control, non-iterative and square input-output formulation to enable many nonlinear control approaches, that require a control-affine and square model such as the sliding mode control (SMC), to be used for control design of small-scale helicopters. A combination of the first-principles approach and system identification is used to derive this model. To complete the nonlinear model of the helicopter required for the control design, the inverse kinematics of the actuating mechanisms of the main and tail rotors are also derived using an approach suitable for the real-time control applications. The parameters of the new control-oriented formulation are identified using a time-domain system identification strategy and the model is validated using flight test data. A robust sliding mode control (SMC) is then designed using the new formulation of the helicopter dynamics and its robustness to parameter uncertainties and wind disturbances is tested in simulations. Next, a hardware-in-the-loop (HIL) testbed is designed to allow for the control implementation and gain tuning as well as testing the robustness of the controller to external disturbances in a controlled
Askari, M.; Markazi, A. H. D.
2012-04-01
A new encoding scheme is presented for a fuzzy-based nonlinear system identification methodology, using the subtractive clustering and non-dominated sorting genetic algorithm. The proposed method consists of two parts. The first part is related to the selection of most relevant or influencing inputs to the system and the second one is related to the tuning of fuzzy rules and parameters of the membership functions. The main purpose of the proposed scheme is to reduce the complexity and increase the accuracy of the model. In particular, three objectives are considered in the process of optimisation, namely, the number of inputs, number of rules and the root mean square of the modelling error. The performance of the developed method is validated by identifying the Box-Jenkins nonlinear benchmark system, and to the modelling of the forward and inverse dynamic behaviours of a magneto-rheological (MR) damper. The latter is also a challenging problem due to the inherent hysteretic and highly nonlinear dynamics of the MR damper. It is shown that the developed evolving Takagi-Sugeno (T-S) fuzzy model can identify and grasp the nonlinear dynamics of both systems very well, while a small number of inputs and fuzzy rules are required for this purpose.
Directory of Open Access Journals (Sweden)
Nicolau Cardoso Neto
2016-09-01
Full Text Available The relationship between humans and pets, especially dogs and cats is taking gigantic proportions, this can be seen by the data recently published by IBGE, where it was verified that the number of pets grows more than the number of child birth (IBGE, 2015. As so foreseen, the purpose of this study is to acknowledge which is the federal juridical basis related to the registration of Domestic Animals in Brazil, comparing them with the international legal standards that can be of reference, such as Canada, United States of America, Republic of Ireland and United Kingdom. This increase in pet population shows problems of many dimensions that relate to several causes in urban centers, especially when referring to zoonosis and conflicts that come from irresponsible ownership, bringing up a public health issue. The intension was to verify the possibility to adequate them to the reality encountered in Brazil, creating a model of identification for the Domestic Animals in urban areas in this country. As for that, the article addresses themes related to responsible ownership, animal welfare and models of registration already accomplished in the mentioned countries. At the end the article presents the proposal of a model for a system of identification of the Domestic Animals.
Angeles, J G C; Ouyang, Z; Aguirre, A M; Lammers, P J; Song, M
2009-09-01
Fungal-plant root associations involve nutrient exchanges, between the partners and the soil, particularly phosphate, that benefit both organisms. Discrete dynamical system (DDS) models are reconstructed to capture gene regulation in the arbuscular mycorrhizae Glomus versiforme-Medicago trunculata root symbiosis. Previously published time-course gene expression data derived from various days post-inoculation were clustered to identify genes co-regulated in mycorrhizal roots. Uncolonised roots grown with high phosphate provide a key nutritional control condition. First-order linear DDS models were created using a data-driven method to fit to the observed gene expression data. The result of the modelling constitutes active gene interactions in the regulatory network of the plant root at 8, 15, 22, 31 and 36 days post-inoculation. These genes are involved in basic metabolism, development, oxidative stress and defense pathways, and show consistent dynamic behaviours in the model. The functions of previously unannotated genes were further elucidated from the developed system maps.
System Identification with Quantized Observations
Wang, Le Yi; Zhang, Jifeng; Zhao, Yanlong
2010-01-01
This book presents recently developed methodologies that utilize quantized information in system identification and explores their potential in extending control capabilities for systems with limited sensor information or networked systems. The results of these methodologies can be applied to signal processing and control design of communication and computer networks, sensor networks, mobile agents, coordinated data fusion, remote sensing, telemedicine, and other fields in which noise-corrupted quantized data need to be processed. Providing a comprehensive coverage of quantized identification,
Optimized Experiment Design for Marine Systems Identification
DEFF Research Database (Denmark)
Blanke, M.; Knudsen, Morten
1999-01-01
Simulation of maneuvring and design of motion controls for marine systems require non-linear mathematical models, which often have more than one-hundred parameters. Model identification is hence an extremely difficult task. This paper discusses experiment design for marine systems identification...... and proposes a sensitivity approach to solve the practical experiment design problem. The applicability of the sensitivity approach is demonstrated on a large non-linear model of surge, sway, roll and yaw of a ship. The use of the method is illustrated for a container-ship where both model and full-scale tests...
Directory of Open Access Journals (Sweden)
Li Wang
2017-02-01
Full Text Available The ability to obtain appropriate parameters for an advanced pressurized water reactor (PWR unit model is of great significance for power system analysis. The attributes of that ability include the following: nonlinear relationships, long transition time, intercoupled parameters and difficult obtainment from practical test, posed complexity and difficult parameter identification. In this paper, a model and a parameter identification method for the PWR primary loop system were investigated. A parameter identification process was proposed, using a particle swarm optimization (PSO algorithm that is based on random perturbation (RP-PSO. The identification process included model variable initialization based on the differential equations of each sub-module and program setting method, parameter obtainment through sub-module identification in the Matlab/Simulink Software (Math Works Inc., Natick, MA, USA as well as adaptation analysis for an integrated model. A lot of parameter identification work was carried out, the results of which verified the effectiveness of the method. It was found that the change of some parameters, like the fuel temperature and coolant temperature feedback coefficients, changed the model gain, of which the trajectory sensitivities were not zero. Thus, obtaining their appropriate values had significant effects on the simulation results. The trajectory sensitivities of some parameters in the core neutron dynamic module were interrelated, causing the parameters to be difficult to identify. The model parameter sensitivity could be different, which would be influenced by the model input conditions, reflecting the parameter identifiability difficulty degree for various input conditions.
Adaptive Filtering and System Identification
National Research Council Canada - National Science Library
Gibson, Steve
2007-01-01
.... Additional application areas include optical wireless communication systems, blind identification and deconvolution in wireless communications, and active control of noise and vibration. This report discusses recent collaborations with the Air Force Research Laboratory (AFRL) and industry.
Linear stochastic systems a geometric approach to modeling, estimation and identification
Lindquist, Anders
2015-01-01
This book presents a treatise on the theory and modeling of second-order stationary processes, including an exposition on selected application areas that are important in the engineering and applied sciences. The foundational issues regarding stationary processes dealt with in the beginning of the book have a long history, starting in the 1940s with the work of Kolmogorov, Wiener, Cramér and his students, in particular Wold, and have since been refined and complemented by many others. Problems concerning the filtering and modeling of stationary random signals and systems have also been addressed and studied, fostered by the advent of modern digital computers, since the fundamental work of R.E. Kalman in the early 1960s. The book offers a unified and logically consistent view of the subject based on simple ideas from Hilbert space geometry and coordinate-free thinking. In this framework, the concepts of stochastic state space and state space modeling, based on the notion of the conditional independence of pas...
Processing system of jaws tomograms for pathology identification and surgical guide modeling
International Nuclear Information System (INIS)
Putrik, M. B.; Ivanov, V. Yu.; Lavrentyeva, Yu. E.
2015-01-01
The aim of the study is to create an image processing system, which allows dentists to find pathological resorption and to build surgical guide surface automatically. X-rays images of jaws from cone beam tomography or spiral computed tomography are the initial data for processing. One patient’s examination always includes up to 600 images (or tomograms), that’s why the development of processing system for fast automation search of pathologies is necessary. X-rays images can be useful not for only illness diagnostic but for treatment planning too. We have studied the case of dental implantation – for successful surgical manipulations surgical guides are used. We have created a processing system that automatically builds jaw and teeth boundaries on the x-ray image. After this step, obtained teeth boundaries used for surgical guide surface modeling and jaw boundaries limit the area for further pathologies search. Criterion for the presence of pathological resorption zones inside the limited area is based on statistical investigation. After described actions, it is possible to manufacture surgical guide using 3D printer and apply it in surgical operation
Directory of Open Access Journals (Sweden)
Manghui Tu
2012-12-01
Full Text Available Forensic readiness can support future forensics investigation or auditing on external/internal attacks, internal sabotage and espionage, and business frauds. To establish forensics readiness, it is essential for an organization to identify what evidences are relevant and where they can be found, to determine whether they are logged in a forensic sound way and whether all the needed evidences are available to reconstruct the events successfully. Â Our goal of this research is to ensure evidence availability. First, both external and internal attacks are molded as augmented attack trees/graphs based on the system vulnerabilities. Second, modeled attacks are conducted against a honeynet simulating an online business information system, and each honeypot's hard drive is forensic sound imaged for each individual attack. Third, an evidence tree/graph will be built after forensics examination on the disk images for each attack. The evidence trees/graphs are expected to be used for automatic crime scene reconstruction and automatic attack/fraud detection in the future.
DEFF Research Database (Denmark)
Li, Chunjian; Andersen, Søren Vang
2007-01-01
noise. For both models, exact EM algorithms are derived for the joint estimation of all system parameters. The exact EM algorithms are obtainable only by appropriate constraints in the model design, and have better convergence properties than algorithms employing generalized EM algorithm or empirical...... iterative schemes. The proposed methods also enjoy good data efficiency since only second order statistics is involved in the computation. When measurement noise is present, a novel Switching Kalman Smoother is incorporated into the EM algorithm, obtaining optimum nonlinear MMSE estimates of the system...
System identification: a frequency domain approach
National Research Council Canada - National Science Library
Pintelon, R; Schoukens, J
2001-01-01
... in the Identification Process 17 1.4.1 Collect Information about the System 17 1.4.2 Select a Model Structure to Represent the System 17 1.4.3 Match the Selected Model Structure to the Measurements 19 1.4.4 Validate the Selected Model 19 1.4.5 Conclusion 19 A Statistical Approach to the Estimation Problem 1.5.1 Least Squares Estimation 20 1.5.2 Weighted Least Squar...
Dynamic System Identification and Modeling of a Rotary Wing UAV for Stability and Control Analysis
National Research Council Canada - National Science Library
McEwen, Matthew
1998-01-01
.... Using aerodynamic parameterization and linear state-space modeling techniques, the Bergen Industrial UAV was modeled for computer simulation to analyze its inherent stability and control characteristics...
LPV Identification of a Heat Distribution System
DEFF Research Database (Denmark)
Trangbæk, K; Bendtsen, Jan Dimon
2010-01-01
This paper deals with incremental system identification of district heating systems to improve control performance. As long as various parameters, e.g. valve settings, are kept fixed, the dynamics of district heating systems can be approximated well by linear models; however, the dynamics change...... significantly when these parameters change. For this reason, we attempt to identify the system using linear parameter varying models. We demonstrate how the so-called ``Hansen Scheme," for linear time-invariant systems, can be employed for incremental identification of linear parameter varying systems as well....... The approach is tested on a laboratory setup emulating a district heating system, where local controllers regulate pumps connected to a common supply. Experiments show that cross-couplings in the system can indeed be identified in closed-loop operation....
System Identification A Frequency Domain Approach
Pintelon, Rik
2012-01-01
System identification is a general term used to describe mathematical tools and algorithms that build dynamical models from measured data. Used for prediction, control, physical interpretation, and the designing of any electrical systems, they are vital in the fields of electrical, mechanical, civil, and chemical engineering. Focusing mainly on frequency domain techniques, System Identification: A Frequency Domain Approach, Second Edition also studies in detail the similarities and differences with the classical time domain approach. It high??lights many of the important steps in the identi
Chen, Yu-Wen; Wang, Yetmen; Chang, Liang-Cheng
2017-04-01
Groundwater resources play a vital role on regional supply. To avoid irreversible environmental impact such as land subsidence, the characteristic identification of groundwater system is crucial before sustainable management of groundwater resource. This study proposes a signal process approach to identify the character of groundwater systems based on long-time hydrologic observations include groundwater level and rainfall. The study process contains two steps. First, a linear signal model (LSM) is constructed and calibrated to simulate the variation of underground hydrology based on the time series of groundwater levels and rainfall. The mass balance equation of the proposed LSM contains three major terms contain net rate of horizontal exchange, rate of rainfall recharge and rate of pumpage and four parameters are required to calibrate. Because reliable records of pumpage is rare, the time-variant groundwater amplitudes of daily frequency (P ) calculated by STFT are assumed as linear indicators of puamage instead of pumpage records. Time series obtained from 39 observation wells and 50 rainfall stations in and around the study area, Pintung Plain, are paired for model construction. Second, the well-calibrated parameters of the linear signal model can be used to interpret the characteristic of groundwater system. For example, the rainfall recharge coefficient (γ) means the transform ratio between rainfall intention and groundwater level raise. The area around the observation well with higher γ means that the saturated zone here is easily affected by rainfall events and the material of unsaturated zone might be gravel or coarse sand with high infiltration ratio. Considering the spatial distribution of γ, the values of γ decrease from the upstream to the downstream of major rivers and also are correlated to the spatial distribution of grain size of surface soil. Via the time-series of groundwater levels and rainfall, the well-calibrated parameters of LSM have
Economic evaluation of national identification and recording systems for pigs.
Saatkamp, H.W.; Dijkhuizen, A.A.; Geers, R.; Huirne, R.B.M.; Noordhuizen, J.P.T.M.; Goedseels, V.
1997-01-01
Four national identification and recording (I&R) systems for the Belgian pig industry were evaluated economically, using a computer simulation model. These systems were: (1) the previous system; (2) a revised system (based on the previous one); (3) a system based on electronic identification;
Wahid, A.; Taqwallah, H. M. H.
2018-03-01
Compressors and a steam reformer are the important units in biohydrogen from biomass plant. The compressors are useful for achieving high-pressure operating conditions while the steam reformer is the main process to produce H2 gas. To control them, in this research used a model predictive control (MPC) expected to have better controller performance than conventional controllers. Because of the explicit model empowerment in MPC, obtaining a better model is the main objective before employing MPC. The common way to get the empirical model is through the identification system, so that obtained a first-order plus dead-time (FOPDT) model. This study has already improved that way since used the system re-identification (SRI) based on closed loop mode. Based on this method the results of the compressor pressure control and temperature control of steam reformer were that MPC based on system re-identification (MPC-SRI) has better performance than MPC without system re-identification (MPCWSRI) and the proportional-integral (PI) controller, by % improvement of 73% against MPCWSRI and 75% against the PI controller.
Modeling, system identification, and control for slosh-free motion of an open container of liquid
Energy Technology Data Exchange (ETDEWEB)
Feddema, J.; Baty, R.; Dykhuizen, R.; Dohrmann, C.; Parker, G.; Robinett, R.; Romero, V.; Schmitt, D.
1996-04-01
This report discusses work performed under a Cooperative Research And Development Agreement (CRADA) with Corning, Inc., to analyze and test various techniques for controlling the motion of a high speed robotic arm carrying an open container of viscous liquid, in this case, molten glass. A computer model was generated to estimate the modes of oscillation of the liquid based on the shape of the container and the viscosity of the liquid. This fluid model was experimentally verified and tuned based on experimental data from a capacitive sensor on the side of the container. A model of the robot dynamics was also developed and verified through experimental tests on a Fanuc S-800 robot arm. These two models were used to estimate the overall modes of oscillation of an open container of liquid being carried by a robot arm. Using the estimated modes, inverse dynamic control techniques were used to determine a motion profile which would eliminate waves on the liquid`s surface. Experimental tests showed that residual surface waves in an open container of water at the end of motion were reduced by over 95% and that in-motion surface waves were reduced by over 75%.
Energy Technology Data Exchange (ETDEWEB)
Vilim, R. B.; Pointer, W. D.; Wei, T. Y. C.; Nuclear Engineering Division
2006-04-30
Quantification of uncertainty is a key requirement for the design of a nuclear power plant and the assurance of its safety. Historically the procedure has been to perform the required uncertainty assessment through comparison of the analytical predictions with experimental simulations. The issue with this historical approach has always been that the simulations through experiments could not be at full scale for the practical reasons of cost and scheduling. Invariably, only parts of the system were tested separately or if integral testing was performed for the complete system, the size or scale of the experimental apparatus was significantly smaller than the actual plant configuration.
International Nuclear Information System (INIS)
Vilim, R. B.; Pointer, W. D.; Wei, T. Y. C.; Nuclear Engineering Division
2006-01-01
Quantification of uncertainty is a key requirement for the design of a nuclear power plant and the assurance of its safety. Historically the procedure has been to perform the required uncertainty assessment through comparison of the analytical predictions with experimental simulations. The issue with this historical approach has always been that the simulations through experiments could not be at full scale for the practical reasons of cost and scheduling. Invariably, only parts of the system were tested separately or if integral testing was performed for the complete system, the size or scale of the experimental apparatus was significantly smaller than the actual plant configuration
A model for the identification of tropical weather systems over South ...
African Journals Online (AJOL)
drinie
2002-07-03
Jul 3, 2002 ... Weather forecasters in South Africa are trained on and experienced in forecasting rainfall from these systems. During late summer .... T = temperature. If the horizontal gradient vector of the average column temperature is zero then the thermal wind will be zero. The thermal wind is defined as the vertical ...
Model and Sensor Based Nonlinear Adaptive Flight Control with Online System Identification
Sun, L.G.
2014-01-01
Consensus exists that many loss-of-control (LOC) in flight accidents caused by severe aircraft damage or system failure could be prevented if flight performance could be recovered using the valid and remaining control authorities. However, the safe maneuverability of a post-failure aircraft will
A model for the identification of tropical weather systems over South ...
African Journals Online (AJOL)
South Africa forms the southern end of Africa with its northern boundary at approximately 22°S and the southern-most point, Cape Agulhas, at approximately 35°S. During most of the year atmospheric circulation over South Africa, especially the central and southern regions, is dominated by extra tropical weather systems ...
Vishwakarma, Vinod
Modified Modal Domain Analysis (MMDA) is a novel method for the development of a reduced-order model (ROM) of a bladed rotor. This method utilizes proper orthogonal decomposition (POD) of Coordinate Measurement Machine (CMM) data of blades' geometries and sector analyses using ANSYS. For the first time ROM of a geometrically mistuned industrial scale rotor (Transonic rotor) with large size of Finite Element (FE) model is generated using MMDA. Two methods for estimating mass and stiffness mistuning matrices are used a) exact computation from sector FE analysis, b) estimates based on POD mistuning parameters. Modal characteristics such as mistuned natural frequencies, mode shapes and forced harmonic response are obtained from ROM for various cases, and results are compared with full rotor ANSYS analysis and other ROM methods such as Subset of Nominal Modes (SNM) and Fundamental Model of Mistuning (FMM). Accuracy of MMDA ROM is demonstrated with variations in number of POD features and geometric mistuning parameters. It is shown for the aforementioned case b) that the high accuracy of ROM studied in previous work with Academic rotor does not directly translate to the Transonic rotor. Reasons for such mismatch in results are investigated and attributed to higher mistuning in Transonic rotor. Alternate solutions such as estimation of sensitivities via least squares, and interpolation of mass and stiffness matrices on manifolds are developed, and their results are discussed. Statistics such as mean and standard deviations of forced harmonic response peak amplitude are obtained from random permutations, and are shown to have similar results as those of Monte Carlo simulations. These statistics are obtained and compared for 3 degree of freedom (DOF) lumped parameter model (LPM) of rotor, Academic rotor and Transonic rotor. A state -- estimator based on MMDA ROM and Kalman filter is also developed for offline or online estimation of harmonic forcing function from
Applied methods and techniques for mechatronic systems modelling, identification and control
Zhu, Quanmin; Cheng, Lei; Wang, Yongji; Zhao, Dongya
2014-01-01
Applied Methods and Techniques for Mechatronic Systems brings together the relevant studies in mechatronic systems with the latest research from interdisciplinary theoretical studies, computational algorithm development and exemplary applications. Readers can easily tailor the techniques in this book to accommodate their ad hoc applications. The clear structure of each paper, background - motivation - quantitative development (equations) - case studies/illustration/tutorial (curve, table, etc.) is also helpful. It is mainly aimed at graduate students, professors and academic researchers in related fields, but it will also be helpful to engineers and scientists from industry. Lei Liu is a lecturer at Huazhong University of Science and Technology (HUST), China; Quanmin Zhu is a professor at University of the West of England, UK; Lei Cheng is an associate professor at Wuhan University of Science and Technology, China; Yongji Wang is a professor at HUST; Dongya Zhao is an associate professor at China University o...
The BESIII muon identification system
International Nuclear Information System (INIS)
Zhang Jiawen; Qian Sen; Chen Jin; Du Zhizhen; Han Jifeng; Li Rubo; Liu Jichen; Liang Hao; Mao, Yajun; Ma Liehua; Wang Yifang; Xie Yigang; Xie Yuguang; Zhang Qingmin; Zhao Jianbing; Zhao, T.; Zhou, Yongzhao
2010-01-01
The muon identification system of BESIII experiment at the IHEP is described. The muon counter (MUC) is composed of resistive plate chambers (RPCs) working in self-quenching streamer mode with the gas mixture Ar/C 2 F 4 H 2 /C 4 H 10 =50/42/8. The design, the construction, the mass production and the quality control result of the detectors are described in detail. The paper also presents the performance of the bare RPCs and the superlayer modules with cosmic rays. Finally, the subsystems of MUC, including the RPC superlayer modules, the gas systems, the HV and LV system and the readout electronic system, are also presented.
identification with model reduction issues
Directory of Open Access Journals (Sweden)
A. Bilbao-Guillerna
2005-01-01
with the multiestimation scheme instead of a high-order one. Depending on the frequency spectrum characteristics of the input and on the estimates evolution, the multiestimation scheme selects on-line the most appropriate model and its related estimation scheme in order to improve the identification and control performances. Robust closed-loop stability is proved even in the presence of unmodeled dynamics of sufficiently small sizes as it has been confirmed by simulation results. The scheme chooses in real time the estimator/controller associated with a particular reduced model possessing the best performance according to an identification performance index by implementing a switching rule between estimators. The switching rule is subject to a minimum residence time at each identifier/adaptive controller parameterization for closed-loop stabilization purposes. A conceptually simple higher-level supervisor, based on heuristic updating rules which estimate on-line the weights of the switching rule between estimation schemes, is discussed.
Identification of material flow systems.
Bauer, G; Deistler, M; Gleiss, A; Glenck, E; Matyus, T
1997-01-01
Material Flow Analysis (MFA) has become an important instrument in environmental science and pollution research. In this paper, we look at the MFA problem as a particularly structured system identification problem. Special emphasis is given to the linear, static case, where we describe a procedure for reconciliating the flow measurements and for estimating the unmeasured flows and the transfer coefficients by taking into account a priori restrictions such as balance equations.
System parameter identification information criteria and algorithms
Chen, Badong; Hu, Jinchun; Principe, Jose C
2013-01-01
Recently, criterion functions based on information theoretic measures (entropy, mutual information, information divergence) have attracted attention and become an emerging area of study in signal processing and system identification domain. This book presents a systematic framework for system identification and information processing, investigating system identification from an information theory point of view. The book is divided into six chapters, which cover the information needed to understand the theory and application of system parameter identification. The authors' research pr
Spegazzini, Nicolas; Siesler, Heinz W; Ozaki, Yukihiro
2012-10-02
A sequential identification approach by two-dimensional (2D) correlation analysis for the identification of a chemical reaction model, activation, and thermodynamic parameters is presented in this paper. The identification task is decomposed into a sequence of subproblems. The first step is the construction of a reaction model with the suggested information by model-free 2D correlation analysis using a novel technique called derivative double 2D correlation spectroscopy (DD2DCOS), which enables one to analyze intensities with nonlinear behavior and overlapped bands. The second step is a model-based 2D correlation analysis where the activation and thermodynamic parameters are estimated by an indirect implicit calibration or a calibration-free approach. In this way, a minimization process for the spectral information by sample-sample 2D correlation spectroscopy and kinetic hard modeling (using ordinary differential equations) of the chemical reaction model is carried out. The sequential identification by 2D correlation analysis is illustrated with reference to the isomeric structure of diphenylurethane synthesized from phenylisocyanate and phenol. The reaction was investigated by FT-IR spectroscopy. The activation and thermodynamic parameters of the isomeric structures of diphenylurethane linked through a hydrogen bonding equilibrium were studied by means of an integration of model-free and model-based 2D correlation analysis called a sequential identification approach. The study determined the enthalpy (ΔH = 15.25 kJ/mol) and entropy (TΔS = 13.20 kJ/mol) of C═O···H hydrogen bonding of diphenylurethane through direct calculation from the differences in the kinetic parameters (δΔ(‡)H, -TδΔ(‡)S) at equilibrium in the chemical reaction system.
System identification of Drosophila olfactory sensory neurons.
Kim, Anmo J; Lazar, Aurel A; Slutskiy, Yevgeniy B
2011-02-01
The lack of a deeper understanding of how olfactory sensory neurons (OSNs) encode odors has hindered the progress in understanding the olfactory signal processing in higher brain centers. Here we employ methods of system identification to investigate the encoding of time-varying odor stimuli and their representation for further processing in the spike domain by Drosophila OSNs. In order to apply system identification techniques, we built a novel low-turbulence odor delivery system that allowed us to deliver airborne stimuli in a precise and reproducible fashion. The system provides a 1% tolerance in stimulus reproducibility and an exact control of odor concentration and concentration gradient on a millisecond time scale. Using this novel setup, we recorded and analyzed the in-vivo response of OSNs to a wide range of time-varying odor waveforms. We report for the first time that across trials the response of OR59b OSNs is very precise and reproducible. Further, we empirically show that the response of an OSN depends not only on the concentration, but also on the rate of change of the odor concentration. Moreover, we demonstrate that a two-dimensional (2D) Encoding Manifold in a concentration-concentration gradient space provides a quantitative description of the neuron's response. We then use the white noise system identification methodology to construct one-dimensional (1D) and two-dimensional (2D) Linear-Nonlinear-Poisson (LNP) cascade models of the sensory neuron for a fixed mean odor concentration and fixed contrast. We show that in terms of predicting the intensity rate of the spike train, the 2D LNP model performs on par with the 1D LNP model, with a root mean-square error (RMSE) increase of about 5 to 10%. Surprisingly, we find that for a fixed contrast of the white noise odor waveforms, the nonlinear block of each of the two models changes with the mean input concentration. The shape of the nonlinearities of both the 1D and the 2D LNP model appears to be
NNSYSID - toolbox for system identification with neural networks
DEFF Research Database (Denmark)
Norgaard, M.; Ravn, Ole; Poulsen, Niels Kjølstad
2002-01-01
The NNSYSID toolset for System Identification has been developed as an add on to MATLAB(R). The NNSYSID toolbox has been designed to assist identification of nonlinear dynamic systems. It contains a number of nonlinear model structures based on neural networks, effective training algorithms...
Expert system based radionuclide identification
International Nuclear Information System (INIS)
Aarnio, P.A.; Ala-Heikkil, J.J.; Hakulinen, T.T.; Nikkinen, M.T.
1998-01-01
An expert system coupled with the gamma spectrum analysis system SAMPO has been developed for automating the qualitative identification of radionuclides as well as for determining the quantitative parameters of the spectrum components. The program is written in C-language and runs in various environments ranging from PCs to UNIX workstations. The expert system utilizes a complete gamma library with over 2600 nuclides and 80,000 lines, and a rule base of about fifty criteria including energies, relative peak intensities, genesis modes, half lives, parent-daughter relationships, etc. The rule base is furthermore extensible by the user. This is not an original contribution but a somewhat updated version of papers and reports previously published elsewhere. (author)
THz identification and Bayes modeling
Sokolnikov, Andre
2017-05-01
THz Identification is a developing technology. Sensing in the THz range potentially gives opportunity for short range radar sensing because THz waves can better penetrate through obscured atmosphere, such as fog, than visible light. The lower scattering of THz as opposed to the visible light results also in significantly better imaging than in IR spectrum. A much higher contrast can be achieved in medical trans-illumination applications than with X-rays or visible light. The same THz radiation qualities produce better tomographical images from hard surfaces, e.g. ceramics. This effect comes from the delay in time of reflected THz pulses detection. For special or commercial applications alike, the industrial quality control of defects is facilitated with a lower cost. The effectiveness of THz wave measurements is increased with computational methods. One of them is Bayes modeling. Examples of this kind of mathematical modeling are considered.
Hohenemser, K. H.; Banerjee, D.
1977-01-01
An introduction to aircraft state and parameter identification methods is presented. A simplified form of the maximum likelihood method is selected to extract analytical aeroelastic rotor models from simulated and dynamic wind tunnel test results for accelerated cyclic pitch stirring excitation. The dynamic inflow characteristics for forward flight conditions from the blade flapping responses without direct inflow measurements were examined. The rotor blades are essentially rigid for inplane bending and for torsion within the frequency range of study, but flexible in out-of-plane bending. Reverse flow effects are considered for high rotor advance ratios. Two inflow models are studied; the first is based on an equivalent blade Lock number, the second is based on a time delayed momentum inflow. In addition to the inflow parameters, basic rotor parameters like the blade natural frequency and the actual blade Lock number are identified together with measurement bias values. The effect of the theoretical dynamic inflow on the rotor eigenvalues is evaluated.
Futia, Gregory L.; Qamar, Lubna; Behbakht, Kian; Gibson, Emily A.
2016-04-01
Circulating tumor cell (CTC) identification has applications in both early detection and monitoring of solid cancers. The rarity of CTCs, expected at ~1-50 CTCs per million nucleated blood cells (WBCs), requires identifying methods based on biomarkers with high sensitivity and specificity for accurate identification. Discovery of biomarkers with ever higher sensitivity and specificity to CTCs is always desirable to potentially find more CTCs in cancer patients thus increasing their clinical utility. Here, we investigate quantitative image cytometry measurements of lipids with the biomarker panel of DNA, Cytokeratin (CK), and CD45 commonly used to identify CTCs. We engineered a device for labeling suspended cell samples with fluorescent antibodies and dyes. We used it to prepare samples for 4 channel confocal laser scanning microscopy. The total data acquired at high resolution from one sample is ~ 1.3 GB. We developed software to perform the automated segmentation of these images into regions of interest (ROIs) containing individual cells. We quantified image features of total signal, spatial second moment, spatial frequency second moment, and their product for each ROI. We performed measurements on pure WBCs, cancer cell line MCF7 and mixed samples. Multivariable regressions and feature selection were used to determine combination features that are more sensitive and specific than any individual feature separately. We also demonstrate that computation of spatial characteristics provides higher sensitivity and specificity than intensity alone. Statistical models allowed quantification of the required sensitivity and specificity for detecting small levels of CTCs in a human blood sample.
Identification of systems with distributed parameters
International Nuclear Information System (INIS)
Moret, J.M.
1990-10-01
The problem of finding a model for the dynamical response of a system with distributed parameters based on measured data is addressed. First a mathematical formalism is developed in order to obtain the specific properties of such a system. Then a linear iterative identification algorithm is proposed that includes these properties, and that produces better results than usual non linear minimisation techniques. This algorithm is further improved by an original data decimation that allow to artificially increase the sampling period without losing between sample information. These algorithms are tested with real laboratory data
Hazard identification based on plant functional modelling
International Nuclear Information System (INIS)
Rasmussen, B.; Whetton, C.
1993-10-01
A major objective of the present work is to provide means for representing a process plant as a socio-technical system, so as to allow hazard identification at a high level. The method includes technical, human and organisational aspects and is intended to be used for plant level hazard identification so as to identify critical areas and the need for further analysis using existing methods. The first part of the method is the preparation of a plant functional model where a set of plant functions link together hardware, software, operations, work organisation and other safety related aspects of the plant. The basic principle of the functional modelling is that any aspect of the plant can be represented by an object (in the sense that this term is used in computer science) based upon an Intent (or goal); associated with each Intent are Methods, by which the Intent is realized, and Constraints, which limit the Intent. The Methods and Constraints can themselves be treated as objects and decomposed into lower-level Intents (hence the procedure is known as functional decomposition) so giving rise to a hierarchical, object-oriented structure. The plant level hazard identification is carried out on the plant functional model using the Concept Hazard Analysis method. In this, the user will be supported by checklists and keywords and the analysis is structured by pre-defined worksheets. The preparation of the plant functional model and the performance of the hazard identification can be carried out manually or with computer support. (au) (4 tabs., 10 ills., 7 refs.)
System Identification for the Clipper Liberty C96 Wind Turbine
Showers, Daniel
System identification techniques are powerful tools that help improve modeling capabilities of real world dynamic systems. These techniques are well established and have been successfully used on countless systems in many areas. However, wind turbines provide a unique challenge for system identification because of the difficulty in measuring its primary input: wind. This thesis first motivates the problem by demonstrating the challenges with wind turbine system identification using both simulations and real data. It then suggests techniques toward successfully identifying a dynamic wind turbine model including the notion of an effective wind speed and how it might be measured. Various levels of simulation complexity are explored for insights into calculating an effective wind speed. In addition, measurements taken from the University of Minnesota's Clipper Liberty C96 research wind turbine are used for a preliminary investigation into the effective wind speed calculation and system identification of a real world wind turbine.
Walker, B. K.; Gai, E.
1978-01-01
A method for determining time-varying Failure Detection and Identification (FDI) thresholds for single sample decision functions is described in the context of a triplex system of inertial platforms. A cost function consisting of the probability of vehicle loss due to FDI decision errors is minimized. A discrete Markov model is constructed from which this cost can be determined as a function of the decision thresholds employed to detect and identify the first and second failures. Optimal thresholds are determined through the use of parameter optimization techniques. The application of this approach to threshold determination is illustrated for the Space Shuttle's inertial measurement instruments.
Pole-zero form fractional model identification in frequency domain
International Nuclear Information System (INIS)
Mansouri, R.; Djamah, T.; Djennoune, S.; Bettayeb, M.
2009-01-01
This paper deals with system identification in the frequency domain using non integer order models given in the pole-zero form. The usual identification techniques cannot be used in this case because of the non integer orders of differentiation which makes the problem strongly nonlinear. A general identification method based on Levenberg-Marquardt algorithm is developed and allows to estimate the (2n+2m+1) parameters of the model. Its application to identify the ''skin effect'' of a squirrel cage induction machine modeling is then presented.
Spears, Janine L.; Parrish, James L., Jr.
2013-01-01
This teaching case introduces students to a relatively simple approach to identifying and documenting security requirements within conceptual models that are commonly taught in systems analysis and design courses. An introduction to information security is provided, followed by a classroom example of a fictitious company, "Fun &…
Ozil, Ipek; Plawecki, Martin H; Doerschuk, Peter C; O'Connor, Sean J
2011-01-01
The influence of family history and genetics on the risk for the development of abuse or dependence is a major theme in alcoholism research. Recent research have used endophenotypes and behavioral paradigms to help detect further genetic contributions to this disease. Electronic tasks, essentially video games, which provide alcohol as a reward in controlled environments and with specified exposures have been developed to explore some of the behavioral and subjective characteristics of individuals with or at risk for alcohol substance use disorders. A generative model (containing parameters with unknown values) of a simple game involving a progressive work paradigm is described along with the associated point process signal processing that allows system identification of the model. The system is demonstrated on human subject data. The same human subject completing the task under different circumstances, e.g., with larger and smaller alcohol reward values, is assigned different parameter values. Potential meanings of the different parameter values are described.
DEFF Research Database (Denmark)
Rasmussen, Jens
1990-01-01
and compatibility of data bases. It is, however, a question whether traditional models of work process or task procedures are suited for design of advanced information systems such as integrated manufacturing systems. Modern technology and the rapid succession of designs, materials and processes require flexible...... are developed to support production planning and control processes as they are found in the present organizations. In this case, the result has been the evolution of "islands of automation" and in the CIM literature, integration is widely discussed in terms of standardization of communication protocols...... should rather aim at creating a resource envelope within which people can adapt their work strategies to the current requirements and personal preferences without loosing support from the system. This requirement implies that for design purposes, models of procedures and processes should be replaced...
Directory of Open Access Journals (Sweden)
S. V. Nikolaev
2015-01-01
motion in the range of main flight operating conditions. This model is aimed at using to not only to support testing, but at subsequent stages of the aviation system life cycle as well. These models are necessary when training the pilots to fly to the ship, when developing the simulators, at the stage of modernization, in examination of aircraft accidents, etc. As a result of the work, have been created a new technique for evaluating the stability and controllability characteristics of the naval aircraft system, including a new procedure for the identification and also the new algorithmic techniques that can be used to visualize the dependence of aerodynamic coefficients as a function of the angle of attack and a new approach to identify longitudinal channel according to lateral test flight modes. All the results are confirmed by processing of flight experiment materials.The work is fulfilled with the support of the Russian Foundation for Basic Research (RFBR, project 15-08-06237.
Genetic Algorithm-Based Identification of Fractional-Order Systems
Directory of Open Access Journals (Sweden)
Shengxi Zhou
2013-05-01
Full Text Available Fractional calculus has become an increasingly popular tool for modeling the complex behaviors of physical systems from diverse domains. One of the key issues to apply fractional calculus to engineering problems is to achieve the parameter identification of fractional-order systems. A time-domain identification algorithm based on a genetic algorithm (GA is proposed in this paper. The multi-variable parameter identification is converted into a parameter optimization by applying GA to the identification of fractional-order systems. To evaluate the identification accuracy and stability, the time-domain output error considering the condition variation is designed as the fitness function for parameter optimization. The identification process is established under various noise levels and excitation levels. The effects of external excitation and the noise level on the identification accuracy are analyzed in detail. The simulation results show that the proposed method could identify the parameters of both commensurate rate and non-commensurate rate fractional-order systems from the data with noise. It is also observed that excitation signal is an important factor influencing the identification accuracy of fractional-order systems.
System Identification and Verification of Rotorcraft UAVs
Carlton, Zachary M.
The task of a controls engineer is to design and implement control logic. To complete this task, it helps tremendously to have an accurate model of the system to be controlled. Obtaining a very accurate system model is not a trivial one, as much time and money is usually associated with the development of such a model. A typical physics based approach can require hundreds of hours of flight time. In an iterative process the model is tuned in such a way that it accurately models the physical system's response. This process becomes even more complicated for unstable and highly non-linear systems such as the dynamics of rotorcraft. An alternate approach to solving this problem is to extract an accurate model by analyzing the frequency response of the system. This process involves recording the system's responses for a frequency range of input excitations. From this data, an accurate system model can then be deduced. Furthermore, it has been shown that with use of the software package CIFER® (Comprehensive Identification from FrEquency Responses), this process can both greatly reduce the cost of modeling a dynamic system and produce very accurate results. The topic of this thesis is to apply CIFER® to a quadcopter to extract a system model for the flight condition of hover. The quadcopter itself is comprised of off-the-shelf components with a Pixhack flight controller board running open source Ardupilot controller logic. In this thesis, both the closed and open loop systems are identified. The model is next compared to dissimilar flight data and verified in the time domain. Additionally, the ESC (Electronic Speed Controller) motor/rotor subsystem, which is comprised of all the vehicle's actuators, is also identified. This process required the development of a test bench environment, which included a GUI (Graphical User Interface), data pre and post processing, as well as the augmentation of the flight controller source code. This augmentation of code allowed for
Separable identification of nonlinear aggregate power system loads
Energy Technology Data Exchange (ETDEWEB)
Rosehart, William; Westwick, David; Jazayeri, Pouyan [Schulich School of Engineering, University of Calgary, Calgary (Canada); Aguado, Jose; Martin, Sebastian [University of Malaga, Malaga (Spain)
2009-02-15
An identification algorithm for a power system load model is proposed in this paper. The overall non-convex identification problem is separated into convex and non-convex subproblems, allowing for a global optimum to be found. Numerical experiments using data from both simulated and physical systems illustrate the accuracy of the proposed algorithm. Experiments are performed to investigate the robustness of the algorithm. (author)
Energy Technology Data Exchange (ETDEWEB)
Gonzalez Gonzalez, R., E-mail: r.gonzalez@ing.unipi.it [San Piero a Grado Nuclear Research Group (GRNSPG), University of Pisa, Via Livornese 1291, 56122 San Piero a Grado, Pisa (Italy); Petruzzi, A., E-mail: a.petruzzi@ing.unipi.it [San Piero a Grado Nuclear Research Group (GRNSPG), University of Pisa, Via Livornese 1291, 56122 San Piero a Grado, Pisa (Italy); D’Auria, F., E-mail: f.dauria@ing.unipi.it [San Piero a Grado Nuclear Research Group (GRNSPG), University of Pisa, Via Livornese 1291, 56122 San Piero a Grado, Pisa (Italy); Mazzantini, O., E-mail: mazzantini@na-sa.com.ar [Nucleo-electrica Argentina Sociedad Anonima (NA-SA), Buenos Aires (Argentina)
2014-08-15
Atucha-2 is a Siemens-designed Pressurized Heavy Water Reactor (PHWR) reactor under construction in the Republic of Argentina. Its geometrical complexity and peculiarity (e.g. oblique Control Rods, Positive Void coefficient) required a developed and validated complex three dimensional (3D) neutron kinetics (NK) coupled thermal hydraulic (TH) model. Reactor shut-down is obtained by oblique CRs and, during accidental conditions, by an emergency shut-down system (JDJ) injecting a highly concentrated boron solution (boron clouds) in the moderator tank. The boron clouds reconstruction is obtained using a Computational Fluid Dynamics (CFD) CFX code calculation. A complete Large Break Loss Of Coolant Accident (LBLOCA) calculation implies the application of the RELAP5-3D{sup ©} system code. Within the framework of the third Agreement “Nucleoelèctrica Argentina-Sociedad Anonima (NA-SA) – University of Pisa/GRNSPG” (Contract, 2009), a new RELAP5-3D control system for the boron injection system was developed and implemented in the validated coupled RELAP5-3D/NESTLE model of the Atucha 2 NPP. The aim of this activity is to find out the limiting case (maximum break area size) for the Peak Cladding Temperature for LOCAs under fixed boundary conditions.
Electro-optical fuel pin identification system
International Nuclear Information System (INIS)
Kirchner, T.L.
1978-09-01
A prototype Electro-Optical Fuel Pin Identification System referred to as the Fuel Pin Identification System (FPIS) has been developed by the Hanford Engineering Development Laboratory (HEDL) in support of the Fast Flux Test Facility (FFTF) presently under construction at HEDL. The system is designed to remotely read an alpha-numeric identification number that is roll stamped on the top of the fuel pin end cap. The prototype FPIS consists of four major subassemblies: optical read head, digital compression electronics, video display, and line printer
Complete functional characterization of sensory neurons by system identification.
Wu, Michael C-K; David, Stephen V; Gallant, Jack L
2006-01-01
System identification is a growing approach to sensory neurophysiology that facilitates the development of quantitative functional models of sensory processing. This approach provides a clear set of guidelines for combining experimental data with other knowledge about sensory function to obtain a description that optimally predicts the way that neurons process sensory information. This prediction paradigm provides an objective method for evaluating and comparing computational models. In this chapter we review many of the system identification algorithms that have been used in sensory neurophysiology, and we show how they can be viewed as variants of a single statistical inference problem. We then review many of the practical issues that arise when applying these methods to neurophysiological experiments: stimulus selection, behavioral control, model visualization, and validation. Finally we discuss several problems to which system identification has been applied recently, including one important long-term goal of sensory neuroscience: developing models of sensory systems that accurately predict neuronal responses under completely natural conditions.
Access control and personal identification systems
Bowers, Dan M
1988-01-01
Access Control and Personal Identification Systems provides an education in the field of access control and personal identification systems, which is essential in selecting the appropriate equipment, dealing intelligently with vendors in purchases of the equipment, and integrating the equipment into a total effective system. Access control devices and systems comprise an important part of almost every security system, but are seldom the sole source of security. In order for the goals of the total system to be met, the other portions of the security system must also be well planned and executed
Thermal Signature Identification System (TheSIS)
Merritt, Scott; Bean, Brian
2015-01-01
We characterize both nonlinear and high order linear responses of fiber-optic and optoelectronic components using spread spectrum temperature cycling methods. This Thermal Signature Identification System (TheSIS) provides much more detail than conventional narrowband or quasi-static temperature profiling methods. This detail allows us to match components more thoroughly, detect subtle reversible shifts in performance, and investigate the cause of instabilities or irreversible changes. In particular, we create parameterized models of athermal fiber Bragg gratings (FBGs), delay line interferometers (DLIs), and distributed feedback (DFB) lasers, then subject the alternative models to selection via the Akaike Information Criterion (AIC). Detailed pairing of components, e.g. FBGs, is accomplished by means of weighted distance metrics or norms, rather than on the basis of a single parameter, such as center wavelength.
Energy Technology Data Exchange (ETDEWEB)
Luz, Lucas Thadeu Orihuela da
1990-12-01
An integrated environment which was developed to give support to the activities of identification, modeling, analysis and simulation of control systems of Electric Power Systems - EPS is presented. It is a computer program to be used in compatible IBM P C microcomputers. The program has two algorithms for systems identification: one for identification in the frequency domain and other to be used in the time domain. For the analysis, were implemented the algorithms of the frequency response and the root locus. One can simulate multivariable and nonlinear models. A set of auxiliary transformations is available to obtain the dynamic equation of the model declared for the simulation and to transform it in a transfer matrix. The hierarchical menus used for the selection of the program functions are presented and the dialogues and the others communication mechanisms with the user are described. The program in EPS's is demonstrated through the design of a Power System Stabilizer. The results obtained with the program are compared with the ones that were obtained in field tests. (author)
2010 United States Automatic Identification System Database
National Oceanic and Atmospheric Administration, Department of Commerce — The 2010 United States Automatic Identification System Database contains vessel traffic data for planning purposes within the U.S. coastal waters. The database is...
2014 United States Automatic Identification System Database
National Oceanic and Atmospheric Administration, Department of Commerce — The 2014 United States Automatic Identification System Database contains vessel traffic data for planning purposes within the U.S. coastal waters. The database is...
2011 United States Automatic Identification System Database
National Oceanic and Atmospheric Administration, Department of Commerce — The 2011 United States Automatic Identification System Database contains vessel traffic data for planning purposes within the U.S. coastal waters. The database is...
2009 United States Automatic Identification System Database
National Oceanic and Atmospheric Administration, Department of Commerce — The 2009 United States Automatic Identification System Database contains vessel traffic data for planning purposes within the U.S. coastal waters. The database is...
2012 United States Automatic Identification System Database
National Oceanic and Atmospheric Administration, Department of Commerce — The 2012 United States Automatic Identification System Database contains vessel traffic data for planning purposes within the U.S. coastal waters. The database is...
Intelligent Storage System Based on Automatic Identification
Directory of Open Access Journals (Sweden)
Kolarovszki Peter
2014-09-01
Full Text Available This article describes RFID technology in conjunction with warehouse management systems. Article also deals with automatic identification and data capture technologies and each processes, which are used in warehouse management system. It describes processes from entering goods into production to identification of goods and also palletizing, storing, bin transferring and removing goods from warehouse. Article focuses on utilizing AMP middleware in WMS processes in Nowadays, the identification of goods in most warehouses is carried through barcodes. In this article we want to specify, how can be processes described above identified through RFID technology. All results are verified by measurement in our AIDC laboratory, which is located at the University of Žilina, and also in Laboratory of Automatic Identification Goods and Services located in GS1 Slovakia. The results of our research bring the new point of view and indicate the ways using of RFID technology in warehouse management system.
The odontology victim identification skill assessment system.
Zohn, Harry K; Dashkow, Sheila; Aschheim, Kenneth W; Dobrin, Lawrence A; Glazer, Howard S; Kirschbaum, Mitchell; Levitt, Daniel; Feldman, Cecile A
2010-05-01
Mass fatality identification efforts involving forensic odontology can involve hundreds of dental volunteers. A literature review was conducted and forensic odontologists and dental educators consulted to identify lessons learned from past mass fatality identification efforts. As a result, the authors propose a skill assessment system, the Odontology Victim Identification Skill Assessment System (OVID-SAS), which details qualifications required to participate on the Antemortem, Postmortem, Ante/Postmortem Comparison, Field, and Shift Leader/Initial Response Teams. For each qualification, specific skills have been identified along with suggested educational pedagogy and skill assessment methods. Courses and assessments can be developed by dental schools, professional associations, or forensic organizations to teach and test for the skills required for dental volunteers to participate on each team. By implementing a system, such as OVID-SAS, forensic odontologists responsible for organizing and managing a forensic odontology mass fatality identification effort will be able to optimally utilize individuals presenting with proven skills.
A Systematic Identification Method for Thermodynamic Property Modelling
DEFF Research Database (Denmark)
Ana Perederic, Olivia; Cunico, Larissa; Sarup, Bent
2017-01-01
In this work, a systematic identification method for thermodynamic property modelling is proposed. The aim of the method is to improve the quality of phase equilibria prediction by group contribution based property prediction models. The method is applied to lipid systems where the Original UNIFAC...
Nuclear Materials Identification System Operational Manual
Chiang, L G
2001-01-01
This report describes the operation and setup of the Nuclear Materials Identification System (NMIS) with a sup 2 sup 5 sup 2 Cf neutron source at the Oak Ridge Y-12 Plant. The components of the system are described with a description of the setup of the system along with an overview of the NMIS measurements for scanning, calibration, and confirmation of inventory items.
Identification of System Parameters by the Random Decrement Technique
DEFF Research Database (Denmark)
Brincker, Rune; Kirkegaard, Poul Henning; Rytter, Anders
1991-01-01
The aim of this paper is to investigate and illustrate the possibilities of using correlation functions estimated by the Random Decrement Technique as a basis for parameter identification. A two-stage system identification system is used: first, the correlation functions are estimated by the Rand......-Walker equations and finally, least-square fitting of the theoretical correlation function. The results are compared to the results of fitting an Auto Regressive Moving Average (ARMA) model directly to the system output from a single-degree-of-freedom system loaded by white noise....
Dynamic mode decomposition for compressive system identification
Bai, Zhe; Kaiser, Eurika; Proctor, Joshua L.; Kutz, J. Nathan; Brunton, Steven L.
2017-11-01
Dynamic mode decomposition has emerged as a leading technique to identify spatiotemporal coherent structures from high-dimensional data. In this work, we integrate and unify two recent innovations that extend DMD to systems with actuation and systems with heavily subsampled measurements. When combined, these methods yield a novel framework for compressive system identification, where it is possible to identify a low-order model from limited input-output data and reconstruct the associated full-state dynamic modes with compressed sensing, providing interpretability of the state of the reduced-order model. When full-state data is available, it is possible to dramatically accelerate downstream computations by first compressing the data. We demonstrate this unified framework on simulated data of fluid flow past a pitching airfoil, investigating the effects of sensor noise, different types of measurements (e.g., point sensors, Gaussian random projections, etc.), compression ratios, and different choices of actuation (e.g., localized, broadband, etc.). This example provides a challenging and realistic test-case for the proposed method, and results indicate that the dominant coherent structures and dynamics are well characterized even with heavily subsampled data.
Identification of material flow systems: Extensions and case study.
Gleiss, A; Matyus, T; Bauer, G; Deistler, M; Glenck, E; Lampert, C
1998-01-01
The paper consists of two main parts. The first part is concerned with different aspects of mathematical modeling of material flow systems for the linear static case. The problems considered are the description of the model class, data reconciliation, identification of subsystems and the analysis of system properties relevant e.g. for simulation. In the second part an application of the modeling tools proposed in the first part to a study on the metabolism of phosphorus in an Austrian region is given.
System Identification of X-33 Neural Network
Aggarwal, Shiv
2003-01-01
Modern flight control research has improved spacecraft survivability as its goal. To this end we need to have a failure detection system on board. In case the spacecraft is performing imperfectly, reconfiguration of control is needed. For that purpose we need to have parameter identification of spacecraft dynamics. Parameter identification of a system is called system identification. We treat the system as a black box which receives some inputs that lead to some outputs. The question is: what kind of parameters for a particular black box can correlate the observed inputs and outputs? Can these parameters help us to predict the outputs for a new given set of inputs? This is the basic problem of system identification. The X33 was supposed to have the onboard capability of evaluating the current performance and if needed to take the corrective measures to adapt to desired performance. The X33 is comprised of both rocket and aircraft vehicle design characteristics and requires, in general, analytical methods for evaluating its flight performance. Its flight consists of four phases: ascent, transition, entry and TAEM (Terminal Area Energy Management). It spends about 200 seconds in ascent phase, reaching an altitude of about 180,000 feet and a speed of about 10 to 15 Mach. During the transition phase which lasts only about 30 seconds, its altitude may increase to about 190,000 feet but its speed is reduced to about 9 Mach. At the beginning of this phase, the Main Engine is Cut Off (MECO) and the control is reconfigured with the help of aerosurfaces (four elevons, two flaps and two rudders) and reaction control system (RCS). The entry phase brings down the altitude of X33 to about 90,000 feet and its speed to about Mach 3. It spends about 250 seconds in this phase. Main engine is still cut off and the vehicle is controlled by complex maneuvers of aerosurfaces. The last phase TAEM lasts for about 450 seconds and the altitude and speed, both are reduced to zero. The
A portable air jet actuator device for mechanical system identification
Belden, Jesse; Staats, Wayne L.; Mazumdar, Anirban; Hunter, Ian W.
2011-03-01
System identification of limb mechanics can help diagnose ailments and can aid in the optimization of robotic limb control parameters and designs. An interesting fluid phenomenon—the Coandă effect—is utilized in a portable actuator to provide a stochastic binary force disturbance to a limb system. The design of the actuator is approached with the goal of creating a portable device which could be deployed on human or robotic limbs for in situ mechanical system identification. The viability of the device is demonstrated by identifying the parameters of an underdamped elastic beam system with fixed inertia and stiffness and variable damping. The nonparametric compliance impulse response yielded from the system identification is modeled as a second-order system and the resultant parameters are found to be in excellent agreement with those found using more traditional system identification techniques. The current design could be further miniaturized and developed as a portable, wireless, unrestrained mechanical system identification instrument for less intrusive and more widespread use.
Parameter identification in the logistic STAR model
DEFF Research Database (Denmark)
Ekner, Line Elvstrøm; Nejstgaard, Emil
We propose a new and simple parametrization of the so-called speed of transition parameter of the logistic smooth transition autoregressive (LSTAR) model. The new parametrization highlights that a consequence of the well-known identification problem of the speed of transition parameter is that th......We propose a new and simple parametrization of the so-called speed of transition parameter of the logistic smooth transition autoregressive (LSTAR) model. The new parametrization highlights that a consequence of the well-known identification problem of the speed of transition parameter...
Cost Optimal System Identification Experiment Design
DEFF Research Database (Denmark)
Kirkegaard, Poul Henning
A structural system identification experiment design method is formulated in the light of decision theory, structural reliability theory and optimization theory. The experiment design is based on a preposterior analysis, well-known from the classical decision theory. I.e. the decisions concerning...... the experiment design are not based on obtained experimental data. Instead the decisions are based on the expected experimental data assumed to be obtained from the measurements, estimated based on prior information and engineering judgement. The design method provides a system identification experiment design...... reflecting the cost of the experiment and the value of obtained additional information. An example concerning design of an experiment for parametric identification of a single degree of freedom structural system shows the applicability of the experiment design method....
CEAI: CCM based Email Authorship Identification Model
DEFF Research Database (Denmark)
Nizamani, Sarwat; Memon, Nasrullah
2013-01-01
In this paper we present a model for email authorship identification (EAI) by employing a Cluster-based Classification (CCM) technique. Traditionally, stylometric features have been successfully employed in various authorship analysis tasks; we extend the traditional feature-set to include some...... reveal that the proposed CCM-based email authorship identification model, along with the proposed feature set, outperforms the state-of-the-art support vector machine (SVM)-based models, as well as the models proposed by Iqbal et al. [1, 2]. The proposed model attains an accuracy rate of 94% for 10...... authors, 89% for 25 authors, and 81% for 50 authors, respectively on Enron data set, while 89.5% accuracy has been achieved on authors' constructed real email data set. The results on Enron data set have been achieved on quite a large number of authors as compared to the models proposed by Iqbal et al. [1...
Bounding approaches to system identification
Norton, John; Piet-Lahanier, Hélène; Walter, Éric
1996-01-01
In response to the growing interest in bounding error approaches, the editors of this volume offer the first collection of papers to describe advances in techniques and applications of bounding of the parameters, or state variables, of uncertain dynamical systems. Contributors explore the application of the bounding approach as an alternative to the probabilistic analysis of such systems, relating its importance to robust control-system design.
System identification of the Arabidopsis plant circadian system
Foo, Mathias; Somers, David E.; Kim, Pan-Jun
2015-02-01
The circadian system generates an endogenous oscillatory rhythm that governs the daily activities of organisms in nature. It offers adaptive advantages to organisms through a coordination of their biological functions with the optimal time of day. In this paper, a model of the circadian system in the plant Arabidopsis (species thaliana) is built by using system identification techniques. Prior knowledge about the physical interactions of the genes and the proteins in the plant circadian system is incorporated in the model building exercise. The model is built by using primarily experimentally-verified direct interactions between the genes and the proteins with the available data on mRNA and protein abundances from the circadian system. Our analysis reveals a great performance of the model in predicting the dynamics of the plant circadian system through the effect of diverse internal and external perturbations (gene knockouts and day-length changes). Furthermore, we found that the circadian oscillatory rhythm is robust and does not vary much with the biochemical parameters except those of a light-sensitive protein P and a transcription factor TOC1. In other words, the circadian rhythmic profile is largely a consequence of the network's architecture rather than its particular parameters. Our work suggests that the current experimental knowledge of the gene-to-protein interactions in the plant Arabidopsis, without considering any additional hypothetical interactions, seems to suffice for system-level modeling of the circadian system of this plant and to present an exemplary platform for the control of network dynamics in complex living organisms.
Non-Linear Systems Identification Using Neural Networks
Chen, S.; Billings, S.A.; Grant, P.M.
1989-01-01
Multi-layered neural networks offer an exciting alternative for modelling complex non-linear systems. This paper investigates the identification of discrete-time non-linear systems using neural networks with a single hidden layer. New parameter estimation algorithms are derived for the neural network model based on a prediction error formulation and the application to both simulated and real data is included to demonstrate the effectiveness of the neural network approach.
System Identification and Robust Control
DEFF Research Database (Denmark)
Tøffner-Clausen, S.
a performance specification may be cast into the same framework. The main limitation in the standard H infinity theory is that it can only handle unstructured complex full block perturbations to the nominal plant. However, often much more detailed perturbation models are available, e.g. from physical modelling...... permitted by m is definitely much more flexible than those used in H inifity. Unfortunately m synthesis is a very difficult mathematical problem which is only well developed for purely complex perturbation sets. In order to develop our main result we will unfortunately need to synthesize m controllers...... quantities like masses, inertias, etc. Which are only known with a certain degree of accuracy. This will give rise to real scalar perturbation to the nominal model. Furthermore working point deviations may also be addressed with real perturbations. However, accurate physical modelling may be a complicated...
Attacks on RFID Identification Systems
Directory of Open Access Journals (Sweden)
D. M. Mikhaylov
2010-09-01
Full Text Available This article is about attacks on RFID systems. Currently antivirus developers are not developing systems that protect from viruses that could exist on RFID tags. Such viruses are considered as not existing because the RFID tag memory is very small. Unfortunately such viruses exist. This article is concerned to such viruses and attacks that hackers could do using such viruses. Based on this article methods to prevent RFID-viruses attacks could be developed.
Model Identification of Integrated ARMA Processes
Stadnytska, Tetiana; Braun, Simone; Werner, Joachim
2008-01-01
This article evaluates the Smallest Canonical Correlation Method (SCAN) and the Extended Sample Autocorrelation Function (ESACF), automated methods for the Autoregressive Integrated Moving-Average (ARIMA) model selection commonly available in current versions of SAS for Windows, as identification tools for integrated processes. SCAN and ESACF can…
Time-Delay System Identification Using Genetic Algorithm
DEFF Research Database (Denmark)
Yang, Zhenyu; Seested, Glen Thane
2013-01-01
problem through an identification approach using the real coded Genetic Algorithm (GA). The desired FOPDT/SOPDT model is directly identified based on the measured system's input and output data. In order to evaluate the quality and performance of this GA-based approach, the proposed method is compared...
Time-Varying FOPDT System Identification with Unknown Disturbance Input
DEFF Research Database (Denmark)
Sun, Zhen; Yang, Zhenyu
2012-01-01
The Time-Varying First Order Plus Dead Time (TV-FOPDT) model is an extension of the conventional FOPDT by allowing the system parameters, which are primarily defined on the transfer function description, i.e., the DC-gain, time constant and time delay, to be time dependent. The TV-FOPDT identific...
Identification of continuous-time systems from samples of input ...
Indian Academy of Sciences (India)
Abstract. This paper presents an introductory survey ofthe methodsthat have been developed for identification of continuous-time systems from samples of input-output data. The two basic approaches may be described as. the indirect method, where first a discrete-time model is estimated from the sampled data and then an ...
Ultra-Wideband Radio Frequency Identification Systems
Nekoogar, Faranak
2012-01-01
Ultra-Wideband Radio Frequency Identification Systems describes the essentials of radio frequency identification systems as well as their target markets. The authors provide a study of commercially available RFID systems and characterizes their performance in terms of read range and reliability in the presence of conductive and dielectric materials. The capabilities and limitations of some commercial RFID systems are reported followed by comprehensive discussions of the advantages and challenges of using ultra-wideband technology for tag/reader communications. The book presents practical aspects of UWB RFID system such as: pulse generation, remote powering, tag and reader antenna design, as well as special applications of UWB RFIDs in a simple and easy-to-understand language.
de Brito, Maria José Azevedo; Nahas, Fábio Xerfan; Ortega, Neli Regina Siqueira; Cordás, Táki Athanássios; Dini, Gal Moreira; Neto, Miguel Sabino; Ferreira, Lydia Masako
2013-09-01
To develop a fuzzy linguistic model to quantify the level of distress of patients seeking cosmetic surgery. Body dysmorphic disorder (BDD) is a mental condition related to body image relatively common among cosmetic surgery patients; it is difficult to diagnose and is a significant cause of morbidity and mortality. Fuzzy cognitive maps are an efficient tool based on human knowledge and experience that can handle uncertainty in identifying or grading BDD symptoms and the degree of body image dissatisfaction. Individuals who seek cosmetic procedures suffer from some degree of dissatisfaction with appearance. A fuzzy model was developed to measure distress levels in cosmetic surgery patients based on the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV), diagnostic criterion B for BDD. We studied 288 patients of both sexes seeking abdominoplasty, rhinoplasty, or rhytidoplasty in a university hospital. Patient distress ranged from "none" to "severe" (range=7.5-31.6; cutoff point=18; area under the ROC curve=0.923). There was a significant agreement between the fuzzy model and DSM-IV criterion B (kappa=0.805; p<0.001). The fuzzy model measured distress levels with good accuracy, indicating that it can be used as a screening tool in cosmetic surgery and psychiatric practice. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.
77 FR 40735 - Unique Device Identification System
2012-07-10
... postmarket surveillance, and better security of devices through more effective detection and removal of... Drug Administration 21 CFR Parts 16, 801, 803, et al. Unique Device Identification System; Proposed..., 806, 810, 814, 820, 821, 822, and 830 [Docket No. FDA-2011-N-0090] RIN 0910-AG31 Unique Device...
System identification with information theoretic criteria
A.A. Stoorvogel; J.H. van Schuppen (Jan)
1995-01-01
textabstractAttention is focused in this paper on the approximation problem of system identification with information theoretic criteria. For a class of problems it is shown that the criterion of mutual information rate is identical to the criterion of exponential-of-quadratic cost and to
Recent phylogenetic studies have used DNA as the target molecule for the development of environmental 16S rDNA clone libraries. As DNA may persist in the environment, DNA-based libraries cannot be used to identify metabolically active bacteria in water systems. In this study, a...
Component codification and identification systems
International Nuclear Information System (INIS)
Pannenbaecker, K.
1977-01-01
The lecture covers the codification in power stations during the erection phase and commercial operation phase. A diagram gives a survey. There are three basic-codifications for application; 1) Kraftwerk-Kennzeichen-System (KKS) for marking each component in orientated systems, for marking electrical orientated positions in cubicals, switch gears etc. and for marking rooms in buildings; 2) Ordnungssystem (OS) for cost calculation and ordering; 3) Unterlagenarten-Schluessel (UAS) for letters, reports etc. and for documentation. The OS is developed on the principle of cost account number and is therefore close to the organization of each supplier and his special form of design and constrution. KKS has only to mark hardware. Therefore all German owners, consultants, authorities and suppliers develop KKS together and conform to it in DIN 407119. (ORU) [de
Teslic, Luka; Hartmann, Benjamin; Nelles, Oliver; Skrjanc, Igor
2011-12-01
This paper deals with the problem of fuzzy nonlinear model identification in the framework of a local model network (LMN). A new iterative identification approach is proposed, where supervised and unsupervised learning are combined to optimize the structure of the LMN. For the purpose of fitting the cluster-centers to the process nonlinearity, the Gustafsson-Kessel (GK) fuzzy clustering, i.e., unsupervised learning, is applied. In combination with the LMN learning procedure, a new incremental method to define the number and the initial locations of the cluster centers for the GK clustering algorithm is proposed. Each data cluster corresponds to a local region of the process and is modeled with a local linear model. Since the validity functions are calculated from the fuzzy covariance matrices of the clusters, they are highly adaptable and thus the process can be described with a very sparse amount of local models, i.e., with a parsimonious LMN model. The proposed method for constructing the LMN is finally tested on a drug absorption spectral process and compared to two other methods, namely, Lolimot and Hilomot. The comparison between the experimental results when using each method shows the usefulness of the proposed identification algorithm.
Raja, Mobeen; Kinne, Rolf K H
2015-08-01
The sodium glucose cotransporter SGLT1 expressed mainly in the intestine and kidney has been explored extensively for understanding the mechanism of sugar cotransport and its inhibition by a classical competitive inhibitor, phlorizin (Pz). It has been shown that inhibition of SGLT1 by Pz involves its interaction followed by major conformational changes in the Pz binding domain (PBD) in C-terminal loop 13. However, the mechanism of Pz inhibition and its interaction with other members of SGLT is not known. In this hypothesis, we performed molecular modeling of SGLT1-loop 13 with Pz and carried out primary sequence analyses and secondary structure predictions to determine qualitatively similar PBDs in C-termini of human SGLT2-4, except for vSGLT, which contains an unstructured short C-terminus. The ranking of predictions of Pz interaction strongly agrees with the following ranking of previously reported Pz inhibition: SGLT2>SGLT1>SGLT4>SGLT3>vSGLT. In addition, the sugar binding residues were found to be quite conserved among all SGLT members investigated here. Based on these preliminary analyses, we propose that other Pz-sensitive SGLTs are also inhibited via mechanism similar to SGLT1 where an aglucone of Pz, phloretin, interacts with PBD and glucoside moiety with sugar binding residues. Our hypothesis sets the stage for future analyses on investigation of Pz interaction with SGLT family and further suggests that Pz modeling may be explored to design novel inhibitors targeting several SGLT members. Copyright © 2015 Elsevier B.V. and Société Française de Biochimie et Biologie Moléculaire (SFBBM). All rights reserved.
Neural System Prediction and Identification Challenge
Directory of Open Access Journals (Sweden)
Ioannis eVlachos
2013-12-01
Full Text Available Can we infer the function of a biological neural network (BNN if we know the connectivity and activity of all its constituent neurons? This question is at the core of neuroscience and, accordingly, various methods have been developed to record the activity and connectivity of as many neurons as possible. Surprisingly, there is no theoretical or computational demonstration that neuronal activity and connectivity are indeed sufficient to infer the function of a BNN. Therefore, we pose the Neural Systems Identification and Prediction Challenge (nuSPIC. We provide the connectivity and activity of all neurons and invite participants (i to infer the functions implemented (hard-wired in spiking neural networks (SNNs by stimulating and recording the activity of neurons and, (ii to implement predefined mathematical/biological functions using SNNs. The nuSPICs can be accessed via a web-interface to the NEST simulator and the user is not required to know any specific programming language. Furthermore, the nuSPICs can be used as a teaching tool. Finally, nuSPICs use the crowd-sourcing model to address scientific issues. With this computational approach we aim to identify which functions can be inferred by systematic recordings of neuronal activity and connectivity. In addition, nuSPICs will help the design and application of new experimental paradigms based on the structure of the SNN and the presumed function which is to be discovered.
Neural system prediction and identification challenge.
Vlachos, Ioannis; Zaytsev, Yury V; Spreizer, Sebastian; Aertsen, Ad; Kumar, Arvind
2013-01-01
Can we infer the function of a biological neural network (BNN) if we know the connectivity and activity of all its constituent neurons?This question is at the core of neuroscience and, accordingly, various methods have been developed to record the activity and connectivity of as many neurons as possible. Surprisingly, there is no theoretical or computational demonstration that neuronal activity and connectivity are indeed sufficient to infer the function of a BNN. Therefore, we pose the Neural Systems Identification and Prediction Challenge (nuSPIC). We provide the connectivity and activity of all neurons and invite participants (1) to infer the functions implemented (hard-wired) in spiking neural networks (SNNs) by stimulating and recording the activity of neurons and, (2) to implement predefined mathematical/biological functions using SNNs. The nuSPICs can be accessed via a web-interface to the NEST simulator and the user is not required to know any specific programming language. Furthermore, the nuSPICs can be used as a teaching tool. Finally, nuSPICs use the crowd-sourcing model to address scientific issues. With this computational approach we aim to identify which functions can be inferred by systematic recordings of neuronal activity and connectivity. In addition, nuSPICs will help the design and application of new experimental paradigms based on the structure of the SNN and the presumed function which is to be discovered.
Abo-Elmagd, M; Sadek, A M
2014-12-01
Can and Bare method is a widely used passive method for measuring the equilibrium factor F through the determination of the track density ratio between bare (D) and filtered (Do) detectors. The dimensions of the used diffusion chamber are altering the deposition ratios of Po-isotopes on the chamber walls as well as the ratios of the existing alpha emitters in air. Then the measured filtered track density and therefore the resultant equilibrium factor is changed according to the diffusion chamber dimensions. For this reason, high uncertainty was expected in the measured F using different diffusion chambers. In the present work, F is derived as a function of both track density ratio (D/Do) and the dimensions of the used diffusion chambers (its volume to the total internal surface area; V/A). The accuracy of the derived formula was verified using the black-box modeling technique via the MATLAB System identification toolbox. The results show that the uncertainty of the calculated F by using the derived formula of F (D/Do, V/A) is only 5%. The obtained uncertainty ensures the quality of the derived function to calculate F using diffusion chambers with wide range of dimensions. Copyright © 2014 Elsevier Ltd. All rights reserved.
System Identification for Nonlinear Control Using Neural Networks
Stengel, Robert F.; Linse, Dennis J.
1990-01-01
An approach to incorporating artificial neural networks in nonlinear, adaptive control systems is described. The controller contains three principal elements: a nonlinear inverse dynamic control law whose coefficients depend on a comprehensive model of the plant, a neural network that models system dynamics, and a state estimator whose outputs drive the control law and train the neural network. Attention is focused on the system identification task, which combines an extended Kalman filter with generalized spline function approximation. Continual learning is possible during normal operation, without taking the system off line for specialized training. Nonlinear inverse dynamic control requires smooth derivatives as well as function estimates, imposing stringent goals on the approximating technique.
International Nuclear Information System (INIS)
Hansen, E.J.; Frisch, C.F.; McDade, R.L. Jr.; Johnston, K.H.
1981-01-01
Outer membrane proteins of Haemophilus influenzae type b which are immunogenic in infant rats were identified by a radioimmunoprecipitation method. Intact cells of H. influenzae type b were radioiodinated by a lactoperoxidase-catalyzed procedure, and an outer membrane-containing fraction was prepared from these cells. These radioiodinated outer membranes were mixed with sera obtained from rats convalescing from systemic H. influenzae type b disease induced at 6 days of age, and the resultant (antibody-outer membrane protein antigen) complexes were extracted from these membranes by treatment with nonionic detergent and ethylenediaminetetraacetic acid. These soluble antibody-antigen complexes were isolated by means of adsorption to protein A-bearing staphylococci, and the radioiodinated protein antigens were identified by gel electrophoresis followed by autoradiography. Infant rats were shown to mount a readily detectable antibody response to several different proteins present in the outer membrane of H. influenzae type b. Individual infant rats were found to vary both qualitatively and quantitatively in their immune response to these immunogenic outer membrane proteins
Energy Technology Data Exchange (ETDEWEB)
Hansen, E.J.; Frisch, C.F.; McDade, R.L. Jr.; Johnston, K.H.
1981-06-01
Outer membrane proteins of Haemophilus influenzae type b which are immunogenic in infant rats were identified by a radioimmunoprecipitation method. Intact cells of H. influenzae type b were radioiodinated by a lactoperoxidase-catalyzed procedure, and an outer membrane-containing fraction was prepared from these cells. These radioiodinated outer membranes were mixed with sera obtained from rats convalescing from systemic H. influenzae type b disease induced at 6 days of age, and the resultant (antibody-outer membrane protein antigen) complexes were extracted from these membranes by treatment with nonionic detergent and ethylenediaminetetraacetic acid. These soluble antibody-antigen complexes were isolated by means of adsorption to protein A-bearing staphylococci, and the radioiodinated protein antigens were identified by gel electrophoresis followed by autoradiography. Infant rats were shown to mount a readily detectable antibody response to several different proteins present in the outer membrane of H. influenzae type b. Individual infant rats were found to vary both qualitatively and quantitatively in their immune response to these immunogenic outer membrane proteins.
Identification of general linear mechanical systems
Sirlin, S. W.; Longman, R. W.; Juang, J. N.
1983-01-01
Previous work in identification theory has been concerned with the general first order time derivative form. Linear mechanical systems, a large and important class, naturally have a second order form. This paper utilizes this additional structural information for the purpose of identification. A realization is obtained from input-output data, and then knowledge of the system input, output, and inertia matrices is used to determine a set of linear equations whereby we identify the remaining unknown system matrices. Necessary and sufficient conditions on the number, type and placement of sensors and actuators are given which guarantee identificability, and less stringent conditions are given which guarantee generic identifiability. Both a priori identifiability and a posteriori identifiability are considered, i.e., identifiability being insured prior to obtaining data, and identifiability being assured with a given data set.
Directory of Open Access Journals (Sweden)
Simon Schleiter
2016-12-01
Full Text Available The determination of dynamic parameters are the central points of the system identification of civil engineering structures under dynamic loading. This paper first gives a brief summary of the recent developments of the system identification methods in civil engineering and describes mathematical models, which enable the identification of the necessary parameters using only stochastic input signals. Relevant methods for this identification use Frequency Domain Decomposition (FDD, Autoregressive Moving Average Models (ARMA and the Autoregressive Models with eXogenous input (ARX. In a first step an elasto-mechanical mdof-system is numerically modeled using FEM and afterwards tested numerically by above mentioned identification methods using stochastic signals. During the second campaign, dynamic measurements are conducted experimentally on a real 7-story RC-building with ambient signal input using sensors. The results are successfully for the relevant system identification methods.
A NEURAL NETWORK BASED IRIS RECOGNITION SYSTEM FOR PERSONAL IDENTIFICATION
Directory of Open Access Journals (Sweden)
Usham Dias
2010-10-01
Full Text Available This paper presents biometric personal identification based on iris recognition using artificial neural networks. Personal identification system consists of localization of the iris region, normalization, enhancement and then iris pattern recognition using neural network. In this paper, through results obtained, we have shown that a person’s left and right eye are unique. In this paper, we also show that the network is sensitive to the initial weights and that over-training gives bad results. We also propose a fast algorithm for the localization of the inner and outer boundaries of the iris region. Results of simulations illustrate the effectiveness of the neural system in personal identification. Finally a hardware iris recognition model is proposed and implementation aspects are discussed.
Dragos, Kosmas; Smarsly, Kay
2016-04-01
System identification has been employed in numerous structural health monitoring (SHM) applications. Traditional system identification methods usually rely on centralized processing of structural response data to extract information on structural parameters. However, in wireless SHM systems the centralized processing of structural response data introduces a significant communication bottleneck. Exploiting the merits of decentralization and on-board processing power of wireless SHM systems, many system identification methods have been successfully implemented in wireless sensor networks. While several system identification approaches for wireless SHM systems have been proposed, little attention has been paid to obtaining information on the physical parameters (e.g. stiffness, damping) of the monitored structure. This paper presents a hybrid system identification methodology suitable for wireless sensor networks based on the principles of component mode synthesis (dynamic substructuring). A numerical model of the monitored structure is embedded into the wireless sensor nodes in a distributed manner, i.e. the entire model is segmented into sub-models, each embedded into one sensor node corresponding to the substructure the sensor node is assigned to. The parameters of each sub-model are estimated by extracting local mode shapes and by applying the equations of the Craig-Bampton method on dynamic substructuring. The proposed methodology is validated in a laboratory test conducted on a four-story frame structure to demonstrate the ability of the methodology to yield accurate estimates of stiffness parameters. Finally, the test results are discussed and an outlook on future research directions is provided.
Identification du modele mathematique d'un helicoptere reduit
Honvo, Japhet
The remote-controlled helicopter remains an interesting topic for research in flight control. This kind of machine, easy to deploy due to their small size, is an ideal candidate to test multiple flight control algorithms. To better understand the dynamics of flight of this vehicle, it is important to have a mathematical model. This thesis follows the logic of obtaining a mathematical model for a stationary hovering helicopter. This thesis aims to provide a testbench for the identification of a mathematical model of a small helicopter and for the application of different flight control laws. First, a review on the identification theory is introduced. The methods presented are applicable to multivariable systems. A particular focus is on the identification of state models. The theory concludes with the presentation of algorithms used in the Matlab/Simulink software. Second, a mathematical model of the helicopter is developed. As part of our research, hypotheses to reduce the model are presented. This model is the basis for determining the right identification methods. The mathematical model provides a guideline for specifying the various components of the test bench. The thesis continues with the presentation of the avionics used in the project. The instrumentation is presented in two parts: the hardware and the software. The acquisition of real-time flight parameters is also presented. Finally, the use of the test bench is detailed for the ground tests and for the flight tests. These tests are designed to collect the data necessary for the deployment of various identification techniques. The thesis concludes with comments on significants results and suggestions of prospects for improving the test bench.
Using Pareto points for model identification in predictive toxicology
2013-01-01
Predictive toxicology is concerned with the development of models that are able to predict the toxicity of chemicals. A reliable prediction of toxic effects of chemicals in living systems is highly desirable in cosmetics, drug design or food protection to speed up the process of chemical compound discovery while reducing the need for lab tests. There is an extensive literature associated with the best practice of model generation and data integration but management and automated identification of relevant models from available collections of models is still an open problem. Currently, the decision on which model should be used for a new chemical compound is left to users. This paper intends to initiate the discussion on automated model identification. We present an algorithm, based on Pareto optimality, which mines model collections and identifies a model that offers a reliable prediction for a new chemical compound. The performance of this new approach is verified for two endpoints: IGC50 and LogP. The results show a great potential for automated model identification methods in predictive toxicology. PMID:23517649
Continuous-time system identification of a smoking cessation intervention
Timms, Kevin P.; Rivera, Daniel E.; Collins, Linda M.; Piper, Megan E.
2014-07-01
Cigarette smoking is a major global public health issue and the leading cause of preventable death in the United States. Toward a goal of designing better smoking cessation treatments, system identification techniques are applied to intervention data to describe smoking cessation as a process of behaviour change. System identification problems that draw from two modelling paradigms in quantitative psychology (statistical mediation and self-regulation) are considered, consisting of a series of continuous-time estimation problems. A continuous-time dynamic modelling approach is employed to describe the response of craving and smoking rates during a quit attempt, as captured in data from a smoking cessation clinical trial. The use of continuous-time models provide benefits of parsimony, ease of interpretation, and the opportunity to work with uneven or missing data.
Upport vector machines for nonlinear kernel ARMA system identification.
Martínez-Ramón, Manel; Rojo-Alvarez, José Luis; Camps-Valls, Gustavo; Muñioz-Marí, Jordi; Navia-Vázquez, Angel; Soria-Olivas, Emilio; Figueiras-Vidal, Aníbal R
2006-11-01
Nonlinear system identification based on support vector machines (SVM) has been usually addressed by means of the standard SVM regression (SVR), which can be seen as an implicit nonlinear autoregressive and moving average (ARMA) model in some reproducing kernel Hilbert space (RKHS). The proposal of this letter is twofold. First, the explicit consideration of an ARMA model in an RKHS (SVM-ARMA2K) is proposed. We show that stating the ARMA equations in an RKHS leads to solving the regularized normal equations in that RKHS, in terms of the autocorrelation and cross correlation of the (nonlinearly) transformed input and output discrete time processes. Second, a general class of SVM-based system identification nonlinear models is presented, based on the use of composite Mercer's kernels. This general class can improve model flexibility by emphasizing the input-output cross information (SVM-ARMA4K), which leads to straightforward and natural combinations of implicit and explicit ARMA models (SVR-ARMA2K and SVR-ARMA4K). Capabilities of these different SVM-based system identification schemes are illustrated with two benchmark problems.
System identification using Nuclear Norm & Tabu Search optimization
Ahmed, Asif A.; Schoen, Marco P.; Bosworth, Ken W.
2018-01-01
In recent years, subspace System Identification (SI) algorithms have seen increased research, stemming from advanced minimization methods being applied to the Nuclear Norm (NN) approach in system identification. These minimization algorithms are based on hard computing methodologies. To the authors’ knowledge, as of now, there has been no work reported that utilizes soft computing algorithms to address the minimization problem within the nuclear norm SI framework. A linear, time-invariant, discrete time system is used in this work as the basic model for characterizing a dynamical system to be identified. The main objective is to extract a mathematical model from collected experimental input-output data. Hankel matrices are constructed from experimental data, and the extended observability matrix is employed to define an estimated output of the system. This estimated output and the actual - measured - output are utilized to construct a minimization problem. An embedded rank measure assures minimum state realization outcomes. Current NN-SI algorithms employ hard computing algorithms for minimization. In this work, we propose a simple Tabu Search (TS) algorithm for minimization. TS algorithm based SI is compared with the iterative Alternating Direction Method of Multipliers (ADMM) line search optimization based NN-SI. For comparison, several different benchmark system identification problems are solved by both approaches. Results show improved performance of the proposed SI-TS algorithm compared to the NN-SI ADMM algorithm.
Nevin, Remington L; Leoutsakos, Jeannie-Marie
2017-03-01
Although mefloquine use is known to be associated with a risk of severe neuropsychiatric adverse reactions that are often preceded by prodromal symptoms, specific combinations of neurologic or psychiatric reactions associated with mefloquine use are not well described in the literature. This study sought to identify a distinct neuropsychiatric syndrome class associated with mefloquine use in reports of adverse events. Latent class modeling of US Food and Drug Administration Adverse Event Reporting System (FAERS) data was performed using indicators defined by the Medical Dictionary for Regulatory Activities neurologic and psychiatric high-level group terms, in a study dataset of FAERS reports (n = 5332) of reactions to common antimalarial drugs. A distinct neuropsychiatric syndrome class was identified that was strongly and significantly associated with reports of mefloquine use (odds ratio = 3.92, 95% confidence interval 2.91-5.28), defined by a very high probability of symptoms of deliria (82.7%) including confusion and disorientation, and a moderate probability of other severe psychiatric and neurologic symptoms including dementia and amnesia (18.6%) and seizures (18.1%). The syndrome class was also associated with symptoms that are considered prodromal including anxiety, depression, sleep disturbance, and abnormal dreams, and neurological symptoms such as dizziness, vertigo, and paresthesias. This study confirms in FAERS reports the existence of a severe mefloquine neuropsychiatric syndrome class associated with common symptoms that may be considered prodromal. Clinical identification of the characteristic symptoms of this syndrome class may aid in improving case finding in pharmacovigilance studies of more serious adverse reactions to the drug.
Identification of Nonlinear Dynamic Systems Possessing Some Non-linearities
Directory of Open Access Journals (Sweden)
Y. N. Pavlov
2015-01-01
Full Text Available The subject of this work is the problem of identification of nonlinear dynamic systems based on the experimental data obtained by applying test signals to the system. The goal is to determinate coefficients of differential equations of systems by experimental frequency hodographs and separate similar, but different, in essence, forces: dissipative forces with the square of the first derivative in the motion equations and dissipative force from the action of dry friction. There was a proposal to use the harmonic linearization method to approximate each of the nonlinearity of "quadratic friction" and "dry friction" by linear friction with the appropriate harmonic linearization coefficient.Assume that a frequency transfer function of the identified system has a known form. Assume as well that there are disturbances while obtaining frequency characteristics of the realworld system. As a result, the points of experimentally obtained hodograph move randomly. Searching for solution of the identification problem was in the hodograph class, specified by the system model, which has the form of the frequency transfer function the same as the form of the frequency transfer function of the system identified. Minimizing a proximity criterion (measure of the experimentally obtained system hodograph and the system hodograph model for all the experimental points described and previously published by one of the authors allowed searching for the unknown coefficients of the frequenc ransfer function of the system model. The paper shows the possibility to identify a nonlinear dynamic system with multiple nonlinearities, obtained on the experimental samples of the frequency system hodograph. The proposed algorithm allows to select the nonlinearity of the type "quadratic friction" and "dry friction", i.e. also in the case where the nonlinearity is dependent on the same dynamic parameter, in particular, on the derivative of the system output value. For the dynamic
Identification problems in linear transformation system
International Nuclear Information System (INIS)
Delforge, Jacques.
1975-01-01
An attempt was made to solve the theoretical and numerical difficulties involved in the identification problem relative to the linear part of P. Delattre's theory of transformation systems. The theoretical difficulties are due to the very important problem of the uniqueness of the solution, which must be demonstrated in order to justify the value of the solution found. Simple criteria have been found when measurements are possible on all the equivalence classes, but the problem remains imperfectly solved when certain evolution curves are unknown. The numerical difficulties are of two kinds: a slow convergence of iterative methods and a strong repercussion of numerical and experimental errors on the solution. In the former case a fast convergence was obtained by transformation of the parametric space, while in the latter it was possible, from sensitivity functions, to estimate the errors, to define and measure the conditioning of the identification problem then to minimize this conditioning as a function of the experimental conditions [fr
System identification on two-phase flow stability
International Nuclear Information System (INIS)
Wu Shaorong; Zhang Youjie; Wang Dazhong; Bo Jinghai; Wang Fei
1996-01-01
The theoretical principle, experimental method and results of interrelation analysis identification for the instability of two-phase flow are described. A completely new concept of test technology and method on two-phase flow stability was developed by using he theory of information science on system stability and system identification for two-phase flow stability in thermo-physics field. Application of this method would make it possible to identify instability boundary of two-phase flow under stable operation conditions of two-phase flow system. The experiment was carried out on the thermohydraulic test system HRTL-5. Using reverse repeated pseudo-random sequences of heating power as input signal sources and flow rate as response function in the test, the two-phase flow stability and stability margin of the natural circulation system are investigated. The effectiveness and feasibility of identifying two-phase flow stability by using this system identification method were experimentally demonstrated. Basic data required for mathematics modeling of two-phase flow and analysis of two-phase flow stability were obtained, which are useful for analyzing, monitoring of the system operation condition, and forecasting of two-phase flow stability in engineering system
Identification of the noise using mathematical modelling
Directory of Open Access Journals (Sweden)
Dobeš Josef
2016-01-01
Full Text Available In engineering applications the noisiness of a component or the whole device is a common problem. Currently, a lot of effort is put to eliminate noise of the already produced devices, to prevent generation of acoustic waves during the design of new components, or to specify the operating problems based on noisiness change. The experimental method and the mathematical modelling method belong to these identification methods. With the power of today’s computers the ability to identify the sources of the noise on the mathematical modelling level is a very appreciated tool for engineers. For example, the noise itself may be generated by the vibration of the solid object, combustion, shock, fluid flow around an object or cavitation at the fluid flow in an object. For the given task generating the noise using fluid flow on the selected geometry and propagation of the acoustic waves and their subsequent identification are solved and evaluated. In this paper the principle of measurement of variables describing the fluid flow field and acoustic field are described. For the solution of fluid flow a mathematical model implemented into the CFD code is used. The mathematical modelling evaluation of the flow field is compared to the experimental data.
Global Nonlinear Model Identification with Multivariate Splines
De Visser, C.C.
2011-01-01
At present, model based control systems play an essential role in many aspects of modern society. Application areas of model based control systems range from food processing to medical imaging, and from process control in oil refineries to the flight control systems of modern aircraft. Central to a
Joint Dynamics Modeling and Parameter Identification for Space Robot Applications
Directory of Open Access Journals (Sweden)
Adenilson R. da Silva
2007-01-01
Full Text Available Long-term mission identification and model validation for in-flight manipulator control system in almost zero gravity with hostile space environment are extremely important for robotic applications. In this paper, a robot joint mathematical model is developed where several nonlinearities have been taken into account. In order to identify all the required system parameters, an integrated identification strategy is derived. This strategy makes use of a robust version of least-squares procedure (LS for getting the initial conditions and a general nonlinear optimization method (MCS—multilevel coordinate search—algorithm to estimate the nonlinear parameters. The approach is applied to the intelligent robot joint (IRJ experiment that was developed at DLR for utilization opportunity on the International Space Station (ISS. The results using real and simulated measurements have shown that the developed algorithm and strategy have remarkable features in identifying all the parameters with good accuracy.
A Review of the Haspert Model for Target Identification
National Research Council Canada - National Science Library
Mortiss, Genevieve
2001-01-01
The Haspert model for target identification using multiple sensors is examined. Haspert takes a total-cost approach in constructing identification rules for engagements in which friendly, hostile and neutral parties are involved...
Identification of Civil Engineering Structures using Multivariate ARMAV and RARMAV Models
DEFF Research Database (Denmark)
Kirkegaard, Poul Henning; Andersen, P.; Brincker, Rune
This paper presents how to make system identification of civil engineering structures using multivariate auto-regressive moving-average vector (ARMAV) models. Further, the ARMAV technique is extended to a recursive technique (RARMAV). The ARMAV model is used to identify measured stationary data....... The results show the usefulness of the approaches for identification of civil engineering structures excited by natural excitation...
A comparative study of non-parametric models for identification of ...
African Journals Online (AJOL)
However, the frequency response method using random binary signals was good for unpredicted white noise characteristics and considered the best method for non-parametric system identifica-tion. The autoregressive external input (ARX) model was very useful for system identification, but on applicati-on, few input ...
Comparison of Two Identification Models Used in Adaptive Control of Continuous-Stirred Tank Reactor
Directory of Open Access Journals (Sweden)
Vojtesek Jiri
2016-01-01
Full Text Available The goal of this paper is to compare two identification methods – continuous-time and discrete-time. The continuous-time identification model is more accurate but not very suitable for on-line identification. This disadvantage was overcome with the use of differential filters. On the other hand, discrete-time identification model has is more suitable for identification but less accurate. Compromise can be found in the delta model as a special type of the discrete-time model parameters of which are related to the sampling period. The adaptive approach is based on the choice of the External Linear Model, parameters of which are identified recursively which satisfies the adaptivity of this system. Proposed control strategy was applied on the mathematical model of the Continuous Stirred-Tank reactor as a typical nonlinear lumped-parameters system used in the industry.
Incremental Closed-loop Identification of Linear Parameter Varying Systems
DEFF Research Database (Denmark)
Bendtsen, Jan Dimon; Trangbæk, Klaus
2011-01-01
, closed-loop system identification is more difficult than open-loop identification. In this paper we prove that the so-called Hansen Scheme, a technique known from linear time-invariant systems theory for transforming closed-loop system identification problems into open-loop-like problems, can be extended......This paper deals with system identification for control of linear parameter varying systems. In practical applications, it is often important to be able to identify small plant changes in an incremental manner without shutting down the system and/or disconnecting the controller; unfortunately...... to accommodate linear parameter varying systems as well....
Algorithms and tools for system identification using prior knowledge
International Nuclear Information System (INIS)
Lindskog, P.
1994-01-01
One of the hardest problems in system identification is that of model structure selection. In this thesis two different kinds of a priori process knowledge are used to address this fundamental problem. Concentrating on linear model structures, the first prior advantage of is knowledge about the systems' dominating time constants and resonance frequencies. The idea is to generalize FIR modelling by replacing the usual delay operator with discrete so-called Laguerre or Kautz filters. The generalization is such that stability, the linear regression structure and the approximation ability of the FIR model structure is retained, whereas the prior is used to reduce the number of parameters needed to arrive at a reasonable model. Tailorized and efficient system identification algorithms for these model structures are detailed in this work. The usefulness of the proposed methods is demonstrated through concrete simulation and application studies. The other approach is referred to as semi-physical modelling. The main idea is to use simple physical insight into the application, often in terms of a set of unstructured equations, in order to come up with suitable nonlinear transformation of the raw measurements, so as to allow for a good model structure. Semi-physical modelling is less ''ambitious'' than physical modelling in that no complete physical structure is sought, just combinations of inputs and outputs that can be subjected to more or less standard model structures, such as linear regressions. The suggested modelling procedure shows a first step where symbolic computations are employed to determine a suitable model structure - a set of regressors. We show how constructive methods from commutative and differential algebra can be applied for this. Subsequently, different numerical schemes for finding a subset of ''good'' regressors and for estimating the corresponding linear-in-the-parameters model are discussed. 107 refs, figs, tabs
Nuclear reactors transients identification and classification system
International Nuclear Information System (INIS)
Bianchi, Paulo Henrique
2008-01-01
This work describes the study and test of a system capable to identify and classify transients in thermo-hydraulic systems, using a neural network technique of the self-organizing maps (SOM) type, with the objective of implanting it on the new generations of nuclear reactors. The technique developed in this work consists on the use of multiple networks to do the classification and identification of the transient states, being each network a specialist at one respective transient of the system, that compete with each other using the quantization error, that is a measure given by this type of neural network. This technique showed very promising characteristics that allow the development of new functionalities in future projects. One of these characteristics consists on the potential of each network, besides responding what transient is in course, could give additional information about that transient. (author)
Modelling and Identification of Induction Machines
Energy Technology Data Exchange (ETDEWEB)
Nestli, T.F.
1995-12-01
To obtain high quality control of the induction machine, field orientation is probably the most frequently used control strategy. Using this strategy requires that one of the flux space vectors be known. Since this cannot be measured, many predictor models for calculation of the rotor flux space vector in real time have been developed. This doctoral thesis presents an analysis method for evaluating and comparing predictor models for flux calculation with respect to sensitivity to parameter deviations and measurement errors and with respect to dynamics. It is concluded that the best predictor models in the minimum sensitivity sense should have properties similar to the current and voltage models at lower and higher frequencies, respectively. To further reduce flux estimation errors, a new saturation model for the Inverse {Gamma}-formulation of the induction machine is developed. It is shown that the leakage reactance varies mainly with stator current, and the magnetizing reactance depends both on stator flux and rotor current magnitudes, i.e., both on magnetization and load. The reactance models are verified by experiments. An off-line identification algorithm is developed to identify the parameters of the reactance model and initial values for the stator and rotor resistances. The algorithm is verified in laboratory experiments, which also demonstrate the temperature dependence of the resistances. 36 refs., 49 figs., 6 tabs.
Systematic approach for the identification of process reference models
CSIR Research Space (South Africa)
Van Der Merwe, A
2009-02-01
Full Text Available Process models are used in different application domains to capture knowledge on the process flow. Process reference models (PRM) are used to capture reusable process models, which should simplify the identification process of process models...
Contribution to the modeling and the identification of haptic interfaces
International Nuclear Information System (INIS)
Janot, A.
2007-12-01
This thesis focuses on the modeling and the identification of haptic interfaces using cable drive. An haptic interface is a force feedback device, which enables its user to interact with a virtual world or a remote environment explored by a slave system. It aims at the matching between the forces and displacements given by the user and those applied to virtual world. Usually, haptic interfaces make use of a mechanical actuated structure whose distal link is equipped with a handle. When manipulating this handle to interact with explored world, the user feels the apparent mass, compliance and friction of the interface. This distortion introduced between the operator and the virtual world must be modeled and identified to enhance the design of the interface and develop appropriate control laws. The first approach has been to adapt the modeling and identification methods of rigid and localized flexibilities robots to haptic interfaces. The identification technique makes use of the inverse dynamic model and the linear least squares with the measurements of joint torques and positions. This approach is validated on a single degree of freedom and a three degree of freedom haptic devices. A new identification method needing only torque data is proposed. It is based on a closed loop simulation using the direct dynamic model. The optimal parameters minimize the 2 norms of the error between the actual torque and the simulated torque assuming the same control law and the same tracking trajectory. This non linear least squares problem dramatically is simplified using the inverse model to calculate the simulated torque. This method is validated on the single degree of freedom haptic device and the SCARA robot. (author)
Accurate Lithium-ion battery parameter estimation with continuous-time system identification methods
International Nuclear Information System (INIS)
Xia, Bing; Zhao, Xin; Callafon, Raymond de; Garnier, Hugues; Nguyen, Truong; Mi, Chris
2016-01-01
Highlights: • Continuous-time system identification is applied in Lithium-ion battery modeling. • Continuous-time and discrete-time identification methods are compared in detail. • The instrumental variable method is employed to further improve the estimation. • Simulations and experiments validate the advantages of continuous-time methods. - Abstract: The modeling of Lithium-ion batteries usually utilizes discrete-time system identification methods to estimate parameters of discrete models. However, in real applications, there is a fundamental limitation of the discrete-time methods in dealing with sensitivity when the system is stiff and the storage resolutions are limited. To overcome this problem, this paper adopts direct continuous-time system identification methods to estimate the parameters of equivalent circuit models for Lithium-ion batteries. Compared with discrete-time system identification methods, the continuous-time system identification methods provide more accurate estimates to both fast and slow dynamics in battery systems and are less sensitive to disturbances. A case of a 2 nd -order equivalent circuit model is studied which shows that the continuous-time estimates are more robust to high sampling rates, measurement noises and rounding errors. In addition, the estimation by the conventional continuous-time least squares method is further improved in the case of noisy output measurement by introducing the instrumental variable method. Simulation and experiment results validate the analysis and demonstrate the advantages of the continuous-time system identification methods in battery applications.
SSNN toolbox for non-linear system identification
Luzar, Marcel; Czajkowski, Andrzej
2015-11-01
The aim of this paper is to develop and design a State Space Neural Network toolbox for a non-linear system identification with an artificial state-space neural networks, which can be used in a model-based robust fault diagnosis and control. Such toolbox is implemented in the MATLAB environment and it uses some of its predefined functions. It is designed in the way that any non-linear multi-input multi-output system is identified and represented in the classical state-space form. The novelty of the proposed approach is that the final result of the identification process is the state, input and output matrices, not only the neural network parameters. Moreover, the toolbox is equipped with the graphical user interface, which makes it useful for the users not familiar with the neural networks theory.
Stability Analysis of Neural Networks-Based System Identification
Directory of Open Access Journals (Sweden)
Talel Korkobi
2008-01-01
Full Text Available This paper treats some problems related to nonlinear systems identification. A stability analysis neural network model for identifying nonlinear dynamic systems is presented. A constrained adaptive stable backpropagation updating law is presented and used in the proposed identification approach. The proposed backpropagation training algorithm is modified to obtain an adaptive learning rate guarantying convergence stability. The proposed learning rule is the backpropagation algorithm under the condition that the learning rate belongs to a specified range defining the stability domain. Satisfying such condition, unstable phenomena during the learning process are avoided. A Lyapunov analysis leads to the computation of the expression of a convenient adaptive learning rate verifying the convergence stability criteria. Finally, the elaborated training algorithm is applied in several simulations. The results confirm the effectiveness of the CSBP algorithm.
Identification of Hammerstein models with cubic spline nonlinearities.
Dempsey, Erika J; Westwick, David T
2004-02-01
This paper considers the use of cubic splines, instead of polynomials, to represent the static nonlinearities in block structured models. It introduces a system identification algorithm for the Hammerstein structure, a static nonlinearity followed by a linear filter, where cubic splines represent the static nonlinearity and the linear dynamics are modeled using a finite impulse response filter. The algorithm uses a separable least squares Levenberg-Marquardt optimization to identify Hammerstein cascades whose nonlinearities are modeled by either cubic splines or polynomials. These algorithms are compared in simulation, where the effects of variations in the input spectrum and distribution, and those of the measurement noise are examined. The two algorithms are used to fit Hammerstein models to stretch reflex electromyogram (EMG) data recorded from a spinal cord injured patient. The model with the cubic spline nonlinearity provides more accurate predictions of the reflex EMG than the polynomial based model, even in novel data.
Compressive System Identification in the Linear Time-Invariant framework
Toth, Roland
2011-12-01
Selection of an efficient model parametrization (model order, delay, etc.) has crucial importance in parametric system identification. It navigates a trade-off between representation capabilities of the model (structural bias) and effects of over-parametrization (variance increase of the estimates). There exists many approaches to this widely studied problem in terms of statistical regularization methods and information criteria. In this paper, an alternative ℓ 1 regularization scheme is proposed for estimation of sparse linear-regression models based on recent results in compressive sensing. It is shown that the proposed scheme provides consistent estimation of sparse models in terms of the so-called oracle property, it is computationally attractive for large-scale over-parameterized models and it is applicable in case of small data sets, i.e., underdetermined estimation problems. The performance of the approach w.r.t. other regularization schemes is demonstrated in an extensive Monte Carlo study. © 2011 IEEE.
Chemical identification using Bayesian model selection
Energy Technology Data Exchange (ETDEWEB)
Burr, Tom; Fry, H. A. (Herbert A.); McVey, B. D. (Brian D.); Sander, E. (Eric)
2002-01-01
Remote detection and identification of chemicals in a scene is a challenging problem. We introduce an approach that uses some of the image's pixels to establish the background characteristics while other pixels represent the target for which we seek to identify all chemical species present. This leads to a generalized least squares problem in which we focus on 'subset selection' to identify the chemicals thought to be present. Bayesian model selection allows us to approximate the posterior probability that each chemical in the library is present by adding the posterior probabilities of all the subsets which include the chemical. We present results using realistic simulated data for the case with 1 to 5 chemicals present in each target and compare performance to a hybrid of forward and backward stepwise selection procedure using the F statistic.
Wiener-Hammerstein system identification - an evolutionary approach
Naitali, Abdessamad; Giri, Fouad
2016-01-01
The problem of identifying parametric Wiener-Hammerstein (WH) systems is addressed within the evolutionary optimisation context. Specifically, a hybrid culture identification method is developed that involves model structure adaptation using genetic recombination and model parameter learning using particle swarm optimisation. The method enjoys three interesting features: (1) the risk of premature convergence of model parameter estimates to local optima is significantly reduced, due to the constantly maintained diversity of model candidates; (2) no prior knowledge is needed except for upper bounds on the system structure indices; (3) the method is fully autonomous as no interaction is needed with the user during the optimum search process. The performances of the proposed method will be illustrated and compared to alternative methods using a well-established WH benchmark.
Identification of MIMO systems with sparse transfer function coefficients
Qiu, Wanzhi; Saleem, Syed Khusro; Skafidas, Efstratios
2012-12-01
We study the problem of estimating transfer functions of multivariable (multiple-input multiple-output--MIMO) systems with sparse coefficients. We note that subspace identification methods are powerful and convenient tools in dealing with MIMO systems since they neither require nonlinear optimization nor impose any canonical form on the systems. However, subspace-based methods are inefficient for systems with sparse transfer function coefficients since they work on state space models. We propose a two-step algorithm where the first step identifies the system order using the subspace principle in a state space format, while the second step estimates coefficients of the transfer functions via L1-norm convex optimization. The proposed algorithm retains good features of subspace methods with improved noise-robustness for sparse systems.
A method for model identification and parameter estimation
International Nuclear Information System (INIS)
Bambach, M; Heinkenschloss, M; Herty, M
2013-01-01
We propose and analyze a new method for the identification of a parameter-dependent model that best describes a given system. This problem arises, for example, in the mathematical modeling of material behavior where several competing constitutive equations are available to describe a given material. In this case, the models are differential equations that arise from the different constitutive equations, and the unknown parameters are coefficients in the constitutive equations. One has to determine the best-suited constitutive equations for a given material and application from experiments. We assume that the true model is one of the N possible parameter-dependent models. To identify the correct model and the corresponding parameters, we can perform experiments, where for each experiment we prescribe an input to the system and observe a part of the system state. Our approach consists of two stages. In the first stage, for each pair of models we determine the experiment, i.e. system input and observation, that best differentiates between the two models, and measure the distance between the two models. Then we conduct N(N − 1) or, depending on the approach taken, N(N − 1)/2 experiments and use the result of the experiments as well as the previously computed model distances to determine the true model. We provide sufficient conditions on the model distances and measurement errors which guarantee that our approach identifies the correct model. Given the model, we identify the corresponding model parameters in the second stage. The problem in the second stage is a standard parameter estimation problem and we use a method suitable for the given application. We illustrate our approach on three examples, including one where the models are elliptic partial differential equations with different parameterized right-hand sides and an example where we identify the constitutive equation in a problem from computational viscoplasticity. (paper)
Time-Delay System Identification Using Genetic Algorithm
DEFF Research Database (Denmark)
Yang, Zhenyu; Seested, Glen Thane
2013-01-01
Due to the unknown dead-time coefficient, the time-delay system identification turns to be a non-convex optimization problem. This paper investigates the identification of a simple time-delay system, named First-Order-Plus-Dead-Time (FOPDT), by using the Genetic Algorithm (GA) technique. The qual......Due to the unknown dead-time coefficient, the time-delay system identification turns to be a non-convex optimization problem. This paper investigates the identification of a simple time-delay system, named First-Order-Plus-Dead-Time (FOPDT), by using the Genetic Algorithm (GA) technique...
Identification of neutral biochemical network models from time series data
Directory of Open Access Journals (Sweden)
Maia Marco
2009-05-01
Full Text Available Abstract Background The major difficulty in modeling biological systems from multivariate time series is the identification of parameter sets that endow a model with dynamical behaviors sufficiently similar to the experimental data. Directly related to this parameter estimation issue is the task of identifying the structure and regulation of ill-characterized systems. Both tasks are simplified if the mathematical model is canonical, i.e., if it is constructed according to strict guidelines. Results In this report, we propose a method for the identification of admissible parameter sets of canonical S-systems from biological time series. The method is based on a Monte Carlo process that is combined with an improved version of our previous parameter optimization algorithm. The method maps the parameter space into the network space, which characterizes the connectivity among components, by creating an ensemble of decoupled S-system models that imitate the dynamical behavior of the time series with sufficient accuracy. The concept of sloppiness is revisited in the context of these S-system models with an exploration not only of different parameter sets that produce similar dynamical behaviors but also different network topologies that yield dynamical similarity. Conclusion The proposed parameter estimation methodology was applied to actual time series data from the glycolytic pathway of the bacterium Lactococcus lactis and led to ensembles of models with different network topologies. In parallel, the parameter optimization algorithm was applied to the same dynamical data upon imposing a pre-specified network topology derived from prior biological knowledge, and the results from both strategies were compared. The results suggest that the proposed method may serve as a powerful exploration tool for testing hypotheses and the design of new experiments.
Identification of neutral biochemical network models from time series data.
Vilela, Marco; Vinga, Susana; Maia, Marco A Grivet Mattoso; Voit, Eberhard O; Almeida, Jonas S
2009-05-05
The major difficulty in modeling biological systems from multivariate time series is the identification of parameter sets that endow a model with dynamical behaviors sufficiently similar to the experimental data. Directly related to this parameter estimation issue is the task of identifying the structure and regulation of ill-characterized systems. Both tasks are simplified if the mathematical model is canonical, i.e., if it is constructed according to strict guidelines. In this report, we propose a method for the identification of admissible parameter sets of canonical S-systems from biological time series. The method is based on a Monte Carlo process that is combined with an improved version of our previous parameter optimization algorithm. The method maps the parameter space into the network space, which characterizes the connectivity among components, by creating an ensemble of decoupled S-system models that imitate the dynamical behavior of the time series with sufficient accuracy. The concept of sloppiness is revisited in the context of these S-system models with an exploration not only of different parameter sets that produce similar dynamical behaviors but also different network topologies that yield dynamical similarity. The proposed parameter estimation methodology was applied to actual time series data from the glycolytic pathway of the bacterium Lactococcus lactis and led to ensembles of models with different network topologies. In parallel, the parameter optimization algorithm was applied to the same dynamical data upon imposing a pre-specified network topology derived from prior biological knowledge, and the results from both strategies were compared. The results suggest that the proposed method may serve as a powerful exploration tool for testing hypotheses and the design of new experiments.
Development of a spoken language identification system for South African languages
CSIR Research Space (South Africa)
Peché, M
2009-12-01
Full Text Available This article introduces the first Spoken Language Identification system developed to distinguish among all eleven of South Africa’s official languages. The PPR-LM (Parallel Phoneme Recognition followed by Language Modeling) architecture...
Mathematics of tsunami: modelling and identification
Krivorotko, Olga; Kabanikhin, Sergey
2015-04-01
Tsunami (long waves in the deep water) motion caused by underwater earthquakes is described by shallow water equations ( { ηtt = div (gH (x,y)-gradη), (x,y) ∈ Ω, t ∈ (0,T ); η|t=0 = q(x,y), ηt|t=0 = 0, (x,y) ∈ Ω. ( (1) Bottom relief H(x,y) characteristics and the initial perturbation data (a tsunami source q(x,y)) are required for the direct simulation of tsunamis. The main difficulty problem of tsunami modelling is a very big size of the computational domain (Ω = 500 × 1000 kilometres in space and about one hour computational time T for one meter of initial perturbation amplitude max|q|). The calculation of the function η(x,y,t) of three variables in Ω × (0,T) requires large computing resources. We construct a new algorithm to solve numerically the problem of determining the moving tsunami wave height S(x,y) which is based on kinematic-type approach and analytical representation of fundamental solution. Proposed algorithm of determining the function of two variables S(x,y) reduces the number of operations in 1.5 times than solving problem (1). If all functions does not depend on the variable y (one dimensional case), then the moving tsunami wave height satisfies of the well-known Airy-Green formula: S(x) = S(0)° --- 4H (0)/H (x). The problem of identification parameters of a tsunami source using additional measurements of a passing wave is called inverse tsunami problem. We investigate two different inverse problems of determining a tsunami source q(x,y) using two different additional data: Deep-ocean Assessment and Reporting of Tsunamis (DART) measurements and satellite altimeters wave-form images. These problems are severely ill-posed. The main idea consists of combination of two measured data to reconstruct the source parameters. We apply regularization techniques to control the degree of ill-posedness such as Fourier expansion, truncated singular value decomposition, numerical regularization. The algorithm of selecting the truncated number of
Qualitative identification of chaotic systems behaviours
International Nuclear Information System (INIS)
Vicha, T.; Dohnal, M.
2008-01-01
There are only three qualitative values positive, negative and zero. This means that there is a maximal number of qualitatively distinguishable scenarios, prescribed by the number of variables and the highest qualitative derivative taken into consideration. There are several chaos related tasks, which can be solved with great difficulties on the numerical level if multidimensional problems are studied. One of them is the identification of all qualitatively different behaviours. To make sure that all distinctive qualitative scenarios are identified a qualitative interpretation of a classical quantitative phase portrait is used. The highest derivatives are usually the second derivatives as it is not possible to safely identify higher derivatives if tasks related to ecology or economics are studied. Two classical models are discussed - Damped oscillation (non chaotic) and Lorenz model (chaotic). There are 191 scenarios of the Lorenz model if only the second derivatives are considered. If the third derivatives are taken into consideration then the number of scenarios is 2619. Complete qualitative results are given in details
System identification advances and case studies
Mehra, Raman K
1976-01-01
In this book, we study theoretical and practical aspects of computing methods for mathematical modelling of nonlinear systems. A number of computing techniques are considered, such as methods of operator approximation with any given accuracy; operator interpolation techniques including a non-Lagrange interpolation; methods of system representation subject to constraints associated with concepts of causality, memory and stationarity; methods of system representation with an accuracy that is the best within a given class of models; methods of covariance matrix estimation;methods for low-rank mat
Task Characterisation and Cross-Platform Programming Through System Identification
Directory of Open Access Journals (Sweden)
Roberto Iglesias
2008-11-01
Full Text Available Developing robust and reliable control code for autonomous mobile robots is difficult, because the interaction between a physical robot and the environment is highly complex, it is subject to noise and variation, and therefore partly unpredictable. This means that to date it is not possible to predict robot behaviour, based on theoretical models. Instead, current methods to develop robot control code still require a substantial trial-and-error component to the software design process. Such iterative refinement could be reduced, we argue, if a more profound theoretical understanding of robot-environment interaction existed. In this paper, we therefore present a modelling method that generates a faithful model of a robot's interaction with its environment, based on data logged while observing a physical robot's behaviour. Because this modelling method - nonlinear modelling using polynomials - is commonly used in the engineering discipline of system identification, we refer to it here as "robot identification". We show in this paper that using robot identification to obtain a computer model of robot environment interaction offers several distinct advantages:
1. Very compact representations (one-line programs of the robot control program are generated
2.The model can be analysed, for example through sensitivity analysis, leading to a better understanding of the essential parameters underlying the robot's behaviour, and
3. The generated, compact robot code can be used for cross-platform robot programming, allowing fast transfer of robot code from one type of robot to another.
We demonstrate these points through experiments with a Magellan Pro and a Nomad 200 mobile robot.
Identification of Dynamic Simulation Models for Variable Speed Pumped Storage Power Plants
Moreira, C.; Fulgêncio, N.; Silva, B.; Nicolet, C.; Béguin, A.
2017-04-01
This paper addresses the identification of reduced order models for variable speed pump-turbine plants, including the representation of the dynamic behaviour of the main components: hydraulic system, turbine governors, electromechanical equipment and power converters. A methodology for the identification of appropriated reduced order models both for turbine and pump operating modes is presented and discussed. The methodological approach consists of three main steps: 1) detailed pumped-storage power plant modelling in SIMSEN; 2) reduced order models identification and 3) specification of test conditions for performance evaluation.
International Nuclear Information System (INIS)
Delforge, Jacques
1984-01-01
In its first part, this research thesis which is the result of studies of the effects of radiations on molecular structures, addresses the search for guiding principles for the elaboration of a model with the best as possible justification (model justification is based on five criteria: rational consistency, refutability, adjustment to experimental data, justification of all model characteristics, uniqueness), recalls the main principles of the theory of transformation systems, and proposes a procedure of model search. The second part addresses mathematical methods. The third part addresses the problem of identifiability of parameters and the fourth part reports examples of model development: study of the effects of gamma irradiation on unicellular algae, study of the effects of radiations on the stem cells of rat gonads. This last part also reports the elaboration of pharmacokinetic model with 21 parameters, and the study of two models of nuclear medicine (assessment of the actual lung volume of hypoxemic patients, study of the methionine brain metabolism)
Systems identification: a theoretical method applied to tracer kinetics in aquatic microcosms
International Nuclear Information System (INIS)
Halfon, E.; Georgia Univ., Athens
1974-01-01
A mathematical model of radionuclide kinetics in a laboratory microcosm was built and the transfer parameters estimated by multiple regression and system identification techniques. Insight into the functioning of the system was obtained from analysis of the model. Methods employed have allowed movements of radioisotopes not directly observable in the experimental systems to be distinguished. Results are generalized to whole ecosystems
System identification of an unmanned quadcopter system using MRAN neural
Pairan, M. F.; Shamsudin, S. S.
2017-12-01
This project presents the performance analysis of the radial basis function neural network (RBF) trained with Minimal Resource Allocating Network (MRAN) algorithm for real-time identification of quadcopter. MRAN’s performance is compared with the RBF with Constant Trace algorithm for 2500 input-output pair data sampling. MRAN utilizes adding and pruning hidden neuron strategy to obtain optimum RBF structure, increase prediction accuracy and reduce training time. The results indicate that MRAN algorithm produces fast training time and more accurate prediction compared with standard RBF. The model proposed in this paper is capable of identifying and modelling a nonlinear representation of the quadcopter flight dynamics.
Liu, Jingbo; Yan, Zihe; Han, Min; Han, Zhijun; Jin, Lingjie; Zhao, Yanlin
2012-01-01
The CapitalBio Mycobacterium identification microarray system is a rapid system for the detection of Mycobacterium tuberculosis. The performance of this system was assessed with 24 reference strains, 486 Mycobacterium tuberculosis clinical isolates, and 40 clinical samples and then compared to the “gold standard” of DNA sequencing. The CapitalBio Mycobacterium identification microarray system showed highly concordant identification results of 100% and 98.4% for Mycobacterium tuberculosis complex (MTC) and nontuberculous mycobacteria (NTM), respectively. The sensitivity and specificity of the CapitalBio Mycobacterium identification array for identification of Mycobacterium tuberculosis isolates were 99.6% and 100%, respectively, for direct detection and identification of clinical samples, and the overall sensitivity was 52.5%. It was 100% for sputum, 16.7% for pleural fluid, and 10% for bronchoalveolar lavage fluid, respectively. The total assay was completed in 6 h, including DNA extraction, PCR, and hybridization. The results of this study confirm the utility of this system for the rapid identification of mycobacteria and suggest that the CapitalBio Mycobacterium identification array is a molecular diagnostic technique with high sensitivity and specificity that has the capacity to quickly identify most mycobacteria. PMID:22090408
A Survey of Modelling and Identification of Quadrotor Robot
Directory of Open Access Journals (Sweden)
Xiaodong Zhang
2014-01-01
Full Text Available A quadrotor is a rotorcraft capable of hover, forward flight, and VTOL and is emerging as a fundamental research and application platform at present with flexibility, adaptability, and ease of construction. Since a quadrotor is basically considered an unstable system with the characteristics of dynamics such as being intensively nonlinear, multivariable, strongly coupled, and underactuated, a precise and practical model is critical to control the vehicle which seems to be simple to operate. As a rotorcraft, the dynamics of a quadrotor is mainly dominated by the complicated aerodynamic effects of the rotors. This paper gives a tutorial of the platform configuration, methodology of modeling, comprehensive nonlinear model, the aerodynamic effects, and model identification for a quadrotor.
Stochastic Modelling of Energy Systems
DEFF Research Database (Denmark)
Andersen, Klaus Kaae
2001-01-01
In this thesis dynamic models of typical components in Danish heating systems are considered. Emphasis is made on describing and evaluating mathematical methods for identification of such models, and on presentation of component models for practical applications. The thesis consists of seven...... of component models, such as e.g. heat exchanger and valve models, adequate for system simulations. Furthermore, the thesis demonstrates and discusses the advantages and disadvantages of using statistical methods in conjunction with physical knowledge in establishing adequate component models of heating...... research papers (case studies) together with a summary report. Each case study takes it's starting point in typical heating system components and both, the applied mathematical modelling methods and the application aspects, are considered. The summary report gives an introduction to the scope...
Particle Identification with the LHCb RICH System
Harnew, Neville
2005-01-01
The LHCb experiment uses a Ring Imaging Cherenkov (RICH) system to provide particle identification over the momentum range 2-100 GeV/c. Two RICH detectors are employed. The upstream detector, RICH1, utilizes both aerogel and C$_4$F$_{10}$ gas radiators whilst the downstream RICH2 uses a CF$_4$ gas radiator. The RICH2 detector has been fabricated and is installed in the LHCb interaction region; RICH1 has a programme of phased design and construction. Novel Hybrid Photon Detectors (HPDs) have been developed in collaboration with industry to detect the Cherenkov photons in the wavelength range 200-600 nm. The HPDs are enclosed in iron shielding and Mumetal cylinders to allow operation in magnetic fields up to 50mT. The performance of pre-series HPDs and the results obtained from a particle test beam using the full LHCb readout chain is presented. The production of a total of 484 HPDs required for the two RICH detectors has recently commenced. The expected performance of the LHCb RICH system, obtained from real...
Particle identification with the LHCb RICH system
Harnew, Neville
2006-07-01
The LHCb experiment uses a Ring Imaging Cherenkov (RICH) system to provide particle identification over the momentum range 2- 100 GeV/c. Two RICH detectors are employed. The upstream detector, RICH 1, utilizes both aerogel and C4F10 gas radiators whilst the downstream RICH 2 uses a CF4 gas radiator. The RICH 2 detector has been fabricated and is installed in the LHCb interaction region; RICH 1 has a programme of phased design and construction. Novel Hybrid Photon Detectors (HPDs) have been developed in collaboration with industry to detect the Cherenkov photons in the wavelength range 200-600 nm. The HPDs are enclosed in iron shielding and Mumetal cylinders to allow operation in magnetic fields up to 50 mT. The performance of pre-series HPDs and the results obtained from a particle test beam using the full LHCb readout chain is presented. The production of a total of 484 HPDs required for the two RICH detectors has recently commenced. The expected performance of the LHCb RICH system, obtained from realistic simulation, is described.
Energy Technology Data Exchange (ETDEWEB)
Alves, Antonio Carlos Pinto Dias
2000-09-01
A nuclear power plant has a myriad of complex system and sub-systems that, working cooperatively, make the control of the whole plant. Nevertheless their operation be automatic most of the time, the integral understanding of their internal- logic can be away of the comprehension of even experienced operators because of the poor interpretability those controls offer. This difficulty does not happens only in nuclear power plants but in almost every a little more complex control system. Neuro-fuzzy models have been used for the last years in a attempt of suppress these difficulties because of their ability of modelling in linguist form even a system which behavior is extremely complex. This is a very intuitive human form of interpretation and neuro-fuzzy model are gathering increasing acceptance. Unfortunately, neuro-fuzzy models can grow up to become of hard interpretation because of the complexity of the systems under modelling. In general, that growing occurs in function of redundant rules or rules that cover a very little domain of the problem. This work presents an identification method for neuro-fuzzy models that not only allows models grow in function of the existent complexity but that beforehand they try to self-adapt to avoid the inclusion of new rules. This form of construction allowed to arrive to highly interpretative neuro-fuzzy models even of very complex systems. The use of this kind of technique in modelling the control of the pressurizer of a PWR nuclear power plant allowed verify its validity and how neuro-fuzzy models so built can be useful in understanding the automatic operation of a nuclear power plant. (author)
Improving substructure identification accuracy of shear structures using virtual control system
Zhang, Dongyu; Yang, Yang; Wang, Tingqiang; Li, Hui
2018-02-01
Substructure identification is a powerful tool to identify the parameters of a complex structure. Previously, the authors developed an inductive substructure identification method for shear structures. The identification error analysis showed that the identification accuracy of this method is significantly influenced by the magnitudes of two key structural responses near a certain frequency; if these responses are unfavorable, the method cannot provide accurate estimation results. In this paper, a novel method is proposed to improve the substructure identification accuracy by introducing a virtual control system (VCS) into the structure. A virtual control system is a self-balanced system, which consists of some control devices and a set of self-balanced forces. The self-balanced forces counterbalance the forces that the control devices apply on the structure. The control devices are combined with the structure to form a controlled structure used to replace the original structure in the substructure identification; and the self-balance forces are treated as known external excitations to the controlled structure. By optimally tuning the VCS’s parameters, the dynamic characteristics of the controlled structure can be changed such that the original structural responses become more favorable for the substructure identification and, thus, the identification accuracy is improved. A numerical example of 6-story shear structure is utilized to verify the effectiveness of the VCS based controlled substructure identification method. Finally, shake table tests are conducted on a 3-story structural model to verify the efficacy of the VCS to enhance the identification accuracy of the structural parameters.
Kuvich, Gary
2005-11-01
One of the major problems of modern industrial robots is a lack of reliable perceptual systems that are similar to human vision in its abilities to understand visual scene and detect and unambiguously identify objects. The traditional linear bottom-up "segmentation-grouping-learning-recognition" approach to image processing and analysis cannot provide a reliable separation of an object from its background or clutter, while human vision unambiguously solves this problem. The modern computer vision can only recognize certain features from visual information, and it plays an auxiliary role, helping to build or choose appropriate 3-dimensional models of objects and visual scene. As result, designers of robotics systems must create for industrial robots artificial environments, which allowing for precise computations of 3-dimensional models within such environments. However, outside of such an artificial environment, the robot is dysfunctional. Biologically-inspired Network-Symbolic models do not compute precise 3-dimensional models, but convert image information into an "understandable" Network-Symbolic format, which is similar to relational knowledge models. Feature, symbol, and predicate are equivalent in the Network-Symbolic systems. A linking mechanism binds these features or symbols into coherent structures, and image converts from a "raster" into a "vector" representation that can be better interpreted by higher-level knowledge structures. Logic of visual scenes can be captured in the Network-Symbolic models and used for the disambiguation of visual information.
Directory of Open Access Journals (Sweden)
Wei Huang
2013-01-01
Full Text Available We introduce a new category of fuzzy inference systems with the aid of a multiobjective opposition-based space search algorithm (MOSSA. The proposed MOSSA is essentially a multiobjective space search algorithm improved by using an opposition-based learning that employs a so-called opposite numbers mechanism to speed up the convergence of the optimization algorithm. In the identification of fuzzy inference system, the MOSSA is exploited to carry out the parametric identification of the fuzzy model as well as to realize its structural identification. Experimental results demonstrate the effectiveness of the proposed fuzzy models.
Aktas, D.; Velissariou, P.; Chassignet, E.; Bourassa, M. A.
2014-12-01
The non-tropical storm, the 12-14 March 1993 Superstorm, which called the Storm of the Century had a wide reaching effect on the Northern Gulf of Mexico region and the East Coast of the United States. Previous studies show that the initial development of the storm could not be simulated accurately enough to represent the intensity and the evolution of the storm over the Gulf of Mexico region. The aim of this study is to identify the effects of the air-sea fluxes, the sea surface temperature (SST) and the model resolution on determining the intensity and the track of the storm more accurately. To this end, the outputs from two-way coupled model runs were examined to analyze the storm characteristics. Model configurations have been set within a coupled system framework that includes the atmospheric model Weather Research & Forecasting Model (WRF) and the ocean model Regional Ocean Model (ROMS). Three WRF domains assigned 15 km, 5 km and ~1.6 km resolutions, respectively and an 8 km resolution ROMS domain were used in the coupled system. The initial and boundary conditions for WRF were extracted from the NCEP Climate Forecast System Reanalysis (CFSR) products and the Hybrid Coordinate Ocean Model (HYCOM) generated SSTs while, the conditions for ROMS were extracted from HYCOM. Comparisons were performed against NOAA buoys and GridSAT brightness temperatures. Minimum mean sea level pressure (MSLP), maximum wind speed and storm locations were examined. Time series for MSLP and wind speed were used to illustrate how air-sea interaction and resolution changes storm intensity along the track. The results showing the RMS differences on the storm location and intensity of the storm are also presented.
Parametric system identification of catamaran for improving controller design
Timpitak, Surasak; Prempraneerach, Pradya; Pengwang, Eakkachai
2018-01-01
This paper presents an estimation of simplified dynamic model for only surge- and yaw- motions of catamaran by using system identification (SI) techniques to determine associated unknown parameters. These methods will enhance the performance of designing processes for the motion control system of Unmanned Surface Vehicle (USV). The simulation results demonstrate an effective way to solve for damping forces and to determine added masses by applying least-square and AutoRegressive Exogenous (ARX) methods. Both methods are then evaluated according to estimated parametric errors from the vehicle’s dynamic model. The ARX method, which yields better estimated accuracy, can then be applied to identify unknown parameters as well as to help improving a controller design of a real unmanned catamaran.
System Identification, Prediction, Simulation and Control with Neural Networks
DEFF Research Database (Denmark)
Sørensen, O.
1997-01-01
a Gauss-Newton search direction is applied. 3) Amongst numerous model types, often met in control applications, only the Non-linear ARMAX (NARMAX) model, representing input/output description, is examined. A simulated example confirms that a neural network has the potential to perform excellent System...... Identification, Prediction, Simulation and Control of a dynamic, non-linear and noisy process. Further, the difficulties to control a practical non-linear laboratory process in a satisfactory way by using a traditional controller are overcomed by using a trained neural network to perform non-linear System......The intention of this paper is to make a systematic examination of the possibilities of applying neural networks in those technical areas, which are familiar to a control engineer. In other words, the potential of neural networks in control applications is given higher priority than a detailed...
Optical Automatic Car Identification (OACI) : Volume 1. Advanced System Specification.
1978-12-01
A performance specification is provided in this report for an Optical Automatic Car Identification (OACI) scanner system which features 6% improved readability over existing industry scanner systems. It also includes the analysis and rationale which ...
System Identification of a Non-Uniformly Sampled Multi-Rate System in Aluminium Electrolysis Cells
Directory of Open Access Journals (Sweden)
Håkon Viumdal
2014-07-01
Full Text Available Standard system identification algorithms are usually designed to generate mathematical models with equidistant sampling instants, that are equal for both input variables and output variables. Unfortunately, real industrial data sets are often disrupted by missing samples, variations of sampling rates in the different variables (also known as multi-rate systems, and intermittent measurements. In industries with varying events based maintenance or manual operational measures, intermittent measurements are performed leading to uneven sampling rates. Such is the case with aluminium smelters, where in addition the materials fed into the cell create even more irregularity in sampling. Both measurements and feeding are mostly manually controlled. A simplified simulation of the metal level in an aluminium electrolysis cell is performed based on mass balance considerations. System identification methods based on Prediction Error Methods (PEM such as Ordinary Least Squares (OLS, and the sub-space method combined Deterministic and Stochastic system identification and Realization (DSR, and its variants are applied to the model of a single electrolysis cell as found in the aluminium smelters. Aliasing phenomena due to large sampling intervals can be crucial in avoiding unsuitable models, but with knowledge about the system dynamics, it is easier to optimize the sampling performance, and hence achieve successful models. The results based on the simulation studies of molten aluminium height in the cells using the various algorithms give results which tally well with the synthetic data sets used. System identification on a smaller data set from a real plant is also implemented in this work. Finally, some concrete suggestions are made for using these models in the smelters.
Orthonormal filters for identification in active control systems
International Nuclear Information System (INIS)
Mayer, Dirk
2015-01-01
Many active noise and vibration control systems require models of the control paths. When the controlled system changes slightly over time, adaptive digital filters for the identification of the models are useful. This paper aims at the investigation of a special class of adaptive digital filters: orthonormal filter banks possess the robust and simple adaptation of the widely applied finite impulse response (FIR) filters, but at a lower model order, which is important when considering implementation on embedded systems. However, the filter banks require prior knowledge about the resonance frequencies and damping of the structure. This knowledge can be supposed to be of limited precision, since in many practical systems, uncertainties in the structural parameters exist. In this work, a procedure using a number of training systems to find the fixed parameters for the filter banks is applied. The effect of uncertainties in the prior knowledge on the model error is examined both with a basic example and in an experiment. Furthermore, the possibilities to compensate for the imprecise prior knowledge by a higher filter order are investigated. Also comparisons with FIR filters are implemented in order to assess the possible advantages of the orthonormal filter banks. Numerical and experimental investigations show that significantly lower computational effort can be reached by the filter banks under certain conditions. (paper)
Orthonormal filters for identification in active control systems
Mayer, Dirk
2015-12-01
Many active noise and vibration control systems require models of the control paths. When the controlled system changes slightly over time, adaptive digital filters for the identification of the models are useful. This paper aims at the investigation of a special class of adaptive digital filters: orthonormal filter banks possess the robust and simple adaptation of the widely applied finite impulse response (FIR) filters, but at a lower model order, which is important when considering implementation on embedded systems. However, the filter banks require prior knowledge about the resonance frequencies and damping of the structure. This knowledge can be supposed to be of limited precision, since in many practical systems, uncertainties in the structural parameters exist. In this work, a procedure using a number of training systems to find the fixed parameters for the filter banks is applied. The effect of uncertainties in the prior knowledge on the model error is examined both with a basic example and in an experiment. Furthermore, the possibilities to compensate for the imprecise prior knowledge by a higher filter order are investigated. Also comparisons with FIR filters are implemented in order to assess the possible advantages of the orthonormal filter banks. Numerical and experimental investigations show that significantly lower computational effort can be reached by the filter banks under certain conditions.
Experimental evaluation of a modal parameter based system identification procedure
Yu, Minli; Feng, Ningsheng; Hahn, Eric J.
2016-02-01
Correct modelling of the foundation of a rotor bearing foundation system (RBFS) is an invaluable asset for the balancing and efficient running of turbomachinery. Numerical experiments have shown that a modal parameter based identification approach could be feasible for this purpose but there is a lack of experimental verification of the suitability of such a modal approach for even the simplest systems. In this paper the approach is tested on a simple experimental rig comprising a clamped horizontal bar with lumped masses. It is shown that apart from damping, the proposed approach can identify reasonably accurately the relevant modal parameters of the rig; and that the resulting equivalent system can predict reasonably well the frequency response of the rig. Hence, the proposed approach shows promise but further testing is required, since application to identifying the foundation of an RBFS involves the additional problem of accurately obtaining the force excitation from motion measurements.
Intelligent system for accident identification in NPP
International Nuclear Information System (INIS)
Hernandez, J.L.
1998-01-01
Accidental situations in NPP are great concern for operators, the facility, regulatory bodies and the environmental. This work proposes a design of intelligent system aimed to assist the operator in the process of decision making initiator events with higher relative contribution to the reactor core damage occur. The intelligent System uses the results of the pre-operational Probabilistic safety Assessment and the Thermal hydraulic Safety Analysis of the NPP Juragua as source for building its knowledge base. The nucleus of the system is presented as a design of an intelligent hybrid from the combination of the artificial intelligence techniques fuzzy logic and artificial neural networks. The system works with variables from the process of the first circuit, second circuit and the containment and it is presented as a model for the integration of safety analyses in the process of decision making by the operator when tackling with accidental situations
Lightning peak current estimation using a system identification approach
Wern, T. L. T.; Mukerjee, R. N.
2006-01-01
A system identification-based lightning peak current estimation algorithm using upper-air radiosonde observations is developed. The preceding convective and precipitative process leading to thunder cloud formation followed by the cloud electrification and the leader processes together with return stroke and the discharge process, is identified by considering it as a deterministic dynamic system, whose undisturbed and unmeasurable output signal the lightning peak current, is contaminated with a stochastic disturbance. The model parameters determined thus, are used to predict the likely temporal lightning return stroke peak current magnitudes. Two alternative parametric estimation models namely Autoregressive with Exogeneous Input (ARX) and Autoregressive with Moving-Average Exogeneous Input (ARMAX) are used to estimate model parameters of the pilot study area and predict the likely lightning return stroke peak current in each case. The relative performances of the models are compared to determine the best model for application in 12-hour and 24-hour ahead predictions. For a short-term (12 hour) prediction, ARMAX2921 giving a best fit of 78.8429% turns out to be the most suitable model. For a longer (24 hour) prediction, the ARX291, giving a best fit of 75.0181% emerges to be the suitable model. These preliminary results indicate that lightning peak current may be estimated to a good performance using upper-air radiosonde observations.
Modelling and identification of a six axes industrial robot
Waiboer, R.R.; Aarts, Ronald G.K.M.; Jonker, Jan B.; ASME,
2005-01-01
This paper deals with the modelling and identification of a six axes industrial St ¨aubli RX90 robot. A non-linear finite element method is used to generate the dynamic equations of motion in a form suitable for both simulation and identification. The latter requires that the equations of motion are
Experimental Modeling of Dynamic Systems
DEFF Research Database (Denmark)
Knudsen, Morten Haack
2006-01-01
An engineering course, Simulation and Experimental Modeling, has been developed that is based on a method for direct estimation of physical parameters in dynamic systems. Compared with classical system identification, the method appears to be easier to understand, apply, and combine with physical...... insight. It is based on a sensitivity approach that is useful for choice of model structure, for experiment design, and for accuracy verification. The method is implemented in the Matlab toolkit Senstools. The method and the presentation have been developed with generally preferred learning styles in mind...
International Nuclear Information System (INIS)
Chen, Zhihuan; Yuan, Xiaohui; Tian, Hao; Ji, Bin
2014-01-01
Highlights: • We propose an improved gravitational search algorithm (IGSA). • IGSA is applied to parameter identification of water turbine regulation system (WTRS). • WTRS is modeled by considering the impact of turbine speed on torque and water flow. • Weighted objective function strategy is applied to parameter identification of WTRS. - Abstract: Parameter identification of water turbine regulation system (WTRS) is crucial in precise modeling hydropower generating unit (HGU) and provides support for the adaptive control and stability analysis of power system. In this paper, an improved gravitational search algorithm (IGSA) is proposed and applied to solve the identification problem for WTRS system under load and no-load running conditions. This newly algorithm which is based on standard gravitational search algorithm (GSA) accelerates convergence speed with combination of the search strategy of particle swarm optimization and elastic-ball method. Chaotic mutation which is devised to stepping out the local optimal with a certain probability is also added into the algorithm to avoid premature. Furthermore, a new kind of model associated to the engineering practices is built and analyzed in the simulation tests. An illustrative example for parameter identification of WTRS is used to verify the feasibility and effectiveness of the proposed IGSA, as compared with standard GSA and particle swarm optimization in terms of parameter identification accuracy and convergence speed. The simulation results show that IGSA performs best for all identification indicators
The NNSYSID Toolbox - A MATLAB Toolbox for System Identification with Neural Networks
Nørgård, Peter Magnus; Ravn, Ole; Hansen, Lars Kai; Poulsen, Niels Kjølstad
1996-01-01
To assist the identification of nonlinear dynamic systems, a set of tools has been developed for the MATLAB(R) environment. The tools include a number of different model structures, highly effective training algorithms, functions for validating trained networks, and pruning algorithms for determination of optimal network architectures. The toolbox should be regarded as a nonlinear extension to the system identification toolbox provided by The MathWorks, Inc. This paper gives a brief overview ...
Robust model identification applied to type 1diabetes
DEFF Research Database (Denmark)
Finan, Daniel Aaron; Jørgensen, John Bagterp; Poulsen, Niels Kjølstad
2010-01-01
In many realistic applications, process noise is known to be neither white nor normally distributed. When identifying models in these cases, it may be more effective to minimize a different penalty function than the standard sum of squared errors (as in a least-squares identification method......). This paper investigates model identification based on two different penalty functions: the 1-norm of the prediction errors and a Huber-type penalty function. For data characteristic of some realistic applications, model identification based on these latter two penalty functions is shown to result in more...... accurate estimates of parameters than the standard least-squares solution, and more accurate model predictions for test data. The identification techniques are demonstrated on a simple toy problem as well as a physiological model of type 1 diabetes....
System identification and robust control of a portable proton exchange membrane full-cell system
Energy Technology Data Exchange (ETDEWEB)
Wang, Fu-Cheng; Yang, Yee-Pien; Huang, Chi-Wei; Chen, Hsuan-Tsung [Department of Mechanical Engineering, National Taiwan University, Taipei (Taiwan); Chang, Hsin-Ping [Chung Shan Institute of Science and Technology (CSIST), Armaments Bureau, M.N.D (Taiwan)
2007-02-10
This paper will discuss the application of system identification techniques and robust control strategies to a proton exchange membrane fuel-cell system. The fuel-cell system's dynamic behaviour is influenced by many factors, such as the reaction mechanism, pressure, flow-rate, composition and temperature change, and is inherently non-linear and time varying. From a system point of view, however, the system can be modelled as a two-input, two-output linear time-invariant system whose inputs are hydrogen and air flow rates, and whose outputs are cell voltage and current. On the other hand, the system's non-linearities and time-varying characteristics can be regarded as system uncertainties and disturbances that are treated by the designed robust controllers. This paper is comprised of three parts. First, system identification techniques were adopted to model the system's transfer functions. Second, the H{sub {infinity}} robust control strategies were applied to stabilise the system. Finally, the system's stability and performance were compromised by introducing weighting functions to the controller's design. From the experimental results, the designed H{sub {infinity}} robust controllers were deemed effective. (author)
Task Characterisation and Cross-Platform Programming Through System Identification
Directory of Open Access Journals (Sweden)
Theocharis Kyriacou
2005-12-01
Full Text Available Developing robust and reliable control code for autonomous mobile robots is difficult, because the interaction between a physical robot and the environment is highly complex, it is subject to noise and variation, and therefore partly unpredictable. This means that to date it is not possible to predict robot behaviour, based on theoretical models. Instead, current methods to develop robot control code still require a substantial trial-and-error component to the software design process. Such iterative refinement could be reduced, we argue, if a more profound theoretical understanding of robot-environment interaction existed. In this paper, we therefore present a modelling method that generates a faithful model of a robot's interaction with its environment, based on data logged while observing a physical robot's behaviour. Because this modelling method — nonlinear modelling using polynomials — is commonly used in the engineering discipline of system identification, we refer to it here as “robot identification”. We show in this paper that using robot identification to obtain a computer model of robot-environment interaction offers several distinct advantages: Very compact representations (one-line programs of the robot control program are generated The model can be analysed, for example through sensitivity analysis, leading to a better understanding of the essential parameters underlying the robot's behaviour, and The generated, compact robot code can be used for cross-platform robot programming, allowing fast transfer of robot code from one type of robot to another. We demonstrate these points through experiments with a Magellan Pro and a Nomad 200 mobile robot.
Analysis of Offshore Knuckle Boom Crane - Part One: Modeling and Parameter Identification
Directory of Open Access Journals (Sweden)
Morten K. Bak
2013-10-01
Full Text Available This paper presents an extensive model of a knuckle boom crane used for pipe handling on offshore drilling rigs. The mechanical system is modeled as a multi-body system and includes the structural flexibility and damping. The motion control system model includes the main components of the crane's electro-hydraulic actuation system. For this a novel black-box model for counterbalance valves is presented, which uses two different pressure ratios to compute the flow through the valve. Experimental data and parameter identification, based on both numerical optimization and manual tuning, are used to verify the crane model. The demonstrated modeling and parameter identification techniques target the system engineer and takes into account the limited access to component data normally encountered by engineers working with design of hydraulic systems.
Energy Technology Data Exchange (ETDEWEB)
Jalashgar, A.
1997-05-01
The main subject of this thesis is to identify hidden failures in process control systems by developing and using a function-oriented system analysis method. Qualitative failure analysis and the characteristics of the classical failure analysis methods and function-oriented modelling methods are covered. The general limitations of the methods in connection with the identification and representation of hidden failures are discussed. The discussion has led to the justification of developing and using a function-oriented system analysis method to identify and represent the capabilities of the system components, which realize different sets of functions in connection with different sets of goals that the system must achieve. A terminology is introduced to define the basic aspects of technical systems including goals, functions, capabilities and physical structure. A function-oriented system analysis method using this terminology and a tailored combination of the two function-oriented modelling approaches, is also introduced. It is then explained how the method can be applied in the identification and representation of hidden failures. The building blocks of a knowledge-oriented system to perform the diagnosis on the basis of the developed method are equally described. A prototype of the knowledge-based system is developed to demonstrate the applicability of the function-oriented system analysis method and the knowledge-based system. The prototype is implemented within the object-oriented software environment G2. (au) 65 ills., 32 refs.
Systematic identification of crystallization kinetics within a generic modelling framework
DEFF Research Database (Denmark)
Abdul Samad, Noor Asma Fazli Bin; Meisler, Kresten Troelstrup; Gernaey, Krist
2012-01-01
A systematic development of constitutive models within a generic modelling framework has been developed for use in design, analysis and simulation of crystallization operations. The framework contains a tool for model identification connected with a generic crystallizer modelling tool-box, a tool...
Optimal Sensor Networks Scheduling in Identification of Distributed Parameter Systems
Patan, Maciej
2012-01-01
Sensor networks have recently come into prominence because they hold the potential to revolutionize a wide spectrum of both civilian and military applications. An ingenious characteristic of sensor networks is the distributed nature of data acquisition. Therefore they seem to be ideally prepared for the task of monitoring processes with spatio-temporal dynamics which constitute one of most general and important classes of systems in modelling of the real-world phenomena. It is clear that careful deployment and activation of sensor nodes are critical for collecting the most valuable information from the observed environment. Optimal Sensor Network Scheduling in Identification of Distributed Parameter Systems discusses the characteristic features of the sensor scheduling problem, analyzes classical and recent approaches, and proposes a wide range of original solutions, especially dedicated for networks with mobile and scanning nodes. Both researchers and practitioners will find the case studies, the proposed al...
Reliability of system identification technique in super high-rise building
Directory of Open Access Journals (Sweden)
Ayumi eIkeda
2015-07-01
Full Text Available A smart physical-parameter based system identification method has been proposed in the previous paper. This method deals with time-variant nonparametric identification of natural frequencies and modal damping ratios using ARX (Auto-Regressive eXogenous models and has been applied to high-rise buildings during the 2011 off the Pacific coast of Tohoku earthquake. In this perspective article, the current state of knowledge in this class of system identification methods is explained briefly and the reliability of this smart method is discussed through the comparison with the result by a more confident technique.
Control Valve Stiction Identification, Modelling, Quantification and Control - A Review
Directory of Open Access Journals (Sweden)
Srinivasan Arumugam
2011-09-01
Full Text Available Most of the processes found in process industries exhibit undesirable nonlinearity due to backlash, saturation, hysteresis, stiction (friction, dead-zone and stuck-fault existing in control valves. The control valve is the actuator for most process control loops and, as the only moving part in the loop, its function is to implement the control action. If the control valve malfunctions, the performance of the control loop is likely to deteriorate, no matter how good the controller is. Commonly encountered control valve problems include nonlinear responses to the demand signal caused by effects such as stiction, dead-band or saturation. Because of these problems, the control loop may be oscillatory, which in turn may cause oscillations in many process variables causing a range of operational problems including increased valve wear. Understanding nonlinear behaviour of control valves in order to maintain the quality of the end products in the industry, this review article surveys the identification, modelling, estimation and design of dynamic models of stiction nonlinearity and providing appropriate controller to obtain optimum responses of the process. The primary objective of this work is to present state-of-art-review of common nonlinear problems associated with mechanical and chemical processes for encouraging researchers, practicing engineers working in this field, so that readers can invent their goals for future research work on nonlinear systems identification and control.
Pescara benchmark: overview of modelling, testing and identification
Energy Technology Data Exchange (ETDEWEB)
Bellino, A; Garibaldi, L; Marchesiello, S [Dynamics/Identification Research Group, Department of Mechanics, Politecnico of Torino, C.so Duca degli Abruzzi 24, 10129 Torino (Italy); Brancaleoni, F; Gabriele, S; Spina, D [Department of Structures, University ' Roma Tre' of Rome, Via C. Segre 4/6, 00146 Rome (Italy); Bregant, L [Department of Mechanical and Marine Engineering , University of Trieste, Via Valerio 8, 34127 Trieste (Italy); Carminelli, A; Catania, G; Sorrentino, S [Diem Department of Mechanical Engineering, University of Bologna, Viale Risorgimento 2, 40136 Bologna (Italy); Di Evangelista, A; Valente, C; Zuccarino, L, E-mail: c.valente@unich.it [Department of Engineering, University ' G. d' Annunzio' of Chieti-Pescara Viale Pindaro 42, 65127 Pescara (Italy)
2011-07-19
The 'Pescara benchmark' is part of the national research project 'BriViDi' (BRIdge VIbrations and DIagnosis) supported by the Italian Ministero dell'Universita e Ricerca. The project is aimed at developing an integrated methodology for the structural health evaluation of railway r/c, p/c bridges. The methodology should provide for applicability in operating conditions, easy data acquisition through common industrial instrumentation, robustness and reliability against structural and environmental uncertainties. The Pescara benchmark consisted in lab tests to get a consistent and large experimental data base and subsequent data processing. Special tests were devised to simulate the train transit effects in actual field conditions. Prestressed concrete beams of current industrial production both sound and damaged at various severity corrosion levels were tested. The results were collected either in a deterministic setting and in a form suitable to deal with experimental uncertainties. Damage identification was split in two approaches: with or without a reference model. In the first case f.e. models were used in conjunction with non conventional updating techniques. In the second case, specialized output-only identification techniques capable to deal with time-variant and possibly non linear systems were developed. The lab tests allowed validating the above approaches and the performances of classical modal based damage indicators.
Pescara benchmark: overview of modelling, testing and identification
Bellino, A.; Brancaleoni, F.; Bregant, L.; Carminelli, A.; Catania, G.; Di Evangelista, A.; Gabriele, S.; Garibaldi, L.; Marchesiello, S.; Sorrentino, S.; Spina, D.; Valente, C.; Zuccarino, L.
2011-07-01
The `Pescara benchmark' is part of the national research project `BriViDi' (BRIdge VIbrations and DIagnosis) supported by the Italian Ministero dell'Universitá e Ricerca. The project is aimed at developing an integrated methodology for the structural health evaluation of railway r/c, p/c bridges. The methodology should provide for applicability in operating conditions, easy data acquisition through common industrial instrumentation, robustness and reliability against structural and environmental uncertainties. The Pescara benchmark consisted in lab tests to get a consistent and large experimental data base and subsequent data processing. Special tests were devised to simulate the train transit effects in actual field conditions. Prestressed concrete beams of current industrial production both sound and damaged at various severity corrosion levels were tested. The results were collected either in a deterministic setting and in a form suitable to deal with experimental uncertainties. Damage identification was split in two approaches: with or without a reference model. In the first case f.e. models were used in conjunction with non conventional updating techniques. In the second case, specialized output-only identification techniques capable to deal with time-variant and possibly non linear systems were developed. The lab tests allowed validating the above approaches and the performances of classical modal based damage indicators.
Subspace identification of Hammer stein models using support vector machines
International Nuclear Information System (INIS)
Al-Dhaifallah, Mujahed
2011-01-01
System identification is the art of finding mathematical tools and algorithms that build an appropriate mathematical model of a system from measured input and output data. Hammerstein model, consisting of a memoryless nonlinearity followed by a dynamic linear element, is often a good trade-off as it can represent some dynamic nonlinear systems very accurately, but is nonetheless quite simple. Moreover, the extensive knowledge about LTI system representations can be applied to the dynamic linear block. On the other hand, finding an effective representation for the nonlinearity is an active area of research. Recently, support vector machines (SVMs) and least squares support vector machines (LS-SVMs) have demonstrated powerful abilities in approximating linear and nonlinear functions. In contrast with other approximation methods, SVMs do not require a-priori structural information. Furthermore, there are well established methods with guaranteed convergence (ordinary least squares, quadratic programming) for fitting LS-SVMs and SVMs. The general objective of this research is to develop new subspace algorithms for Hammerstein systems based on SVM regression.
New methodology for a person identification system
Indian Academy of Sciences (India)
Abstract. Reliable person identification is a key factor for any safety measure. Unlike other biometrics such as the palm, retina, gait, face and fingerprints, the characteristic of the iris is stable in a person's lifetime. Iris patterns are chaotically distributed and well suited for recognizing persons throughout their lifetime with.
Advanced 3D Object Identification System, Phase I
National Aeronautics and Space Administration — Optra will build an Advanced 3D Object Identification System utilizing three or more high resolution imagers spaced around a launch platform. Data from each imager...
Energy Technology Data Exchange (ETDEWEB)
Bhatt, Uma S. [Univ. of Alaska, Fairbanks, AK (United States). Dept. of Atmospheric Sciences; Wackerbauer, Renate [Univ. of Alaska, Fairbanks, AK (United States). Dept. of Physics; Polyakov, Igor V. [Univ. of Alaska, Fairbanks, AK (United States). Dept. of Atmospheric Sciences; Newman, David E. [Univ. of Alaska, Fairbanks, AK (United States). Dept. of Physics; Sanchez, Raul E. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Fusion Energy Division; Univ. Carlos III de Madrid (Spain)
2015-11-13
The goal of this research was to apply fractional and non-linear analysis techniques in order to develop a more complete characterization of climate change and variability for the oceanic, sea ice and atmospheric components of the Earth System. This research applied two measures of dynamical characteristics of time series, the R/S method of calculating the Hurst exponent and Renyi entropy, to observational and modeled climate data in order to evaluate how well climate models capture the long-term dynamics evident in observations. Fractional diffusion analysis was applied to ARGO ocean buoy data to quantify ocean transport. Self organized maps were applied to North Pacific sea level pressure and analyzed in ways to improve seasonal predictability for Alaska fire weather. This body of research shows that these methods can be used to evaluate climate models and shed light on climate mechanisms (i.e., understanding why something happens). With further research, these methods show promise for improving seasonal to longer time scale forecasts of climate.
Closed-loop System Identification with New Sensors
DEFF Research Database (Denmark)
Bendtsen, Jan Dimon; Trangbæk, K; Stoustrup, Jakob
2008-01-01
This paper deals with system identification of new system dynamics revealed by online introduction of new sensors in existing multi-variable linear control systems. The so-called "Hansen Scheme" utilises the dual Youla-Kucera parameterisation of all systems stabilised by a given linear controller...
A single mode method for the analysis and identification of nonlinear MDOF systems
Huang, Liping; Iwan, W. D.
In order to apply mode approach to describe a nonlinear system, the concept of modal response of nonlinear systems is examined, and an amplitude-dependent modal model is presented based on an analysis of a single mode of response. The effectiveness of this model is examined under different types and various levels of excitation. A corresponding identification procedure for cubic systems is proposed and applied to the analysis of a 3DOF soltening nonlinear system.
Modelling and control of dynamic systems using gaussian process models
Kocijan, Juš
2016-01-01
This monograph opens up new horizons for engineers and researchers in academia and in industry dealing with or interested in new developments in the field of system identification and control. It emphasizes guidelines for working solutions and practical advice for their implementation rather than the theoretical background of Gaussian process (GP) models. The book demonstrates the potential of this recent development in probabilistic machine-learning methods and gives the reader an intuitive understanding of the topic. The current state of the art is treated along with possible future directions for research. Systems control design relies on mathematical models and these may be developed from measurement data. This process of system identification, when based on GP models, can play an integral part of control design in data-based control and its description as such is an essential aspect of the text. The background of GP regression is introduced first with system identification and incorporation of prior know...
Stochastic Models in the Identification Process
Czech Academy of Sciences Publication Activity Database
Slovák, Dalibor; Zvárová, Jana
2011-01-01
Roč. 7, č. 1 (2011), s. 44-50 ISSN 1801-5603 R&D Projects: GA MŠk(CZ) 1M06014 Institutional research plan: CEZ:AV0Z10300504 Keywords : identification process * weight-of evidence formula * coancestry coefficient * beta- binomial sampling formula * DNA mixtures Subject RIV: IN - Informatics, Computer Science http://www.ejbi.eu/images/2011-1/Slovak_en.pdf
Padfield, G. D.; Duval, R. K.
1982-01-01
A set of results on rotorcraft system identification is described. Flight measurements collected on an experimental Puma helicopter are reviewed and some notable characteristics highlighted. Following a brief review of previous work in rotorcraft system identification, the results of state estimation and model structure estimation processes applied to the Puma data are presented. The results, which were obtained using NASA developed software, are compared with theoretical predictions of roll, yaw and pitching moment derivatives for a 6 degree of freedom model structure. Anomalies are reported. The theoretical methods used are described. A framework for reduced order modelling is outlined.
Hall, W. E., Jr.; Gupta, N. K.; Hansen, R. S.
1978-01-01
An integrated approach to rotorcraft system identification is described. This approach consists of sequential application of (1) data filtering to estimate states of the system and sensor errors, (2) model structure estimation to isolate significant model effects, and (3) parameter identification to quantify the coefficient of the model. An input design algorithm is described which can be used to design control inputs which maximize parameter estimation accuracy. Details of each aspect of the rotorcraft identification approach are given. Examples of both simulated and actual flight data processing are given to illustrate each phase of processing. The procedure is shown to provide means of calibrating sensor errors in flight data, quantifying high order state variable models from the flight data, and consequently computing related stability and control design models.
Closed-loop System Identification with New Sensors
DEFF Research Database (Denmark)
Bendtsen, Jan Dimon; Trangbæk, K; Stoustrup, Jakob
2008-01-01
This paper deals with system identification of new system dynamics revealed by online introduction of new sensors in existing multi-variable linear control systems. The so-called "Hansen Scheme" utilises the dual Youla-Kucera parameterisation of all systems stabilised by a given linear controller...... to transform closed-loop system identification problems into open-loop-like problems. We show that this scheme can be formally extended to accomodate extra sensors in a nice way. The approach is illustrated on a simple simulation example....
Particle identification system based on dense aerogel
Energy Technology Data Exchange (ETDEWEB)
Barnyakov, A.Yu. [Budker Institute of Nuclear Physics, 11, akademika Lavrentieva prospect, Novosibirsk 630090 (Russian Federation); Barnyakov, M.Yu. [Budker Institute of Nuclear Physics, 11, akademika Lavrentieva prospect, Novosibirsk 630090 (Russian Federation); Novosibirsk State Technical University, 20, Karl Marx prospect, Novosibirsk, 630092 (Russian Federation); Beloborodov, K.I., E-mail: K.I.Beloborodov@inp.nsk.su [Budker Institute of Nuclear Physics, 11, akademika Lavrentieva prospect, Novosibirsk 630090 (Russian Federation); Novosibirsk State University, 2, Pirogova Street, Novosibirsk 630090 (Russian Federation); Bobrovnikov, V.S.; Buzykaev, A.R. [Budker Institute of Nuclear Physics, 11, akademika Lavrentieva prospect, Novosibirsk 630090 (Russian Federation); Danilyuk, A.F. [Boreskov Institute of Catalysis, 5, akademika Lavrentieva prospect, Novosibirsk 630090 (Russian Federation); Golubev, V.B. [Budker Institute of Nuclear Physics, 11, akademika Lavrentieva prospect, Novosibirsk 630090 (Russian Federation); Novosibirsk State University, 2, Pirogova Street, Novosibirsk 630090 (Russian Federation); Gulevich, V.V. [Budker Institute of Nuclear Physics, 11, akademika Lavrentieva prospect, Novosibirsk 630090 (Russian Federation); Kononov, S.A.; Kravchenko, E.A. [Budker Institute of Nuclear Physics, 11, akademika Lavrentieva prospect, Novosibirsk 630090 (Russian Federation); Novosibirsk State University, 2, Pirogova Street, Novosibirsk 630090 (Russian Federation); Onuchin, A.P.; Martin, K.A. [Budker Institute of Nuclear Physics, 11, akademika Lavrentieva prospect, Novosibirsk 630090 (Russian Federation); Novosibirsk State Technical University, 20, Karl Marx prospect, Novosibirsk, 630092 (Russian Federation); Serednyakov, S.I. [Budker Institute of Nuclear Physics, 11, akademika Lavrentieva prospect, Novosibirsk 630090 (Russian Federation); Novosibirsk State University, 2, Pirogova Street, Novosibirsk 630090 (Russian Federation); and others
2013-12-21
A threshold Cherenkov counter based on dense aerogel with refraction index n=1.13 is described. This counter is used for kaon identification at momenta below 1 GeV/c in the SND detector, which takes data at the VEPP-2000 e{sup +}e{sup −} collider. The results of measurements of the counter efficiency using electrons, muons, pions, and kaons produced in e{sup +}e{sup −} annihilation are presented.
[Rapid identification system for seedlings of medicinal Chrysanthemum morifolium].
Mao, Pengfei; Guo, Qiaosheng; Wang, Tao; Shao, Qingsong
2012-04-01
To achieve the rapid identification for seedlings of medicinal Chrysanthemum morifolium, the discriminant equation was established and the software for rapid identification was designed. Leaf structure of medicinal Chrysanthemum of 12 cultivars was analyzed to establish the discriminant equation based on variance analysis and discriminant analysis. On this basis, the identification program and software (based on the python language) were designed. Through the analysis of variance and multiple comparisons for the 11 leaf parameter index data of 12 different cultivars, it was found that that the leaf parameters were significant different from each other and reached significant levels. The discriminant equation and the rapid identification software were set up based on the analysis of various indicators. The rapid identification system of seedlings of medicinal Chrysanthemum could be achieved through the establishment of discriminant equation combined with computer technology.
CEAI: CCM-based email authorship identification model
Directory of Open Access Journals (Sweden)
Sarwat Nizamani
2013-11-01
Full Text Available In this paper we present a model for email authorship identification (EAI by employing a Cluster-based Classification (CCM technique. Traditionally, stylometric features have been successfully employed in various authorship analysis tasks; we extend the traditional feature set to include some more interesting and effective features for email authorship identification (e.g., the last punctuation mark used in an email, the tendency of an author to use capitalization at the start of an email, or the punctuation after a greeting or farewell. We also included Info Gain feature selection based content features. It is observed that the use of such features in the authorship identification process has a positive impact on the accuracy of the authorship identification task. We performed experiments to justify our arguments and compared the results with other base line models. Experimental results reveal that the proposed CCM-based email authorship identification model, along with the proposed feature set, outperforms the state-of-the-art support vector machine (SVM-based models, as well as the models proposed by Iqbal et al. (2010, 2013 [1,2]. The proposed model attains an accuracy rate of 94% for 10 authors, 89% for 25 authors, and 81% for 50 authors, respectively on Enron dataset, while 89.5% accuracy has been achieved on authors’ constructed real email dataset. The results on Enron dataset have been achieved on quite a large number of authors as compared to the models proposed by Iqbal et al. [1,2].
A portable system for nuclear, chemical agent, and explosives identification
International Nuclear Information System (INIS)
Parker, W.E.; Buckley, W.M.; Kreek, S.A.; Mauger, G.J.; Lavietes, A.D.; Dougan, A.D.; Caffrey, A.J.
2001-01-01
The FRIS/PINS hybrid integrates the LLNL-developed Field Radionuclide Identification System (FRIS) with the INEEL-developed Portable Isotopic Neutron Spectroscopy (PINS) chemical assay system to yield a combined general radioisotope, special nuclear material, and chemical weapons/explosives detection and identification system. The PINS system uses a neutron source and a high-purity germanium γ-ray detector. The FRIS system uses an electromechanically cooled germanium detector and its own analysis software to detect and identify special nuclear material and other radioisotopes. The FRIS/PINS combined system also uses the electromechanically-cooled germanium detector. There is no other currently available integrated technology that can combine a prompt-gamma neutron-activation analysis capability for CWE with a passive radioisotope measurement and identification capability for special nuclear material
A Portable System for Nuclear, Chemical Agent and Explosives Identification
International Nuclear Information System (INIS)
Parker, W.E.; Buckley, W.M.; Kreek, S.A.; Caffrey, A.J.; Mauger, G.J.; Lavietes, A.D.; Dougan, A.D.
2000-01-01
The FRIS/PINS hybrid integrates the LLNL-developed Field Radionuclide Identification System (FRIS) with the INEEL-developed Portable Isotopic Neutron Spectroscopy (PINS) chemical assay system to yield a combined general radioisotope, special nuclear material, and chemical weapons/explosives detection and identification system. The PINS system uses a neutron source and a high-purity germanium γ-ray detector. The FRIS system uses an electrochemically cooled germanium detector and its own analysis software to detect and identify special nuclear material and other radioisotopes. The FRIS/PINS combined system also uses the electromechanically-cooled germanium detector. There is no other currently available integrated technology that can combine an active neutron interrogation and analysis capability for CWE with a passive radioisotope measurement and identification capability for special nuclear material
Robust Kernel Clustering Algorithm for Nonlinear System Identification
Directory of Open Access Journals (Sweden)
Mohamed Bouzbida
2017-01-01
Full Text Available In engineering field, it is necessary to know the model of the real nonlinear systems to ensure its control and supervision; in this context, fuzzy modeling and especially the Takagi-Sugeno fuzzy model has drawn the attention of several researchers in recent decades owing to their potential to approximate nonlinear behavior. To identify the parameters of Takagi-Sugeno fuzzy model several clustering algorithms are developed such as the Fuzzy C-Means (FCM algorithm, Possibilistic C-Means (PCM algorithm, and Possibilistic Fuzzy C-Means (PFCM algorithm. This paper presents a new clustering algorithm for Takagi-Sugeno fuzzy model identification. Our proposed algorithm called Robust Kernel Possibilistic Fuzzy C-Means (RKPFCM algorithm is an extension of the PFCM algorithm based on kernel method, where the Euclidean distance used the robust hyper tangent kernel function. The proposed algorithm can solve the nonlinear separable problems found by FCM, PCM, and PFCM algorithms. Then an optimization method using the Particle Swarm Optimization (PSO method combined with the RKPFCM algorithm is presented to overcome the convergence to a local minimum of the objective function. Finally, validation results of examples are given to demonstrate the effectiveness, practicality, and robustness of our proposed algorithm in stochastic environment.
Identification of the Skirt Piled Gullfaks C Gravity Platform using ARMAV Models
DEFF Research Database (Denmark)
Kirkegaard, Poul Henning; Andersen, P.; Brincker, Rune
This paper presents the results from the system identification of the Gullfaks C gravity offshore platform excited by natural loads. The paper describes how modal parameters and mode shapes can be estimated by use of ARMAV models. The results estimated by an ARMAV model are compared with results ...
Identification of the Skirt Piled Gullfaks C Gravity Platform using ARMAV Models
DEFF Research Database (Denmark)
Kirkegaard, Poul Henning; Andersen, P.; Brincker, Rune
1996-01-01
This paper presents the results from the system identification of the Gullfaks C gravity offshore platform excited by natural loads. The paper describes how modal parameters and mode shapes can be estimated by use of ARMAV models. The results estimated by an ARMAV model are compared with results ...
Sonneveld, P.; Mulder, J.A.
1981-01-01
The distribution kinetics of adriamycin in the rat were analyzed by using a multicompartment mathematical model. The set of a priori unknown model parameters, the drug rate constants, were estimated from 48 hr multicompartment drug distribution data by applying multivariate system identification
NNSYSID and NNCTRL Tools for system identification and control with neural networks
DEFF Research Database (Denmark)
Nørgaard, Magnus; Ravn, Ole; Poulsen, Niels Kjølstad
2001-01-01
choose among several designs such as direct inverse control, internal model control, nonlinear feedforward, feedback linearisation, optimal control, gain scheduling based on instantaneous linearisation of neural network models and nonlinear model predictive control. This article gives an overview......Two toolsets for use with MATLAB have been developed: the neural network based system identification toolbox (NNSYSID) and the neural network based control system design toolkit (NNCTRL). The NNSYSID toolbox has been designed to assist identification of nonlinear dynamic systems. It contains...... a number of nonlinear model structures based on neural networks, effective training algorithms and tools for model validation and model structure selection. The NNCTRL toolkit is an add-on to NNSYSID and provides tools for design and simulation of control systems based on neural networks. The user can...
NNSYSID and NNCTRL Tools for system identification and control with neural networks
DEFF Research Database (Denmark)
Nørgaard, Magnus; Ravn, Ole; Poulsen, Niels Kjølstad
2001-01-01
Two toolsets for use with MATLAB have been developed: the neural network based system identification toolbox (NNSYSID) and the neural network based control system design toolkit (NNCTRL). The NNSYSID toolbox has been designed to assist identification of nonlinear dynamic systems. It contains...... a number of nonlinear model structures based on neural networks, effective training algorithms and tools for model validation and model structure selection. The NNCTRL toolkit is an add-on to NNSYSID and provides tools for design and simulation of control systems based on neural networks. The user can...... choose among several designs such as direct inverse control, internal model control, nonlinear feedforward, feedback linearisation, optimal control, gain scheduling based on instantaneous linearisation of neural network models and nonlinear model predictive control. This article gives an overview...
Arakawa, Hiroyuki; Blanchard, D Caroline; Blanchard, Robert J
2007-01-10
Deficits in social interaction are primary characteristics of autism, which has strong genetic components. Genetically manipulated mouse models may provide a useful research tool to advance the investigation of genes associated with autism. To identify these genes using mouse models, behavioral assays for social relationships in the background strains must be developed. The present study examined colony formation in groups of one male and three female mice (Experiment 1) and, groups of three male mice (Experiment 2) of the C57BL/6J strain in a semi-natural visible burrow system. For adult mixed-sex colonies, 4-h observations during both the dark and light cycles for 15 days demonstrated day-dependent increases in huddling together in the chamber accompanied by decreased frequencies of active social behaviors. Sequential analyses of social interactions indicated that approaches to the back of the approached animal typically elicited flight, while approaches to the front of the approached animal failed to do so. This was seen for female to female, and for female to male approaches, as well as male to female approaches, strongly counterindicating a view that rear approach/flight specifically reflects female responsivirity to unwanted male sexual approach. For adult male colonies, similar protocols found that these social behaviors were similar to those of adult mixed-sex colonies. These findings suggest two potentially useful measures of eusocial behavior in mice, of possible value for genetic mouse models of autism; that is, huddling together and approaches to the front but not the back, of conspecifics.
Identification of bilinear systems using differential evolution algorithm
Indian Academy of Sciences (India)
Abstract. In this work, a novel identification method based on differential evolu- tion algorithm has been applied to bilinear systems and its performance has been compared to that of genetic algorithm. Box–Jenkins system and different type bilinear systems have been identified using differential evolution and genetic ...
Online identification of continuous bimodal and trimodal piecewise affine systems
Le, Q.T.; van den Boom, A.J.J.; Baldi, S.; Rantzer, Anders; Bagterp Jørgensen, John; Stoustrup, Jakob
2016-01-01
This paper investigates the identification of continuous piecewise affine systems in state space form with jointly unknown partition and subsystem matrices. The partition of the system is generated by the so-called centers. By representing continuous piecewise affine systems in the max-form and
Proceedings of the IASTED conference on modelling, identification and control : MIC 2010
Energy Technology Data Exchange (ETDEWEB)
Hangos, K. (ed.)
2010-03-09
This conference on energy and power systems provided a forum to discuss the latest research and innovative technologies related to power system modelling, identification and control. It was divided into the following tracks: applications in vehicle and transportation systems; economic, business and social applications; process and energy systems; mechanical and electrical applications; stability and controller design; mechatronics and robotics; identification, estimation and simulation; and control theory. The conference featured 64 presentations, of which 15 have been catalogued separately for inclusion in this database. refs., tabs., figs.
Hsu, Ling-Yuan; Chen, Tsung-Lin
2012-11-13
This paper presents a vehicle dynamics prediction system, which consists of a sensor fusion system and a vehicle parameter identification system. This sensor fusion system can obtain the six degree-of-freedom vehicle dynamics and two road angles without using a vehicle model. The vehicle parameter identification system uses the vehicle dynamics from the sensor fusion system to identify ten vehicle parameters in real time, including vehicle mass, moment of inertial, and road friction coefficients. With above two systems, the future vehicle dynamics is predicted by using a vehicle dynamics model, obtained from the parameter identification system, to propagate with time the current vehicle state values, obtained from the sensor fusion system. Comparing with most existing literatures in this field, the proposed approach improves the prediction accuracy both by incorporating more vehicle dynamics to the prediction system and by on-line identification to minimize the vehicle modeling errors. Simulation results show that the proposed method successfully predicts the vehicle dynamics in a left-hand turn event and a rollover event. The prediction inaccuracy is 0.51% in a left-hand turn event and 27.3% in a rollover event.
Hsu, Ling-Yuan; Chen, Tsung-Lin
2012-01-01
This paper presents a vehicle dynamics prediction system, which consists of a sensor fusion system and a vehicle parameter identification system. This sensor fusion system can obtain the six degree-of-freedom vehicle dynamics and two road angles without using a vehicle model. The vehicle parameter identification system uses the vehicle dynamics from the sensor fusion system to identify ten vehicle parameters in real time, including vehicle mass, moment of inertial, and road friction coefficients. With above two systems, the future vehicle dynamics is predicted by using a vehicle dynamics model, obtained from the parameter identification system, to propagate with time the current vehicle state values, obtained from the sensor fusion system. Comparing with most existing literatures in this field, the proposed approach improves the prediction accuracy both by incorporating more vehicle dynamics to the prediction system and by on-line identification to minimize the vehicle modeling errors. Simulation results show that the proposed method successfully predicts the vehicle dynamics in a left-hand turn event and a rollover event. The prediction inaccuracy is 0.51% in a left-hand turn event and 27.3% in a rollover event. PMID:23202231
Parameter identification based synchronization for a class of chaotic systems with offset vectors
International Nuclear Information System (INIS)
Chen Cailian; Feng Gang; Guan Xinping
2004-01-01
Based on a parameter identification scheme, a novel synchronization method is presented for a class of chaotic systems with offset vectors which can be represented by the so-called T-S fuzzy model. It is shown that the slave system can synchronize the master system and the unknown parameters of the master system can be identified simultaneously. The delayed feedback technique is also developed in order to reduce the energy and time required for the identification and synchronization. Numerical simulations demonstrate the effectiveness of the proposed method
2013-01-31
... SECURITY Coast Guard 33 CFR Parts 173, 174, 181, and 187 RIN 1625-AB45 Changes to Standard Numbering System, Vessel Identification System, and Boating Accident Report Database AGENCY: Coast Guard, DHS. ACTION: Rule... Numbering System, Vessel Identification System, and Boating Accident Report Database rule became effective...
MAC, A System for Automatically IPR Identification, Collection and Distribution
Serrão, Carlos
Controlling Intellectual Property Rights (IPR) in the Digital World is a very hard challenge. The facility to create multiple bit-by-bit identical copies from original IPR works creates the opportunities for digital piracy. One of the most affected industries by this fact is the Music Industry. The Music Industry has supported huge losses during the last few years due to this fact. Moreover, this fact is also affecting the way that music rights collecting and distributing societies are operating to assure a correct music IPR identification, collection and distribution. In this article a system for automating this IPR identification, collection and distribution is presented and described. This system makes usage of advanced automatic audio identification system based on audio fingerprinting technology. This paper will present the details of the system and present a use-case scenario where this system is being used.
Overhead longwave infrared hyperspectral material identification using radiometric models
Energy Technology Data Exchange (ETDEWEB)
Zelinski, M. E. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
2018-01-09
Material detection algorithms used in hyperspectral data processing are computationally efficient but can produce relatively high numbers of false positives. Material identification performed as a secondary processing step on detected pixels can help separate true and false positives. This paper presents a material identification processing chain for longwave infrared hyperspectral data of solid materials collected from airborne platforms. The algorithms utilize unwhitened radiance data and an iterative algorithm that determines the temperature, humidity, and ozone of the atmospheric profile. Pixel unmixing is done using constrained linear regression and Bayesian Information Criteria for model selection. The resulting product includes an optimal atmospheric profile and full radiance material model that includes material temperature, abundance values, and several fit statistics. A logistic regression method utilizing all model parameters to improve identification is also presented. This paper details the processing chain and provides justification for the algorithms used. Several examples are provided using modeled data at different noise levels.
Identification of the reduced order models of a BWR reactor
International Nuclear Information System (INIS)
Hernandez S, A.
2004-01-01
The present work has as objective to analyze the relative stability of a BWR type reactor. It is analyzed that so adaptive it turns out to identify the parameters of a model of reduced order so that this it reproduces a condition of given uncertainty. This will take of a real fact happened in the La Salle plant under certain operation conditions of power and flow of coolant. The parametric identification is carried out by means of an algorithm of recursive least square and an Output Error model (Output Error), measuring the output power of the reactor when the instability is present, and considering that it is produced by a change in the reactivity of the system in the same way that a sign of type step. Also it is carried out an analytic comparison of the relative stability, analyzing two types of answers: the original answer of the uncertainty of the reactor vs. the obtained response identifying the parameters of the model of reduced order, reaching the conclusion that it is very viable to adapt a model of reduced order to study the stability of a reactor, under the only condition to consider that the dynamics of the reactivity is of step type. (Author)
Improved Stochastic Subspace System Identification for Structural Health Monitoring
Chang, Chia-Ming; Loh, Chin-Hsiung
2015-07-01
Structural health monitoring acquires structural information through numerous sensor measurements. Vibrational measurement data render the dynamic characteristics of structures to be extracted, in particular of the modal properties such as natural frequencies, damping, and mode shapes. The stochastic subspace system identification has been recognized as a power tool which can present a structure in the modal coordinates. To obtain qualitative identified data, this tool needs to spend computational expense on a large set of measurements. In study, a stochastic system identification framework is proposed to improve the efficiency and quality of the conventional stochastic subspace system identification. This framework includes 1) measured signal processing, 2) efficient space projection, 3) system order selection, and 4) modal property derivation. The measured signal processing employs the singular spectrum analysis algorithm to lower the noise components as well as to present a data set in a reduced dimension. The subspace is subsequently derived from the data set presented in a delayed coordinate. With the proposed order selection criteria, the number of structural modes is determined, resulting in the modal properties. This system identification framework is applied to a real-world bridge for exploring the feasibility in real-time applications. The results show that this improved system identification method significantly decreases computational time, while qualitative modal parameters are still attained.
A review on modeling, identification and servo control of robotic ...
African Journals Online (AJOL)
Robotic excavator is a hydraulic actuated 4 DOF manipulator mounted on a mobile chassis which implements automatic excavations. This article reviews modeling, identification, and low level control of the robotic excavator. First, modeling of the nonlinear hydraulic dynamics, coupling manipulator dynamics, and soil-tool ...
Nonlinear system identification NARMAX methods in the time, frequency, and spatio-temporal domains
Billings, Stephen A
2013-01-01
Nonlinear System Identification: NARMAX Methods in the Time, Frequency, and Spatio-Temporal Domains describes a comprehensive framework for the identification and analysis of nonlinear dynamic systems in the time, frequency, and spatio-temporal domains. This book is written with an emphasis on making the algorithms accessible so that they can be applied and used in practice. Includes coverage of: The NARMAX (nonlinear autoregressive moving average with exogenous inputs) modelThe orthogonal least squares algorithm that allows models to be built term by
The NNSYSID Toolbox - A MATLAB Toolbox for System Identification with Neural Networks
DEFF Research Database (Denmark)
Nørgård, Peter Magnus; Ravn, Ole; Hansen, Lars Kai
1996-01-01
To assist the identification of nonlinear dynamic systems, a set of tools has been developed for the MATLAB(R) environment. The tools include a number of different model structures, highly effective training algorithms, functions for validating trained networks, and pruning algorithms for determi......To assist the identification of nonlinear dynamic systems, a set of tools has been developed for the MATLAB(R) environment. The tools include a number of different model structures, highly effective training algorithms, functions for validating trained networks, and pruning algorithms...
Improved system blind identification based on second-order ...
Indian Academy of Sciences (India)
An improved system blind identification method based on second-order cyclostationary statistics and the properties of group delay, has been proposed. This is achieved by applying a correction to the estimated phase (by the spectral correlation density of the system output) for the poles, in the group delay domain.
Identification of Stellar Sequences in Various Stellar Systems ...
Indian Academy of Sciences (India)
GIREESH C. JOSHI
2017-11-27
Nov 27, 2017 ... (CMRD) approach is used to separate the stellar sequences of cluster systems. The age, distance and reddening of each ... gravitational bound systems (Joshi & Tyagi 2015). Gen- erally, these objects are identified ...... identification of stellar clusters within embedded region of the Galactic disk and highly ...
A study on switched linear system identification using game ...
African Journals Online (AJOL)
This study deals with application of game-theoretic strategies and neural computing to switched linear system identification, wherein some of the subsystems may be in failed, standby, or working states. The controller is to detect failed subsystems, and switch standby and working subsystems to maintain stable system ...
Improved system blind identification based on second-order ...
Indian Academy of Sciences (India)
However, many systems, like data commu- nication channels, acoustic paths and vocal tract, are of non-minimum phase nature and their actual identification is essential. Higher order statistics (HOS), like the bispectrum, have complete system phase information in a hidden manner and many methods to extract phase from ...
Combined parametric-nonparametric identification of block-oriented systems
Mzyk, Grzegorz
2014-01-01
This book considers a problem of block-oriented nonlinear dynamic system identification in the presence of random disturbances. This class of systems includes various interconnections of linear dynamic blocks and static nonlinear elements, e.g., Hammerstein system, Wiener system, Wiener-Hammerstein ("sandwich") system and additive NARMAX systems with feedback. Interconnecting signals are not accessible for measurement. The combined parametric-nonparametric algorithms, proposed in the book, can be selected dependently on the prior knowledge of the system and signals. Most of them are based on the decomposition of the complex system identification task into simpler local sub-problems by using non-parametric (kernel or orthogonal) regression estimation. In the parametric stage, the generalized least squares or the instrumental variables technique is commonly applied to cope with correlated excitations. Limit properties of the algorithms have been shown analytically and illustrated in simple experiments.
Transfer Function Identification Using Orthogonal Fourier Transform Modeling Functions
Morelli, Eugene A.
2013-01-01
A method for transfer function identification, including both model structure determination and parameter estimation, was developed and demonstrated. The approach uses orthogonal modeling functions generated from frequency domain data obtained by Fourier transformation of time series data. The method was applied to simulation data to identify continuous-time transfer function models and unsteady aerodynamic models. Model fit error, estimated model parameters, and the associated uncertainties were used to show the effectiveness of the method for identifying accurate transfer function models from noisy data.
2010-08-16
... SECURITY Coast Guard 33 CFR Parts 173, 174, 181, and 187 RIN 1625-AB45 Changes to Standard Numbering System, Vessel Identification System, and Boating Accident Report Database AGENCY: Coast Guard, DHS. ACTION..., Vessel Identification System, and Boating Accident Report Database. DATES: Comments and related material...
Identification of the non-linear systems using internal recurrent neural networks
Directory of Open Access Journals (Sweden)
Bogdan CODRES
2006-12-01
Full Text Available In the past years utilization of neural networks took a distinct ampleness because of the following properties: distributed representation of information, capacity of generalization in case of uncontained situation in training data set, tolerance to noise, resistance to partial destruction, parallel processing. Another major advantage of neural networks is that they allow us to obtain the model of the investigated system, systems that is not necessarily to be linear. In fact, the true value of neural networks is seen in the case of identification and control of nonlinear systems. In this paper there are presented some identification techniques using neural networks.
On-line identification of hybrid systems using an adaptive growing and pruning RBF neural network
DEFF Research Database (Denmark)
Alizadeh, Tohid
2008-01-01
This paper introduces an adaptive growing and pruning radial basis function (GAP-RBF) neural network for on-line identification of hybrid systems. The main idea is to identify a global nonlinear model that can predict the continuous outputs of hybrid systems. In the proposed approach, GAP......-RBF neural network uses a modified unscented kalman filter (UKF) with forgetting factor scheme as the required on-line learning algorithm. The effectiveness of the resulting identification approach is tested and evaluated on a simulated benchmark hybrid system....
Early identification systems for emerging foodborne hazards
Marvin, H.J.P.; Kleter, G.A.; Pradini, A.; Dekkers, S.; Bolton, D.J.
2009-01-01
This paper provides a non-exhausting overview of early warning systems for emerging foodborne hazards that are operating in the various places in the world. Special attention is given to endpoint-focussed early warning systems (i.e. ECDC, ISIS and GPHIN) and hazard-focussed early warning systems
Immune System Toxicity and Immunotoxicity Hazard Identification
Exposure to chemicals may alter immune system health, increasing the risk of infections, allergy and autoimmune diseases. The chapter provides a concise overview of the immune system, host factors that affect immune system heal, and the effects that xenobiotic exposure may have ...
Identification and communication of uncertainties of phenomenological models in PSA
International Nuclear Information System (INIS)
Pulkkinen, U.; Simola, K.
2001-11-01
This report aims at presenting a view upon uncertainty analysis of phenomenological models with an emphasis on the identification and documentation of various types of uncertainties and assumptions in the modelling of the phenomena. In an uncertainty analysis, it is essential to include and document all unclear issues, in order to obtain a maximal coverage of unresolved issues. This holds independently on their nature or type of the issues. The classification of uncertainties is needed in the decomposition of the problem and it helps in the identification of means for uncertainty reduction. Further, an enhanced documentation serves to evaluate the applicability of the results to various risk-informed applications. (au)
Rapisarda, P.; Trentelman, H.L.
We illustrate procedures to identify a state-space representation of a lossless or dissipative system from a given noise-free trajectory; important special cases are passive systems and bounded-real systems. Computing a rank-revealing factorization of a Gramian-like matrix constructed from the data,
A biometric identification system based on eigenpalm and eigenfinger features.
Ribaric, Slobodan; Fratric, Ivan
2005-11-01
This paper presents a multimodal biometric identification system based on the features of the human hand. We describe a new biometric approach to personal identification using eigenfinger and eigenpalm features, with fusion applied at the matching-score level. The identification process can be divided into the following phases: capturing the image; preprocessing; extracting and normalizing the palm and strip-like finger subimages; extracting the eigenpalm and eigenfinger features based on the K-L transform; matching and fusion; and, finally, a decision based on the (k, l)-NN classifier and thresholding. The system was tested on a database of 237 people (1,820 hand images). The experimental results showed the effectiveness of the system in terms of the recognition rate (100 percent), the equal error rate (EER = 0.58 percent), and the total error rate (TER = 0.72 percent).
System identification by methods from the statistical signal theory, history and state of the art
International Nuclear Information System (INIS)
Christensen, Palle; Gundersen, Vidar B.
1999-01-01
Condition monitoring is an important area in which the OECD Halden Reactor Project has developed several tools. This paper presents a general overview of methods utilised in diagnosis systems, signal validation systems and process optimisation systems such as EFD, Mocom, Aladdin and PEANO. An overview of lessons learned on diagnosis of technical systems with special reference to system identification is reported. The analysis of input-output behaviour by special, suitable methods may be used as a tool for diagnosis. An overview of methods for empirical modelling and data analysis and their major differences is presented. It is explained how system identification methods and transforms may be used to build models based on observed data from a system. According to the Webster dictionary diagnosis is 'Investigation or analysis of the cause or nature of a condition, situation or a problem.' By examining data collected from a process the aim is to detect abnormal conditions and if possible understand what has been the cause of the observed problem. Section 1 gives a retrospective view at the development in the field of system identification. Section 2 presents a classification of the methods, while section 3 provides some practical advice on how diagnosis can be carried out by means of system identification methods (author) (ml)
Towards model evaluation and identification using Self-Organizing Maps
Directory of Open Access Journals (Sweden)
M. Herbst
2008-04-01
Full Text Available The reduction of information contained in model time series through the use of aggregating statistical performance measures is very high compared to the amount of information that one would like to draw from it for model identification and calibration purposes. It has been readily shown that this loss imposes important limitations on model identification and -diagnostics and thus constitutes an element of the overall model uncertainty. In this contribution we present an approach using a Self-Organizing Map (SOM to circumvent the identifiability problem induced by the low discriminatory power of aggregating performance measures. Instead, a Self-Organizing Map is used to differentiate the spectrum of model realizations, obtained from Monte-Carlo simulations with a distributed conceptual watershed model, based on the recognition of different patterns in time series. Further, the SOM is used instead of a classical optimization algorithm to identify those model realizations among the Monte-Carlo simulation results that most closely approximate the pattern of the measured discharge time series. The results are analyzed and compared with the manually calibrated model as well as with the results of the Shuffled Complex Evolution algorithm (SCE-UA. In our study the latter slightly outperformed the SOM results. The SOM method, however, yields a set of equivalent model parameterizations and therefore also allows for confining the parameter space to a region that closely represents a measured data set. This particular feature renders the SOM potentially useful for future model identification applications.
The Talent Search Model of Gifted Identification
Assouline, Susan G.; Lupkowski-Shoplik, Ann
2012-01-01
The Talent Search model, founded at Johns Hopkins University by Dr. Julian C. Stanley, is fundamentally an above-level testing program. This simplistic description belies the enduring impact that the Talent Search model has had on the lives of hundreds of thousands of gifted students as well as their parents and teachers. In this article, we…
Robust System Identification and Control Design
National Research Council Canada - National Science Library
Zhou, Kemin
2001-01-01
..., some advanced nonlinear control techniques including bifurcation stabilization and compressor stabilization techniques, model reduction techniques, fault detection and fault tolerant control methods...
Energy Technology Data Exchange (ETDEWEB)
Cai, Jie [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Kim, Donghun [Purdue University; Braun, James E. [Purdue University
2017-07-03
It is important to have practical methods for constructing a good mathematical model for a building's thermal system for energy audits, retrofit analysis and advanced building controls, e.g. model predictive control. Identification approaches based on semi-physical model structures are popular in building science for those purposes. However conventional gray box identification approaches applied to thermal networks would fail when significant unmeasured heat gains present in estimation data. Although this situation is very common and practical, there has been little research to tackle this issue in building science. This paper presents an overall identification approach to alleviate influences of unmeasured disturbances, and hence to obtain improved gray-box building models. The approach was applied to an existing open space building and the performance is demonstrated.
Identification of Nonlinear Systems: Volterra Series Simplification
Directory of Open Access Journals (Sweden)
A. Novák
2007-01-01
Full Text Available Traditional measurement of multimedia systems, e.g. linear impulse response and transfer function, are sufficient but not faultless. For these methods the pure linear system is considered and nonlinearities, which are usually included in real systems, are disregarded. One of the ways to describe and analyze a nonlinear system is by using Volterra Series representation. However, this representation uses an enormous number of coefficients. In this work a simplification of this method is proposed and an experiment with an audio amplifier is shown.
Deterministic System Identification Using RBF Networks
Directory of Open Access Journals (Sweden)
Joilson Batista de Almeida Rego
2014-01-01
Full Text Available This paper presents an artificial intelligence application using a nonconventional mathematical tool: the radial basis function (RBF networks, aiming to identify the current plant of an induction motor or other nonlinear systems. Here, the objective is to present the RBF response to different nonlinear systems and analyze the obtained results. A RBF network is trained and simulated in order to obtain the dynamical solution with basin of attraction and equilibrium point for known and unknown system and establish a relationship between these dynamical systems and the RBF response. On the basis of several examples, the results indicating the effectiveness of this approach are demonstrated.
Dror, Shahar
1992-01-01
Approved for public release; distribution is unlimited Identification and control of non-linear dynamical systems is a very complex task which requires new methods of approaching. This research addresses the problem of emulation and control via the use of distributed parallel processing, namely artificial neural networks. Four models for describing non-linear MIMO dynamical systems are presented. Based on these models a combined feedforward and recurrent neural networks are structured t...
Transforming Graphical System Models to Graphical Attack Models
DEFF Research Database (Denmark)
Ivanova, Marieta Georgieva; Probst, Christian W.; Hansen, Rene Rydhof
2016-01-01
Manually identifying possible attacks on an organisation is a complex undertaking; many different factors must be considered, and the resulting attack scenarios can be complex and hard to maintain as the organisation changes. System models provide a systematic representation of organisations...... that helps in structuring attack identification and can integrate physical, virtual, and social components. These models form a solid basis for guiding the manual identification of attack scenarios. Their main benefit, however, is in the analytic generation of attacks. In this work we present a systematic...... approach to transforming graphical system models to graphical attack models in the form of attack trees. Based on an asset in the model, our transformations result in an attack tree that represents attacks by all possible actors in the model, after which the actor in question has obtained the asset....
Music Identification System Using MPEG-7 Audio Signature Descriptors
You, Shingchern D.; Chen, Wei-Hwa; Chen, Woei-Kae
2013-01-01
This paper describes a multiresolution system based on MPEG-7 audio signature descriptors for music identification. Such an identification system may be used to detect illegally copied music circulated over the Internet. In the proposed system, low-resolution descriptors are used to search likely candidates, and then full-resolution descriptors are used to identify the unknown (query) audio. With this arrangement, the proposed system achieves both high speed and high accuracy. To deal with the problem that a piece of query audio may not be inside the system's database, we suggest two different methods to find the decision threshold. Simulation results show that the proposed method II can achieve an accuracy of 99.4% for query inputs both inside and outside the database. Overall, it is highly possible to use the proposed system for copyright control. PMID:23533359
National Research Council Canada - National Science Library
Feiler, Peter
2007-01-01
.... The Society of Automotive Engineers (SAE) Architecture Analysis & Design Language (AADL) is an industry-standard, architecture-modeling notation specifically designed to support a component- based approach to modeling embedded systems...
Modelling Railway Interlocking Systems
DEFF Research Database (Denmark)
Lindegaard, Morten Peter; Viuf, P.; Haxthausen, Anne Elisabeth
2000-01-01
In this report we present a model of interlocking systems, and describe how the model may be validated by simulation. Station topologies are modelled by graphs in which the nodes denote track segments, and the edges denote connectivity for train traÆc. Points and signals are modelled by annotatio...
CEAI: CCM based Email Authorship Identification Model
DEFF Research Database (Denmark)
Nizamani, Sarwat; Memon, Nasrullah
2013-01-01
authors, 89% for 25 authors, and 81% for 50 authors, respectively on Enron data set, while 89.5% accuracy has been achieved on authors' constructed real email data set. The results on Enron data set have been achieved on quite a large number of authors as compared to the models proposed by Iqbal et al. [1...
On the identification of fractionally cointegrated VAR models with the F(d) condition
DEFF Research Database (Denmark)
Santucci de Magistris, Paolo; Carlini, Federico
for any choice of the lag-length when the true cointegration rank is known. The properties of these multiple non-identified models are studied and a necessary and sufficient condition for the identification of the fractional parameters of the system is provided. The condition is named F(d......). This is a generalization of the well-known I(1) condition to the fractional case. Imposing a proper restriction on the fractional integration parameter, d, is sufficient to guarantee identification of all model parameters and the validity of the F(d) condition. The paper also illustrates the indeterminacy between...
Modeling, Identification, Estimation, and Simulation of Urban Traffic Flow in Jakarta and Bandung
Directory of Open Access Journals (Sweden)
Herman Y. Sutarto
2015-06-01
Full Text Available This paper presents an overview of urban traffic flow from the perspective of system theory and stochastic control. The topics of modeling, identification, estimation and simulation techniques are evaluated and validated using actual traffic flow data from the city of Jakarta and Bandung, Indonesia, and synthetic data generated from traffic micro-simulator VISSIM. The results on particle filter (PF based state estimation and Expectation-Maximization (EM based parameter estimation (identification confirm the proposed model gives satisfactory results that capture the variation of urban traffic flow. The combination of the technique and the simulator platform assembles possibility to develop a real-time traffic light controller.
On the identification of fractionally cointegrated VAR models with the F(d) condition
DEFF Research Database (Denmark)
Carlini, Federico; Santucci de Magistris, Paolo
with different fractional integration and cointegration parameters. The properties of these multiple non-identified sub-models are studied and a necessary and sufficient condition for the identification of the fractional parameters of the system is provided. The condition is named F(d). The assessment of the F(d...
Identification of continuous-time systems from samples of input ...
Indian Academy of Sciences (India)
Identification of process parameters for control purposes must often be done using a digital computer, from samples of input±output observations. On the other hand, the .... As stated earlier, it is convenient to divide the problem into two subproblems. The first of these is the determination of a suitable discrete-time model from ...
Open and Closed Loop Parametric System Identification in Compact Disk Players
DEFF Research Database (Denmark)
Vidal, Enrique Sanchez; Stoustrup, Jakob; Andersen, Palle
2001-01-01
By measuring the current through the coil of the actuators in the optical pick-up in a compact disk player, open loop parametric system identification can be performed. The parameters are identified by minimizing the least-squares loss function of the ARX model. The only parameter which cannot be...
Music Identification System Using MPEG-7 Audio Signature Descriptors
Directory of Open Access Journals (Sweden)
Shingchern D. You
2013-01-01
Full Text Available This paper describes a multiresolution system based on MPEG-7 audio signature descriptors for music identification. Such an identification system may be used to detect illegally copied music circulated over the Internet. In the proposed system, low-resolution descriptors are used to search likely candidates, and then full-resolution descriptors are used to identify the unknown (query audio. With this arrangement, the proposed system achieves both high speed and high accuracy. To deal with the problem that a piece of query audio may not be inside the system’s database, we suggest two different methods to find the decision threshold. Simulation results show that the proposed method II can achieve an accuracy of 99.4% for query inputs both inside and outside the database. Overall, it is highly possible to use the proposed system for copyright control.
Directory of Open Access Journals (Sweden)
Dhiadeen Mohammed Salih
2015-01-01
Full Text Available A single hidden layer feedforward neural network (SLFN with online sequential extreme learning machine (OSELM algorithm has been introduced and applied in many regression problems successfully. However, using SLFN with OSELM as black-box for nonlinear system identification may lead to building models for the identified plant with inconsistency responses from control perspective. The reason can refer to the random initialization procedure of the SLFN hidden node parameters with OSELM algorithm. In this paper, a single hidden layer feedforward wavelet network (WN is introduced with OSELM for nonlinear system identification aimed at getting better generalization performances by reducing the effect of a random initialization procedure.
New methodology for a person identification system
Indian Academy of Sciences (India)
Home; Journals; Sadhana; Volume 31; Issue 3. New methodology for a person identiﬁcation system. R Bremananth A Chitra. Volume 31 Issue 3 June 2006 pp 259-276 ... Experimental results illustrate that the proposed method has been easily espoused in elections, bank transactions and other security applications.
Quadrotor system identification using the multivariate multiplex b-spline
Visser, T.; De Visser, C.C.; Van Kampen, E.J.
2015-01-01
A novel method for aircraft system identification is presented that is based on a new multivariate spline type; the multivariate multiplex B-spline. The multivariate multiplex B-spline is a generalization of the recently introduced tensor-simplex B-spline. Multivariate multiplex splines obtain
Friction ridge skin - Automated Fingerprint Identification System (AFIS)
Meuwly, Didier
2013-01-01
This contribution describes the development and the forensic use of automated fingerprint identification systems (AFISs). AFISs were initially developed in order to overcome the limitations of the paper-based fingerprint collections, by digitizing the ten-print cards in computerized databases and to
Comparison of System Identification Methods using Ambient Bridge Test Data
DEFF Research Database (Denmark)
Andersen, P.; Brincker, Rune; Peeters, B.
1999-01-01
In this paper the performance of four different system identification methods is compared using operational data obtained from an ambient vibration test of the Swiss Z24 highway bridge. The four methods are the frequency domain based peak-picking methods, the polyreference LSCE method, the stocha...
Journal of EEA, Vol. 27, 2010 WRITER IDENTIFICATION SYSTEM ...
African Journals Online (AJOL)
messy
These text blocks are scanned and stored for further processing by the identification system. Two approaches have been employed for feature extraction from the handwritten images: texture level using multi-channel Gabor Energy Features and the character-shape (allograph) level using codebook of connected component ...
Identification of Parameters in Active Magnetic Bearing Systems
DEFF Research Database (Denmark)
Lauridsen, Jonas Skjødt; Voigt, Andreas Jauernik; Mandrup-Poulsen, Christian
2016-01-01
A method for identifying uncertain parameters in Active Magnetic Bearing (AMB) based rotordynamic systems is introduced and adapted for experimental application. The Closed Loop Identification (CLI) method is utilised to estimate the current/force factors Ki and the displacement/force factors Ks...
Box & Jenkins Model Identification:A Comparison of Methodologies
Directory of Open Access Journals (Sweden)
Maria Augusta Soares Machado
2012-12-01
Full Text Available This paper focuses on a presentation of a comparison of a neuro-fuzzy back propagation network and Forecast automatic model Identification to identify automatically Box & Jenkins non seasonal models.Recently some combinations of neural networks and fuzzy logic technologies have being used to deal with uncertain and subjective problems. It is concluded on the basis of the obtained results that this type of approach is very powerful to be used.
Directory of Open Access Journals (Sweden)
Man Zhu
2017-03-01
Full Text Available Determination of ship maneuvering models is a tough task of ship maneuverability prediction. Among several prime approaches of estimating ship maneuvering models, system identification combined with the full-scale or free- running model test is preferred. In this contribution, real-time system identification programs using recursive identification method, such as the recursive least square method (RLS, are exerted for on-line identification of ship maneuvering models. However, this method seriously depends on the objects of study and initial values of identified parameters. To overcome this, an intelligent technology, i.e., support vector machines (SVM, is firstly used to estimate initial values of the identified parameters with finite samples. As real measured motion data of the Mariner class ship always involve noise from sensors and external disturbances, the zigzag simulation test data include a substantial quantity of Gaussian white noise. Wavelet method and empirical mode decomposition (EMD are used to filter the data corrupted by noise, respectively. The choice of the sample number for SVM to decide initial values of identified parameters is extensively discussed and analyzed. With de-noised motion data as input-output training samples, parameters of ship maneuvering models are estimated using RLS and SVM-RLS, respectively. The comparison between identification results and true values of parameters demonstrates that both the identified ship maneuvering models from RLS and SVM-RLS have reasonable agreements with simulated motions of the ship, and the increment of the sample for SVM positively affects the identification results. Furthermore, SVM-RLS using data de-noised by EMD shows the highest accuracy and best convergence.
The application of an artificial immune system for solving the identification problem
Directory of Open Access Journals (Sweden)
Astachova Irina
2017-01-01
Full Text Available Ecological prognosis sets the identification task, which is to find the capacity of pollution sources based on the available experimental data. This problem is an inverse problem, for the solution of which the method of symbolic regression is considered. The distributed artificial immune system is used as an algorithm for the problem solving. The artificial immune system (AIS is a model that allows solving various problems of identification, its concept was borrowed from biology. The solution is sought using a distributed version of the artificial immune system, which is implemented through a network. This distributed network can operate in any heterogeneous environment, which is achieved through the use of cross-platform Python programming language. AIS demonstrates the ability to restore the original function in the problem of identification. The obtained solution for the test data is represented by the graph.
Identification of protective antigens for vaccination against systemic salmonellosis
Directory of Open Access Journals (Sweden)
Dirk eBumann
2014-08-01
Full Text Available There is an urgent medical need for improved vaccines with broad serovar coverage and high efficacy against systemic salmonellosis. Subunit vaccines offer excellent safety profiles but require identification of protective antigens, which remains a challenging task. Here, I review crucial properties of Salmonella antigens that might help to narrow down the number of potential candidates from more than 4000 proteins encoded in Salmonella genomes, to a more manageable number of 50-200 most promising antigens. I also discuss complementary approaches for antigen identification and potential limitations of current pre-clinical vaccine testing.
Rule based deterioration identification and management system
International Nuclear Information System (INIS)
Kataoka, S.; Pavinich, W.; Lapides, M.
1993-01-01
Under the sponsorship of IHI and EPRI, a rule-based screening system has been developed that can be used by utility engineers to determine which deterioration mechanisms are acting on specific LWR components, and to evaluate the efficacy of an age-related deterioration management program. The screening system was developed using the rule-based shell, NEXPERT, which provides traceability to the data sources used in the logic development. The system addresses all the deterioration mechanisms of specific metals encountered in either BWRs or PWRs. Deterioration mechanisms are listed with reasons why they may occur during the design life of LWRs, considering the plant environment, manufacturing process, service history, material chemical composition, etc. of components in a specific location of a LWR. To eliminate the evaluation of inactive deterioration quickly, a tier structure is applied to the rules. The reasons why deterioration will occur are extracted automatically by backward chaining. To reduce the amount of user input, plant environmental data are stored in files as default environmental data. (author)
Biometric identification systems: the science of transaction facilitation
Rogers, Robert R.
1994-10-01
The future ofthe "secure transaction" and the success ofall undertakings that depend on absolute certainty that the individuals involved really are who and what they represent themselves to be is dependent upon the successful development of absolutely accurate, low-cost and easy-to-operate Biometric Identification Systems. Whether these transactions are political, military, financial or administrative (e.g. health cards, drivers licenses, welfare entitlement, national identification cards, credit card transactions, etc.), the need for such secure and positive identification has never been greater -and yet we are only at the beginning ofan era in which we will see the emergence and proliferation of Biometric Identification Systems in nearly every field ofhuman endeavor. Proper application ofthese systems will change the way the world operates, and that is precisely the goal ofComparator Systems Corporation. Just as with the photo-copier 40 years ago and the personal computer 20 years ago, the potential applications for positive personal identification are going to make the Biometric Identification System a commonplace component in the standard practice ofbusiness, and in interhuman relationships ofall kinds. The development of new and specific application hardware, as well as the necessary algorithms and related software required for integration into existing operating procedures and newly developed systems alike, has been a more-than-a-decade-long process at Comparator -and we are now on the verge of delivering these systems to the world markets so urgently in need of them. An individual could feel extremely confident and satisfied ifhe could present his credit, debit, or ATM card at any point of sale and, after inserting his card, could simply place his finger on a glass panel and in less than a second be positively accepted as being the person that the card purported him to be; not to mention the security and satisfaction of the vendor involved in knowing that
Attendance fingerprint identification system using arduino and single board computer
Muchtar, M. A.; Seniman; Arisandi, D.; Hasanah, S.
2018-03-01
Fingerprint is one of the most unique parts of the human body that distinguishes one person from others and is easily accessed. This uniqueness is supported by technology that can automatically identify or recognize a person called fingerprint sensor. Yet, the existing Fingerprint Sensor can only do fingerprint identification on one machine. For the mentioned reason, we need a method to be able to recognize each user in a different fingerprint sensor. The purpose of this research is to build fingerprint sensor system for fingerprint data management to be centralized so identification can be done in each Fingerprint Sensor. The result of this research shows that by using Arduino and Raspberry Pi, data processing can be centralized so that fingerprint identification can be done in each fingerprint sensor with 98.5 % success rate of centralized server recording.
Energy Technology Data Exchange (ETDEWEB)
Mohanty, Subhasish M. [Argonne National Lab. (ANL), Argonne, IL (United States); Jagielo, Bryan J. [Argonne National Lab. (ANL), Argonne, IL (United States); Oakland Univ., Rochester, MI (United States); Iverson, William I. [Argonne National Lab. (ANL), Argonne, IL (United States); Univ. of Illinois at Urbana-Champaign, Champaign, IL (United States); Bhan, Chi Bum [Argonne National Lab. (ANL), Argonne, IL (United States); Pusan National Univ., Busan (Korea, Republic of); Soppet, William S. [Argonne National Lab. (ANL), Argonne, IL (United States); Majumdar, Saurin M. [Argonne National Lab. (ANL), Argonne, IL (United States); Natesan, Ken N. [Argonne National Lab. (ANL), Argonne, IL (United States)
2014-12-10
Nuclear reactors in the United States account for roughly 20% of the nation's total electric energy generation, and maintaining their safety in regards to key component structural integrity is critical not only for long term use of such plants but also for the safety of personnel and the public living around the plant. Early detection of damage signature such as of stress corrosion cracking, thermal-mechanical loading related material degradation in safety-critical components is a necessary requirement for long-term and safe operation of nuclear power plant systems.
Directory of Open Access Journals (Sweden)
Shahin Ismail
2005-01-01
Full Text Available Speaker identification systems perform well under the neutral talking condition; however, they suffer sharp degradation under the shouted talking condition. In this paper, the second-order hidden Markov models (HMM2s have been used to improve the recognition performance of isolated-word text-dependent speaker identification systems under the shouted talking condition. Our results show that HMM2s significantly improve the speaker identification performance compared to the first-order hidden Markov models (HMM1s. The average speaker identification performance under the shouted talking condition based on HMM1s is . On the other hand, the average speaker identification performance based on HMM2s is .
RSMASS system model development
International Nuclear Information System (INIS)
Marshall, A.C.; Gallup, D.R.
1998-01-01
RSMASS system mass models have been used for more than a decade to make rapid estimates of space reactor power system masses. This paper reviews the evolution of the RSMASS models and summarizes present capabilities. RSMASS has evolved from a simple model used to make rough estimates of space reactor and shield masses to a versatile space reactor power system model. RSMASS uses unique reactor and shield models that permit rapid mass optimization calculations for a variety of space reactor power and propulsion systems. The RSMASS-D upgrade of the original model includes algorithms for the balance of the power system, a number of reactor and shield modeling improvements, and an automatic mass optimization scheme. The RSMASS-D suite of codes cover a very broad range of reactor and power conversion system options as well as propulsion and bimodal reactor systems. Reactor choices include in-core and ex-core thermionic reactors, liquid metal cooled reactors, particle bed reactors, and prismatic configuration reactors. Power conversion options include thermoelectric, thermionic, Stirling, Brayton, and Rankine approaches. Program output includes all major component masses and dimensions, efficiencies, and a description of the design parameters for a mass optimized system. In the past, RSMASS has been used as an aid to identify and select promising concepts for space power applications. The RSMASS modeling approach has been demonstrated to be a valuable tool for guiding optimization of the power system design; consequently, the model is useful during system design and development as well as during the selection process. An improved in-core thermionic reactor system model RSMASS-T is now under development. The current development of the RSMASS-T code represents the next evolutionary stage of the RSMASS models. RSMASS-T includes many modeling improvements and is planned to be more user-friendly. RSMASS-T will be released as a fully documented, certified code at the end of
Directory of Open Access Journals (Sweden)
Guangsheng Chen
2015-08-01
Full Text Available This study proposed a dynamic parameters’ identification method for the feeding system of computer numerical control machine tools based on internal sensor. A simplified control model and linear identification model of the feeding system were established, in which the input and output signals are from sensors embedded in computer numerical control machine tools, and the dynamic parameters of the feeding system, including the equivalent inertia, equivalent damping, worktable damping, and the overall stiffness of the mechanical system, were solved by the least square method. Using the high-order Taylor expansion, the nonlinear Stribeck friction model was linearized and the parameters of the Stribeck friction model were obtained by the same way. To verify the validity and effectiveness of the identification method, identification experiments, circular motion testing, and simulations were conducted. The results obtained were stable and suggested that inertia and damping identification experiments converged fast. Stiffness identification experiments showed some deviation from simulation due to the influences of geometric error and nonlinear of stiffness. However, the identification results were still of reference significance and the method is convenient, effective, and suited for industrial condition.
Energy Technology Data Exchange (ETDEWEB)
Mays, Gary T [ORNL; Belles, Randy [ORNL; Blevins, Brandon R [ORNL; Hadley, Stanton W [ORNL; Harrison, Thomas J [ORNL; Jochem, Warren C [ORNL; Neish, Bradley S [ORNL; Omitaomu, Olufemi A [ORNL; Rose, Amy N [ORNL
2012-05-01
Oak Ridge National Laboratory (ORNL) initiated an internal National Electric Generation Siting Study, which is an ongoing multiphase study addressing several key questions related to our national electrical energy supply. This effort has led to the development of a tool, OR-SAGE (Oak Ridge Siting Analysis for power Generation Expansion), to support siting evaluations. The objective in developing OR-SAGE was to use industry-accepted approaches and/or develop appropriate criteria for screening sites and employ an array of Geographic Information Systems (GIS) data sources at ORNL to identify candidate areas for a power generation technology application. The initial phase of the study examined nuclear power generation. These early nuclear phase results were shared with staff from the Electric Power Research Institute (EPRI), which formed the genesis and support for an expansion of the work to several other power generation forms, including advanced coal with carbon capture and storage (CCS), solar, and compressed air energy storage (CAES). Wind generation was not included in this scope of work for EPRI. The OR-SAGE tool is essentially a dynamic visualization database. The results shown in this report represent a single static set of results using a specific set of input parameters. In this case, the GIS input parameters were optimized to support an economic study conducted by EPRI. A single set of individual results should not be construed as an ultimate energy solution, since US energy policy is very complex. However, the strength of the OR-SAGE tool is that numerous alternative scenarios can be quickly generated to provide additional insight into electrical generation or other GIS-based applications. The screening process divides the contiguous United States into 100 x 100 m (1-hectare) squares (cells), applying successive power generation-appropriate site selection and evaluation criteria (SSEC) to each cell. There are just under 700 million cells representing the
Timm, Thomas; Grabitzki, Julia; Severcan, Cinar; Muratoglu, Suzan; Ewald, Lisa; Yilmaz, Yavuz; Lochnit, Guenter
2016-03-01
In multicellular parasites (e.g., nematodes and protozoa), proteins and glycolipids have been found to be decorated with phosphorylcholine (PC). PC can provoke various effects on immune cells leading to an immunomodulation of the host's immune system. This immunomodulation allows long-term persistence but also prevents severe pathology due to downregulation of cellular immune responses. PC-containing antigens have been found to interfere with key proliferative signaling pathways in B and T cells, development of dendritic cells and macrophages, and mast cell degranulation. These effects contribute to the observed modulated cytokine levels and impairment of lymphocyte proliferation. In contrast to glycosphingolipids, little is known about the PC-epitopes of proteins. So far, only a limited number of PC-modified proteins from nematodes have been identified. In this project, PC-substituted proteins and glycolipids in Ascaris suum have been localized by immunohistochemistry in specific tissues of the body wall, intestine, and reproductive tract. Subsequently, we investigated the PCome of A. suum by 2D gel-based proteomics and detection by Western blotting using the PC-specific antibody TEPC-15. By peptide-mass-fingerprint matrix-assisted laser-desorption ionization time-of-flight mass spectrometry (MALDI-TOF-MS), we could identify 59 PC-substituted proteins, which are in involved multiple cellular processes. In addition to membrane proteins like vitellogenin-6, we found proteins with structural (e.g., tubulins) and metabolic (e.g., pyruvate dehydrogenase) functions or which can act in the defense against the host's immune response (e.g., serpins). Initial characterization of the PC-epitopes revealed a predominant linkage of PC to the proteins via N-glycans. Our data form the basis for more detailed investigations of the PC-epitope structures as a prerequisite for comprehensive understanding of the molecular mechanisms of immunomodulation.
Identification of Complex Dynamical Systems with Neural Networks (2/2)
CERN. Geneva
2016-01-01
The identification and analysis of high dimensional nonlinear systems is obviously a challenging task. Neural networks have been proven to be universal approximators but this still leaves the identification task a hard one. To do it efficiently, we have to violate some of the rules of classical regression theory. Furthermore we should focus on the interpretation of the resulting model to overcome its black box character. First, we will discuss function approximation with 3 layer feedforward neural networks up to new developments in deep neural networks and deep learning. These nets are not only of interest in connection with image analysis but are a center point of the current artificial intelligence developments. Second, we will focus on the analysis of complex dynamical system in the form of state space models realized as recurrent neural networks. After the introduction of small open dynamical systems we will study dynamical systems on manifolds. Here manifold and dynamics have to be identified in parall...
Identification of Complex Dynamical Systems with Neural Networks (1/2)
CERN. Geneva
2016-01-01
The identification and analysis of high dimensional nonlinear systems is obviously a challenging task. Neural networks have been proven to be universal approximators but this still leaves the identification task a hard one. To do it efficiently, we have to violate some of the rules of classical regression theory. Furthermore we should focus on the interpretation of the resulting model to overcome its black box character. First, we will discuss function approximation with 3 layer feedforward neural networks up to new developments in deep neural networks and deep learning. These nets are not only of interest in connection with image analysis but are a center point of the current artificial intelligence developments. Second, we will focus on the analysis of complex dynamical system in the form of state space models realized as recurrent neural networks. After the introduction of small open dynamical systems we will study dynamical systems on manifolds. Here manifold and dynamics have to be identified in parall...
Fotouhi, Abbas; Propp, Karsten; Auger, Daniel J.
2015-01-01
This paper describes a study demonstrating a new method of state-of-charge (SoC) estimation for batteries in real-world electric vehicle applications. This method combines realtime model identification with an adaptive neuro-fuzzy inference system (ANFIS). In the study, investigations were carried down on a small-scale battery pack. An equivalent circuit network model of the pack was developed and validated using pulse-discharge experiments. The pack was then subjected to demands representing...
Comparing Different Fault Identification Algorithms in Distributed Power System
Alkaabi, Salim
A power system is a huge complex system that delivers the electrical power from the generation units to the consumers. As the demand for electrical power increases, distributed power generation was introduced to the power system. Faults may occur in the power system at any time in different locations. These faults cause a huge damage to the system as they might lead to full failure of the power system. Using distributed generation in the power system made it even harder to identify the location of the faults in the system. The main objective of this work is to test the different fault location identification algorithms while tested on a power system with the different amount of power injected using distributed generators. As faults may lead the system to full failure, this is an important area for research. In this thesis different fault location identification algorithms have been tested and compared while the different amount of power is injected from distributed generators. The algorithms were tested on IEEE 34 node test feeder using MATLAB and the results were compared to find when these algorithms might fail and the reliability of these methods.
International Nuclear Information System (INIS)
Lundberg, Jonas; Johansson, Björn JE
2015-01-01
It has been realized that resilience as a concept involves several contradictory definitions, both for instance resilience as agile adjustment and as robust resistance to situations. Our analysis of resilience concepts and models suggest that beyond simplistic definitions, it is possible to draw up a systemic resilience model (SyRes) that maintains these opposing characteristics without contradiction. We outline six functions in a systemic model, drawing primarily on resilience engineering, and disaster response: anticipation, monitoring, response, recovery, learning, and self-monitoring. The model consists of four areas: Event-based constraints, Functional Dependencies, Adaptive Capacity and Strategy. The paper describes dependencies between constraints, functions and strategies. We argue that models such as SyRes should be useful both for envisioning new resilience methods and metrics, as well as for engineering and evaluating resilient systems. - Highlights: • The SyRes model resolves contradictions between previous resilience definitions. • SyRes is a core model for envisioning and evaluating resilience metrics and models. • SyRes describes six functions in a systemic model. • They are anticipation, monitoring, response, recovery, learning, self-monitoring. • The model describes dependencies between constraints, functions and strategies
2014-04-24
capable instrumentation. • A system reliant on RTK GPS would not be very practical and we show it to be unnecessary. 7/6/2014 Vehicle - Ground...includes: – Moblility logs (post-processed RTK - GPS pose, wheel odometry) for 3 different terrain (grass, dirt, parking lot) on the LandTamer (6x6...Platform Retrofit 7/6/2014 Vehicle - Ground Model Identification 12 AVT GT1920C GigE Camera Pose System: Novatel OEMV-3 GPS Receiver + Honeywell
Model-based version management system framework
International Nuclear Information System (INIS)
Mehmood, W.
2016-01-01
In this paper we present a model-based version management system. Version Management System (VMS) a branch of software configuration management (SCM) aims to provide a controlling mechanism for evolution of software artifacts created during software development process. Controlling the evolution requires many activities to perform, such as, construction and creation of versions, identification of differences between versions, conflict detection and merging. Traditional VMS systems are file-based and consider software systems as a set of text files. File based VMS systems are not adequate for performing software configuration management activities such as, version control on software artifacts produced in earlier phases of the software life cycle. New challenges of model differencing, merge, and evolution control arise while using models as central artifact. The goal of this work is to present a generic framework model-based VMS which can be used to overcome the problem of tradition file-based VMS systems and provide model versioning services. (author)
Recursive Model Identification for the Evaluation of Baroreflex Sensitivity.
Le Rolle, Virginie; Beuchée, Alain; Praud, Jean-Paul; Samson, Nathalie; Pladys, Patrick; Hernández, Alfredo I
2016-12-01
A method for the recursive identification of physiological models of the cardiovascular baroreflex is proposed and applied to the time-varying analysis of vagal and sympathetic activities. The proposed method was evaluated with data from five newborn lambs, which were acquired during injection of vasodilator and vasoconstrictors and the results show a close match between experimental and simulated signals. The model-based estimation of vagal and sympathetic contributions were consistent with physiological knowledge and the obtained estimators of vagal and sympathetic activities were compared to traditional markers associated with baroreflex sensitivity. High correlations were observed between traditional markers and model-based indices.
Schmidt-Eisenlohr, F.; Puñal, O.; Klagges, K.; Kirsche, M.
Apart from the general issue of modeling the channel, the PHY and the MAC of wireless networks, there are specific modeling assumptions that are considered for different systems. In this chapter we consider three specific wireless standards and highlight modeling options for them. These are IEEE 802.11 (as example for wireless local area networks), IEEE 802.16 (as example for wireless metropolitan networks) and IEEE 802.15 (as example for body area networks). Each section on these three systems discusses also at the end a set of model implementations that are available today.
Validation of the measurement model concept for error structure identification
International Nuclear Information System (INIS)
Shukla, Pavan K.; Orazem, Mark E.; Crisalle, Oscar D.
2004-01-01
The development of different forms of measurement models for impedance has allowed examination of key assumptions on which the use of such models to assess error structure are based. The stochastic error structures obtained using the transfer-function and Voigt measurement models were identical, even when non-stationary phenomena caused some of the data to be inconsistent with the Kramers-Kronig relations. The suitability of the measurement model for assessment of consistency with the Kramers-Kronig relations, however, was found to be more sensitive to the confidence interval for the parameter estimates than to the number of parameters in the model. A tighter confidence interval was obtained for Voigt measurement model, which made the Voigt measurement model a more sensitive tool for identification of inconsistencies with the Kramers-Kronig relations
Aggressive symbolic model identification in 13 year-old youths
Directory of Open Access Journals (Sweden)
Miguel A. Vidal
2009-01-01
Full Text Available Although a great amount of research has been carried out about the effects of media on the audience, few studies deal with the process that determines why the viewers identify with a specific symbolic model instead of choosing any other. In this descriptive study we try to highlight similarity identification, focusing on aggressive model identification. A sample of 203 participants, both male and female, aged 13, and with a high socioeconomic level viewed different films sequences. They were asked to answer to a questionnaire both before and after watching the clip. This questionnaire included an adjective list about the traits that best defined themselves, their favorite characters, and characters they didn’t like. Results show a clear correspondence between the participants’ self-perceived traits and those perceived for the main characters in the film. Self-perceived traits were opposed to those perceived in the main characters opponents.
A corticothalamic circuit model for sound identification in complex scenes.
Directory of Open Access Journals (Sweden)
Gonzalo H Otazu
Full Text Available The identification of the sound sources present in the environment is essential for the survival of many animals. However, these sounds are not presented in isolation, as natural scenes consist of a superposition of sounds originating from multiple sources. The identification of a source under these circumstances is a complex computational problem that is readily solved by most animals. We present a model of the thalamocortical circuit that performs level-invariant recognition of auditory objects in complex auditory scenes. The circuit identifies the objects present from a large dictionary of possible elements and operates reliably for real sound signals with multiple concurrently active sources. The key model assumption is that the activities of some cortical neurons encode the difference between the observed signal and an internal estimate. Reanalysis of awake auditory cortex recordings revealed neurons with patterns of activity corresponding to such an error signal.
System Identification and Control Design of an Unmanned Helicopter Using a PI-MPC Controller
Le Tri, Quang; Lai, Ying-Chih
2017-03-01
This paper presents the study of the system identification and controller design for an unmanned helicopter using the integration of Proportional Integral (PI) and Model Predictive Control (MPC). Since the dynamic model of a helicopter is highly nonlinear and contains many uncertainties, the system identification and control are challenging and complicated. To accelerate the development, the autonomous flight and trajectory tracking of an unmanned helicopter, this study first setup a software simulation environment of the helicopter using the X-Plane flight simulator. The prediction-error minimization (PEM) and subspace methods were applied in this study to identify the dynamic model of the interested flight trim conditions. The lateral, longitudinal, heave, and yaw dynamic models were predicted by using the System Identification Toolbox of MATLAB. To enhance the stability and eliminate the uncertainty of the control system, the Integration of Proportional Integral (PI) and MPC were introduced. The developed control system was then applied to perform the trajectory tracking of a helicopter. The simulation results show that the performance of the proposed approach can track the desired trajectory.
Stochastic subspace system identification using multivariate time-frequency distributions
Chang, Chia-Ming; Huang, Shieh-Kung
2017-04-01
Structural health monitoring assesses structural integrity by processing the measured responses of structures. One particular group in the structural health monitoring research is to conduct the operational modal analysis and then to extract the dynamic characteristics of structures from vibrational responses. These characteristics include natural frequencies, damping ratios, and mode shapes. Deviations in these characteristics represent the changes in structural properties and also imply possible damage to structures. In this study, a new stochastic system identification is developed using multivariate time-frequency distributions. These time-frequency distributions are derived from the short-time Fourier transform and subsequently yield a time-frequency matrix by stacking them with respect to time. As the derivation in the data-driven stochastic subspace system identification, the future time-frequency matrix is projected onto the past time-frequency matrix. By exploiting the singular value decomposition, the system and measurement matrices of a stochastic state-space representation are derived. Consequently, the dynamic characteristics of a structure are obtained. As compared to the time-domain stochastic subspace system identification, the proposed method utilizes the past and future matrices with a lower dimension in projection. A spectral magnitude envelope can be applied to the time-frequency matrix to highlight the major frequency components as well as to eliminate the components with less influence. To validate the proposed method, a numerical example is developed. This method is also applied to experimental data in order to evaluate its effectiveness. As a result, performance of the proposed method is superior to the time-domain stochastic subspace system identification.
Matthäus, Franziska; Pahle, Jürgen
2017-01-01
This contributed volume comprises research articles and reviews on topics connected to the mathematical modeling of cellular systems. These contributions cover signaling pathways, stochastic effects, cell motility and mechanics, pattern formation processes, as well as multi-scale approaches. All authors attended the workshop on "Modeling Cellular Systems" which took place in Heidelberg in October 2014. The target audience primarily comprises researchers and experts in the field, but the book may also be beneficial for graduate students.
Optimal policies for identification of stochastic linear systems
Lopez-Toledo, A. A.; Athans, M.
1975-01-01
The problem of designing closed-loop policies for identification of multiinput-multioutput linear discrete-time systems with random time-varying parameters is considered in this paper using a Bayesian approach. A sensitivity index gives a measure of performance for the closed-loop laws. The computation of the optimal laws is shown to be nontrivial, an exercise in stochastic control, but open-loop, affine, and open-loop feedback optimal inputs are shown to yield tractable problems. Numerical examples are given. For time-invariant systems, the criterion considered is shown to be related to the trace of the information matrix associated with the system.
Automated dental identification system: An aid to forensic odontology
Directory of Open Access Journals (Sweden)
Parvathi Devi
2011-01-01
Full Text Available Automated dental identification system is computer-aided software for the postmortem identification of deceased individuals based on dental characteristics specifically radiographs. This system is receiving increased attention because of the large number of victims encountered in the mass disasters and it is 90% more time saving and accurate than the conventional radiographic methods. This technique is based on the intensity of the overall region of tooth image and therefore it does not necessitate the presence of sharp boundary between the teeth. It provides automated search and matching capabilities for digitized radiographs and photographic dental images and compares the teeth present in multiple digitized dental records in order to access their similarity. This paper highlights the functionality of its components and techniques used in realizing these components.
Robust uncertainty evaluation for system identification on distributed wireless platforms
Crinière, Antoine; Döhler, Michael; Le Cam, Vincent; Mevel, Laurent
2016-04-01
Health monitoring of civil structures by system identification procedures from automatic control is now accepted as a valid approach. These methods provide frequencies and modeshapes from the structure over time. For a continuous monitoring the excitation of a structure is usually ambient, thus unknown and assumed to be noise. Hence, all estimates from the vibration measurements are realizations of random variables with inherent uncertainty due to (unknown) process and measurement noise and finite data length. The underlying algorithms are usually running under Matlab under the assumption of large memory pool and considerable computational power. Even under these premises, computational and memory usage are heavy and not realistic for being embedded in on-site sensor platforms such as the PEGASE platform. Moreover, the current push for distributed wireless systems calls for algorithmic adaptation for lowering data exchanges and maximizing local processing. Finally, the recent breakthrough in system identification allows us to process both frequency information and its related uncertainty together from one and only one data sequence, at the expense of computational and memory explosion that require even more careful attention than before. The current approach will focus on presenting a system identification procedure called multi-setup subspace identification that allows to process both frequencies and their related variances from a set of interconnected wireless systems with all computation running locally within the limited memory pool of each system before being merged on a host supervisor. Careful attention will be given to data exchanges and I/O satisfying OGC standards, as well as minimizing memory footprints and maximizing computational efficiency. Those systems are built in a way of autonomous operations on field and could be later included in a wide distributed architecture such as the Cloud2SM project. The usefulness of these strategies is illustrated on
Identification of chaotic memristor systems based on piecewise adaptive Legendre filters
International Nuclear Information System (INIS)
Zhao, Yibo; Zhang, Xiuzai; Xu, Jin; Guo, Yecai
2015-01-01
Memristor is a nonlinear device, which plays an important role in the design and implementation of chaotic systems. In order to be able to understand in-depth the complex nonlinear dynamic behaviors in chaotic memristor systems, modeling or identification of its nonlinear model is very important premise. This paper presents a chaotic memristor system identification method based on piecewise adaptive Legendre filters. The threshold decomposition is carried out for the input vector, and also the input signal subintervals via decomposition satisfy the convergence condition of the adaptive Legendre filters. Then the adaptive Legendre filter structure and adaptive weight update algorithm are derived. Final computer simulation results show the effectiveness as well as fast convergence characteristics.
Proportionate Minimum Error Entropy Algorithm for Sparse System Identification
Directory of Open Access Journals (Sweden)
Zongze Wu
2015-08-01
Full Text Available Sparse system identification has received a great deal of attention due to its broad applicability. The proportionate normalized least mean square (PNLMS algorithm, as a popular tool, achieves excellent performance for sparse system identification. In previous studies, most of the cost functions used in proportionate-type sparse adaptive algorithms are based on the mean square error (MSE criterion, which is optimal only when the measurement noise is Gaussian. However, this condition does not hold in most real-world environments. In this work, we use the minimum error entropy (MEE criterion, an alternative to the conventional MSE criterion, to develop the proportionate minimum error entropy (PMEE algorithm for sparse system identification, which may achieve much better performance than the MSE based methods especially in heavy-tailed non-Gaussian situations. Moreover, we analyze the convergence of the proposed algorithm and derive a sufficient condition that ensures the mean square convergence. Simulation results confirm the excellent performance of the new algorithm.
Systems Engineering Model for ART Energy Conversion
Energy Technology Data Exchange (ETDEWEB)
Mendez Cruz, Carmen Margarita [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Rochau, Gary E. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Wilson, Mollye C. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
2017-02-01
The near-term objective of the EC team is to establish an operating, commercially scalable Recompression Closed Brayton Cycle (RCBC) to be constructed for the NE - STEP demonstration system (demo) with the lowest risk possible. A systems engineering approach is recommended to ensure adequate requirements gathering, documentation, and mode ling that supports technology development relevant to advanced reactors while supporting crosscut interests in potential applications. A holistic systems engineering model was designed for the ART Energy Conversion program by leveraging Concurrent Engineering, Balance Model, Simplified V Model, and Project Management principles. The resulting model supports the identification and validation of lifecycle Brayton systems requirements, and allows designers to detail system-specific components relevant to the current stage in the lifecycle, while maintaining a holistic view of all system elements.
CTBT integrated verification system evaluation model supplement
Energy Technology Data Exchange (ETDEWEB)
EDENBURN,MICHAEL W.; BUNTING,MARCUS; PAYNE JR.,ARTHUR C.; TROST,LAWRENCE C.
2000-03-02
Sandia National Laboratories has developed a computer based model called IVSEM (Integrated Verification System Evaluation Model) to estimate the performance of a nuclear detonation monitoring system. The IVSEM project was initiated in June 1994, by Sandia's Monitoring Systems and Technology Center and has been funded by the U.S. Department of Energy's Office of Nonproliferation and National Security (DOE/NN). IVSEM is a simple, ''top-level,'' modeling tool which estimates the performance of a Comprehensive Nuclear Test Ban Treaty (CTBT) monitoring system and can help explore the impact of various sensor system concepts and technology advancements on CTBT monitoring. One of IVSEM's unique features is that it integrates results from the various CTBT sensor technologies (seismic, in sound, radionuclide, and hydroacoustic) and allows the user to investigate synergy among the technologies. Specifically, IVSEM estimates the detection effectiveness (probability of detection), location accuracy, and identification capability of the integrated system and of each technology subsystem individually. The model attempts to accurately estimate the monitoring system's performance at medium interfaces (air-land, air-water) and for some evasive testing methods such as seismic decoupling. The original IVSEM report, CTBT Integrated Verification System Evaluation Model, SAND97-25 18, described version 1.2 of IVSEM. This report describes the changes made to IVSEM version 1.2 and the addition of identification capability estimates that have been incorporated into IVSEM version 2.0.
CTBT integrated verification system evaluation model supplement
International Nuclear Information System (INIS)
EDENBURN, MICHAEL W.; BUNTING, MARCUS; PAYNE, ARTHUR C. JR.; TROST, LAWRENCE C.
2000-01-01
Sandia National Laboratories has developed a computer based model called IVSEM (Integrated Verification System Evaluation Model) to estimate the performance of a nuclear detonation monitoring system. The IVSEM project was initiated in June 1994, by Sandia's Monitoring Systems and Technology Center and has been funded by the U.S. Department of Energy's Office of Nonproliferation and National Security (DOE/NN). IVSEM is a simple, ''top-level,'' modeling tool which estimates the performance of a Comprehensive Nuclear Test Ban Treaty (CTBT) monitoring system and can help explore the impact of various sensor system concepts and technology advancements on CTBT monitoring. One of IVSEM's unique features is that it integrates results from the various CTBT sensor technologies (seismic, in sound, radionuclide, and hydroacoustic) and allows the user to investigate synergy among the technologies. Specifically, IVSEM estimates the detection effectiveness (probability of detection), location accuracy, and identification capability of the integrated system and of each technology subsystem individually. The model attempts to accurately estimate the monitoring system's performance at medium interfaces (air-land, air-water) and for some evasive testing methods such as seismic decoupling. The original IVSEM report, CTBT Integrated Verification System Evaluation Model, SAND97-25 18, described version 1.2 of IVSEM. This report describes the changes made to IVSEM version 1.2 and the addition of identification capability estimates that have been incorporated into IVSEM version 2.0
2010-08-20
...-2009-0640; FRL-9191-5] RIN-2050-AE81 Hazardous and Solid Waste Management System; Identification and...-2009-0640. Mail: Send your comments to the Hazardous and Solid Waste Management System; Identification... copies of your comments to the Hazardous and Solid Waste Management System; Identification and Listing of...
2010-04-01
... 21 Food and Drugs 8 2010-04-01 2010-04-01 false Implantable radiofrequency transponder system for... radiofrequency transponder system for patient identification and health information. (a) Identification. An implantable radiofrequency transponder system for patient identification and health information is a device...
A Portable, Air-Jet-Actuator-Based Device for System Identification
Staats, Wayne; Belden, Jesse; Mazumdar, Anirban; Hunter, Ian
2010-11-01
System identification (ID) of human and robotic limbs could help in diagnosis of ailments and aid in optimization of control parameters and future redesigns. We present a self-contained actuator, which uses the Coanda effect to rapidly switch the direction of a high speed air jet to create a binary stochastic force input to a limb for system ID. The design of the actuator is approached with the goal of creating a portable device, which could deployed on robot or human limbs for in situ identification. The viability of the device is demonstrated by performing stochastic system ID on an underdamped elastic beam system with fixed inertia and stiffness, and variable damping. The non-parametric impulse response yielded from the stochastic system ID is modeled as a second order system, and the resultant parameters are found to be in excellent agreement with those found using more traditional system ID techniques. The current design could be further miniaturized and developed as a portable, wireless, on-site multi-axis system identification system for less intrusive and more widespread use.
Modelling of wastewater systems
DEFF Research Database (Denmark)
Bechmann, Henrik
Oxygen Demand) flux and SS flux in the inlet to the WWTP. COD is measured by means of a UV absorption sensor while SS is measured by a turbidity sensor. These models include a description of the deposit of COD and SS amounts, respectively, in the sewer system, and the models can thus be used to quantify......In this thesis, models of pollution fluxes in the inlet to 2 Danish wastewater treatment plants (WWTPs) as well as of suspended solids (SS) concentrations in the aeration tanks of an alternating WWTP and in the effluent from the aeration tanks are developed. The latter model is furthermore used...... to analyze and quantify the effect of the Aeration Tank Settling (ATS) operating mode, which is used during rain events. Furthermore, the model is used to propose a control algorithm for the phase lengths during ATS operation. The models are mainly formulated as state space model in continuous time...
A neural network model of lateralization during letter identification.
Shevtsova, N; Reggia, J A
1999-03-01
The causes of cerebral lateralization of cognitive and other functions are currently not well understood. To investigate one aspect of function lateralization, a bihemispheric neural network model for a simple visual identification task was developed that has two parallel interacting paths of information processing. The model is based on commonly accepted concepts concerning neural connectivity, activity dynamics, and synaptic plasticity. A combination of both unsupervised (Hebbian) and supervised (Widrow-Hoff) learning rules is used to train the model to identify a small set of letters presented as input stimuli in the left visual hemifield, in the central position, and in the right visual hemifield. Each visual hemifield projects onto the contralateral hemisphere, and the two hemispheres interact via a simulated corpus callosum. The contribution of each individual hemisphere to the process of input stimuli identification was studied for a variety of underlying asymmetries. The results indicate that multiple asymmetries may cause lateralization. Lateralization occurred toward the side having larger size, higher excitability, or higher learning rate parameters. It appeared more intensively with strong inhibitory callosal connections, supporting the hypothesis that the corpus callosum plays a functionally inhibitory role. The model demonstrates clearly the dependence of lateralization on different hemisphere parameters and suggests that computational models can be useful in better understanding the mechanisms underlying emergence of lateralization.
An Efficient Human Identification through MultiModal Biometric System
Directory of Open Access Journals (Sweden)
K. Meena
Full Text Available ABSTRACT Human identification is essential for proper functioning of society. Human identification through multimodal biometrics is becoming an emerging trend, and one of the reasons is to improve recognition accuracy. Unimodal biometric systems are affected by various problemssuch as noisy sensor data,non-universality, lack of individuality, lack of invariant representation and susceptibility to circumvention.A unimodal system has limited accuracy. Hence, Multimodal biometric systems by combining more than one biometric feature in different levels are proposed in order to enhance the performance of the system. A supervisor module combines the different opinions or decisions delivered by each subsystem and then make a final decision. In this paper, a multimodal biometrics authentication is proposed by combining face, iris and finger features. Biometric features are extracted by Local Derivative Ternary Pattern (LDTP in Contourlet domain and an extensive evaluation of LDTP is done using Support Vector Machine and Nearest Neighborhood Classifier. The experimental evaluations are performed on a public dataset demonstrating the accuracy of the proposed system compared with the existing systems. It is observed that, the combination of face, fingerprint and iris gives better performance in terms of accuracy, False Acceptance Rate, False Rejection Rate with minimum computation time.
Identification of fractional order systems using modulating functions method
Liu, Dayan
2013-06-01
The modulating functions method has been used for the identification of linear and nonlinear systems. In this paper, we generalize this method to the on-line identification of fractional order systems based on the Riemann-Liouville fractional derivatives. First, a new fractional integration by parts formula involving the fractional derivative of a modulating function is given. Then, we apply this formula to a fractional order system, for which the fractional derivatives of the input and the output can be transferred into the ones of the modulating functions. By choosing a set of modulating functions, a linear system of algebraic equations is obtained. Hence, the unknown parameters of a fractional order system can be estimated by solving a linear system. Using this method, we do not need any initial values which are usually unknown and not equal to zero. Also we do not need to estimate the fractional derivatives of noisy output. Moreover, it is shown that the proposed estimators are robust against high frequency sinusoidal noises and the ones due to a class of stochastic processes. Finally, the efficiency and the stability of the proposed method is confirmed by some numerical simulations.
IDENTIFICATION METHOD FOR PENDULUM SYSTEM MOMENT OF INERTIA WITH VISCOUS DAMPING
Directory of Open Access Journals (Sweden)
A. S. Alyshev
2016-09-01
Full Text Available The paper proposes a method for identification of axial moment of inertia of the mechanical system called reaction wheel pendulum with a viscous friction in the bearings of the suspension. The method is based on the reversible symmetric motions. Pendulum system motion includes a free measured motion and reverse symmetrical motion at the same angular interval. The pendulum includes a rod with a low-power DC motor with a flywheel attached to the end of the rod. The angle of rotation and velocity of the rod and the flywheel are measured by encoders. The paper introduces a new method,presents a design formula,a mathematical model of the pendulum system and a robust motor control law for it. The method is based on energy algorithm and control residing in electric motor operational changes by means of a flywheel. The mechanical system moves symmetrically that is provided by nonuniform controlled flywheel rotation. As a result, the influence of dissipative factors on identification results is eliminated. Dynamic modeling is carried out for the pendulum system and proves high accuracy of the method. The research results can be used for identification of complex mechanical systems under the action of resistance, dissipative and other forces.
Design and development of a prototype hot spot identification system
International Nuclear Information System (INIS)
Jain, Amit; Thakur, Vaishali M.; Anilkumar, Rekha; Sawant, Pravin; Chaudhury, Probal; Pradeepkumar, K.S.
2015-01-01
The proper assessment of radiological environments inside nuclear facilities require accurate spatial mapping of the gamma ray field. A prototype Hotspot Identification System has been designed and developed in-house for gamma ray imaging by combining a gamma spectrometer with a pinhole collimator and a digital camera. The system can rapidly determine the location, distribution and intensity of gamma ray sources by carrying a scan of the suspected locations. The measured data was compared with simulated values for NaI(Tl) response, generated using the MCNP 4B Transport code. The data obtained by experimental and theoretical method are in good agreement. (author)
Developing a Speaker Identification System for the DARPA RATS Project
DEFF Research Database (Denmark)
Plchot, O; Matsoukas, S; Matejka, P
2013-01-01
present results using multiple SID systems differing mainly in the algorithm used for voice activity detection (VAD) and feature extraction. We show that (a) unsupervised VAD performs as well supervised methods in terms of downstream SID performance, (b) noise-robust feature extraction methods......This paper describes the speaker identification (SID) system developed by the Patrol team for the first phase of the DARPA RATS (Robust Automatic Transcription of Speech) program, which seeks to advance state of the art detection capabilities on audio from highly degraded communication channels. We...
High Resolution Spectrometer (HRS) particle-identification system
International Nuclear Information System (INIS)
Pratt, J.C.; Spencer, J.E.; Whitten, C.A.
1977-08-01
The functions of the particle-identification system (PIDS) designed for the High Resolution Spectrometer facility (HRS) at LAMPF are described, together with the mechanical layout, counter hardware, and associated electronics. The system was designed for easy use and to be applicable to currently proposed experiments at HRS. The several strobe signals that can be generated correspond to different event types or characteristics, and logic configuration and timing can be remotely controlled by computer. Concepts of discrete pattern recognition and multidimensional, analog pulse discrimination are used to distinguish between different event types
System for identification of microorganism and detection of infectious disorder
DEFF Research Database (Denmark)
2013-01-01
Methods for the identification of microorganisms or infectious disorders are disclosed, comprising obtaining a suitable sample from sources such as persons, animals, plants, food, water or soil. The methods also comprise providing tailored nucleic acid substrate(s) designed to react with a type 1...... topoisomerase from one or more microorganism(s) or infectious agent(s), and incubating said substrate with said sample, or extracts or preparations from the sample, so that the substrate is processed by said topoisomerase if said microorganism(s) or infectious agent(s) is present in the sample. Finally......, processed substrates are identified and potentially quantified by one or more of a range of standard molecular biology methods and read-out systems. The identification and potential quantification of microorganisms and infectious agents, including but not limited to Plasmodium falciparum and Mycobacterium...
The electronic identification, signature and security of information systems
Directory of Open Access Journals (Sweden)
Horovèák Pavel
2002-12-01
Full Text Available The contribution deals with the actual methods and technologies of information and communication systems security. It introduces the overview of electronic identification elements such as static password, dynamic password and single sign-on. Into this category belong also biometric and dynamic characteristics of verified person. Widespread is authentication based on identification elements ownership, such as various cards and authentication calculators. In the next part is specified a definition and characterization of electronic signature, its basic functions and certificate categories. Practical utilization of electronic signature consists of electronic signature acquirement, signature of outgoing email message, receiving of electronic signature and verification of electronic signature. The use of electronic signature is continuously growing and in connection with legislation development it exercises in all resorts.
Sensor network based vehicle classification and license plate identification system
Energy Technology Data Exchange (ETDEWEB)
Frigo, Janette Rose [Los Alamos National Laboratory; Brennan, Sean M [Los Alamos National Laboratory; Rosten, Edward J [Los Alamos National Laboratory; Raby, Eric Y [Los Alamos National Laboratory; Kulathumani, Vinod K [WEST VIRGINIA UNIV.
2009-01-01
Typically, for energy efficiency and scalability purposes, sensor networks have been used in the context of environmental and traffic monitoring applications in which operations at the sensor level are not computationally intensive. But increasingly, sensor network applications require data and compute intensive sensors such video cameras and microphones. In this paper, we describe the design and implementation of two such systems: a vehicle classifier based on acoustic signals and a license plate identification system using a camera. The systems are implemented in an energy-efficient manner to the extent possible using commercially available hardware, the Mica motes and the Stargate platform. Our experience in designing these systems leads us to consider an alternate more flexible, modular, low-power mote architecture that uses a combination of FPGAs, specialized embedded processing units and sensor data acquisition systems.
Boccara, Nino
2010-01-01
Modeling Complex Systems, 2nd Edition, explores the process of modeling complex systems, providing examples from such diverse fields as ecology, epidemiology, sociology, seismology, and economics. It illustrates how models of complex systems are built and provides indispensable mathematical tools for studying their dynamics. This vital introductory text is useful for advanced undergraduate students in various scientific disciplines, and serves as an important reference book for graduate students and young researchers. This enhanced second edition includes: . -recent research results and bibliographic references -extra footnotes which provide biographical information on cited scientists who have made significant contributions to the field -new and improved worked-out examples to aid a student’s comprehension of the content -exercises to challenge the reader and complement the material Nino Boccara is also the author of Essentials of Mathematica: With Applications to Mathematics and Physics (Springer, 2007).
Behavioral pattern identification for structural health monitoring in complex systems
Gupta, Shalabh
Estimation of structural damage and quantification of structural integrity are critical for safe and reliable operation of human-engineered complex systems, such as electromechanical, thermofluid, and petrochemical systems. Damage due to fatigue crack is one of the most commonly encountered sources of structural degradation in mechanical systems. Early detection of fatigue damage is essential because the resulting structural degradation could potentially cause catastrophic failures, leading to loss of expensive equipment and human life. Therefore, for reliable operation and enhanced availability, it is necessary to develop capabilities for prognosis and estimation of impending failures, such as the onset of wide-spread fatigue crack damage in mechanical structures. This dissertation presents information-based online sensing of fatigue damage using the analytical tools of symbolic time series analysis ( STSA). Anomaly detection using STSA is a pattern recognition method that has been recently developed based upon a fixed-structure, fixed-order Markov chain. The analysis procedure is built upon the principles of Symbolic Dynamics, Information Theory and Statistical Pattern Recognition. The dissertation demonstrates real-time fatigue damage monitoring based on time series data of ultrasonic signals. Statistical pattern changes are measured using STSA to monitor the evolution of fatigue damage. Real-time anomaly detection is presented as a solution to the forward (analysis) problem and the inverse (synthesis) problem. (1) the forward problem - The primary objective of the forward problem is identification of the statistical changes in the time series data of ultrasonic signals due to gradual evolution of fatigue damage. (2) the inverse problem - The objective of the inverse problem is to infer the anomalies from the observed time series data in real time based on the statistical information generated during the forward problem. A computer-controlled special
International Nuclear Information System (INIS)
Schreckenberg, M
2004-01-01
This book by Nino Boccara presents a compilation of model systems commonly termed as 'complex'. It starts with a definition of the systems under consideration and how to build up a model to describe the complex dynamics. The subsequent chapters are devoted to various categories of mean-field type models (differential and recurrence equations, chaos) and of agent-based models (cellular automata, networks and power-law distributions). Each chapter is supplemented by a number of exercises and their solutions. The table of contents looks a little arbitrary but the author took the most prominent model systems investigated over the years (and up until now there has been no unified theory covering the various aspects of complex dynamics). The model systems are explained by looking at a number of applications in various fields. The book is written as a textbook for interested students as well as serving as a comprehensive reference for experts. It is an ideal source for topics to be presented in a lecture on dynamics of complex systems. This is the first book on this 'wide' topic and I have long awaited such a book (in fact I planned to write it myself but this is much better than I could ever have written it!). Only section 6 on cellular automata is a little too limited to the author's point of view and one would have expected more about the famous Domany-Kinzel model (and more accurate citation!). In my opinion this is one of the best textbooks published during the last decade and even experts can learn a lot from it. Hopefully there will be an actualization after, say, five years since this field is growing so quickly. The price is too high for students but this, unfortunately, is the normal case today. Nevertheless I think it will be a great success! (book review)
Source term identification in atmospheric modelling via sparse optimization
Adam, Lukas; Branda, Martin; Hamburger, Thomas
2015-04-01
Inverse modelling plays an important role in identifying the amount of harmful substances released into atmosphere during major incidents such as power plant accidents or volcano eruptions. Another possible application of inverse modelling lies in the monitoring the CO2 emission limits where only observations at certain places are available and the task is to estimate the total releases at given locations. This gives rise to minimizing the discrepancy between the observations and the model predictions. There are two standard ways of solving such problems. In the first one, this discrepancy is regularized by adding additional terms. Such terms may include Tikhonov regularization, distance from a priori information or a smoothing term. The resulting, usually quadratic, problem is then solved via standard optimization solvers. The second approach assumes that the error term has a (normal) distribution and makes use of Bayesian modelling to identify the source term. Instead of following the above-mentioned approaches, we utilize techniques from the field of compressive sensing. Such techniques look for a sparsest solution (solution with the smallest number of nonzeros) of a linear system, where a maximal allowed error term may be added to this system. Even though this field is a developed one with many possible solution techniques, most of them do not consider even the simplest constraints which are naturally present in atmospheric modelling. One of such examples is the nonnegativity of release amounts. We believe that the concept of a sparse solution is natural in both problems of identification of the source location and of the time process of the source release. In the first case, it is usually assumed that there are only few release points and the task is to find them. In the second case, the time window is usually much longer than the duration of the actual release. In both cases, the optimal solution should contain a large amount of zeros, giving rise to the
Energy Technology Data Exchange (ETDEWEB)
Ojima, D. [ed.
1992-12-31
The 1990 Global Change Institute (GCI) on Earth System Modeling is the third of a series organized by the Office for Interdisciplinary Earth Studies to look in depth at particular issues critical to developing a better understanding of the earth system. The 1990 GCI on Earth System Modeling was organized around three themes: defining critical gaps in the knowledge of the earth system, developing simplified working models, and validating comprehensive system models. This book is divided into three sections that reflect these themes. Each section begins with a set of background papers offering a brief tutorial on the subject, followed by working group reports developed during the institute. These reports summarize the joint ideas and recommendations of the participants and bring to bear the interdisciplinary perspective that imbued the institute. Since the conclusion of the 1990 Global Change Institute, research programs, nationally and internationally, have moved forward to implement a number of the recommendations made at the institute, and many of the participants have maintained collegial interactions to develop research projects addressing the needs identified during the two weeks in Snowmass.
Performance study of LMS based adaptive algorithms for unknown system identification
International Nuclear Information System (INIS)
Javed, Shazia; Ahmad, Noor Atinah
2014-01-01
Adaptive filtering techniques have gained much popularity in the modeling of unknown system identification problem. These techniques can be classified as either iterative or direct. Iterative techniques include stochastic descent method and its improved versions in affine space. In this paper we present a comparative study of the least mean square (LMS) algorithm and some improved versions of LMS, more precisely the normalized LMS (NLMS), LMS-Newton, transform domain LMS (TDLMS) and affine projection algorithm (APA). The performance evaluation of these algorithms is carried out using adaptive system identification (ASI) model with random input signals, in which the unknown (measured) signal is assumed to be contaminated by output noise. Simulation results are recorded to compare the performance in terms of convergence speed, robustness, misalignment, and their sensitivity to the spectral properties of input signals. Main objective of this comparative study is to observe the effects of fast convergence rate of improved versions of LMS algorithms on their robustness and misalignment
Identification of walking human model using agent-based modelling
Shahabpoor, Erfan; Pavic, Aleksandar; Racic, Vitomir
2018-03-01
The interaction of walking people with large vibrating structures, such as footbridges and floors, in the vertical direction is an important yet challenging phenomenon to describe mathematically. Several different models have been proposed in the literature to simulate interaction of stationary people with vibrating structures. However, the research on moving (walking) human models, explicitly identified for vibration serviceability assessment of civil structures, is still sparse. In this study, the results of a comprehensive set of FRF-based modal tests were used, in which, over a hundred test subjects walked in different group sizes and walking patterns on a test structure. An agent-based model was used to simulate discrete traffic-structure interactions. The occupied structure modal parameters found in tests were used to identify the parameters of the walking individual's single-degree-of-freedom (SDOF) mass-spring-damper model using 'reverse engineering' methodology. The analysis of the results suggested that the normal distribution with the average of μ = 2.85Hz and standard deviation of σ = 0.34Hz can describe human SDOF model natural frequency. Similarly, the normal distribution with μ = 0.295 and σ = 0.047 can describe the human model damping ratio. Compared to the previous studies, the agent-based modelling methodology proposed in this paper offers significant flexibility in simulating multi-pedestrian walking traffics, external forces and simulating different mechanisms of human-structure and human-environment interaction at the same time.
Identification of single-input-single-output quantum linear systems
Levitt, Matthew; GuÅ£ǎ, Mǎdǎlin
2017-03-01
The purpose of this paper is to investigate system identification for single-input-single-output general (active or passive) quantum linear systems. For a given input we address the following questions: (1) Which parameters can be identified by measuring the output? (2) How can we construct a system realization from sufficient input-output data? We show that for time-dependent inputs, the systems which cannot be distinguished are related by symplectic transformations acting on the space of system modes. This complements a previous result of Guţă and Yamamoto [IEEE Trans. Autom. Control 61, 921 (2016), 10.1109/TAC.2015.2448491] for passive linear systems. In the regime of stationary quantum noise input, the output is completely determined by the power spectrum. We define the notion of global minimality for a given power spectrum, and characterize globally minimal systems as those with a fully mixed stationary state. We show that in the case of systems with a cascade realization, the power spectrum completely fixes the transfer function, so the system can be identified up to a symplectic transformation. We give a method for constructing a globally minimal subsystem direct from the power spectrum. Restricting to passive systems the analysis simplifies so that identifiability may be completely understood from the eigenvalues of a particular system matrix.
Closed-loop model identification of cooperative manipulators holding deformable objects
Alkathiri, A. A.; Akmeliawati, R.; Azlan, N. Z.
2017-11-01
This paper presents system identification to obtain the closed-loop models of a couple of cooperative manipulators in a system, which function to hold deformable objects. The system works using the master-slave principle. In other words, one of the manipulators is position-controlled through encoder feedback, while a force sensor gives feedback to the other force-controlled manipulator. Using the closed-loop input and output data, the closed-loop models, which are useful for model-based control design, are estimated. The criteria for model validation are a 95% fit between the measured and simulated output of the estimated models and residual analysis. The results show that for both position and force control respectively, the fits are 95.73% and 95.88%.
The KKS power plant identification system. 3. ed.
International Nuclear Information System (INIS)
1988-01-01
The previous first and second editions of the KKS, system for power plant identification, consisted of the following: introduction; instructions for application with a comparative presentation of the DIN/KKS systems and subject index; keys (functional key, equipment key, operating media key). The third edition now available incorporates the following revisions and additions: instructions for application refer exclusively to the KKS system; key updates; revised coordinating file for the equipment key and operating media key; a completely new section entitled 'Agreements for coordination of project activities', in an annex to the KKS instructions; comparison DIN/KKS adapted to new version of KKS instructions; the subject index of the 2nd edition has been extended by a keyword index referring to the explanations for application of the KKS system. (orig./HP) [de
Dhiadeen Mohammed Salih; Samsul Bahari Mohd Noor; Mohammad Hamiruce Merhaban; Raja Mohd Kamil
2015-01-01
A single hidden layer feedforward neural network (SLFN) with online sequential extreme learning machine (OSELM) algorithm has been introduced and applied in many regression problems successfully. However, using SLFN with OSELM as black-box for nonlinear system identification may lead to building models for the identified plant with inconsistency responses from control perspective. The reason can refer to the random initialization procedure of the SLFN hidden node parameters with OSELM algorit...
Active Vibration damping of Smart composite beams based on system identification technique
Bendine, Kouider; Satla, Zouaoui; Boukhoulda, Farouk Benallel; Nouari, Mohammed
2018-03-01
In the present paper, the active vibration control of a composite beam using piezoelectric actuator is investigated. The space state equation is determined using system identification technique based on the structure input output response provided by ANSYS APDL finite element package. The Linear Quadratic (LQG) control law is designed and integrated into ANSYS APDL to perform closed loop simulations. Numerical examples for different types of excitation loads are presented to test the efficiency and the accuracy of the proposed model.
Deak, R.; Bodia, L.; Aarts, H.J.M.; Maraz, A.
2004-01-01
Identification of clinical yeast isolates causing candidiasis is routinely performed by commercial yeast identification systems based on biochemical, morphological and physiological tests. These systems require 3-5 days and the proportion of identifications that are incorrect is high. Our novel and
System Identification of Mistuned Bladed Disks from Traveling Wave Response Measurements
Feiner, D. M.; Griffin, J. H.; Jones, K. W.; Kenyon, J. A.; Mehmed, O.; Kurkov, A. P.
2003-01-01
A new approach to modal analysis is presented. By applying this technique to bladed disk system identification methods, one can determine the mistuning in a rotor based on its response to a traveling wave excitation. This allows system identification to be performed under rotating conditions, and thus expands the applicability of existing mistuning identification techniques from integrally bladed rotors to conventional bladed disks.
Text-Independent Speaker Identification Using the Histogram Transform Model
DEFF Research Database (Denmark)
Ma, Zhanyu; Yu, Hong; Tan, Zheng-Hua
2016-01-01
In this paper, we propose a novel probabilistic method for the task of text-independent speaker identification (SI). In order to capture the dynamic information during SI, we design a super-MFCCs features by cascading three neighboring Mel-frequency Cepstral coefficients (MFCCs) frames together....... These super-MFCC vectors are utilized for probabilistic model training such that the speaker’s characteristics can be sufficiently captured. The probability density function (PDF) of the aforementioned super-MFCCs features is estimated by the recently proposed histogram transform (HT) method. To recedes...
Energy Technology Data Exchange (ETDEWEB)
Nicolau, Andressa dos Santos; Schirru, Roberto, E-mail: andressa@lmp.ufrj.br, E-mail: schirru@lmp.ufrj.br [Universidade Federal do Rio de Janeiro (UFRJ), Rio de Janeiro, RJ (Brazil); Lima, Alan Miranda Monteiro de [Coordenacao dos Programas de Pos-Graduacao em Engenharia (COPPE/UFRJ), Rio de Janeiro, RJ (Brazil)
2011-07-01
Transient identification systems have been proposed in order to maintain the plant operating in safe conditions and help operators in make decisions in emergency short time interval with maximum certainty associated. This article presents a system, time independent and without the use of an event that can be used as a starting point for t = 0 (reactor scram, for instance), for transient/accident identification of a pressurized water nuclear reactor (PWR). The model was developed in order to be able to recognize the normal condition and three accidents of the design basis list of the Nuclear Power Plant Angra 2, postulated in the Final Safety Analysis Report (FSAR). Were used several sets of process variables in order to establish a minimum set of variables considered necessary and sufficient. The optimization step of the identification algorithm is based upon the paradigm of Quantum Computing. In this case, the optimization metaheuristic Quantum Inspired Evolutionary Algorithm (QEA) was implemented and works as a data mining tool. The results obtained with the QEA without the time variable are compatible to the techniques in the reference literature, for the transient identification problem, with less computational effort (number of evaluations). This system allows a solution that approximates the ideal solution, the Voronoi Vectors with only one partition for the classes of accidents with robustness. (author)
International Nuclear Information System (INIS)
Nicolau, Andressa dos Santos; Schirru, Roberto; Lima, Alan Miranda Monteiro de
2011-01-01
Transient identification systems have been proposed in order to maintain the plant operating in safe conditions and help operators in make decisions in emergency short time interval with maximum certainty associated. This article presents a system, time independent and without the use of an event that can be used as a starting point for t = 0 (reactor scram, for instance), for transient/accident identification of a pressurized water nuclear reactor (PWR). The model was developed in order to be able to recognize the normal condition and three accidents of the design basis list of the Nuclear Power Plant Angra 2, postulated in the Final Safety Analysis Report (FSAR). Were used several sets of process variables in order to establish a minimum set of variables considered necessary and sufficient. The optimization step of the identification algorithm is based upon the paradigm of Quantum Computing. In this case, the optimization metaheuristic Quantum Inspired Evolutionary Algorithm (QEA) was implemented and works as a data mining tool. The results obtained with the QEA without the time variable are compatible to the techniques in the reference literature, for the transient identification problem, with less computational effort (number of evaluations). This system allows a solution that approximates the ideal solution, the Voronoi Vectors with only one partition for the classes of accidents with robustness. (author)
Rotor-System Log-Decrement Identification Using Short-Time Fourier-Transform Filter
Directory of Open Access Journals (Sweden)
Qihang Li
2015-01-01
Full Text Available With the increase of the centrifugal compressor capability, such as large scale LNG and CO2 reinjection, the stability margin evaluation is crucial to assure the compressor work in the designed operating conditions in field. Improving the precision of parameter identification of stability is essential and necessary as well. Based on the time-varying characteristics of response vibration during the sine-swept process, a short-time Fourier transform (STFT filter was introduced to increase the signal-noise ratio and improve the accuracy of the estimated stability parameters. A finite element model was established to simulate the sine-swept process, and the simulated vibration signals were used to study the filtering effect and demonstrate the feasibility to identify the stability parameters by using Multiple-Input and Multiple-Output system identification method that combines the prediction error method and instrumental variable method. Simulation results show that the identification method with STFT filter improves the estimated accuracy much well and makes the curves of frequency response function clearer. Experiment was carried out on a test rig as well, which indicates the identification method is feasible in stability identification, and the results of experiment indicate that STFT filter works very well.
Real-time diagnostics of the reusable rocket engine using on-line system identification
Guo, T.-H.; Merrill, W.; Duyar, A.
1990-01-01
A model-based failure diagnosis system has been proposed for real-time diagnosis of SSME failures. Actuation, sensor, and system degradation failure modes are all considered by the proposed system. In the case of SSME actuation failures, it was shown that real-time identification can effectively be used for failure diagnosis purposes. It is a direct approach since it reduces the detection, isolation, and the estimation of the extent of the failures to the comparison of parameter values before and after the failure. As with any model-based failure detection system, the proposed approach requires a fault model that embodies the essential characteristics of the failure process. The proposed diagnosis approach has the added advantage that it can be used as part of an intelligent control system for failure accommodation purposes.
An Asymmetric Hysteresis Model and Parameter Identification Method for Piezoelectric Actuator
Directory of Open Access Journals (Sweden)
Haichen Qin
2014-01-01
Full Text Available Hysteresis behaviour degrades the positioning accuracy of PZT actuator for ultrahigh-precision positioning applications. In this paper, a corrected hysteresis model based on Bouc-Wen model for modelling the asymmetric hysteresis behaviour of PZT actuator is established by introducing an input bias φ and an asymmetric factor ΔΦ into the standard Bouc-Wen hysteresis model. A modified particle swarm optimization (MPSO algorithm is established and realized to identify and optimize the model parameters. Feasibility and effectiveness of MPSO are proved by experiment and numerical simulation. The research results show that the corrected hysteresis model can represent the asymmetric hysteresis behaviour of the PZT actuator more accurately than the noncorrected hysteresis model based on the Bouc-Wen model. The MPSO parameter identification method can effectively identify the parameters of the corrected and noncorrected hysteresis models. Some cases demonstrate the corrected hysteresis model and the MPSO parameter identification method can be used to model smart materials and structure systems with the asymmetric hysteresis behaviour.
Directory of Open Access Journals (Sweden)
Xingjian Wang
2016-01-01
Full Text Available Attainment of high-performance motion/velocity control objectives for the Direct-Drive Rotary (DDR torque motor should fully consider practical nonlinearities in controller design, such as dynamic friction. The LuGre model has been widely utilized to describe nonlinear friction behavior; however, parameter identification for the LuGre model remains a challenge. A new dynamic friction parameter identification method for LuGre model is proposed in this study. Static parameters are identified through a series of constant velocity experiments, while dynamic parameters are obtained through a presliding process. Novel evolutionary algorithm (NEA is utilized to increase identification accuracy. Experimental results gathered from the identification experiments conducted in the study for a practical DDR torque motor control system validate the effectiveness of the proposed method.
International Nuclear Information System (INIS)
Maitri, Rohit V.; Zhang, Chao; Jiang, Jin
2017-01-01
The supercritical water cooled reactor (SCWR) is one of the six Generation IV nuclear reactors. A novel control system design method for the Canadian SCWR, known as CANDU SCWR, is developed in this study. The main dynamic of this reactor can be represented as a multiple input and multiple output (MIMO) system governed by highly non-linear partial differential equations. Even though the non-linear governing equations of such a reactor can be solved using computational fluid dynamics (CFD) techniques, it is difficult to convert the existing non-linear partial differential equations to linear dynamic models to facilitate its control system design. To deal with this problem, a new approach is developed herein, which uses the results from CFD simulations to derive the linear dynamic models around several chosen operating points based on system identification techniques. The derived linear dynamic models have been validated by comparing it with the data from the non-linear dynamic model.
An iterated cubature unscented Kalman filter for large-DoF systems identification with noisy data
Ghorbani, Esmaeil; Cha, Young-Jin
2018-04-01
Structural and mechanical system identification under dynamic loading has been an important research topic over the last three or four decades. Many Kalman-filtering-based approaches have been developed for linear and nonlinear systems. For example, to predict nonlinear systems, an unscented Kalman filter was applied. However, from extensive literature reviews, the unscented Kalman filter still showed weak performance on systems with large degrees of freedom. In this research, a modified unscented Kalman filter is proposed by integration of a cubature Kalman filter to improve the system identification performance of systems with large degrees of freedom. The novelty of this work lies on conjugating the unscented transform with the cubature integration concept to find a more accurate output from the transformation of the state vector and its related covariance matrix. To evaluate the proposed method, three different numerical models (i.e., the single degree-of-freedom Bouc-Wen model, the linear 3-degrees-of-freedom system, and the 10-degrees-of-freedom system) are investigated. To evaluate the robustness of the proposed method, high levels of noise in the measured response data are considered. The results show that the proposed method is significantly superior to the traditional UKF for noisy measured data in systems with large degrees of freedom.
Directory of Open Access Journals (Sweden)
Mohammad Reza Zakerzadeh
2011-01-01
Full Text Available Preisach model is a well-known hysteresis identification method in which the hysteresis is modeled by linear combination of hysteresis operators. Although Preisach model describes the main features of system with hysteresis behavior, due to its rigorous numerical nature, it is not convenient to use in real-time control applications. Here a novel neural network approach based on the Preisach model is addressed, provides accurate hysteresis nonlinearity modeling in comparison with the classical Preisach model and can be used for many applications such as hysteresis nonlinearity control and identification in SMA and Piezo actuators and performance evaluation in some physical systems such as magnetic materials. To evaluate the proposed approach, an experimental apparatus consisting one-dimensional flexible aluminum beam actuated with an SMA wire is used. It is shown that the proposed ANN-based Preisach model can identify hysteresis nonlinearity more accurately than the classical one. It also has powerful ability to precisely predict the higher-order hysteresis minor loops behavior even though only the first-order reversal data are in use. It is also shown that to get the same precise results in the classical Preisach model, many more data should be used, and this directly increases the experimental cost.
A Gamma Memory Neural Network for System Identification
Motter, Mark A.; Principe, Jose C.
1992-01-01
A gamma neural network topology is investigated for a system identification application. A discrete gamma memory structure is used in the input layer, providing delayed values of both the control inputs and the network output to the input layer. The discrete gamma memory structure implements a tapped dispersive delay line, with the amount of dispersion regulated by a single, adaptable parameter. The network is trained using static back propagation, but captures significant features of the system dynamics. The system dynamics identified with the network are the Mach number dynamics of the 16 Foot Transonic Tunnel at NASA Langley Research Center, Hampton, Virginia. The training data spans an operating range of Mach numbers from 0.4 to 1.3.
ANALYSIS OF SOFTWARE THREATS TO THE AUTOMATIC IDENTIFICATION SYSTEM
Directory of Open Access Journals (Sweden)
Marijan Gržan
2017-01-01
Full Text Available Automatic Identification System (AIS represents an important improvement in the fields of maritime security and vessel tracking. It is used by the signatory countries to the SOLAS Convention and by private and public providers. Its main advantage is that it can be used as an additional navigation aids, especially in avoiding collision at sea and in search and rescue operations. The present work analyses the functioning of the AIS System and the ways of exchanging data among the users. We also study one of the vulnerabilities of the System that can be abused by malicious users. The threat itself is analysed in detail in order to provide insight into the very process from the creation of a program to its implementation.
Hidden Markov model using Dirichlet process for de-identification.
Chen, Tao; Cullen, Richard M; Godwin, Marshall
2015-12-01
For the 2014 i2b2/UTHealth de-identification challenge, we introduced a new non-parametric Bayesian hidden Markov model using a Dirichlet process (HMM-DP). The model intends to reduce task-specific feature engineering and to generalize well to new data. In the challenge we developed a variational method to learn the model and an efficient approximation algorithm for prediction. To accommodate out-of-vocabulary words, we designed a number of feature functions to model such words. The results show the model is capable of understanding local context cues to make correct predictions without manual feature engineering and performs as accurately as state-of-the-art conditional random field models in a number of categories. To incorporate long-range and cross-document context cues, we developed a skip-chain conditional random field model to align the results produced by HMM-DP, which further improved the performance. Copyright © 2015 Elsevier Inc. All rights reserved.
Modeling and Model Identification of Autonomous Underwater Vehicles
2015-06-01
shape of the Standard REMUS AUV. Figure 4. Standard Hydroid REMUS 100 The REMUS AUV uses a single DC brushless motor to power a 3 bladed...The standard model comes with six brushless DC thrusters, four of them placed to control planar motion and whose angles can be manually changed prior...Reduction Project (0704-0188) Washington DC 20503. 1. AGENCY USE ONLY (Leave blank) 2. REPORT DATE June 2015 3. REPORT TYPE AND DATES COVERED
EHPS Handling Stability Analysis of Electric Bus Based on System Identification Method
Zhang, Ni; Liu, Hai-mei; Bei, Shao-yi; Li, Bo; Zhao, Jing-bo
2017-09-01
Electric hydraulic assist force steering system (EHPS system) is the steering system of electric bus, this paper presents a method of EHPS handling stability analysis based on system identification method according to the handling stability of EHPS system for electric bus. The simulation model of electro-hydraulic assist force steering system EHPS is established by using the software AMESim, and making a quantitative analysis on the characteristics of the electric assist force assisted steering system, the assist force response and stability. At the same time, we study the stability of vehicle, including hunting, transient response, return experiment, the results show that the HPS and EHPS by comparing the simulation: It improves the portability, road sense, transient response and return performance after loading the system, which verifiy the effectiveness of the control strategy that improves vehicle steering performance, and it provides the basis for the optimization of control methods in the future.
Evolution of the US Coast Guard's oil identification system
International Nuclear Information System (INIS)
Hendrick, M.S.; Reilly, T.R.
1993-01-01
The U.S. Coast Guard, tasked with the development of open-quotes procedures and techniques to be employed in identifying ... oil and hazardous substances . . . open-quotes by the 1972 Federal Water Pollution Control Act (FWPCA), developed the Oil Identification System (OIS). The OIS was based on four analytical laboratory techniques: infrared (IR) and fluorescence (FL) spectroscopy, gas chromatography (GC), and thin- layer chromatography (TLC). A Central Oil Identification Laboratory (COIL) began operation in 1977, and field laboratories (FOILS) using two of the techniques (FL and TLC) were established in many Marine Safety Offices to screen possible sources. Development of the OIS was documented in two formal reports, in 1974 and 1977. The current implementation of the OIS at COIL is still based on a multimethod approach, but it incorporates today's state-of-the-art technology and responds to the current needs of the Coast Guard. One pervasive force for change has been the affordability of computers. The rapid development of computerized instruments has resulted in improvements in the performance, ruggedness, and prices of analytical laboratory equipment. All the instruments in the authors' laboratory at the present time are interfaced to or have internal computerized data-handling systems. Fourier-transform infrared spectrometers (FTIR) have replaced older mechanically scanning, dispersive IR instruments. High-performance liquid chromatography (HPLC) has replaced TLC completely. A gas chromatography/mass spectrometer (GC/MS), a room-size research tool in 1977, sits on a benchtop in the laboratory today, and a standard method for oil identification is being developed for this technique. Laboratory strategies are now based on finding the most efficient use of resources, as rapid response times are not necessary in all cases. It may also be possible in the near future to resume field testing
Modeling Supermarket Refrigeration Systems for Supervisory Control in Smart Grid
DEFF Research Database (Denmark)
Shafiei, Seyed Ehsan; Rasmussen, Henrik; Stoustrup, Jakob
2013-01-01
A modular modeling approach of supermarket refrigeration systems (SRS) which is appropriate for smart grid control purposes is presented in this paper. Modeling and identification are performed by just knowing the system configuration and measured data disregarding the physical details. So...
Directory of Open Access Journals (Sweden)
Ando Tetsuo
2009-01-01
Full Text Available In the early deployment of electric toll collecting (ETC system, multipath interference has caused the malfunction of the system. Therefore, radio absorbers are installed in the toll gate to suppress the scattering effects. This paper presents a novel radio propagation measurement system using the beamforming with 8-elmenet antenna array to examine the power intensity distribution of the ETC gate in real time without closing the toll gates that are already open for traffic. In addition, an identification method of the individual scattering objects with 3D visualization by using virtual reality modeling language will be proposed and the validity is also demonstrated by applying to the measurement data.
Ward, Jodie; Gilmore, Simon R; Robertson, James; Peakall, Rod
2009-11-01
Plant material is frequently encountered in criminal investigations but often overlooked as potential evidence. We designed a DNA-based molecular identification system for 100 Australian grasses that consisted of a series of polymerase chain reaction assays that enabled the progressive identification of grasses to different taxonomic levels. The identification system was based on DNA sequence variation at four chloroplast and two mitochondrial loci. Seventeen informative indels and 68 single-nucleotide polymorphisms were utilized as molecular markers for subfamily to species-level identification. To identify an unknown sample to subfamily level required a minimum of four markers or nine markers for species identification. The accuracy of the system was confirmed by blind tests. We have demonstrated "proof of concept" of a molecular identification system for trace botanical samples. Our evaluation suggests that the adoption of a system that combines this approach with DNA sequencing could assist the morphological identification of grasses found as forensic evidence.
Information Systems Efficiency Model
Directory of Open Access Journals (Sweden)
Milos Koch
2017-07-01
Full Text Available This contribution discusses the basic concept of creating a new model for the efficiency and effectiveness assessment of company information systems. The present trends in this field are taken into account, and the attributes are retained of measuring the optimal solutions for a company’s ICT (the implementation, functionality, service, innovations, safety, relationships, costs, etc.. The proposal of a new model of assessment comes from our experience with formerly implemented and employed methods, methods which we have modified in time and adapted to companies’ needs but also to the necessaries of our research that has been done through the ZEFIS portal. The most noteworthy of them is the HOS method that we have discussed in a number of forums. Its main feature is the fact that it respects the complexity of an information system in correlation with the balanced state of its individual parts.
Reliability of System Identification Techniques to Assess Standing Balance in Healthy Elderly.
Pasma, Jantsje H; Engelhart, Denise; Maier, Andrea B; Aarts, Ronald G K M; van Gerven, Joop M A; Arendzen, J Hans; Schouten, Alfred C; Meskers, Carel G M; van der Kooij, Herman
2016-01-01
System identification techniques have the potential to assess the contribution of the underlying systems involved in standing balance by applying well-known disturbances. We investigated the reliability of standing balance parameters obtained with multivariate closed loop system identification techniques. In twelve healthy elderly balance tests were performed twice a day during three days. Body sway was measured during two minutes of standing with eyes closed and the Balance test Room (BalRoom) was used to apply four disturbances simultaneously: two sensory disturbances, to the proprioceptive and the visual system, and two mechanical disturbances applied at the leg and trunk segment. Using system identification techniques, sensitivity functions of the sensory disturbances and the neuromuscular controller were estimated. Based on the generalizability theory (G theory), systematic errors and sources of variability were assessed using linear mixed models and reliability was assessed by computing indexes of dependability (ID), standard error of measurement (SEM) and minimal detectable change (MDC). A systematic error was found between the first and second trial in the sensitivity functions. No systematic error was found in the neuromuscular controller and body sway. The reliability of 15 of 25 parameters and body sway were moderate to excellent when the results of two trials on three days were averaged. To reach an excellent reliability on one day in 7 out of 25 parameters, it was predicted that at least seven trials must be averaged. This study shows that system identification techniques are a promising method to assess the underlying systems involved in standing balance in elderly. However, most of the parameters do not appear to be reliable unless a large number of trials are collected across multiple days. To reach an excellent reliability in one third of the parameters, a training session for participants is needed and at least seven trials of two minutes must be
Reliability of System Identification Techniques to Assess Standing Balance in Healthy Elderly.
Directory of Open Access Journals (Sweden)
Jantsje H Pasma
Full Text Available System identification techniques have the potential to assess the contribution of the underlying systems involved in standing balance by applying well-known disturbances. We investigated the reliability of standing balance parameters obtained with multivariate closed loop system identification techniques.In twelve healthy elderly balance tests were performed twice a day during three days. Body sway was measured during two minutes of standing with eyes closed and the Balance test Room (BalRoom was used to apply four disturbances simultaneously: two sensory disturbances, to the proprioceptive and the visual system, and two mechanical disturbances applied at the leg and trunk segment. Using system identification techniques, sensitivity functions of the sensory disturbances and the neuromuscular controller were estimated. Based on the generalizability theory (G theory, systematic errors and sources of variability were assessed using linear mixed models and reliability was assessed by computing indexes of dependability (ID, standard error of measurement (SEM and minimal detectable change (MDC.A systematic error was found between the first and second trial in the sensitivity functions. No systematic error was found in the neuromuscular controller and body sway. The reliability of 15 of 25 parameters and body sway were moderate to excellent when the results of two trials on three days were averaged. To reach an excellent reliability on one day in 7 out of 25 parameters, it was predicted that at least seven trials must be averaged.This study shows that system identification techniques are a promising method to assess the underlying systems involved in standing balance in elderly. However, most of the parameters do not appear to be reliable unless a large number of trials are collected across multiple days. To reach an excellent reliability in one third of the parameters, a training session for participants is needed and at least seven trials of two
Ayvaz, M Tamer
2010-09-20
This study proposes a linked simulation-optimization model for solving the unknown groundwater pollution source identification problems. In the proposed model, MODFLOW and MT3DMS packages are used to simulate the flow and transport processes in the groundwater system. These models are then integrated with an optimization model which is based on the heuristic harmony search (HS) algorithm. In the proposed simulation-optimization model, the locations and release histories of the pollution sources are treated as the explicit decision variables and determined through the optimization model. Also, an implicit solution procedure is proposed to determine the optimum number of pollution sources which is an advantage of this model. The performance of the proposed model is evaluated on two hypothetical examples for simple and complex aquifer geometries, measurement error conditions, and different HS solution parameter sets. Identified results indicated that the proposed simulation-optimization model is an effective way and may be used to solve the inverse pollution source identification problems. Copyright (c) 2010 Elsevier B.V. All rights reserved.
Applying the Team Identification-Social Psychological Health Model to Older Sport Fans
Wann, Daniel L.; Rogers, Kelly; Dooley, Keith; Foley, Mary
2011-01-01
According to the Team Identification-Social Psychological Health Model (Wann, 2006b), team identification and social psychological health should be positively correlated because identification leads to important social connections which, in turn, facilitate well-being. Although past research substantiates the hypothesized positive relationship…
Vibratory gyroscopes : identification of mathematical model from test data
CSIR Research Space (South Africa)
Shatalov, MY
2007-05-01
Full Text Available from the experimental data is based on transformation of the system of linear differential equations of the model into an overdetermined system of linear algebraic equations with subsequent matching of the system parameters by means of the least squares...
Laamiri, Imen; Khouaja, Anis; Messaoud, Hassani
2015-03-01
In this paper we provide a convergence analysis of the alternating RGLS (Recursive Generalized Least Square) algorithm used for the identification of the reduced complexity Volterra model describing stochastic non-linear systems. The reduced Volterra model used is the 3rd order SVD-PARAFC-Volterra model provided using the Singular Value Decomposition (SVD) and the Parallel Factor (PARAFAC) tensor decomposition of the quadratic and the cubic kernels respectively of the classical Volterra model. The Alternating RGLS (ARGLS) algorithm consists on the execution of the classical RGLS algorithm in alternating way. The ARGLS convergence was proved using the Ordinary Differential Equation (ODE) method. It is noted that the algorithm convergence canno׳t be ensured when the disturbance acting on the system to be identified has specific features. The ARGLS algorithm is tested in simulations on a numerical example by satisfying the determined convergence conditions. To raise the elegies of the proposed algorithm, we proceed to its comparison with the classical Alternating Recursive Least Squares (ARLS) presented in the literature. The comparison has been built on a non-linear satellite channel and a benchmark system CSTR (Continuous Stirred Tank Reactor). Moreover the efficiency of the proposed identification approach is proved on an experimental Communicating Two Tank system (CTTS). Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.
Fuzzy-Rule-Based Object Identification Methodology for NAVI System
Directory of Open Access Journals (Sweden)
Yaacob Sazali
2005-01-01
Full Text Available We present an object identification methodology applied in a navigation assistance for visually impaired (NAVI system. The NAVI has a single board processing system (SBPS, a digital video camera mounted headgear, and a pair of stereo earphones. The captured image from the camera is processed by the SBPS to generate a specially structured stereo sound suitable for vision impaired people in understanding the presence of objects/obstacles in front of them. The image processing stage is designed to identify the objects in the captured image. Edge detection and edge-linking procedures are applied in the processing of image. A concept of object preference is included in the image processing scheme and this concept is realized using a fuzzy-rule base. The blind users are trained with the stereo sound produced by NAVI for achieving a collision-free autonomous navigation.
Fuzzy-Rule-Based Object Identification Methodology for NAVI System
Nagarajan, R.; Sainarayanan, G.; Yaacob, Sazali; Porle, Rosalyn R.
2005-12-01
We present an object identification methodology applied in a navigation assistance for visually impaired (NAVI) system. The NAVI has a single board processing system (SBPS), a digital video camera mounted headgear, and a pair of stereo earphones. The captured image from the camera is processed by the SBPS to generate a specially structured stereo sound suitable for vision impaired people in understanding the presence of objects/obstacles in front of them. The image processing stage is designed to identify the objects in the captured image. Edge detection and edge-linking procedures are applied in the processing of image. A concept of object preference is included in the image processing scheme and this concept is realized using a fuzzy-rule base. The blind users are trained with the stereo sound produced by NAVI for achieving a collision-free autonomous navigation.
Identification of reverse logistics decision types from mathematical models
Directory of Open Access Journals (Sweden)
Pascual Cortés Pellicer
2018-04-01
Full Text Available Purpose: The increase in social awareness, politics and environmental regulation, the scarcity of raw materials and the desired “green” image, are some of the reasons that lead companies to decide for implement processes of Reverse Logistics (RL. At the time when incorporate new RL processes as key business processes, new and important decisions need to be made. Identification and knowledge of these decisions, including the information available and the implications for the company or supply chain, will be fundamental for decision-makers to achieve the best results. In the present work, the main types of RL decisions are identified. Design/methodology/approach: This paper is based on the analysis of mathematical models designed as tools to aid decision making in the field of RL. Once the types of interest work to be analyzed are defined, those studies that really deal about the object of study are searched and analyzed. The decision variables that are taken at work are identified and grouped according to the type of decision and, finally, are showed the main types of decisions used in mathematical models developed in the field of RL. Findings: The principal conclusion of the research is that the most commonly addressed decisions with mathematical models in the field of RL are those related to the network’s configuration, followed by tactical/operative decisions such as the selections of product’s treatments to realize and the policy of returns or prices, among other decisions. Originality/value: The identification of the main decisions types of the reverse logistics will allow the managers of these processes to know and understand them better, while offer an integrated vision of them, favoring the achievement of better results.
An application of multilevel flow modelling method for nuclear plant state identification
International Nuclear Information System (INIS)
Businaro, T.; Di Lorenzo, A.; Meo, G.B.; Rabbani, M.I.; Rubino, E.
1986-01-01
With the advent of advanced digital techniques it has been rendered possible, necessity of which has long since been recognised, to develop a computer based man-machine interface and hance an expert system based on knowledge based decision making for operator support in the control rooms of nuclear plants. The Multilevel Flow Modelling method developed at RISO Laboratories, Denmark, has been applied in the present experiment to model Italian PEC reactor and to verify applicability of this method in plant state identification. In MFM method functional structure of a process plant is described in terms of a set of interrelated mass and energy flow structures on different levels of physical aggregation
On the identification of fractionally cointegrated VAR models with the F(d) condition
DEFF Research Database (Denmark)
Carlini, Federico; Santucci de Magistris, Paolo
for any choice of the lag length, also when the true cointegration rank is known. The properties of these multiple non-identified models are studied and a necessary and sufficient condition for the identification of the fractional parameters of the system is provided. The condition is named F(d......) and it is a generalization to the fractional case of the I(1) condition in the VECM model. The assessment of the F(d) condition in the empirical analysis is relevant for the determination of the fractional parameters as well as the number of lags. The paper also illustrates the indeterminacy between the cointegration rank...
Dynamics of Practical Premixed Flames, Part I: Model Structure and Identification
Directory of Open Access Journals (Sweden)
A. Huber
2009-06-01
Full Text Available For the analysis of thermoacoustic instabilities it is most important to determine the dynamic flame response to acoustic disturbances. Premixed flames are often modelled as single-input single-output system, where the “output” (the overall rate of heat release responds to a single “input” variable (often the velocity at the exit of the burner nozzle. However, for practical premixed flames, where perturbations of pressure or velocity at the fuel injector will modulate the fuel equivalence ratio, the heat release rate will respond to fluctuations of equivalence ratio as well as nozzle mass flow rate. In this case, a multiple-input, single-output (MISO model structure for the flame is appropriate. Such a model structure is developed in the present paper. Staged fuel injection as well as fuel line impedances can be taken into account, the integration with low-order or finite-element based models for stability analysis is straightforward. In order to determine unit impulse and frequency response functions for such a model structure, an identification scheme based on unsteady CFD calculation with broadband excitation followed by correlation analysis is proposed and validated successfully. Identification of MISO model coefficients is a challenging task, especially in the presence of noise. Therefore criteria are introduced which allow to ascertain a posteriori how well the identified model represents the true system dynamics. Using these criteria, it is investigated how excitation signal type, time series length and signal-to-noise ratio influence the results of the identification process. Consequences for passive design strategies based on multi-stage fuel injection and experimental work on practical premixed flame dynamics are discussed.
Directory of Open Access Journals (Sweden)
Zhaohua Gong
2012-01-01
Full Text Available Mathematical modeling and parameter estimation are critical steps in the optimization of biotechnological processes. In the 1,3-propanediol (1,3-PD production by glycerol fermentation process under anaerobic conditions, 3-hydroxypropionaldehyde (3-HPA accumulation would arouse an irreversible cessation of the fermentation process. Considering 3-HPA inhibitions to cells growth and to activities of enzymes, we propose a novel mathematical model to describe glycerol continuous cultures. Some properties of the above model are discussed. On the basis of the concentrations of extracellular substances, a parameter identification model is established to determine the kinetic parameters in the presented system. Through the penalty function technique combined with an extension of the state space method, an improved genetic algorithm is then constructed to solve the parameter identification model. An illustrative numerical example shows the appropriateness of the proposed model and the validity of optimization algorithm. Since it is difficult to measure the concentrations of intracellular substances, a quantitative robustness analysis method is given to infer whether the model is plausible for the intracellular substances. Numerical results show that the proposed model is of good robustness.
On the Optimal Location of Sensors for Parametric Identification of Linear Systems
DEFF Research Database (Denmark)
Kirkegaard, Poul Henning; Brincker, Rune
1994-01-01
. It is assumed most often that the results of the measurements are statistically independent random variables. In an example the importance of considering the measurements as statistically dependent random variables is shown. The covariance of the model parameters expected to be obtained is investigated......An outline of the field of optimal location of sensors for parametric identification of linear structural systems is presented. There are few papers devoted to the case of optimal location of sensors in which the measurements are modeled by a random field with non-trivial covariance function...
Zaychik, Kirill B.; Cardullo, Frank M.
2012-01-01
Results have been obtained using conventional techniques to model the generic human operator?s control behavior, however little research has been done to identify an individual based on control behavior. The hypothesis investigated is that different operators exhibit different control behavior when performing a given control task. Two enhancements to existing human operator models, which allow personalization of the modeled control behavior, are presented. One enhancement accounts for the testing control signals, which are introduced by an operator for more accurate control of the system and/or to adjust the control strategy. This uses the Artificial Neural Network which can be fine-tuned to model the testing control. Another enhancement takes the form of an equiripple filter which conditions the control system power spectrum. A novel automated parameter identification technique was developed to facilitate the identification process of the parameters of the selected models. This utilizes a Genetic Algorithm based optimization engine called the Bit-Climbing Algorithm. Enhancements were validated using experimental data obtained from three different sources: the Manual Control Laboratory software experiments, Unmanned Aerial Vehicle simulation, and NASA Langley Research Center Visual Motion Simulator studies. This manuscript also addresses applying human operator models to evaluate the effectiveness of motion feedback when simulating actual pilot control behavior in a flight simulator.
International Nuclear Information System (INIS)
Dominguez, Manuel
1998-01-01
In the frame of complex systems modelization, we describe in this report the contribution of neural networks to mechanical friction modelization. This thesis is divided in three parts, each one corresponding to every stage of the realized work. The first part takes stock of the properties of neural networks by replacing them in the statistic frame of learning theory (particularly: non-linear and non-parametric regression models) and by showing the existing links with other more 'classic' techniques from automatics. We show then how identification models can be integrated in the neural networks description as a larger nonlinear model class. A methodology of neural networks use have been developed. We focused on validation techniques using correlation functions for non-linear systems, and on the use of regularization methods. The second part deals with the problematic of friction in mechanical systems. Particularly, we present the main current identified physical phenomena, which are integrated in advanced friction modelization. Characterization of these phenomena allows us to state a priori knowledge to be used in the identification stage. We expose some of the most well-known friction models: Dahl's model, Reset Integrator and Canuda's dynamical model, which are then used in simulation studies. The last part links the former one by illustrating a real-world application: an electric jack from SFIM-Industries, used in the Very Large Telescope (VLT) control scheme. This part begins with physical system presentation. The results are compared with more 'classic' methods. We finish using neural networks compensation scheme in closed-loop control. (author) [fr
System identification of timber masonry walls using shaking table test
Roy, Timir B.; Guerreiro, Luis; Bagchi, Ashutosh
2017-04-01
Dynamic study is important in order to design, repair and rehabilitation of structures. It has played an important role in the behavior characterization of structures; such as: bridges, dams, high rise buildings etc. There had been substantial development in this area over the last few decades, especially in the field of dynamic identification techniques of structural systems. Frequency Domain Decomposition (FDD) and Time Domain Decomposition are most commonly used methods to identify modal parameters; such as: natural frequency, modal damping and mode shape. The focus of the present research is to study the dynamic characteristics of typical timber masonry walls commonly used in Portugal. For that purpose, a multi-storey structural prototype of such wall has been tested on a seismic shake table at the National Laboratory for Civil Engineering, Portugal (LNEC). Signal processing has been performed of the output response, which is collected from the shaking table experiment of the prototype using accelerometers. In the present work signal processing of the output response, based on the input response has been done in two ways: FDD and Stochastic Subspace Identification (SSI). In order to estimate the values of the modal parameters, algorithms for FDD are formulated and parametric functions for the SSI are computed. Finally, estimated values from both the methods are compared to measure the accuracy of both the techniques.
Structural system identification based on variational mode decomposition
Bagheri, Abdollah; Ozbulut, Osman E.; Harris, Devin K.
2018-03-01
In this paper, a new structural identification method is proposed to identify the modal properties of engineering structures based on dynamic response decomposition using the variational mode decomposition (VMD). The VMD approach is a decomposition algorithm that has been developed as a means to overcome some of the drawbacks and limitations of the empirical mode decomposition method. The VMD-based modal identification algorithm decomposes the acceleration signal into a series of distinct modal responses and their respective center frequencies, such that when combined their cumulative modal responses reproduce the original acceleration response. The decaying amplitude of the extracted modal responses is then used to identify the modal damping ratios using a linear fitting function on modal response data. Finally, after extracting modal responses from available sensors, the mode shape vector for each of the decomposed modes in the system is identified from all obtained modal response data. To demonstrate the efficiency of the algorithm, a series of numerical, laboratory, and field case studies were evaluated. The laboratory case study utilized the vibration response of a three-story shear frame, whereas the field study leveraged the ambient vibration response of a pedestrian bridge to characterize the modal properties of the structure. The modal properties of the shear frame were computed using analytical approach for a comparison with the experimental modal frequencies. Results from these case studies demonstrated that the proposed method is efficient and accurate in identifying modal data of the structures.
Encryption and validation of multiple signals for optical identification systems
Energy Technology Data Exchange (ETDEWEB)
Perez-Cabre, E [Universitat PoliteGcnica de Catalunya, Department Optica i Optometria, Violinista Vellsola 37, 08222 Terrassa (Spain); Millan, M S [Universitat PoliteGcnica de Catalunya, Department Optica i Optometria, Violinista Vellsola 37, 08222 Terrassa (Spain); Javidi, B [University of Connecticut, Electrical and Computer Engineering Department, 371 Fairfield Road, CT 06269 Storrs (United States)
2007-07-15
Multifactor encryption-authentication technique reinforces optical security by allowing the simultaneous A N D-verification of more than one primary image. Instead of basing the identification on a unique signature or piece of information, our goal is to authenticate a given person, object, vehicle by the simultaneous recognition of several factors. Some of them are intrinsic to the person and object or vehicle under control. Other factors, act as keys of the authentication step. Such a system is proposed for situations such as the access control to restricted areas, where the demand of security is high. The multifactor identification method involves double random-phase encoding, fully phase-based encryption and a combined nonlinear joint transform correlator and a classical 4f-correlator for simultaneous recognition and authentication of multiple images. The encoded signal fulfils the general requirements of invisible content, extreme difficulty in counterfeiting and real-time automatic verification. Four reference double-phase encoded images are compared with the retrieved input images obtained in situ from the person or the vehicle whose authentication is wanted and from a database. A recognition step based on the correlation between the signatures and the stored references determines the authentication or rejection of the person and object under surveillance.
Revised Culture-Based System for Identification of Malassezia Species▿
Kaneko, Takamasa; Makimura, Koichi; Abe, Michiko; Shiota, Ryoko; Nakamura, Yuka; Kano, Rui; Hasegawa, Atsuhiko; Sugita, Takashi; Shibuya, Shuichi; Watanabe, Shinichi; Yamaguchi, Hideyo; Abe, Shigeru; Okamura, Noboru
2007-01-01
Forty-six strains of Malassezia spp. with atypical biochemical features were isolated from 366 fresh clinical isolates from human subjects and dogs. Isolates obtained in this study included 2 (4.7%) lipid-dependent M. pachydermatis isolates; 1 (2.4%) precipitate-producing and 6 (14.6%) non-polyethoxylated castor oil (Cremophor EL)-assimilating M. furfur isolates; and 37 (34.3%) M. slooffiae isolates that were esculin hydrolyzing, 17 (15.7%) that were non-tolerant of growth at 40°C, and 2 (1.9%) that assimilated polyethoxylated castor oil. Although their colony morphologies and sizes were characteristic on CHROMagar Malassezia medium (CHROM), all strains of M. furfur developed large pale pink and wrinkled colonies, and all strains of M. slooffiae developed small (Malassezia species, M. globosa, M. restricta, and M. furfur, were correctly identified by their biochemical characteristics and colony morphologies. The results presented here indicate that our proposed identification system will be useful as a routine tool for the identification of clinically important Malassezia species in clinical laboratories. PMID:17881545
Portable bacterial identification system based on elastic light scatter patterns
Directory of Open Access Journals (Sweden)
Bae Euiwon
2012-08-01
Full Text Available Abstract Background Conventional diagnosis and identification of bacteria requires shipment of samples to a laboratory for genetic and biochemical analysis. This process can take days and imposes significant delay to action in situations where timely intervention can save lives and reduce associated costs. To enable faster response to an outbreak, a low-cost, small-footprint, portable microbial-identification instrument using forward scatterometry has been developed. Results This device, weighing 9 lb and measuring 12 × 6 × 10.5 in., utilizes elastic light scatter (ELS patterns to accurately capture bacterial colony characteristics and delivers the classification results via wireless access. The overall system consists of two CCD cameras, one rotational and one translational stage, and a 635-nm laser diode. Various software algorithms such as Hough transform, 2-D geometric moments, and the traveling salesman problem (TSP have been implemented to provide colony count and circularity, centering process, and minimized travel time among colonies. Conclusions Experiments were conducted with four bacteria genera using pure and mixed plate and as proof of principle a field test was conducted in four different locations where the average classification rate ranged between 95 and 100%.
Nonlinear System Identification and Its Applications in Fault Detection and Diagnosis
DEFF Research Database (Denmark)
Sun, Zhen
Interest in nonlinear system identification has grown significantly in recent years. It is much more difficult to develop general results than the concern for linear models since the nonlinear model structures are often much more complicated. As a consequence, the thesis only considers two differ...... be performed by identifying these fault related parameters. Afterwards, the decision whether the fault happened or how large the fault is can be made by comparison and analysis based on the estimated values....... and then for a space robot system. Secondly, the system considered is described by a nonlinear FOPDT model. This type of FOPDT model is an extension of the traditional FOPDT model which pre-assumes all the model parameters are constants. The nonlinearity that is defined in the model is reflected in its two categories...... refrigeration system. The proposed models and methods are further extended for the purpose of Fault Detection and Diagnosis (FDD). In a system where it exists possible parametric fault, if some fault happens, one or several parameters related to fault may change their values. Then the FDD procedure can...
Integration of system identification and robust controller designs for flexible structures in space
Juang, Jer-Nan; Lew, Jiann-Shiun
1990-01-01
A novel approach is developed using experimental data from the structural testing of a physical system to identify a reduced-order model and its error for a robust controller design. There are three steps involved in the approach. First, an approximately balanced model is identified using the eigensystem realization algorithm, which is an identification algorithm. Second, the model error is calculated and described in frequency domain in terms of the H(infinity) norm. Third, a pole-placement technique in combination with an H(infinity) control method is applied to design a controller for the system. A set of experimental data from an existing setup, namely the Mini-Mast system, is used to illustrate and verify the approach development in this paper.
WH-EA: An Evolutionary Algorithm for Wiener-Hammerstein System Identification
Directory of Open Access Journals (Sweden)
J. Zambrano
2018-01-01
Full Text Available Current methods to identify Wiener-Hammerstein systems using Best Linear Approximation (BLA involve at least two steps. First, BLA is divided into obtaining front and back linear dynamics of the Wiener-Hammerstein model. Second, a refitting procedure of all parameters is carried out to reduce modelling errors. In this paper, a novel approach to identify Wiener-Hammerstein systems in a single step is proposed. This approach is based on a customized evolutionary algorithm (WH-EA able to look for the best BLA split, capturing at the same time the process static nonlinearity with high precision. Furthermore, to correct possible errors in BLA estimation, the locations of poles and zeros are subtly modified within an adequate search space to allow a fine-tuning of the model. The performance of the proposed approach is analysed by using a demonstration example and a nonlinear system identification benchmark.
Identification of the 1PL Model with Guessing Parameter: Parametric and Semi-Parametric Results
San Martin, Ernesto; Rolin, Jean-Marie; Castro, Luis M.
2013-01-01
In this paper, we study the identification of a particular case of the 3PL model, namely when the discrimination parameters are all constant and equal to 1. We term this model, 1PL-G model. The identification analysis is performed under three different specifications. The first specification considers the abilities as unknown parameters. It is…
Deciphering the Crowd: Modeling and Identification of Pedestrian Group Motion
Directory of Open Access Journals (Sweden)
Norihiro Hagita
2013-01-01
Full Text Available Associating attributes to pedestrians in a crowd is relevant for various areas like surveillance, customer profiling and service providing. The attributes of interest greatly depend on the application domain and might involve such social relations as friends or family as well as the hierarchy of the group including the leader or subordinates. Nevertheless, the complex social setting inherently complicates this task. We attack this problem by exploiting the small group structures in the crowd. The relations among individuals and their peers within a social group are reliable indicators of social attributes. To that end, this paper identifies social groups based on explicit motion models integrated through a hypothesis testing scheme. We develop two models relating positional and directional relations. A pair of pedestrians is identified as belonging to the same group or not by utilizing the two models in parallel, which defines a compound hypothesis testing scheme. By testing the proposed approach on three datasets with different environmental properties and group characteristics, it is demonstrated that we achieve an identification accuracy of 87% to 99%. The contribution of this study lies in its definition of positional and directional relation models, its description of compound evaluations, and the resolution of ambiguities with our proposed uncertainty measure based on the local and global indicators of group relation.
Deciphering the crowd: modeling and identification of pedestrian group motion.
Yücel, Zeynep; Zanlungo, Francesco; Ikeda, Tetsushi; Miyashita, Takahiro; Hagita, Norihiro
2013-01-14
Associating attributes to pedestrians in a crowd is relevant for various areas like surveillance, customer profiling and service providing. The attributes of interest greatly depend on the application domain and might involve such social relations as friends or family as well as the hierarchy of the group including the leader or subordinates. Nevertheless, the complex social setting inherently complicates this task. We attack this problem by exploiting the small group structures in the crowd. The relations among individuals and their peers within a social group are reliable indicators of social attributes. To that end, this paper identifies social groups based on explicit motion models integrated through a hypothesis testing scheme. We develop two models relating positional and directional relations. A pair of pedestrians is identified as belonging to the same group or not by utilizing the two models in parallel, which defines a compound hypothesis testing scheme. By testing the proposed approach on three datasets with different environmental properties and group characteristics, it is demonstrated that we achieve an identification accuracy of 87% to 99%. The contribution of this study lies in its definition of positional and directional relation models, its description of compound evaluations, and the resolution of ambiguities with our proposed uncertainty measure based on the local and global indicators of group relation.
Identification of a Time-Varying, Box-Jenkins Model of Intrinsic Joint Compliance.
Guarin, Diego L; Kearney, Robert E
2017-08-01
The mechanical properties of a joint are determined by the combination of intrinsic and reflex mechanisms. However, in some situations the reflex contributions are small so that intrinsic mechanisms play the dominant role in the control of posture and movement. The intrinsic mechanisms, characterized by the joint compliance, can be described well by a second order, linear model for small perturbations around an operating point defined by mean position and torque. However, the compliance parameters depend strongly on the operating point. Thus, for functional activities, such as walking, where position and torque undergo large, rapid changes, the joint compliance will also present large, fast changes and so will appear to be Time-Varying (TV). Therefore, a TV system identification algorithm must be used to characterize these changes. This paper introduces a novel TV system identification algorithm that achieves this. The method extends an instrumental-variable based algorithm for the identification of linear, TV, parametric, Box-Jenkins models to use periodic data. Simulation studies demonstrate that the new algorithm accurately tracks the changes in intrinsic joint compliance expected during walking. Moreover, the method performs well with the complex noise encountered in practice. Consequently the new method should be a valuable tool for the study of joint mechanics during functional activities.
Hou, Zeyu; Lu, Wenxi
2018-05-01
Knowledge of groundwater contamination sources is critical for effectively protecting groundwater resources, estimating risks, mitigating disaster, and designing remediation strategies. Many methods for groundwater contamination source identification (GCSI) have been developed in recent years, including the simulation-optimization technique. This study proposes utilizing a support vector regression (SVR) model and a kernel extreme learning machine (KELM) model to enrich the content of the surrogate model. The surrogate model was itself key in replacing the simulation model, reducing the huge computational burden of iterations in the simulation-optimization technique to solve GCSI problems, especially in GCSI problems of aquifers contaminated by dense nonaqueous phase liquids (DNAPLs). A comparative study between the Kriging, SVR, and KELM models is reported. Additionally, there is analysis of the influence of parameter optimization and the structure of the training sample dataset on the approximation accuracy of the surrogate model. It was found that the KELM model was the most accurate surrogate model, and its performance was significantly improved after parameter optimization. The approximation accuracy of the surrogate model to the simulation model did not always improve with increasing numbers of training samples. Using the appropriate number of training samples was critical for improving the performance of the surrogate model and avoiding unnecessary computational workload. It was concluded that the KELM model developed in this work could reasonably predict system responses in given operation conditions. Replacing the simulation model with a KELM model considerably reduced the computational burden of the simulation-optimization process and also maintained high computation accuracy.
A knowledge-based approach to identification and adaptation in dynamical systems control
Glass, B. J.; Wong, C. M.
1988-01-01
Artificial intelligence techniques are applied to the problems of model form and parameter identification of large-scale dynamic systems. The object-oriented knowledge representation is discussed in the context of causal modeling and qualitative reasoning. Structured sets of rules are used for implementing qualitative component simulations, for catching qualitative discrepancies and quantitative bound violations, and for making reconfiguration and control decisions that affect the physical system. These decisions are executed by backward-chaining through a knowledge base of control action tasks. This approach was implemented for two examples: a triple quadrupole mass spectrometer and a two-phase thermal testbed. Results of tests with both of these systems demonstrate that the software replicates some or most of the functionality of a human operator, thereby reducing the need for a human-in-the-loop in the lower levels of control of these complex systems.
Modelling and Analysing Socio-Technical Systems
DEFF Research Database (Denmark)
Aslanyan, Zaruhi; Ivanova, Marieta Georgieva; Nielson, Flemming
2015-01-01
Modern organisations are complex, socio-technical systems consisting of a mixture of physical infrastructure, human actors, policies and processes. An in-creasing number of attacks on these organisations exploits vulnerabilities on all different levels, for example combining a malware attack...... with social engineering. Due to this combination of attack steps on technical and social levels, risk assessment in socio-technical systems is complex. Therefore, established risk assessment methods often abstract away the internal structure of an organisation and ignore human factors when modelling...... and assessing attacks. In our work we model all relevant levels of socio-technical systems, and propose evaluation techniques for analysing the security properties of the model. Our approach simplifies the identification of possible attacks and provides qualified assessment and ranking of attacks based...
Shokravi, H.; Bakhary, NH
2017-11-01
Subspace System Identification (SSI) is considered as one of the most reliable tools for identification of system parameters. Performance of a SSI scheme is considerably affected by the structure of the associated identification algorithm. Weight matrix is a variable in SSI that is used to reduce the dimensionality of the state-space equation. Generally one of the weight matrices of Principle Component (PC), Unweighted Principle Component (UPC) and Canonical Variate Analysis (CVA) are used in the structure of a SSI algorithm. An increasing number of studies in the field of structural health monitoring are using SSI for damage identification. However, studies that evaluate the performance of the weight matrices particularly in association with accuracy, noise resistance, and time complexity properties are very limited. In this study, the accuracy, noise-robustness, and time-efficiency of the weight matrices are compared using different qualitative and quantitative metrics. Three evaluation metrics of pole analysis, fit values and elapsed time are used in the assessment process. A numerical model of a mass-spring-dashpot and operational data is used in this research paper. It is observed that the principal components obtained using PC algorithms are more robust against noise uncertainty and give more stable results for the pole distribution. Furthermore, higher estimation accuracy is achieved using UPC algorithm. CVA had the worst performance for pole analysis and time efficiency analysis. The superior performance of the UPC algorithm in the elapsed time is attributed to using unit weight matrices. The obtained results demonstrated that the process of reducing dimensionality in CVA and PC has not enhanced the time efficiency but yield an improved modal identification in PC.
Experimental Grey Box Model Identification of an Active Gas Bearing
DEFF Research Database (Denmark)
Theisen, Lukas Roy Svane; Pierart Vásquez, Fabián Gonzalo; Niemann, Hans Henrik
2014-01-01
Gas bearings have inherent dynamics that gives rise to low damping and potential instability at certain rotational speeds. Required damping and stabilization properties can be achieved by active ow control if bearing parameters are known. This paper deals with identifacation of parameters...... in a dynamic model of an active gas bearing and subsequent control loop design. A grey box model is determined based on experiments where piezo actuated valves are used to perturb the journal and hence excite the rotor-bearing system. Such modelling from actuator to output is shown to effciently support...... controller design, in contrast to impact models that focus on resonance dynamics. The identified model is able to accurately reproduce the lateral dynamics of the rotor-bearing system in a desired operating range, in this case around the first two natural frequencies. The identified models are validated...
Anti-collision radio-frequency identification system using passive SAW tags
Sorokin, A. V.; Shepeta, A. P.
2017-06-01
Modern multi sensor systems should have high operating speed and resistance to climate impacts. Radiofrequency systems use passive SAW tags for identification items and vehicles. These tags find application in industry, traffic remote control systems, and railway remote traffic control systems for identification and speed measuring. However, collision of the passive SAW RFID tags hinders development passive RFID SAW technology in Industry. The collision problem for passive SAW tags leads for incorrect identification and encoding each tag. In our researching, we suggest approach for identification of several passive SAW tags in collision case.
Medical isotope identification with large mobile detection systems
Mukhopadhyay, Sanjoy; Maurer, Richard
2012-10-01
The Remote Sensing laboratory (RSL) of National Security Technologies Inc. has built an array of large (5.08 - cm x 10.16 - cm x 40.6 - cm) thallium doped sodium iodide (NaI: Tl) scintillators to locate and screen gamma-ray emitting radioisotopes that are of interests to radiological emergency responders [1]. These vehicle mounted detectors provide the operators with rapid, simple, specific information for radiological threat assessment. Applications include large area inspection, customs inspection, border protection, emergency response, and monitoring of radiological facilities. These RSL mobile units are currently being upgraded to meet the Defense Threat Reduction Agency mission requirements for a next-generation system capable of detecting and identifying nuclear threat materials. One of the challenging problems faced by these gamma-ray detectors is the unambiguous identification of medical isotopes like 131I (364.49 keV [81.7%], 636.99 keV [7.17%]), 99Tcm (140.51 keV [89.1%]) and 67Ga (184.6 keV [19.7%], 300.2 [16.0%], 393.5 [4.5%] that are used in radionuclide therapy and often have overlapping gamma-ray energy regions of interest (ROI). The problem is made worse by short (about 5 seconds) acquisition time of the spectral data necessary for dynamic mobile detectors. This article describes attempts to identify medical isotopes from data collected from this mobile detection system in a short period of time (not exceeding 5 secs) and a large standoff distance (typically 10 meters) The mobile units offer identification capabilities that are based on hardware auto stabilization of the amplifier gain. The 1461 keV gamma-energy line from 40K is tracked. It uses gamma-ray energy windowing along with embedded mobile Gamma Detector Response and Analysis Software (GADRAS) [2] simultaneously to deconvolve any overlapping gamma-energy ROIs. These high sensitivity detectors are capable of resolving complex masking scenarios and exceed all ANSI N42.34 (2006) requirements
Design of the TORCH detector: A Cherenkov based Time-of-Flight system for particle identification
AUTHOR|(CDS)2078663; Rademacker, Jonas
The LHCb detector at the LHC collider has been very successfully operated over the past years, providing new and profound insights into the Standard Model, in particular through study of $b$-hadrons to achieve a better understanding of CP violation. One of the key components of LHCb is its particle identification system, comprised of two RICH detectors, which allow for high precision separation of particle species over a large momentum range. In order to retain and improve the performance of the particle identification system in light of the LHCb upgrade, the TORCH detector has been proposed to supplement the RICH system at low momentum (2-10 GeV/c). The TORCH detector provides (charged) particle identification through precision timing of particles passing through it. Assuming a known momentum from the tracking, it is possible to derive the species of a particle from the time of flight from its primary vertex. This measurement is achieved by timing and combining photons generated in a solid radiator. The geom...
Identification and simulation evaluation of an AH-64 helicopter hover math model
Schroeder, J. A.; Watson, D. C.; Tischler, M. B.; Eshow, M. M.
1991-01-01
Frequency-domain parameter-identification techniques were used to develop a hover mathematical model of the AH-64 Apache helicopter from flight data. The unstable AH-64 bare-airframe characteristics without a stability-augmentation system were parameterized in the convectional stability-derivative form. To improve the model's vertical response, a simple transfer-function model approximating the effects of dynamic inflow was developed. Additional subcomponents of the vehicle were also modeled and simulated, such as a basic engine response for hover and the vehicle stick dynamic characteristics. The model, with and without stability augmentation, was then evaluated by AH-64 pilots in a moving-base simulation. It was the opinion of the pilots that the simulation was a satisfactory representation of the aircraft for the tasks of interest. The principal negative comment was that height control was more difficult in the simulation than in the aircraft.
Development of an Effective System Identification and Control Capability for Quad-copter UAVs
Wei, Wei
In recent years, with the promise of extensive commercial applications, the popularity of Unmanned Aerial Vehicles (UAVs) has dramatically increased as witnessed by publications and mushrooming research and educational programs. Over the years, multi-copter aircraft have been chosen as a viable configuration for small-scale VTOL UAVs in the form of quad-copters, hexa-copters and octo-copters. Compared to the single main rotor configuration such as the conventional helicopter, multi-copter airframes require a simpler feedback control system and fewer mechanical parts. These characteristics make these UAV platforms, such as quad-copter which is the main emphasis in this dissertation, a rugged and competitive candidate for many applications in both military and civil areas. Because of its configuration and relative size, the small-scale quad-copter UAV system is inherently very unstable. In order to develop an effective control system through simulation techniques, obtaining an accurate dynamic model of a given quad-copter is imperative. Moreover, given the anticipated stringent safety requirements, fault tolerance will be a crucial component of UAV certification. Accurate dynamic modeling and control of this class of UAV is an enabling technology and is imperative for future commercial applications. In this work, the dynamic model of a quad-copter system in hover flight was identified using frequency-domain system identification techniques. A new and unique experimental system, data acquisition and processing procedure was developed catering specifically to the class of electric powered multi-copter UAV systems. The Comprehensive Identification from FrEquency Responses (CIFER RTM) software package, developed by US Army Aviation Development Directorate -- AFDD, was utilized along with flight tests to develop dynamic models of the quad-copter system. A new set of flight tests were conducted and the predictive capability of the dynamic models were successfully validated
Identification of System Parameters by the Random Decrement Technique
DEFF Research Database (Denmark)
Brincker, Rune; Kirkegaard, Poul Henning; Rytter, Anders
-Walker equations and finally least square fitting of the theoretical correlation function. The results are compared to the results of fitting an Auto Regressive Moving Average(ARMA) model directly to the system output. All investigations are performed on the simulated output from a single degree-off-freedom system...
Spoken language identification system adaptation in under-resourced environments
CSIR Research Space (South Africa)
Kleynhans, N
2013-12-01
Full Text Available Speech Recognition (ASR) systems in the developing world is severely inhibited. Given that few task-specific corpora exist and speech technology systems perform poorly when deployed in a new environment, we investigate the use of acoustic model adaptation...
On the Optimal Location of Sensors for Parametric Identification of Linear Structural Systems
DEFF Research Database (Denmark)
Kirkegaard, Poul Henning; Brincker, Rune
A survey of the field of optimal location of sensors for parametric identification of linear structural systems is presented. The survey shows that few papers are devoted to the case of optimal location sensors in which the measurements are modelled by a random field with non-trivial covariance...... function. Most often it is assumed that the results of the measurements are statistically independent variables. In an example the importance of considering the measurements as statistically dependent random variables is shown. The example is concerned with optimal location of sensors for parametric...... identification of modal parameters for a vibrating beam under random loading. The covariance of the modal parameters expected to be obtained is investigated to variations of number and location of sensors. Further, the influence of the noise on the optimal location of the sensors is investigated....
Reliability of system identification techniques to assess standing balance in healthy elderly
Pasma, Jantsje H.; Engelhart, Denise; Maier, Andrea B.; Aarts, Ronald G.K.M.; Van Gerven, Joop M.A.; Arendzen, J. Hans; Schouten, Alfred C.; Meskers, Carel G.M.; Van Kooij, Herman Der
2016-01-01
Objectives System identification techniques have the potential to assess the contribution of the underlying systems involved in standing balance by applying well-known disturbances. We investigated the reliability of standing balance parameters obtained with multivariate closed loop system
Reliability of System Identification Techniques to Assess Standing Balance in Healthy Elderly
Pasma, J.H.; Engelhart, D.; Maier, A.B.; Aarts, R.G.K.M.; Van Gerven, J.M.A.; Arendzen, J.H.; Schouten, A.C.; Meskers, C.G.M.; Van der Kooij, H.
2016-01-01
Objectives System identification techniques have the potential to assess the contribution of the underlying systems involved in standing balance by applying well-known disturbances. We investigated the reliability of standing balance parameters obtained with multivariate closed loop system
Ho Pham Huy Anh; Nguyen Thanh Nam
2012-01-01
In this paper, a novel forward adaptive neural MIMO NARX model is used for modelling and identifying the forward kinematics of an industrial 3‐DOF robot arm system. The nonlinear features of the forward kinematics of the industrial robot arm drive are thoroughly modelled based on the forward adaptive neural NARX model‐based identification process using experimental input‐output training data. This paper proposes a novel use of a back propagation (BP) algorithm to generate the forward neural M...
Semantic policy and adversarial modeling for cyber threat identification and avoidance
DeFrancesco, Anton; McQueary, Bruce
2009-05-01
Today's enterprise networks undergo a relentless barrage of attacks from foreign and domestic adversaries. These attacks may be perpetrated with little to no funding, but may wreck incalculable damage upon the enterprises security, network infrastructure, and services. As more services come online, systems that were once in isolation now provide information that may be combined dynamically with information from other systems to create new meaning on the fly. Security issues are compounded by the potential to aggregate individual pieces of information and infer knowledge at a higher classification than any of its constituent parts. To help alleviate these challenges, in this paper we introduce the notion of semantic policy and discuss how it's use is evolving from a robust approach to access control to preempting and combating attacks in the cyber domain, The introduction of semantic policy and adversarial modeling to network security aims to ask 'where is the network most vulnerable', 'how is the network being attacked', and 'why is the network being attacked'. The first aspect of our approach is integration of semantic policy into enterprise security to augment traditional network security with an overall awareness of policy access and violations. This awareness allows the semantic policy to look at the big picture - analyzing trends and identifying critical relations in system wide data access. The second aspect of our approach is to couple adversarial modeling with semantic policy to move beyond reactive security measures and into a proactive identification of system weaknesses and areas of vulnerability. By utilizing Bayesian-based methodologies, the enterprise wide meaning of data and semantic policy is applied to probability and high-level risk identification. This risk identification will help mitigate potential harm to enterprise networks by enabling resources to proactively isolate, lock-down, and secure systems that are most vulnerable.
Optimization of inverse model identification for multi-axial test rig control
Directory of Open Access Journals (Sweden)
Müller Tino
2016-01-01
Full Text Available Laboratory testing of multi-axial fatigue situations improves repeatability and allows a time condensing of tests which can be carried out until component failure, compared to field testing. To achieve realistic and convincing durability results, precise load data reconstruction is necessary. Cross-talk and a high number of degrees of freedom negatively affect the control accuracy. Therefore a multiple input/multiple output (MIMO model of the system, capturing all inherent cross-couplings is identified. In a first step the model order is estimated based on the physical fundamentals of a one channel hydraulic-servo system. Subsequently, the structure of the MIMO model is optimized using correlation of the outputs, to increase control stability and reduce complexity of the parameter optimization. The identification process is successfully applied to the iterative control of a multi-axial suspension rig. The results show accurate control, with increased stability compared to control without structure optimization.
Abidi, Yassine; Bellassoued, Mourad; Mahjoub, Moncef; Zemzemi, Nejib
2018-03-01
In this paper, we consider the inverse problem of space dependent multiple ionic parameters identification in cardiac electrophysiology modelling from a set of observations. We use the monodomain system known as a state-of-the-art model in cardiac electrophysiology and we consider a general Hodgkin-Huxley formalism to describe the ionic exchanges at the microscopic level. This formalism covers many physiological transmembrane potential models including those in cardiac electrophysiology. Our main result is the proof of the uniqueness and a Lipschitz stability estimate of ion channels conductance parameters based on some observations on an arbitrary subdomain. The key idea is a Carleman estimate for a parabolic operator with multiple coefficients and an ordinary differential equation system.
Time-Varying FOPDT Modeling and On-line Parameter Identification
DEFF Research Database (Denmark)
Yang, Zhenyu; Sun, Zhen
2013-01-01
A type of Time-Varying First-Order Plus Dead-Time (TV-FOPDT) model is extended from SISO format into a MISO version by explicitly taking the disturbance input into consideration. Correspondingly, a set of on-line parameter identification algorithms oriented to MISO TV-FOPDT model are proposed based...... on the Mixed-Integer-Nonlinear Programming, Least-Mean-Square and sliding window techniques. The proposed approaches can simultaneously estimate the time-dependent system parameters, as well as the unknown disturbance input if it is the case, in an on-line manner. The proposed concepts and algorithms...... are firstly illustrated through a numerical example, and then applied to investigate transient superheat dynamic modeling in a supermarket refrigeration system....
Closed-loop Identification for Control of Linear Parameter Varying Systems
DEFF Research Database (Denmark)
Bendtsen, Jan Dimon; Trangbæk, Klaus
2014-01-01
, closed- loop system identification is more difficult than open-loop identification. In this paper we prove that the so-called Hansen Scheme, a technique known from linear time-invariant systems theory for transforming closed-loop system identification problems into open-loop-like problems, can......This paper deals with system identification for control of linear parameter varying systems. In practical applications, it is often important to be able to identify small plant changes in an incremental manner without shutting down the system and/or disconnecting the controller; unfortunately...... be extended to accommodate linear parameter varying systems as well. We investigate the identified subsystem’s parameter dependency and observe that, under mild assumptions, the identified subsystem is affine in the parameter vector. Various identification methods are compared in direct and Hansen Scheme...
Combined non-parametric and parametric approach for identification of time-variant systems
Dziedziech, Kajetan; Czop, Piotr; Staszewski, Wieslaw J.; Uhl, Tadeusz
2018-03-01
Identification of systems, structures and machines with variable physical parameters is a challenging task especially when time-varying vibration modes are involved. The paper proposes a new combined, two-step - i.e. non-parametric and parametric - modelling approach in order to determine time-varying vibration modes based on input-output measurements. Single-degree-of-freedom (SDOF) vibration modes from multi-degree-of-freedom (MDOF) non-parametric system representation are extracted in the first step with the use of time-frequency wavelet-based filters. The second step involves time-varying parametric representation of extracted modes with the use of recursive linear autoregressive-moving-average with exogenous inputs (ARMAX) models. The combined approach is demonstrated using system identification analysis based on the experimental mass-varying MDOF frame-like structure subjected to random excitation. The results show that the proposed combined method correctly captures the dynamics of the analysed structure, using minimum a priori information on the model.
Özbek, Necdet Sinan; Eker, Ilyas
2015-01-01
This study describes a set of real-time interactive experiments that address system identification and model reference adaptive control (MRAC) techniques. In constructing laboratory experiments that contribute to efficient teaching, experimental design and instructional strategy are crucial, but a process for doing this has yet to be defined. This…
Talent identification model for sprinter using discriminant factor
Kusnanik, N. W.; Hariyanto, A.; Herdyanto, Y.; Satia, A.
2018-01-01
The main purpose of this study was to identify young talented sprinter using discriminant factor. The research was conducted in 3 steps including item pool, screening of item pool, and trial of instruments at the small and big size of samples. 315 male elementary school students participated in this study with mean age of 11-13 years old. Data were collected by measuring anthropometry (standing height, sitting height, body mass, and leg length); testing physical fitness (40m sprint for speed, shuttle run for agility, standing broad jump for power, multistage fitness test for endurance). Data were analyzed using discriminant factor. The result of this study found that there were 5 items that selected as an instrument to identify young talented sprinter: sitting height, body mass, leg length, sprint 40m, and multistage fitness test. Model of Discriminant for talent identification in sprinter was D = -24,497 + (0,155 sitting height) + (0,080 body mass) + (0,148 leg length) + (-1,225 Sprint 40m) + (0,563 MFT). The conclusion of this study: instrument tests that have been selected and discriminant model that have been found can be applied to identify young talented as a sprinter.
Identification and impact of discoverers in online social systems
Medo, Matúš; Mariani, Manuel S.; Zeng, An; Zhang, Yi-Cheng
2016-09-01
Understanding the behavior of users in online systems is of essential importance for sociology, system design, e-commerce, and beyond. Most existing models assume that individuals in diverse systems, ranging from social networks to e-commerce platforms, tend to what is already popular. We propose a statistical time-aware framework to identify the users who differ from the usual behavior by being repeatedly and persistently among the first to collect the items that later become hugely popular. Since these users effectively discover future hits, we refer them as discoverers. We use the proposed framework to demonstrate that discoverers are present in a wide range of real systems. Once identified, discoverers can be used to predict the future success of new items. We finally introduce a simple network model which reproduces the discovery patterns observed in the real data. Our results open the door to quantitative study of detailed temporal patterns in social systems.
1999-03-01
Netherlands HOST NATION COORDINATOR Prof. Jose-Luis LOPEZ-RUIZ SENER, Ingenieria y Sistema, S.A. Parque Tecnologico de Madrid Calle Severo Ochoa s/n 28760... de - veloped by SD Scicon, a UK software house, and is an .where x can be any one of the six degrees of freedom. update of an earlier version...CEDEX, FRANCE RTO MEETING PROCEEDINGS 11 System Identification for Integrated Aircraft Development and Flight Testing (1’Identification des systemes
Yang, Chunguang G; Granite, Stephen J; Van Eyk, Jennifer E; Winslow, Raimond L
2006-11-01
Protein identification using MS is an important technique in proteomics as well as a major generator of proteomics data. We have designed the protein identification data object model (PDOM) and developed a parser based on this model to facilitate the analysis and storage of these data. The parser works with HTML or XML files saved or exported from MASCOT MS/MS ions search in peptide summary report or MASCOT PMF search in protein summary report. The program creates PDOM objects, eliminates redundancy in the input file, and has the capability to output any PDOM object to a relational database. This program facilitates additional analysis of MASCOT search results and aids the storage of protein identification information. The implementation is extensible and can serve as a template to develop parsers for other search engines. The parser can be used as a stand-alone application or can be driven by other Java programs. It is currently being used as the front end for a system that loads HTML and XML result files of MASCOT searches into a relational database. The source code is freely available at http://www.ccbm.jhu.edu and the program uses only free and open-source Java libraries.
Directory of Open Access Journals (Sweden)
Roger Skjetne
2004-01-01
Full Text Available Complete nonlinear dynamic manoeuvering models of ships, with numerical values, are hard to find in the literature. This paper presents a modeling, identification, and control design where the objective is to manoeuver a ship along desired paths at different velocities. Material from a variety of references have been used to describe the ship model, its difficulties, limitations, and possible simplifications for the purpose of automatic control design. The numerical values of the parameters in the model is identified in towing tests and adaptive manoeuvering experiments for a small ship in a marine control laboratory.
Identification of simultaneous equation models with measurement error : a computerized evaluation
Merckens, Arjen; Bekker, Paul
1993-01-01
Rank conditions for identification in structural models are often difficult evaluate. Here we consider simultaneous equation models with measurement error and we show that previously published rank conditions for identification are not well-suited for evaluation. An alternative rank condition is
Bloemen, H.H.J.; Chou, C.T.; van den Boom, T.J.J.; Verdult, V.; Verhaegen, M.H.G.; Backx, T.C.
2001-01-01
The benefits of using the Wiener model based identification and control methodology presented in this paper, compared to linear techniques, are demonstrated for dual composition control of a moderate–high purity distillation column simulation model. An identification experiment design is presented
Model Identification using Continuous Glucose Monitoring Data for Type 1 Diabetes
DEFF Research Database (Denmark)
Boiroux, Dimitri; Hagdrup, Morten; Mahmoudi, Zeinab
2016-01-01
This paper addresses model identification of continuous-discrete nonlinear models for people with type 1 diabetes using sampled data from a continuous glucose monitor (CGM). We compare five identification techniques: least squares, weighted least squares, Huber regression, maximum likelihood with...
Low power proton exchange membrane fuel cell system identification and adaptive control
Energy Technology Data Exchange (ETDEWEB)
Yang, Yee-Pien; Wang, Fu-Cheng; Ma, Ying-Wei [Department of Mechanical Engineering, National Taiwan University, Taipei (Taiwan); Chang, Hsin-Ping; Weng, Biing-Jyh [Chung Shan Institute of Science and Technology (CSIST), Armaments Bureau, M.N.D. (Taiwan)
2007-02-10
This paper proposes a systematic method of system identification and control of a proton exchange membrane (PEM) fuel cell. This fuel cell can be used for low-power communication devices involving complex electrochemical reactions of nonlinear and time-varying dynamic properties. From a system point of view, the dynamic model of PEM fuel cell is reduced to a configuration of two inputs, hydrogen and air flow rates, and two outputs, cell voltage and current. The corresponding transfer functions describe linearized subsystem dynamics with finite orders and time-varying parameters, which are expressed as discrete-time auto-regression moving-average with auxiliary input models for system identification by the recursive least square algorithm. In the experiments, a pseudo-random binary sequence of hydrogen or air flow rate is fed to a single fuel cell device to excite its dynamics. By measuring the corresponding output signals, each subsystem transfer function of reduced order is identified, while the unmodeled, higher-order dynamics and disturbances are described by the auxiliary input term. This provides a basis of adaptive control strategy to improve the fuel cell performance in terms of efficiency, as well as transient and steady state specifications. Simulation shows that adaptive controller is robust to the variation of fuel cell system dynamics, and it has proved promising from the experimental results. (author)
Advanced 3D Object Identification System, Phase II
National Aeronautics and Space Administration — During the Phase I effort, OPTRA developed object detection, tracking, and identification algorithms and successfully tested these algorithms on computer-generated...
A neural network model for non invasive subsurface stratigraphic identification
International Nuclear Information System (INIS)
Sullivan, John M. Jr.; Ludwig, Reinhold; Lai Qiang
2000-01-01
Ground-Penetrating Radar (GRP) is a powerful tool to examine the stratigraphy below ground surface for remote sensing. Increasingly GPR has also found applications in microwave NDE as an interrogation tool to assess dielectric layers. Unfortunately, GPR data is characterized by a high degree of uncertainty and natural physical ambiguity. Robust decomposition routines are sparse for this application. We have developed a hierarchical set of neural network modules which split the task of layer profiling into consecutive stages. Successful GPR profiling of the subsurface stratigraphy is of key importance for many remote sensing applications including microwave NDE. Neural network modules were designed to accomplish the two main processing goals of recognizing the 'subsurface pattern' followed by the identification of the depths of the subsurface layers like permafrost, groundwater table, and bedrock. We used an adaptive transform technique to transform raw GPR data into a small feature vector containing the most representative and discriminative features of the signal. This information formed the input for the neural network processing units. This strategy reduced the number of required training samples for the neural network by orders of magnitude. The entire processing system was trained using the adaptive transformed feature vector inputs and tested with real measured GPR data. The successful results of this system establishes the feasibility the feasibility of delineating subsurface layering nondestructively
Neural Networks and other Techniques for Fault Identification and Isolation of Aircraft Systems
Innocenti, M.; Napolitano, M.
2003-01-01
Fault identification, isolation, and accomodation have become critical issues in the overall performance of advanced aircraft systems. Neural Networks have shown to be a very attractive alternative to classic adaptation methods for identification and control of non-linear dynamic systems. The purpose of this paper is to show the improvements in neural network applications achievable through the use of learning algorithms more efficient than the classic Back-Propagation, and through the implementation of the neural schemes in parallel hardware. The results of the analysis of a scheme for Sensor Failure, Detection, Identification and Accommodation (SFDIA) using experimental flight data of a research aircraft model are presented. Conventional approaches to the problem are based on observers and Kalman Filters while more recent methods are based on neural approximators. The work described in this paper is based on the use of neural networks (NNs) as on-line learning non-linear approximators. The performances of two different neural architectures were compared. The first architecture is based on a Multi Layer Perceptron (MLP) NN trained with the Extended Back Propagation algorithm (EBPA). The second architecture is based on a Radial Basis Function (RBF) NN trained with the Extended-MRAN (EMRAN) algorithms. In addition, alternative methods for communications links fault detection and accomodation are presented, relative to multiple unmanned aircraft applications.
Data Fusion for System Identification of Ming-Chu Basin in Taiwan
Chen, C.; Liu, H.; Hsu, N.; Chiang, C.
2012-12-01
Among many possible sources, Ming-Chu basin is considered as a high potential zone for groundwater development. Ming-Chu basin is located at the midstream segment of Jhuoshuei River, Taiwan. Within the basin, Qingshui River and Dongpurui River converge to Jhuoshuei River. Recently, due to lack of proper operation and management of groundwater resources in the Jhuoshuei River alluvial fan, land subsidence, seawater intrusion and other hazards are occured, and the safety of high-speed railway is also threatened. Therefore, the development for additional water resources is urgent for this area. The purpose of this study is to use data fusion for system identification of Ming-Chu basin. The identification requires the collection of hydrogeologic data and the identification of the groundwater system, including the boundary condition, stratification, and hydrologic input and output. In this paper, we utilize groundwater hydrograph method to quantify each hydrologic variable. The natural recharge from rainfall infiltration and stream leakage is further quantified with the utilization of isotope analysis method. Through groundwater hydrograph method, the recharge, pumping, loss and outflow from mountain pass can be evaluated. These hydrologic variables are the input data of future groundwater numerical model for further calibration and simulation.
Macko, S. A.; O'Connell, M. T.; Fu, Y.
2016-12-01
The Najinhe watershed is a topographically diverse, heavily agricultural watershed in northeastern China that provides opportunities for identification of the impact of land use on nitrogen cycling. Land use, both historic and current, influences the biological processing of nitrogen in a particular area. Soil conditions, including moisture, texture, and organic content, control the capacity of a parcel for processing reactive nitrogen. Compounds derived from natural and anthropogenic sources exhibit characteristic ratios of stable isotopes of nitrogen and oxygen that serve as tracers of origin as well as integrators of biological processes. A distributed hydrologic model coupled with one focusing on reactive transport is able to help determine locations with the highest impact on the dissolved N in this system. Gaussian Markov Random Fields were used to determine the biogeochemical influence of model locations whereas δ15N measurements from NO3- and NH4+ in soil extracts were used to calibrate and validate model predictions based on measured precipitation and streamflow values. Sources were integrated using a Bayesian mixing model to determine likely fate and transport parameters for various N inputs to the watershed. The application of the coupled hydrologic and transport models to a village scale catchment suggests integration and expansion to larger watersheds on the basin scale. Identification of sensitive parcels on multiple spatial scales can direct targeted land management efforts to mitigate ecological and health effects of reactive N in surface waters.
Energy Technology Data Exchange (ETDEWEB)
Janot, A
2007-12-15
This thesis focuses on the modeling and the identification of haptic interfaces using cable drive. An haptic interface is a force feedback device, which enables its user to interact with a virtual world or a remote environment explored by a slave system. It aims at the matching between the forces and displacements given by the user and those applied to virtual world. Usually, haptic interfaces make use of a mechanical actuated structure whose distal link is equipped with a handle. When manipulating this handle to interact with explored world, the user feels the apparent mass, compliance and friction of the interface. This distortion introduced between the operator and the virtual world must be modeled and identified to enhance the design of the interface and develop appropriate control laws. The first approach has been to adapt the modeling and identification methods of rigid and localized flexibilities robots to haptic interfaces. The identification technique makes use of the inverse dynamic model and the linear least squares with the measurements of joint torques and positions. This approach is validated on a single degree of freedom and a three degree of freedom haptic devices. A new identification method needing only torque data is proposed. It is based on a closed loop simulation using the direct dynamic model. The optimal parameters minimize the 2 norms of the error between the actual torque and the simulated torque assuming the same control law and the same tracking trajectory. This non linear least squares problem dramatically is simplified using the inverse model to calculate the simulated torque. This method is validated on the single degree of freedom haptic device and the SCARA robot. (author)
An Automated System for Garment Texture Design Class Identification
Directory of Open Access Journals (Sweden)
Emon Kumar Dey
2015-09-01
Full Text Available Automatic identification of garment design class might play an important role in the garments and fashion industry. To achieve this, essential initial works are found in the literature. For example, construction of a garment database, automatic segmentation of garments from real life images, categorizing them into the type of garments such as shirts, jackets, tops, skirts, etc. It is now essential to find a system such that it will be possible to identify the particular design (printed, striped or single color of garment product for an automated system to recommend the garment trends. In this paper, we have focused on this specific issue and thus propose two new descriptors namely Completed CENTRIST (cCENTRIST and Ternary CENTRIST (tCENTRIST. To test these descriptors, we used two different publically available databases. The experimental results of these databases demonstrate that both cCENTRIST and tCENTRIST achieve nearly about 3% more accuracy than the existing state-of-the art methods.
Modeling, Parameters Identification, and Control of High Pressure Fuel Cell Back-Pressure Valve
Directory of Open Access Journals (Sweden)
Fengxiang Chen
2014-01-01
Full Text Available The reactant pressure is crucial to the efficiency and lifespan of a high pressure PEMFC engine. This paper analyses a regulated back-pressure valve (BPV for the cathode outlet flow in a high pressure PEMFC engine, which can achieve precisely pressure control. The modeling, parameters identification, and nonlinear controller design of a BPV system are considered. The identified parameters are used in designing active disturbance rejection controller (ADRC. Simulations and extensive experiments are conducted with the xPC Target and show that the proposed controller can not only achieve good dynamic and static performance but also have strong robustness against parameters’ disturbance and external disturbance.
Applying the Team Identification-Social Psychological Health Model to older sport fans.
Wann, Daniel L; Rogers, Kelly; Dooley, Keith; Foley, Mary
2011-01-01
According to the Team Identification-Social Psychological Health Model (Wann, 2006b), team identification and social psychological health should be positively correlated because identification leads to important social connections which, in turn, facilitate well-being. Although past research substantiates the hypothesized positive relationship between team identification and well-being, earlier studies focused solely on college student populations. The current study extended past work in this area by investigating the team identification/well-being relationship among older sport fans. A sample of older adults (N = 96; M age = 70.82) completed scales assessing demographics, identification with a local college basketball team, and measures of social psychological well-being. As hypothesized, team identification accounted for a significant proportion of unique variance in two measures of social psychological health (collective self-esteem and loneliness).
Psychometric Properties of the Dyskinesia Identification System: Condensed User Scale (DISCUS).
Sprague, Robert L.; And Others
1989-01-01
Individuals with developmental disability (n=400) were assessed with the "Dyskinesia Identification System: Condensed User Scale" (DISCUS), a 15-item tardive dyskinesia rating scale shortened from the "Dyskinesia Identification System-Coldwater." Based on interrater reliability, 2-week stability, and other analyses, DISCUS is…
33 CFR 164.43 - Automatic Identification System Shipborne Equipment-Prince William Sound.
2010-07-01
... 33 Navigation and Navigable Waters 2 2010-07-01 2010-07-01 false Automatic Identification System Shipborne Equipment-Prince William Sound. 164.43 Section 164.43 Navigation and Navigable Waters COAST GUARD... Automatic Identification System Shipborne Equipment—Prince William Sound. (a) Until December 31, 2004, each...
Directory of Open Access Journals (Sweden)
Roberto da Cunha Follador
2016-04-01
Full Text Available The Operational Modal Analysis technique is a methodology very often applied for the identification of dynamic systems when the input signal is unknown. The applied methodology is based on a technique to estimate the Frequency Response Functions and extract the modal parameters using only the structural dynamic response data, without assuming the knowledge of the excitation forces. Such approach is an adequate way for measuring the aircraft aeroelastic response due to random input, like atmospheric turbulence. The in-flight structural response has been measured by accelerometers distributed along the aircraft wings, fuselage and empennages. The Enhanced Frequency Domain Decomposition technique was chosen to identify the airframe dynamic parameters. This technique is based on the hypothesis that the system is randomly excited with a broadband spectrum with almost constant power spectral density. The system identification procedure is based on the Single Value Decomposition of the power spectral densities of system output signals, estimated by the usual Fast Fourier Transform method. This procedure has been applied to different flight conditions to evaluate the modal parameters and the aeroelastic stability trends of the airframe under investigation. The experimental results obtained by this methodology were compared with the predicted results supplied by aeroelastic numerical models in order to check the consistency of the proposed output-only methodology. The objective of this paper is to compare in-flight measured aeroelastic damping against the corresponding parameters computed from numerical aeroelastic models. Different aerodynamic modeling approaches should be investigated such as the use of source panel body models, cruciform and flat plate projection. As a result of this investigation it is expected the choice of the better aeroelastic modeling and Operational Modal Analysis techniques to be included in a standard aeroelastic
Mathematical model of statistical identification of information support of road transport
Directory of Open Access Journals (Sweden)
V. G. Kozlov
2016-01-01
Full Text Available In this paper based on the statistical identification method using the theory of self-organizing systems, built multifactor model the relationship of road transport and training system. Background information for the model represented by a number of parameters of average annual road transport operations and information provision, including training complex system parameters (inputs, road management and output parameters. Ask two criteria: stability criterion model and test correlation. The program determines their minimum, and is the only model of optimal complexity. The predetermined number of parameters established mathematical relationship of each output parameter with the others. To improve the accuracy and regularity of the forecast of the interpolation nodes allocated in the test data sequence. Other data form the training sequence. Decision model based on the principle of selection. Running it with the gradual complication of the mathematical description and exhaustive search of all possible variants of the models on the specified criteria. Advantages of the proposed model: adequately reflects the actual process, allows you to enter any additional input parameters and determine their impact on the individual output parameters of the road transport, allows in turn change the values of key parameters in a certain ratio and to determine the appropriate changes the output parameters of the road transport, allows to predict the output parameters road transport operations.
Optical Verification Laboratory Demonstration System for High Security Identification Cards
Javidi, Bahram
1997-01-01
Document fraud including unauthorized duplication of identification cards and credit cards is a serious problem facing the government, banks, businesses, and consumers. In addition, counterfeit products such as computer chips, and compact discs, are arriving on our shores in great numbers. With the rapid advances in computers, CCD technology, image processing hardware and software, printers, scanners, and copiers, it is becoming increasingly easy to reproduce pictures, logos, symbols, paper currency, or patterns. These problems have stimulated an interest in research, development and publications in security technology. Some ID cards, credit cards and passports currently use holograms as a security measure to thwart copying. The holograms are inspected by the human eye. In theory, the hologram cannot be reproduced by an unauthorized person using commercially-available optical components; in practice, however, technology has advanced to the point where the holographic image can be acquired from a credit card-photographed or captured with by a CCD camera-and a new hologram synthesized using commercially-available optical components or hologram-producing equipment. Therefore, a pattern that can be read by a conventional light source and a CCD camera can be reproduced. An optical security and anti-copying device that provides significant security improvements over existing security technology was demonstrated. The system can be applied for security verification of credit cards, passports, and other IDs so that they cannot easily be reproduced. We have used a new scheme of complex phase/amplitude patterns that cannot be seen and cannot be copied by an intensity-sensitive detector such as a CCD camera. A random phase mask is bonded to a primary identification pattern which could also be phase encoded. The pattern could be a fingerprint, a picture of a face, or a signature. The proposed optical processing device is designed to identify both the random phase mask and the
Dos Santos, P Lopes; Deshpande, Sunil; Rivera, Daniel E; Azevedo-Perdicoúlis, T-P; Ramos, J A; Younger, Jarred
2013-12-31
There is good evidence that naltrexone, an opioid antagonist, has a strong neuroprotective role and may be a potential drug for the treatment of fibromyalgia. In previous work, some of the authors used experimental clinical data to identify input-output linear time invariant models that were used to extract useful information about the effect of this drug on fibromyalgia symptoms. Additional factors such as anxiety, stress, mood, and headache, were considered as additive disturbances. However, it seems reasonable to think that these factors do not affect the drug actuation, but only the way in which a participant perceives how the drug actuates on herself. Under this hypothesis the linear time invariant models can be replaced by State-Space Affine Linear Parameter Varying models where the disturbances are seen as a scheduling signal signal only acting at the parameters of the output equation. In this paper a new algorithm for identifying such a model is proposed. This algorithm minimizes a quadratic criterion of the output error. Since the output error is a linear function of some parameters, the Affine Linear Parameter Varying system identification is formulated as a separable nonlinear least squares problem. Likewise other identification algorithms using gradient optimization methods several parameter derivatives are dynamical systems that must be simulated. In order to increase time efficiency a canonical parametrization that minimizes the number of systems to be simulated is chosen. The effectiveness of the algorithm is assessed in a case study where an Affine Parameter Varying Model is identified from the experimental data used in the previous study and compared with the time-invariant model.
Identification and monitoring of a PEM electrolyser based on dynamical modelling
Energy Technology Data Exchange (ETDEWEB)
Lebbal, M.E.; Lecoeuche, S. [Ecole des Mines de Douai, Departement Informatique et Automatique, 941 rue Charles Bourseul, 59508 Douai Cedex (France)
2009-07-15
To improve the efficiency and the safety of hydrogen electrolysis stations, some technological studies are still under investigation both on methods and materials. As methods, control, monitoring and diagnosis algorithms are relevant tools. This work focuses on the dynamical modelling and the monitoring of Proton Exchange Membrane (PEM) electrolyser. Our contribution consists of three parts: to propose a model of an analytical-dynamical PEM electrolyser, dedicated to control and monitoring; to identify the model parameters and to propose adequate monitoring tools. The proposed model is deduced from physical laws and electrochemical equations and consists of a steady-state electric model coupled with a dynamic thermal model. The estimation of the model parameters is achieved using identification and data fitting techniques based on experimental measurements. Taking into account the information given by the proposed analytical model and the experimentation data (temperature T, voltage U and current I) given by a PEM electrolyser, the model parameters are identified. After estimating the dynamical model, model-based diagnosis is used to monitor the PEM electrolyser and to ensure its safety. We illustrate how our algorithm can detect and isolate faults on actuators, on sensors or on electrolyser system. (author)
The role of fuzzy logic in modeling, identification and control
Directory of Open Access Journals (Sweden)
Lotfi A. Zadeh
1994-07-01
Full Text Available In the nearly four decades which have passed since the launching of the Sputnik, great progress has been achieved in our understanding of how to model, identify and control complex systems. However, to be able to design systems having high MIQ (Machine Intelligence Quotient, a profound change in the orientation of control theory may be required. More specifically, what may be needed is the employment of soft computing - rather than hard computing - in systems analysis and design. Soft computing - unlike hard computing - is tolerant of imprecision, uncertainty and partial truth.
Modeling and Identification of Harmonic Instability Problems In Wind Farms
DEFF Research Database (Denmark)
Ebrahimzadeh, Esmaeil; Blaabjerg, Frede; Wang, Xiongfei
2016-01-01
to identify harmonic instability problems in wind farms, where many wind turbines, cables, transformers, capacitor banks, shunt reactors, etc, typically are located. This methodology introduces the wind farm as a Multi-Input Multi-Outpur (MIMO) control system, where the linearized models of fast inner control...
Optimal experiment design for identification of grey-box models
DEFF Research Database (Denmark)
Sadegh, Payman; Melgaard, Henrik; Madsen, Henrik
1994-01-01
Optimal experiment design is investigated for stochastic dynamic systems where the prior partial information about the system is given as a probability distribution function in the system parameters. The concept of information is related to entropy reduction in the system through Lindley's measure...... estimation results in a considerable reduction of the experimental length. Besides, it is established that the physical knowledge of the system enables us to design experiments, with the goal of maximizing information about the physical parameters of interest....... of average information, and the relationship between the choice of information related criteria and some estimators (MAP and MLE) is established. A continuous time physical model of the heat dynamics of a building is considered and the results show that performing an optimal experiment corresponding to a MAP...
Peng, Jinzhu; Dubay, Rickey
2011-10-01
In this paper, an adaptive control approach based on the neural networks is presented to control a DC motor system with dead-zone characteristics (DZC), where two neural networks are proposed to formulate the traditional identification and control approaches. First, a Wiener-type neural network (WNN) is proposed to identify the motor DZC, which formulates the Wiener model with a linear dynamic block in cascade with a nonlinear static gain. Second, a feedforward neural network is proposed to formulate the traditional PID controller, termed as PID-type neural network (PIDNN), which is then used to control and compensate for the DZC. In this way, the DC motor system with DZC is identified by the WNN identifier, which provides model information to the PIDNN controller in order to make it adaptive. Back-propagation algorithms are used to train both neural networks. Also, stability and convergence analysis are conducted using the Lyapunov theorem. Finally, experiments on the DC motor system demonstrated accurate identification and good compensation for dead-zone with improved control performance over the conventional PID control. Copyright © 2011 ISA. Published by Elsevier Ltd. All rights reserved.