Subcritical flutter testing and system identification

NASA Technical Reports Server (NTRS)

Houbolt, J. C.

1974-01-01

Treatment is given of system response evaluation, especially in application to subcritical flight and wind tunnel flutter testing of aircraft. An evaluation is made of various existing techniques, in conjuction with a companion survey which reports theoretical and analog experiments made to study the identification of system response characteristics. Various input excitations are considered, and new techniques for analyzing response are explored, particularly in reference to the prevalent practical case where unwanted input noise is present, such as caused by gusts or wind tunnel turbulence. Further developments are also made of system parameter identification techniques.

A Full Dynamic Compound Inverse Method for output-only element-level system identification and input estimation from earthquake response signals

NASA Astrophysics Data System (ADS)

Pioldi, Fabio; Rizzi, Egidio

2016-08-01

This paper proposes a new output-only element-level system identification and input estimation technique, towards the simultaneous identification of modal parameters, input excitation time history and structural features at the element-level by adopting earthquake-induced structural response signals. The method, named Full Dynamic Compound Inverse Method (FDCIM), releases strong assumptions of earlier element-level techniques, by working with a two-stage iterative algorithm. Jointly, a Statistical Average technique, a modification process and a parameter projection strategy are adopted at each stage to achieve stronger convergence for the identified estimates. The proposed method works in a deterministic way and is completely developed in State-Space form. Further, it does not require continuous- to discrete-time transformations and does not depend on initialization conditions. Synthetic earthquake-induced response signals from different shear-type buildings are generated to validate the implemented procedure, also with noise-corrupted cases. The achieved results provide a necessary condition to demonstrate the effectiveness of the proposed identification method.

Experimental parameter identification of a multi-scale musculoskeletal model controlled by electrical stimulation: application to patients with spinal cord injury.

PubMed

Benoussaad, Mourad; Poignet, Philippe; Hayashibe, Mitsuhiro; Azevedo-Coste, Christine; Fattal, Charles; Guiraud, David

2013-06-01

We investigated the parameter identification of a multi-scale physiological model of skeletal muscle, based on Huxley's formulation. We focused particularly on the knee joint controlled by quadriceps muscles under electrical stimulation (ES) in subjects with a complete spinal cord injury. A noninvasive and in vivo identification protocol was thus applied through surface stimulation in nine subjects and through neural stimulation in one ES-implanted subject. The identification protocol included initial identification steps, which are adaptations of existing identification techniques to estimate most of the parameters of our model. Then we applied an original and safer identification protocol in dynamic conditions, which required resolution of a nonlinear programming (NLP) problem to identify the serial element stiffness of quadriceps. Each identification step and cross validation of the estimated model in dynamic condition were evaluated through a quadratic error criterion. The results highlighted good accuracy, the efficiency of the identification protocol and the ability of the estimated model to predict the subject-specific behavior of the musculoskeletal system. From the comparison of parameter values between subjects, we discussed and explored the inter-subject variability of parameters in order to select parameters that have to be identified in each patient.

Advances in parameter estimation techniques applied to flexible structures

NASA Technical Reports Server (NTRS)

Maben, Egbert; Zimmerman, David C.

1994-01-01

In this work, various parameter estimation techniques are investigated in the context of structural system identification utilizing distributed parameter models and 'measured' time-domain data. Distributed parameter models are formulated using the PDEMOD software developed by Taylor. Enhancements made to PDEMOD for this work include the following: (1) a Wittrick-Williams based root solving algorithm; (2) a time simulation capability; and (3) various parameter estimation algorithms. The parameter estimations schemes will be contrasted using the NASA Mini-Mast as the focus structure.

A Parameter Identification Method for Helicopter Noise Source Identification and Physics-Based Semi-Empirical Modeling

NASA Technical Reports Server (NTRS)

Greenwood, Eric, II; Schmitz, Fredric H.

2010-01-01

A new physics-based parameter identification method for rotor harmonic noise sources is developed using an acoustic inverse simulation technique. This new method allows for the identification of individual rotor harmonic noise sources and allows them to be characterized in terms of their individual non-dimensional governing parameters. This new method is applied to both wind tunnel measurements and ground noise measurements of two-bladed rotors. The method is shown to match the parametric trends of main rotor Blade-Vortex Interaction (BVI) noise, allowing accurate estimates of BVI noise to be made for operating conditions based on a small number of measurements taken at different operating conditions.

A system identification technique based on the random decrement signatures. Part 1: Theory and simulation

NASA Technical Reports Server (NTRS)

Bedewi, Nabih E.; Yang, Jackson C. S.

1987-01-01

Identification of the system parameters of a randomly excited structure may be treated using a variety of statistical techniques. Of all these techniques, the Random Decrement is unique in that it provides the homogeneous component of the system response. Using this quality, a system identification technique was developed based on a least-squares fit of the signatures to estimate the mass, damping, and stiffness matrices of a linear randomly excited system. The mathematics of the technique is presented in addition to the results of computer simulations conducted to demonstrate the prediction of the response of the system and the random forcing function initially introduced to excite the system.

An Eigensystem Realization Algorithm (ERA) for modal parameter identification and model reduction

NASA Technical Reports Server (NTRS)

Juang, J. N.; Pappa, R. S.

1985-01-01

A method, called the Eigensystem Realization Algorithm (ERA), is developed for modal parameter identification and model reduction of dynamic systems from test data. A new approach is introduced in conjunction with the singular value decomposition technique to derive the basic formulation of minimum order realization which is an extended version of the Ho-Kalman algorithm. The basic formulation is then transformed into modal space for modal parameter identification. Two accuracy indicators are developed to quantitatively identify the system modes and noise modes. For illustration of the algorithm, examples are shown using simulation data and experimental data for a rectangular grid structure.

Estimation and identification study for flexible vehicles

NASA Technical Reports Server (NTRS)

Jazwinski, A. H.; Englar, T. S., Jr.

1973-01-01

Techniques are studied for the estimation of rigid body and bending states and the identification of model parameters associated with the single-axis attitude dynamics of a flexible vehicle. This problem is highly nonlinear but completely observable provided sufficient attitude and attitude rate data is available and provided all system bending modes are excited in the observation interval. A sequential estimator tracks the system states in the presence of model parameter errors. A batch estimator identifies all model parameters with high accuracy.

An approximation theory for nonlinear partial differential equations with applications to identification and control

NASA Technical Reports Server (NTRS)

Banks, H. T.; Kunisch, K.

1982-01-01

Approximation results from linear semigroup theory are used to develop a general framework for convergence of approximation schemes in parameter estimation and optimal control problems for nonlinear partial differential equations. These ideas are used to establish theoretical convergence results for parameter identification using modal (eigenfunction) approximation techniques. Results from numerical investigations of these schemes for both hyperbolic and parabolic systems are given.

Simultaneous Intrinsic and Extrinsic Parameter Identification of a Hand-Mounted Laser-Vision Sensor

PubMed Central

Lee, Jong Kwang; Kim, Kiho; Lee, Yongseok; Jeong, Taikyeong

2011-01-01

In this paper, we propose a simultaneous intrinsic and extrinsic parameter identification of a hand-mounted laser-vision sensor (HMLVS). A laser-vision sensor (LVS), consisting of a camera and a laser stripe projector, is used as a sensor component of the robotic measurement system, and it measures the range data with respect to the robot base frame using the robot forward kinematics and the optical triangulation principle. For the optimal estimation of the model parameters, we applied two optimization techniques: a nonlinear least square optimizer and a particle swarm optimizer. Best-fit parameters, including both the intrinsic and extrinsic parameters of the HMLVS, are simultaneously obtained based on the least-squares criterion. From the simulation and experimental results, it is shown that the parameter identification problem considered was characterized by a highly multimodal landscape; thus, the global optimization technique such as a particle swarm optimization can be a promising tool to identify the model parameters for a HMLVS, while the nonlinear least square optimizer often failed to find an optimal solution even when the initial candidate solutions were selected close to the true optimum. The proposed optimization method does not require good initial guesses of the system parameters to converge at a very stable solution and it could be applied to a kinematically dissimilar robot system without loss of generality. PMID:22164104

Modern control concepts in hydrology. [parameter identification in adaptive stochastic control approach

NASA Technical Reports Server (NTRS)

Duong, N.; Winn, C. B.; Johnson, G. R.

1975-01-01

Two approaches to an identification problem in hydrology are presented, based upon concepts from modern control and estimation theory. The first approach treats the identification of unknown parameters in a hydrologic system subject to noisy inputs as an adaptive linear stochastic control problem; the second approach alters the model equation to account for the random part in the inputs, and then uses a nonlinear estimation scheme to estimate the unknown parameters. Both approaches use state-space concepts. The identification schemes are sequential and adaptive and can handle either time-invariant or time-dependent parameters. They are used to identify parameters in the Prasad model of rainfall-runoff. The results obtained are encouraging and confirm the results from two previous studies; the first using numerical integration of the model equation along with a trial-and-error procedure, and the second using a quasi-linearization technique. The proposed approaches offer a systematic way of analyzing the rainfall-runoff process when the input data are imbedded in noise.

Dynamic Parameter Identification of Subject-Specific Body Segment Parameters Using Robotics Formalism: Case Study Head Complex.

PubMed

Díaz-Rodríguez, Miguel; Valera, Angel; Page, Alvaro; Besa, Antonio; Mata, Vicente

2016-05-01

Accurate knowledge of body segment inertia parameters (BSIP) improves the assessment of dynamic analysis based on biomechanical models, which is of paramount importance in fields such as sport activities or impact crash test. Early approaches for BSIP identification rely on the experiments conducted on cadavers or through imaging techniques conducted on living subjects. Recent approaches for BSIP identification rely on inverse dynamic modeling. However, most of the approaches are focused on the entire body, and verification of BSIP for dynamic analysis for distal segment or chain of segments, which has proven to be of significant importance in impact test studies, is rarely established. Previous studies have suggested that BSIP should be obtained by using subject-specific identification techniques. To this end, our paper develops a novel approach for estimating subject-specific BSIP based on static and dynamics identification models (SIM, DIM). We test the validity of SIM and DIM by comparing the results using parameters obtained from a regression model proposed by De Leva (1996, "Adjustments to Zatsiorsky-Seluyanov's Segment Inertia Parameters," J. Biomech., 29(9), pp. 1223-1230). Both SIM and DIM are developed considering robotics formalism. First, the static model allows the mass and center of gravity (COG) to be estimated. Second, the results from the static model are included in the dynamics equation allowing us to estimate the moment of inertia (MOI). As a case study, we applied the approach to evaluate the dynamics modeling of the head complex. Findings provide some insight into the validity not only of the proposed method but also of the application proposed by De Leva (1996, "Adjustments to Zatsiorsky-Seluyanov's Segment Inertia Parameters," J. Biomech., 29(9), pp. 1223-1230) for dynamic modeling of body segments.

Development of advanced techniques for rotorcraft state estimation and parameter identification

NASA Technical Reports Server (NTRS)

Hall, W. E., Jr.; Bohn, J. G.; Vincent, J. H.

1980-01-01

An integrated methodology for rotorcraft system identification consists of rotorcraft mathematical modeling, three distinct data processing steps, and a technique for designing inputs to improve the identifiability of the data. These elements are as follows: (1) a Kalman filter smoother algorithm which estimates states and sensor errors from error corrupted data. Gust time histories and statistics may also be estimated; (2) a model structure estimation algorithm for isolating a model which adequately explains the data; (3) a maximum likelihood algorithm for estimating the parameters and estimates for the variance of these estimates; and (4) an input design algorithm, based on a maximum likelihood approach, which provides inputs to improve the accuracy of parameter estimates. Each step is discussed with examples to both flight and simulated data cases.

System Identification Applied to Dynamic CFD Simulation and Wind Tunnel Data

NASA Technical Reports Server (NTRS)

Murphy, Patrick C.; Klein, Vladislav; Frink, Neal T.; Vicroy, Dan D.

2011-01-01

Demanding aerodynamic modeling requirements for military and civilian aircraft have provided impetus for researchers to improve computational and experimental techniques. Model validation is a key component for these research endeavors so this study is an initial effort to extend conventional time history comparisons by comparing model parameter estimates and their standard errors using system identification methods. An aerodynamic model of an aircraft performing one-degree-of-freedom roll oscillatory motion about its body axes is developed. The model includes linear aerodynamics and deficiency function parameters characterizing an unsteady effect. For estimation of unknown parameters two techniques, harmonic analysis and two-step linear regression, were applied to roll-oscillatory wind tunnel data and to computational fluid dynamics (CFD) simulated data. The model used for this study is a highly swept wing unmanned aerial combat vehicle. Differences in response prediction, parameters estimates, and standard errors are compared and discussed

User's manual for a parameter identification technique. [with options for model simulation for fixed input forcing functions and identification from wind tunnel and flight measurements

NASA Technical Reports Server (NTRS)

Kanning, G.

1975-01-01

A digital computer program written in FORTRAN is presented that implements the system identification theory for deterministic systems using input-output measurements. The user supplies programs simulating the mathematical model of the physical plant whose parameters are to be identified. The user may choose any one of three options. The first option allows for a complete model simulation for fixed input forcing functions. The second option identifies up to 36 parameters of the model from wind tunnel or flight measurements. The third option performs a sensitivity analysis for up to 36 parameters. The use of each option is illustrated with an example using input-output measurements for a helicopter rotor tested in a wind tunnel.

Experimental validation of a new heterogeneous mechanical test design

NASA Astrophysics Data System (ADS)

Aquino, J.; Campos, A. Andrade; Souto, N.; Thuillier, S.

2018-05-01

Standard material parameters identification strategies generally use an extensive number of classical tests for collecting the required experimental data. However, a great effort has been made recently by the scientific and industrial communities to support this experimental database on heterogeneous tests. These tests can provide richer information on the material behavior allowing the identification of a more complete set of material parameters. This is a result of the recent development of full-field measurements techniques, like digital image correlation (DIC), that can capture the heterogeneous deformation fields on the specimen surface during the test. Recently, new specimen geometries were designed to enhance the richness of the strain field and capture supplementary strain states. The butterfly specimen is an example of these new geometries, designed through a numerical optimization procedure where an indicator capable of evaluating the heterogeneity and the richness of strain information. However, no experimental validation was yet performed. The aim of this work is to experimentally validate the heterogeneous butterfly mechanical test in the parameter identification framework. For this aim, DIC technique and a Finite Element Model Up-date inverse strategy are used together for the parameter identification of a DC04 steel, as well as the calculation of the indicator. The experimental tests are carried out in a universal testing machine with the ARAMIS measuring system to provide the strain states on the specimen surface. The identification strategy is accomplished with the data obtained from the experimental tests and the results are compared to a reference numerical solution.

Stochastic subspace identification for operational modal analysis of an arch bridge

NASA Astrophysics Data System (ADS)

Loh, Chin-Hsiung; Chen, Ming-Che; Chao, Shu-Hsien

2012-04-01

In this paer the application of output-only system identification technique, known as Stochastic Subspace Identification (SSI) algorithms, for civil infrastructures is carried out. The ability of covariance driven stochastic subspace identification (SSI-COV) was proved through the analysis of the ambient data of an arch bridge under operational condition. A newly developed signal processing technique, Singular Spectrum analysis (SSA), capable to smooth noisy signals, is adopted for pre-processing the recorded data before the SSI. The conjunction of SSA and SSICOV provides a useful criterion for the system order determination. With the aim of estimating accurate modal parameters of the structure in off-line analysis, a stabilization diagram is constructed by plotting the identified poles of the system with increasing the size of data Hankel matrix. Identification task of a real structure, Guandu Bridge, is carried out to identify the system natural frequencies and mode shapes. The uncertainty of the identified model parameters from output-only measurement of the bridge under operation condition, such as temperature and traffic loading conditions, is discussed.

LIFES: Laser Induced Fluorescence and Environmental Sensing. [remote sensing technique for marine environment

NASA Technical Reports Server (NTRS)

Houston, W. R.; Stephenson, D. G.; Measures, R. M.

1975-01-01

A laboratory investigation has been conducted to evaluate the detection and identification capabilities of laser induced fluorescence as a remote sensing technique for the marine environment. The relative merits of fluorescence parameters including emission and excitation profiles, intensity and lifetime measurements are discussed in relation to the identification of specific targets of the marine environment including crude oils, refined petroleum products, fish oils and algae. Temporal profiles displaying the variation of lifetime with emission wavelength have proven to add a new dimension of specificity and simplicity to the technique.

Provably secure identity-based identification and signature schemes from code assumptions

PubMed Central

Zhao, Yiming

2017-01-01

Code-based cryptography is one of few alternatives supposed to be secure in a post-quantum world. Meanwhile, identity-based identification and signature (IBI/IBS) schemes are two of the most fundamental cryptographic primitives, so several code-based IBI/IBS schemes have been proposed. However, with increasingly profound researches on coding theory, the security reduction and efficiency of such schemes have been invalidated and challenged. In this paper, we construct provably secure IBI/IBS schemes from code assumptions against impersonation under active and concurrent attacks through a provably secure code-based signature technique proposed by Preetha, Vasant and Rangan (PVR signature), and a security enhancement Or-proof technique. We also present the parallel-PVR technique to decrease parameter values while maintaining the standard security level. Compared to other code-based IBI/IBS schemes, our schemes achieve not only preferable public parameter size, private key size, communication cost and signature length due to better parameter choices, but also provably secure. PMID:28809940

Provably secure identity-based identification and signature schemes from code assumptions.

PubMed

Song, Bo; Zhao, Yiming

2017-01-01

Code-based cryptography is one of few alternatives supposed to be secure in a post-quantum world. Meanwhile, identity-based identification and signature (IBI/IBS) schemes are two of the most fundamental cryptographic primitives, so several code-based IBI/IBS schemes have been proposed. However, with increasingly profound researches on coding theory, the security reduction and efficiency of such schemes have been invalidated and challenged. In this paper, we construct provably secure IBI/IBS schemes from code assumptions against impersonation under active and concurrent attacks through a provably secure code-based signature technique proposed by Preetha, Vasant and Rangan (PVR signature), and a security enhancement Or-proof technique. We also present the parallel-PVR technique to decrease parameter values while maintaining the standard security level. Compared to other code-based IBI/IBS schemes, our schemes achieve not only preferable public parameter size, private key size, communication cost and signature length due to better parameter choices, but also provably secure.

Assessment of Students with Emotional and Behavioral Disorders

ERIC Educational Resources Information Center

Plotts, Cynthia A.

2012-01-01

Assessment and identification of children with emotional and behavioral disorders (EBD) is complex and involves multiple techniques, levels, and participants. While federal law sets the general parameters for identification in school settings, these criteria are vague and may lead to inconsistencies in selection and interpretation of assessment…

Single Nucleobase Identification Using Biophysical Signatures from Nanoelectronic Quantum Tunneling.

PubMed

Korshoj, Lee E; Afsari, Sepideh; Khan, Sajida; Chatterjee, Anushree; Nagpal, Prashant

2017-03-01

Nanoelectronic DNA sequencing can provide an important alternative to sequencing-by-synthesis by reducing sample preparation time, cost, and complexity as a high-throughput next-generation technique with accurate single-molecule identification. However, sample noise and signature overlap continue to prevent high-resolution and accurate sequencing results. Probing the molecular orbitals of chemically distinct DNA nucleobases offers a path for facile sequence identification, but molecular entropy (from nucleotide conformations) makes such identification difficult when relying only on the energies of lowest-unoccupied and highest-occupied molecular orbitals (LUMO and HOMO). Here, nine biophysical parameters are developed to better characterize molecular orbitals of individual nucleobases, intended for single-molecule DNA sequencing using quantum tunneling of charges. For this analysis, theoretical models for quantum tunneling are combined with transition voltage spectroscopy to obtain measurable parameters unique to the molecule within an electronic junction. Scanning tunneling spectroscopy is then used to measure these nine biophysical parameters for DNA nucleotides, and a modified machine learning algorithm identified nucleobases. The new parameters significantly improve base calling over merely using LUMO and HOMO frontier orbital energies. Furthermore, high accuracies for identifying DNA nucleobases were observed at different pH conditions. These results have significant implications for developing a robust and accurate high-throughput nanoelectronic DNA sequencing technique. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

A hybrid approach to parameter identification of linear delay differential equations involving multiple delays

NASA Astrophysics Data System (ADS)

Marzban, Hamid Reza

2018-05-01

In this paper, we are concerned with the parameter identification of linear time-invariant systems containing multiple delays. The approach is based upon a hybrid of block-pulse functions and Legendre's polynomials. The convergence of the proposed procedure is established and an upper error bound with respect to the L2-norm associated with the hybrid functions is derived. The problem under consideration is first transformed into a system of algebraic equations. The least squares technique is then employed for identification of the desired parameters. Several multi-delay systems of varying complexity are investigated to evaluate the performance and capability of the proposed approximation method. It is shown that the proposed approach is also applicable to a class of nonlinear multi-delay systems. It is demonstrated that the suggested procedure provides accurate results for the desired parameters.

Automatic limb identification and sleeping parameters assessment for pressure ulcer prevention.

PubMed

Baran Pouyan, Maziyar; Birjandtalab, Javad; Nourani, Mehrdad; Matthew Pompeo, M D

2016-08-01

Pressure ulcers (PUs) are common among vulnerable patients such as elderly, bedridden and diabetic. PUs are very painful for patients and costly for hospitals and nursing homes. Assessment of sleeping parameters on at-risk limbs is critical for ulcer prevention. An effective assessment depends on automatic identification and tracking of at-risk limbs. An accurate limb identification can be used to analyze the pressure distribution and assess risk for each limb. In this paper, we propose a graph-based clustering approach to extract the body limbs from the pressure data collected by a commercial pressure map system. A robust signature-based technique is employed to automatically label each limb. Finally, an assessment technique is applied to evaluate the experienced stress by each limb over time. The experimental results indicate high performance and more than 94% average accuracy of the proposed approach. Copyright © 2016 Elsevier Ltd. All rights reserved.

Parameter identification for nonlinear aerodynamic systems

NASA Technical Reports Server (NTRS)

Pearson, Allan E.

1992-01-01

Continuing work on frequency analysis for transfer function identification is discussed. A new study was initiated into a 'weighted' least squares algorithm within the context of the Fourier modulating function approach. The first phase of applying these techniques to the F-18 flight data is nearing completion, and these results are summarized.

A Galerkin method for the estimation of parameters in hybrid systems governing the vibration of flexible beams with tip bodies

NASA Technical Reports Server (NTRS)

Banks, H. T.; Rosen, I. G.

1985-01-01

An approximation scheme is developed for the identification of hybrid systems describing the transverse vibrations of flexible beams with attached tip bodies. In particular, problems involving the estimation of functional parameters are considered. The identification problem is formulated as a least squares fit to data subject to the coupled system of partial and ordinary differential equations describing the transverse displacement of the beam and the motion of the tip bodies respectively. A cubic spline-based Galerkin method applied to the state equations in weak form and the discretization of the admissible parameter space yield a sequence of approximating finite dimensional identification problems. It is shown that each of the approximating problems admits a solution and that from the resulting sequence of optimal solutions a convergent subsequence can be extracted, the limit of which is a solution to the original identification problem. The approximating identification problems can be solved using standard techniques and readily available software.

Data mining for the identification of metabolic syndrome status

PubMed Central

Worachartcheewan, Apilak; Schaduangrat, Nalini; Prachayasittikul, Virapong; Nantasenamat, Chanin

2018-01-01

Metabolic syndrome (MS) is a condition associated with metabolic abnormalities that are characterized by central obesity (e.g. waist circumference or body mass index), hypertension (e.g. systolic or diastolic blood pressure), hyperglycemia (e.g. fasting plasma glucose) and dyslipidemia (e.g. triglyceride and high-density lipoprotein cholesterol). It is also associated with the development of diabetes mellitus (DM) type 2 and cardiovascular disease (CVD). Therefore, the rapid identification of MS is required to prevent the occurrence of such diseases. Herein, we review the utilization of data mining approaches for MS identification. Furthermore, the concept of quantitative population-health relationship (QPHR) is also presented, which can be defined as the elucidation/understanding of the relationship that exists between health parameters and health status. The QPHR modeling uses data mining techniques such as artificial neural network (ANN), support vector machine (SVM), principal component analysis (PCA), decision tree (DT), random forest (RF) and association analysis (AA) for modeling and construction of predictive models for MS characterization. The DT method has been found to outperform other data mining techniques in the identification of MS status. Moreover, the AA technique has proved useful in the discovery of in-depth as well as frequently occurring health parameters that can be used for revealing the rules of MS development. This review presents the potential benefits on the applications of data mining as a rapid identification tool for classifying MS. PMID:29383020

Data mining for the identification of metabolic syndrome status.

PubMed

Worachartcheewan, Apilak; Schaduangrat, Nalini; Prachayasittikul, Virapong; Nantasenamat, Chanin

2018-01-01

Metabolic syndrome (MS) is a condition associated with metabolic abnormalities that are characterized by central obesity (e.g. waist circumference or body mass index), hypertension (e.g. systolic or diastolic blood pressure), hyperglycemia (e.g. fasting plasma glucose) and dyslipidemia (e.g. triglyceride and high-density lipoprotein cholesterol). It is also associated with the development of diabetes mellitus (DM) type 2 and cardiovascular disease (CVD). Therefore, the rapid identification of MS is required to prevent the occurrence of such diseases. Herein, we review the utilization of data mining approaches for MS identification. Furthermore, the concept of quantitative population-health relationship (QPHR) is also presented, which can be defined as the elucidation/understanding of the relationship that exists between health parameters and health status. The QPHR modeling uses data mining techniques such as artificial neural network (ANN), support vector machine (SVM), principal component analysis (PCA), decision tree (DT), random forest (RF) and association analysis (AA) for modeling and construction of predictive models for MS characterization. The DT method has been found to outperform other data mining techniques in the identification of MS status. Moreover, the AA technique has proved useful in the discovery of in-depth as well as frequently occurring health parameters that can be used for revealing the rules of MS development. This review presents the potential benefits on the applications of data mining as a rapid identification tool for classifying MS.

An adaptive optimal control for smart structures based on the subspace tracking identification technique

NASA Astrophysics Data System (ADS)

Ripamonti, Francesco; Resta, Ferruccio; Borroni, Massimo; Cazzulani, Gabriele

2014-04-01

A new method for the real-time identification of mechanical system modal parameters is used in order to design different adaptive control logics aiming to reduce the vibrations in a carbon fiber plate smart structure. It is instrumented with three piezoelectric actuators, three accelerometers and three strain gauges. The real-time identification is based on a recursive subspace tracking algorithm whose outputs are elaborated by an ARMA model. A statistical approach is finally applied to choose the modal parameter correct values. These are given in input to model-based control logics such as a gain scheduling and an adaptive LQR control.

Comparison of modal identification techniques using a hybrid-data approach

NASA Technical Reports Server (NTRS)

Pappa, Richard S.

1986-01-01

Modal identification of seemingly simple structures, such as the generic truss is often surprisingly difficult in practice due to high modal density, nonlinearities, and other nonideal factors. Under these circumstances, different data analysis techniques can generate substantially different results. The initial application of a new hybrid-data method for studying the performance characteristics of various identification techniques with such data is summarized. This approach offers new pieces of information for the system identification researcher. First, it allows actual experimental data to be used in the studies, while maintaining the traditional advantage of using simulated data. That is, the identification technique under study is forced to cope with the complexities of real data, yet the performance can be measured unquestionably for the artificial modes because their true parameters are known. Secondly, the accuracy achieved for the true structural modes in the data can be estimated from the accuracy achieved for the artificial modes if the results show similar characteristics. This similarity occurred in the study, for example, for a weak structural mode near 56 Hz. It may even be possible--eventually--to use the error information from the artificial modes to improve the identification accuracy for the structural modes.

A robust star identification algorithm with star shortlisting

NASA Astrophysics Data System (ADS)

Mehta, Deval Samirbhai; Chen, Shoushun; Low, Kay Soon

2018-05-01

A star tracker provides the most accurate attitude solution in terms of arc seconds compared to the other existing attitude sensors. When no prior attitude information is available, it operates in "Lost-In-Space (LIS)" mode. Star pattern recognition, also known as star identification algorithm, forms the most crucial part of a star tracker in the LIS mode. Recognition reliability and speed are the two most important parameters of a star pattern recognition technique. In this paper, a novel star identification algorithm with star ID shortlisting is proposed. Firstly, the star IDs are shortlisted based on worst-case patch mismatch, and later stars are identified in the image by an initial match confirmed with a running sequential angular match technique. The proposed idea is tested on 16,200 simulated star images having magnitude uncertainty, noise stars, positional deviation, and varying size of the field of view. The proposed idea is also benchmarked with the state-of-the-art star pattern recognition techniques. Finally, the real-time performance of the proposed technique is tested on the 3104 real star images captured by a star tracker SST-20S currently mounted on a satellite. The proposed technique can achieve an identification accuracy of 98% and takes only 8.2 ms for identification on real images. Simulation and real-time results depict that the proposed technique is highly robust and achieves a high speed of identification suitable for actual space applications.

Parametric Robust Control and System Identification: Unified Approach

NASA Technical Reports Server (NTRS)

Keel, L. H.

1996-01-01

During the period of this support, a new control system design and analysis method has been studied. This approach deals with control systems containing uncertainties that are represented in terms of its transfer function parameters. Such a representation of the control system is common and many physical parameter variations fall into this type of uncertainty. Techniques developed here are capable of providing nonconservative analysis of such control systems with parameter variations. We have also developed techniques to deal with control systems when their state space representations are given rather than transfer functions. In this case, the plant parameters will appear as entries of state space matrices. Finally, a system modeling technique to construct such systems from the raw input - output frequency domain data has been developed.

Identification of propulsion systems

NASA Technical Reports Server (NTRS)

Merrill, Walter; Guo, Ten-Huei; Duyar, Ahmet

1991-01-01

This paper presents a tutorial on the use of model identification techniques for the identification of propulsion system models. These models are important for control design, simulation, parameter estimation, and fault detection. Propulsion system identification is defined in the context of the classical description of identification as a four step process that is unique because of special considerations of data and error sources. Propulsion system models are described along with the dependence of system operation on the environment. Propulsion system simulation approaches are discussed as well as approaches to propulsion system identification with examples for both air breathing and rocket systems.

Modal Parameter Identification of a Flexible Arm System

NASA Technical Reports Server (NTRS)

Barrington, Jason; Lew, Jiann-Shiun; Korbieh, Edward; Wade, Montanez; Tantaris, Richard

1998-01-01

In this paper an experiment is designed for the modal parameter identification of a flexible arm system. This experiment uses a function generator to provide input signal and an oscilloscope to save input and output response data. For each vibrational mode, many sets of sine-wave inputs with frequencies close to the natural frequency of the arm system are used to excite the vibration of this mode. Then a least-squares technique is used to analyze the experimental input/output data to obtain the identified parameters for this mode. The identified results are compared with the analytical model obtained by applying finite element analysis.

Parameter estimation techniques and application in aircraft flight testing

NASA Technical Reports Server (NTRS)

1974-01-01

Technical papers presented at the symposium by selected representatives from industry, universities, and various Air Force, Navy, and NASA installations are given. The topics covered include the newest developments in identification techniques, the most recent flight-test experience, and the projected potential for the near future.

Rotorcraft system identification techniques for handling qualities and stability and control evaluation

NASA Technical Reports Server (NTRS)

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.

Performance of wavelet analysis and neural networks for pathological voices identification

NASA Astrophysics Data System (ADS)

Salhi, Lotfi; Talbi, Mourad; Abid, Sabeur; Cherif, Adnane

2011-09-01

Within the medical environment, diverse techniques exist to assess the state of the voice of the patient. The inspection technique is inconvenient for a number of reasons, such as its high cost, the duration of the inspection, and above all, the fact that it is an invasive technique. This study focuses on a robust, rapid and accurate system for automatic identification of pathological voices. This system employs non-invasive, non-expensive and fully automated method based on hybrid approach: wavelet transform analysis and neural network classifier. First, we present the results obtained in our previous study while using classic feature parameters. These results allow visual identification of pathological voices. Second, quantified parameters drifting from the wavelet analysis are proposed to characterise the speech sample. On the other hand, a system of multilayer neural networks (MNNs) has been developed which carries out the automatic detection of pathological voices. The developed method was evaluated using voice database composed of recorded voice samples (continuous speech) from normophonic or dysphonic speakers. The dysphonic speakers were patients of a National Hospital 'RABTA' of Tunis Tunisia and a University Hospital in Brussels, Belgium. Experimental results indicate a success rate ranging between 75% and 98.61% for discrimination of normal and pathological voices using the proposed parameters and neural network classifier. We also compared the average classification rate based on the MNN, Gaussian mixture model and support vector machines.

Computational system identification of continuous-time nonlinear systems using approximate Bayesian computation

NASA Astrophysics Data System (ADS)

Krishnanathan, Kirubhakaran; Anderson, Sean R.; Billings, Stephen A.; Kadirkamanathan, Visakan

2016-11-01

In this paper, we derive a system identification framework for continuous-time nonlinear systems, for the first time using a simulation-focused computational Bayesian approach. Simulation approaches to nonlinear system identification have been shown to outperform regression methods under certain conditions, such as non-persistently exciting inputs and fast-sampling. We use the approximate Bayesian computation (ABC) algorithm to perform simulation-based inference of model parameters. The framework has the following main advantages: (1) parameter distributions are intrinsically generated, giving the user a clear description of uncertainty, (2) the simulation approach avoids the difficult problem of estimating signal derivatives as is common with other continuous-time methods, and (3) as noted above, the simulation approach improves identification under conditions of non-persistently exciting inputs and fast-sampling. Term selection is performed by judging parameter significance using parameter distributions that are intrinsically generated as part of the ABC procedure. The results from a numerical example demonstrate that the method performs well in noisy scenarios, especially in comparison to competing techniques that rely on signal derivative estimation.

Ares I-X In-Flight Modal Identification

NASA Technical Reports Server (NTRS)

Bartkowicz, Theodore J.; James, George H., III

2011-01-01

Operational modal analysis is a procedure that allows the extraction of modal parameters of a structure in its operating environment. It is based on the idealized premise that input to the structure is white noise. In some cases, when free decay responses are corrupted by unmeasured random disturbances, the response data can be processed into cross-correlation functions that approximate free decay responses. Modal parameters can be computed from these functions by time domain identification methods such as the Eigenvalue Realization Algorithm (ERA). The extracted modal parameters have the same characteristics as impulse response functions of the original system. Operational modal analysis is performed on Ares I-X in-flight data. Since the dynamic system is not stationary due to propellant mass loss, modal identification is only possible by analyzing the system as a series of linearized models over short periods of time via a sliding time-window of short time intervals. A time-domain zooming technique was also employed to enhance the modal parameter extraction. Results of this study demonstrate that free-decay time domain modal identification methods can be successfully employed for in-flight launch vehicle modal extraction.

Modal parameter identification based on combining transmissibility functions and blind source separation techniques

NASA Astrophysics Data System (ADS)

Araújo, Iván Gómez; Sánchez, Jesús Antonio García; Andersen, Palle

2018-05-01

Transmissibility-based operational modal analysis is a recent and alternative approach used to identify the modal parameters of structures under operational conditions. This approach is advantageous compared with traditional operational modal analysis because it does not make any assumptions about the excitation spectrum (i.e., white noise with a flat spectrum). However, common methodologies do not include a procedure to extract closely spaced modes with low signal-to-noise ratios. This issue is relevant when considering that engineering structures generally have closely spaced modes and that their measured responses present high levels of noise. Therefore, to overcome these problems, a new combined method for modal parameter identification is proposed in this work. The proposed method combines blind source separation (BSS) techniques and transmissibility-based methods. Here, BSS techniques were used to recover source signals, and transmissibility-based methods were applied to estimate modal information from the recovered source signals. To achieve this combination, a new method to define a transmissibility function was proposed. The suggested transmissibility function is based on the relationship between the power spectral density (PSD) of mixed signals and the PSD of signals from a single source. The numerical responses of a truss structure with high levels of added noise and very closely spaced modes were processed using the proposed combined method to evaluate its ability to identify modal parameters in these conditions. Colored and white noise excitations were used for the numerical example. The proposed combined method was also used to evaluate the modal parameters of an experimental test on a structure containing closely spaced modes. The results showed that the proposed combined method is capable of identifying very closely spaced modes in the presence of noise and, thus, may be potentially applied to improve the identification of damping ratios.

Cyclo-stationary linear parameter time-varying subspace realization method applied for identification of horizontal-axis wind turbines

NASA Astrophysics Data System (ADS)

Velazquez, Antonio; Swartz, R. Andrew

2013-04-01

Wind energy is becoming increasingly important worldwide as an alternative renewable energy source. Economical, maintenance and operation are critical issues for large slender dynamic structures, especially for remote offshore wind farms. Health monitoring systems are very promising instruments to assure reliability and good performance of the structure. These sensing and control technologies are typically informed by models based on mechanics or data-driven identification techniques in the time and/or frequency domain. Frequency response functions are popular but are difficult to realize autonomously for structures of higher order and having overlapping frequency content. Instead, time-domain techniques have shown powerful advantages from a practical point of view (e.g. embedded algorithms in wireless-sensor networks), being more suitable to differentiate closely-related modes. Customarily, time-varying effects are often neglected or dismissed to simplify the analysis, but such is not the case for wind loaded structures with spinning multibodies. A more complex scenario is constituted when dealing with both periodic mechanisms responsible for the vibration shaft of the rotor-blade system, and the wind tower substructure interaction. Transformations of the cyclic effects on the vibration data can be applied to isolate inertia quantities different from rotating-generated forces that are typically non-stationary in nature. After applying these transformations, structural identification can be carried out by stationary techniques via data-correlated Eigensystem realizations. In this paper an exploration of a periodic stationary or cyclo-stationary subspace identification technique is presented here by means of a modified Eigensystem Realization Algorithm (ERA) via Stochastic Subspace Identification (SSI) and Linear Parameter Time-Varying (LPTV) techniques. Structural response is assumed under stationary ambient excitation produced by a Gaussian (white) noise assembled in the operative range bandwidth of horizontal-axis wind turbines. ERA-OKID analysis is driven by correlation-function matrices from the stationary ambient response aiming to reduce noise effects. Singular value decomposition (SVD) and eigenvalue analysis are computed in a last stage to get frequencies and mode shapes. Proposed assumptions are carefully weighted to account for the uncertainty of the environment the wind turbines are subjected to. A numerical example is presented based on data acquisition carried out in a BWC XL.1 low power wind turbine device installed in University of California at Davis. Finally, comments and observations are provided on how this subspace realization technique can be extended for modal-parameter identification using exclusively ambient vibration data.

Output-only cyclo-stationary linear-parameter time-varying stochastic subspace identification method for rotating machinery and spinning structures

NASA Astrophysics Data System (ADS)

Velazquez, Antonio; Swartz, R. Andrew

2015-02-01

Economical maintenance and operation are critical issues for rotating machinery and spinning structures containing blade elements, especially large slender dynamic beams (e.g., wind turbines). Structural health monitoring systems represent promising instruments to assure reliability and good performance from the dynamics of the mechanical systems. However, such devices have not been completely perfected for spinning structures. These sensing technologies are typically informed by both mechanistic models coupled with data-driven identification techniques in the time and/or frequency domain. Frequency response functions are popular but are difficult to realize autonomously for structures of higher order, especially when overlapping frequency content is present. Instead, time-domain techniques have shown to possess powerful advantages from a practical point of view (i.e. low-order computational effort suitable for real-time or embedded algorithms) and also are more suitable to differentiate closely-related modes. Customarily, time-varying effects are often neglected or dismissed to simplify this analysis, but such cannot be the case for sinusoidally loaded structures containing spinning multi-bodies. A more complex scenario is constituted when dealing with both periodic mechanisms responsible for the vibration shaft of the rotor-blade system and the interaction of the supporting substructure. Transformations of the cyclic effects on the vibrational data can be applied to isolate inertial quantities that are different from rotation-generated forces that are typically non-stationary in nature. After applying these transformations, structural identification can be carried out by stationary techniques via data-correlated eigensystem realizations. In this paper, an exploration of a periodic stationary or cyclo-stationary subspace identification technique is presented here for spinning multi-blade systems by means of a modified Eigensystem Realization Algorithm (ERA) via stochastic subspace identification (SSI) and linear parameter time-varying (LPTV) techniques. Structural response is assumed to be stationary ambient excitation produced by a Gaussian (white) noise within the operative range bandwidth of the machinery or structure in study. ERA-OKID analysis is driven by correlation-function matrices from the stationary ambient response aiming to reduce noise effects. Singular value decomposition (SVD) and eigenvalue analysis are computed in a last stage to identify frequencies and complex-valued mode shapes. Proposed assumptions are carefully weighted to account for the uncertainty of the environment. A numerical example is carried out based a spinning finite element (SFE) model, and verified using ANSYS® Ver. 12. Finally, comments and observations are provided on how this subspace realization technique can be extended to the problem of modal-parameter identification using only ambient vibration data.

Joint OSNR monitoring and modulation format identification in digital coherent receivers using deep neural networks.

PubMed

Khan, Faisal Nadeem; Zhong, Kangping; Zhou, Xian; Al-Arashi, Waled Hussein; Yu, Changyuan; Lu, Chao; Lau, Alan Pak Tao

2017-07-24

We experimentally demonstrate the use of deep neural networks (DNNs) in combination with signals' amplitude histograms (AHs) for simultaneous optical signal-to-noise ratio (OSNR) monitoring and modulation format identification (MFI) in digital coherent receivers. The proposed technique automatically extracts OSNR and modulation format dependent features of AHs, obtained after constant modulus algorithm (CMA) equalization, and exploits them for the joint estimation of these parameters. Experimental results for 112 Gbps polarization-multiplexed (PM) quadrature phase-shift keying (QPSK), 112 Gbps PM 16 quadrature amplitude modulation (16-QAM), and 240 Gbps PM 64-QAM signals demonstrate OSNR monitoring with mean estimation errors of 1.2 dB, 0.4 dB, and 1 dB, respectively. Similarly, the results for MFI show 100% identification accuracy for all three modulation formats. The proposed technique applies deep machine learning algorithms inside standard digital coherent receiver and does not require any additional hardware. Therefore, it is attractive for cost-effective multi-parameter estimation in next-generation elastic optical networks (EONs).

Use of system identification techniques for improving airframe finite element models using test data

NASA Technical Reports Server (NTRS)

Hanagud, Sathya V.; Zhou, Weiyu; Craig, James I.; Weston, Neil J.

1991-01-01

A method for using system identification techniques to improve airframe finite element models was developed and demonstrated. The method uses linear sensitivity matrices to relate changes in selected physical parameters to changes in total system matrices. The values for these physical parameters were determined using constrained optimization with singular value decomposition. The method was confirmed using both simple and complex finite element models for which pseudo-experimental data was synthesized directly from the finite element model. The method was then applied to a real airframe model which incorporated all the complexities and details of a large finite element model and for which extensive test data was available. The method was shown to work, and the differences between the identified model and the measured results were considered satisfactory.

Pole-zero form fractional model identification in frequency domain

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mansouri, R.; Djamah, T.; Djennoune, S.

2009-03-05

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.

Identification of piecewise affine systems based on fuzzy PCA-guided robust clustering technique

NASA Astrophysics Data System (ADS)

Khanmirza, Esmaeel; Nazarahari, Milad; Mousavi, Alireza

2016-12-01

Hybrid systems are a class of dynamical systems whose behaviors are based on the interaction between discrete and continuous dynamical behaviors. Since a general method for the analysis of hybrid systems is not available, some researchers have focused on specific types of hybrid systems. Piecewise affine (PWA) systems are one of the subsets of hybrid systems. The identification of PWA systems includes the estimation of the parameters of affine subsystems and the coefficients of the hyperplanes defining the partition of the state-input domain. In this paper, we have proposed a PWA identification approach based on a modified clustering technique. By using a fuzzy PCA-guided robust k-means clustering algorithm along with neighborhood outlier detection, the two main drawbacks of the well-known clustering algorithms, i.e., the poor initialization and the presence of outliers, are eliminated. Furthermore, this modified clustering technique enables us to determine the number of subsystems without any prior knowledge about system. In addition, applying the structure of the state-input domain, that is, considering the time sequence of input-output pairs, provides a more efficient clustering algorithm, which is the other novelty of this work. Finally, the proposed algorithm has been evaluated by parameter identification of an IGV servo actuator. Simulation together with experiment analysis has proved the effectiveness of the proposed method.

Analysis of blind identification methods for estimation of kinetic parameters in dynamic medical imaging

NASA Astrophysics Data System (ADS)

Riabkov, Dmitri

Compartment modeling of dynamic medical image data implies that the concentration of the tracer over time in a particular region of the organ of interest is well-modeled as a convolution of the tissue response with the tracer concentration in the blood stream. The tissue response is different for different tissues while the blood input is assumed to be the same for different tissues. The kinetic parameters characterizing the tissue responses can be estimated by blind identification methods. These algorithms use the simultaneous measurements of concentration in separate regions of the organ; if the regions have different responses, the measurement of the blood input function may not be required. In this work it is shown that the blind identification problem has a unique solution for two-compartment model tissue response. For two-compartment model tissue responses in dynamic cardiac MRI imaging conditions with gadolinium-DTPA contrast agent, three blind identification algorithms are analyzed here to assess their utility: Eigenvector-based Algorithm for Multichannel Blind Deconvolution (EVAM), Cross Relations (CR), and Iterative Quadratic Maximum Likelihood (IQML). Comparisons of accuracy with conventional (not blind) identification techniques where the blood input is known are made as well. The statistical accuracies of estimation for the three methods are evaluated and compared for multiple parameter sets. The results show that the IQML method gives more accurate estimates than the other two blind identification methods. A proof is presented here that three-compartment model blind identification is not unique in the case of only two regions. It is shown that it is likely unique for the case of more than two regions, but this has not been proved analytically. For the three-compartment model the tissue responses in dynamic FDG PET imaging conditions are analyzed with the blind identification algorithms EVAM and Separable variables Least Squares (SLS). A method of identification that assumes that FDG blood input in the brain can be modeled as a function of time and several parameters (IFM) is analyzed also. Nonuniform sampling SLS (NSLS) is developed due to the rapid change of the FDG concentration in the blood during the early postinjection stage. Comparisons of accuracy of EVAM, SLS, NSLS and IFM identification techniques are made.

Exploratory Study for Continuous-time Parameter Estimation of Ankle Dynamics

NASA Technical Reports Server (NTRS)

Kukreja, Sunil L.; Boyle, Richard D.

2014-01-01

Recently, a parallel pathway model to describe ankle dynamics was proposed. This model provides a relationship between ankle angle and net ankle torque as the sum of a linear and nonlinear contribution. A technique to identify parameters of this model in discrete-time has been developed. However, these parameters are a nonlinear combination of the continuous-time physiology, making insight into the underlying physiology impossible. The stable and accurate estimation of continuous-time parameters is critical for accurate disease modeling, clinical diagnosis, robotic control strategies, development of optimal exercise protocols for longterm space exploration, sports medicine, etc. This paper explores the development of a system identification technique to estimate the continuous-time parameters of ankle dynamics. The effectiveness of this approach is assessed via simulation of a continuous-time model of ankle dynamics with typical parameters found in clinical studies. The results show that although this technique improves estimates, it does not provide robust estimates of continuous-time parameters of ankle dynamics. Due to this we conclude that alternative modeling strategies and more advanced estimation techniques be considered for future work.

AGARD Flight Test Techniques Series. Volume 14. Introduction to Flight Test Engineering (Introduction a la Technique d’essais en vol)

DTIC Science & Technology

1995-09-01

path and aircraft attitude and other flight or aircraft parameters • Calculations in the frequency domain ( Fast Fourier Transform) • Data analysis...Signal filtering Image processing of video and radar data Parameter identification Statistical analysis Power spectral density Fast Fourier Transform...airspeeds both fast and slow, altitude, load factor both above and below 1g, centers of gravity (fore and aft), and with system/subsystem failures. Whether

Estimation et validation des derivees de stabilite et controle du modele dynamique non-lineaire d'un drone a voilure fixe

NASA Astrophysics Data System (ADS)

Courchesne, Samuel

Knowledge of the dynamic characteristics of a fixed-wing UAV is necessary to design flight control laws and to conceive a high quality flight simulator. The basic features of a flight mechanic model include the properties of mass, inertia and major aerodynamic terms. They respond to a complex process involving various numerical analysis techniques and experimental procedures. This thesis focuses on the analysis of estimation techniques applied to estimate problems of stability and control derivatives from flight test data provided by an experimental UAV. To achieve this objective, a modern identification methodology (Quad-M) is used to coordinate the processing tasks from multidisciplinary fields, such as parameter estimation modeling, instrumentation, the definition of flight maneuvers and validation. The system under study is a non-linear model with six degrees of freedom with a linear aerodynamic model. The time domain techniques are used for identification of the drone. The first technique, the equation error method is used to determine the structure of the aerodynamic model. Thereafter, the output error method and filter error method are used to estimate the aerodynamic coefficients values. The Matlab scripts for estimating the parameters obtained from the American Institute of Aeronautics and Astronautics (AIAA) are used and modified as necessary to achieve the desired results. A commendable effort in this part of research is devoted to the design of experiments. This includes an awareness of the system data acquisition onboard and the definition of flight maneuvers. The flight tests were conducted under stable flight conditions and with low atmospheric disturbance. Nevertheless, the identification results showed that the filter error method is most effective for estimating the parameters of the drone due to the presence of process noise and measurement. The aerodynamic coefficients are validated using a numerical analysis of the vortex method. In addition, a simulation model incorporating the estimated parameters is used to compare the behavior of states measured. Finally, a good correspondence between the results is demonstrated despite a limited number of flight data. Keywords: drone, identification, estimation, nonlinear, flight test, system, aerodynamic coefficient.

Modern control concepts in hydrology

NASA Technical Reports Server (NTRS)

Duong, N.; Johnson, G. R.; Winn, C. B.

1974-01-01

Two approaches to an identification problem in hydrology are presented based upon concepts from modern control and estimation theory. The first approach treats the identification of unknown parameters in a hydrologic system subject to noisy inputs as an adaptive linear stochastic control problem; the second approach alters the model equation to account for the random part in the inputs, and then uses a nonlinear estimation scheme to estimate the unknown parameters. Both approaches use state-space concepts. The identification schemes are sequential and adaptive and can handle either time invariant or time dependent parameters. They are used to identify parameters in the Prasad model of rainfall-runoff. The results obtained are encouraging and conform with results from two previous studies; the first using numerical integration of the model equation along with a trial-and-error procedure, and the second, by using a quasi-linearization technique. The proposed approaches offer a systematic way of analyzing the rainfall-runoff process when the input data are imbedded in noise.

On-Orbit System Identification

NASA Technical Reports Server (NTRS)

Mettler, E.; Milman, M. H.; Bayard, D.; Eldred, D. B.

1987-01-01

Information derived from accelerometer readings benefits important engineering and control functions. Report discusses methodology for detection, identification, and analysis of motions within space station. Techniques of vibration and rotation analyses, control theory, statistics, filter theory, and transform methods integrated to form system for generating models and model parameters that characterize total motion of complicated space station, with respect to both control-induced and random mechanical disturbances.

High Resolution Ultrasonic Method for 3D Fingerprint Recognizable Characteristics in Biometrics Identification

NASA Astrophysics Data System (ADS)

Maev, R. Gr.; Bakulin, E. Yu.; Maeva, A.; Severin, F.

Biometrics is a rapidly evolving scientific and applied discipline that studies possible ways of personal identification by means of unique biological characteristics. Such identification is important in various situations requiring restricted access to certain areas, information and personal data and for cases of medical emergencies. A number of automated biometric techniques have been developed, including fingerprint, hand shape, eye and facial recognition, thermographic imaging, etc. All these techniques differ in the recognizable parameters, usability, accuracy and cost. Among these, fingerprint recognition stands alone since a very large database of fingerprints has already been acquired. Also, fingerprints are key evidence left at a crime scene and can be used to indentify suspects. Therefore, of all automated biometric techniques, especially in the field of law enforcement, fingerprint identification seems to be the most promising. We introduce a newer development of the ultrasonic fingerprint imaging. The proposed method obtains a scan only once and then varies the C-scan gate position and width to visualize acoustic reflections from any appropriate depth inside the skin. Also, B-scans and A-scans can be recreated from any position using such data array, which gives the control over the visualization options. By setting the C-scan gate deeper inside the skin, distribution of the sweat pores (which are located along the ridges) can be easily visualized. This distribution should be unique for each individual so this provides a means of personal identification, which is not affected by any changes (accidental or intentional) of the fingers' surface conditions. This paper discusses different setups, acoustic parameters of the system, signal and image processing options and possible ways of 3-dimentional visualization that could be used as a recognizable characteristic in biometric identification.

System identification for modeling for control of flexible structures

NASA Technical Reports Server (NTRS)

Mettler, Edward; Milman, Mark

1986-01-01

The major components of a design and operational flight strategy for flexible structure control systems are presented. In this strategy an initial distributed parameter control design is developed and implemented from available ground test data and on-orbit identification using sophisticated modeling and synthesis techniques. The reliability of this high performance controller is directly linked to the accuracy of the parameters on which the design is based. Because uncertainties inevitably grow without system monitoring, maintaining the control system requires an active on-line system identification function to supply parameter updates and covariance information. Control laws can then be modified to improve performance when the error envelopes are decreased. In terms of system safety and stability the covariance information is of equal importance as the parameter values themselves. If the on-line system ID function detects an increase in parameter error covariances, then corresponding adjustments must be made in the control laws to increase robustness. If the error covariances exceed some threshold, an autonomous calibration sequence could be initiated to restore the error enveloped to an acceptable level.

An adaptive learning control system for large flexible structures

NASA Technical Reports Server (NTRS)

Thau, F. E.

1985-01-01

The objective of the research has been to study the design of adaptive/learning control systems for the control of large flexible structures. In the first activity an adaptive/learning control methodology for flexible space structures was investigated. The approach was based on using a modal model of the flexible structure dynamics and an output-error identification scheme to identify modal parameters. In the second activity, a least-squares identification scheme was proposed for estimating both modal parameters and modal-to-actuator and modal-to-sensor shape functions. The technique was applied to experimental data obtained from the NASA Langley beam experiment. In the third activity, a separable nonlinear least-squares approach was developed for estimating the number of excited modes, shape functions, modal parameters, and modal amplitude and velocity time functions for a flexible structure. In the final research activity, a dual-adaptive control strategy was developed for regulating the modal dynamics and identifying modal parameters of a flexible structure. A min-max approach was used for finding an input to provide modal parameter identification while not exceeding reasonable bounds on modal displacement.

Inverse problems and optimal experiment design in unsteady heat transfer processes identification

NASA Technical Reports Server (NTRS)

Artyukhin, Eugene A.

1991-01-01

Experimental-computational methods for estimating characteristics of unsteady heat transfer processes are analyzed. The methods are based on the principles of distributed parameter system identification. The theoretical basis of such methods is the numerical solution of nonlinear ill-posed inverse heat transfer problems and optimal experiment design problems. Numerical techniques for solving problems are briefly reviewed. The results of the practical application of identification methods are demonstrated when estimating effective thermophysical characteristics of composite materials and thermal contact resistance in two-layer systems.

Use of system identification techniques for improving airframe finite element models using test data

NASA Technical Reports Server (NTRS)

Hanagud, Sathya V.; Zhou, Weiyu; Craig, James I.; Weston, Neil J.

1993-01-01

A method for using system identification techniques to improve airframe finite element models using test data was developed and demonstrated. The method uses linear sensitivity matrices to relate changes in selected physical parameters to changes in the total system matrices. The values for these physical parameters were determined using constrained optimization with singular value decomposition. The method was confirmed using both simple and complex finite element models for which pseudo-experimental data was synthesized directly from the finite element model. The method was then applied to a real airframe model which incorporated all of the complexities and details of a large finite element model and for which extensive test data was available. The method was shown to work, and the differences between the identified model and the measured results were considered satisfactory.

Thermoluminescence of Antarctic meteorites: A rapid screening technique for terrestrial age estimation, pairing studies and identification of specimens with unusual prefall histories

NASA Technical Reports Server (NTRS)

Sutton, S. R.; Walker, R. M.

1986-01-01

Thermoluminescence (TL) is a promising technique for rapid screening of the large numbers of Antarctic meteorites, permitting identification of interesting specimens that can then be studied in detail by other, more definite techniques. Specifically, TL permits determination of rough terrestrial age, identification of potential paired groups and location of specimens with unusual pre-fall histories. Meteorites with long terrestrial ages are particularly valuable for studying transport and weathering mechanisms. Pairing studies are possible because TL variations among meteorites are large compared to variations within individual objects, especially for natural TL. Available TL data for several L3 fragments, three of which were paired by other techniques, are presented as an example of the use of TL parameters in pairing studies. Additional TL measurements, specifically a blind test, are recommended to satisfactorily establish the reliability of this pairing property. The TL measurements also identify fragments with unusual pre-fall histories, such an near-Sun orbits.

The Accuracy of Parameter Estimation in System Identification of Noisy Aircraft Load Measurement. Ph.D. Thesis

NASA Technical Reports Server (NTRS)

Kong, Jeffrey

1994-01-01

This thesis focuses on the subject of the accuracy of parameter estimation and system identification techniques. Motivated by a complicated load measurement from NASA Dryden Flight Research Center, advanced system identification techniques are needed. The objective of this problem is to accurately predict the load experienced by the aircraft wing structure during flight determined from a set of calibrated load and gage response relationship. We can then model the problem as a black box input-output system identification from which the system parameter has to be estimated. Traditional LS (Least Square) techniques and the issues of noisy data and model accuracy are addressed. A statistical bound reflecting the change in residual is derived in order to understand the effects of the perturbations on the data. Due to the intrinsic nature of the LS problem, LS solution faces the dilemma of the trade off between model accuracy and noise sensitivity. A method of conflicting performance indices is presented, thus allowing us to improve the noise sensitivity while at the same time configuring the degredation of the model accuracy. SVD techniques for data reduction are studied and the equivalence of the Correspondence Analysis (CA) and Total Least Squares Criteria are proved. We also looked at nonlinear LS problems with NASA F-111 data set as an example. Conventional methods are neither easily applicable nor suitable for the specific load problem since the exact model of the system is unknown. Neural Network (NN) does not require prior information on the model of the system. This robustness motivated us to apply the NN techniques on our load problem. Simulation results for the NN methods used in both the single load and the 'warning signal' problems are both useful and encouraging. The performance of the NN (for single load estimate) is better than the LS approach, whereas no conventional approach was tried for the 'warning signals' problems. The NN design methodology is also presented. The use of SVD, CA and Collinearity Index methods are used to reduce the number of neurons in a layer.

Analytical techniques for the study of some parameters of multispectral scanner systems for remote sensing

NASA Technical Reports Server (NTRS)

Wiswell, E. R.; Cooper, G. R. (Principal Investigator)

1978-01-01

The author has identified the following significant results. The concept of average mutual information in the received spectral random process about the spectral scene was developed. Techniques amenable to implementation on a digital computer were also developed to make the required average mutual information calculations. These techniques required identification of models for the spectral response process of scenes. Stochastic modeling techniques were adapted for use. These techniques were demonstrated on empirical data from wheat and vegetation scenes.

Least-squares sequential parameter and state estimation for large space structures

NASA Technical Reports Server (NTRS)

Thau, F. E.; Eliazov, T.; Montgomery, R. C.

1982-01-01

This paper presents the formulation of simultaneous state and parameter estimation problems for flexible structures in terms of least-squares minimization problems. The approach combines an on-line order determination algorithm, with least-squares algorithms for finding estimates of modal approximation functions, modal amplitudes, and modal parameters. The approach combines previous results on separable nonlinear least squares estimation with a regression analysis formulation of the state estimation problem. The technique makes use of sequential Householder transformations. This allows for sequential accumulation of matrices required during the identification process. The technique is used to identify the modal prameters of a flexible beam.

The integrated manual and automatic control of complex flight systems

NASA Technical Reports Server (NTRS)

Schmidt, D. K.

1984-01-01

A unified control synthesis methodology for complex and/or non-conventional flight vehicles are developed. Prediction techniques for the handling characteristics of such vehicles and pilot parameter identification from experimental data are addressed.

Modeling, estimation and identification methods for static shape determination of flexible structures. [for large space structure design

NASA Technical Reports Server (NTRS)

Rodriguez, G.; Scheid, R. E., Jr.

1986-01-01

This paper outlines methods for modeling, identification and estimation for static determination of flexible structures. The shape estimation schemes are based on structural models specified by (possibly interconnected) elliptic partial differential equations. The identification techniques provide approximate knowledge of parameters in elliptic systems. The techniques are based on the method of maximum-likelihood that finds parameter values such that the likelihood functional associated with the system model is maximized. The estimation methods are obtained by means of a function-space approach that seeks to obtain the conditional mean of the state given the data and a white noise characterization of model errors. The solutions are obtained in a batch-processing mode in which all the data is processed simultaneously. After methods for computing the optimal estimates are developed, an analysis of the second-order statistics of the estimates and of the related estimation error is conducted. In addition to outlining the above theoretical results, the paper presents typical flexible structure simulations illustrating performance of the shape determination methods.

Simulation verification techniques study. Subsystem simulation validation techniques

NASA Technical Reports Server (NTRS)

Duncan, L. M.; Reddell, J. P.; Schoonmaker, P. B.

1974-01-01

Techniques for validation of software modules which simulate spacecraft onboard systems are discussed. An overview of the simulation software hierarchy for a shuttle mission simulator is provided. A set of guidelines for the identification of subsystem/module performance parameters and critical performance parameters are presented. Various sources of reference data to serve as standards of performance for simulation validation are identified. Environment, crew station, vehicle configuration, and vehicle dynamics simulation software are briefly discussed from the point of view of their interfaces with subsystem simulation modules. A detailed presentation of results in the area of vehicle subsystems simulation modules is included. A list of references, conclusions and recommendations are also given.

On-line adaptive battery impedance parameter and state estimation considering physical principles in reduced order equivalent circuit battery models part 2. Parameter and state estimation

NASA Astrophysics Data System (ADS)

Fleischer, Christian; Waag, Wladislaw; Heyn, Hans-Martin; Sauer, Dirk Uwe

2014-09-01

Lithium-ion battery systems employed in high power demanding systems such as electric vehicles require a sophisticated monitoring system to ensure safe and reliable operation. Three major states of the battery are of special interest and need to be constantly monitored. These include: battery state of charge (SoC), battery state of health (capacity fade determination, SoH), and state of function (power fade determination, SoF). The second paper concludes the series by presenting a multi-stage online parameter identification technique based on a weighted recursive least quadratic squares parameter estimator to determine the parameters of the proposed battery model from the first paper during operation. A novel mutation based algorithm is developed to determine the nonlinear current dependency of the charge-transfer resistance. The influence of diffusion is determined by an on-line identification technique and verified on several batteries at different operation conditions. This method guarantees a short response time and, together with its fully recursive structure, assures a long-term stable monitoring of the battery parameters. The relative dynamic voltage prediction error of the algorithm is reduced to 2%. The changes of parameters are used to determine the states of the battery. The algorithm is real-time capable and can be implemented on embedded systems.

A photometric mode identification method, including an improved non-adiabatic treatment of the atmosphere

NASA Astrophysics Data System (ADS)

Dupret, M.-A.; De Ridder, J.; De Cat, P.; Aerts, C.; Scuflaire, R.; Noels, A.; Thoul, A.

2003-02-01

We present an improved version of the method of photometric mode identification of Heynderickx et al. (\\cite{hey}). Our new version is based on the inclusion of precise non-adiabatic eigenfunctions determined in the outer stellar atmosphere according to the formalism recently proposed by Dupret et al. (\\cite{dup}). Our improved photometric mode identification technique is therefore no longer dependent on ad hoc parameters for the non-adiabatic effects. It contains the complete physical conditions of the outer atmosphere of the star, provided that rotation does not play a key role. We apply our method to the two slowly pulsating B stars HD 74560 and HD 138764 and to the beta Cephei star EN (16) Lac. Besides identifying the degree l of the pulsating stars, our method is also a tool for improving the knowledge of stellar interiors and atmospheres, by imposing constraints on parameters such as the metallicity and the mixing-length parameter alpha (a procedure we label non-adiabatic asteroseismology). The non-adiabatic eigenfunctions needed for the mode identification are available upon request from the authors.

A theoretical framework for convergence and continuous dependence of estimates in inverse problems for distributed parameter systems

NASA Technical Reports Server (NTRS)

Banks, H. T.; Ito, K.

1988-01-01

Numerical techniques for parameter identification in distributed-parameter systems are developed analytically. A general convergence and stability framework (for continuous dependence on observations) is derived for first-order systems on the basis of (1) a weak formulation in terms of sesquilinear forms and (2) the resolvent convergence form of the Trotter-Kato approximation. The extension of this framework to second-order systems is considered.

Viscoelastic material properties' identification using high speed full field measurements on vibrating plates

NASA Astrophysics Data System (ADS)

Giraudeau, A.; Pierron, F.

2010-06-01

The paper presents an experimental application of a method leading to the identification of the elastic and damping material properties of isotropic vibrating plates. The theory assumes that the searched parameters can be extracted from curvature and deflection fields measured on the whole surface of the plate at two particular instants of the vibrating motion. The experimental application consists in an original excitation fixture, a particular adaptation of an optical full-field measurement technique, a data preprocessing giving the curvature and deflection fields and finally in the identification process using the Virtual Fields Method (VFM). The principle of the deflectometry technique used for the measurements is presented. First results of identification on an acrylic plate are presented and compared to reference values. Details about a new experimental arrangement, currently in progress, is presented. It uses a high speed digital camera to over sample the full-field measurements.

MID Plot: a new lithology technique. [Matrix identification plot

DOE Office of Scientific and Technical Information (OSTI.GOV)

Clavier, C.; Rust, D.H.

1976-01-01

Lithology interpretation by the Litho-Porosity (M-N) method has been used for years, but is evidently too cumbersome and ambiguous for widespread acceptance as a field technique. To set aside these objections, another method has been devised. Instead of the log-derived parameters M and N, the MID Plot uses quasi-physical quantities, (rho/sub ma/)/sub a/ and (..delta..t/sub ma/)/sub a/, as its porosity-independent variables. These parameters, taken from suitably scaled Neutron-Density and Sonic-Neutron crossplots, define a unique matrix mineral or mixture for each point on the logs. The matrix points on the MID Plot thus remain constant in spite of changes in mudmore » filtrate, porosity, or neutron tool types (all of which significantly affect the M-N Plot). This new development is expected to bring welcome relief in areas where lithology identification is a routine part of log analysis.« less

Comparing SVM and ANN based Machine Learning Methods for Species Identification of Food Contaminating Beetles.

PubMed

Bisgin, Halil; Bera, Tanmay; Ding, Hongjian; Semey, Howard G; Wu, Leihong; Liu, Zhichao; Barnes, Amy E; Langley, Darryl A; Pava-Ripoll, Monica; Vyas, Himansu J; Tong, Weida; Xu, Joshua

2018-04-25

Insect pests, such as pantry beetles, are often associated with food contaminations and public health risks. Machine learning has the potential to provide a more accurate and efficient solution in detecting their presence in food products, which is currently done manually. In our previous research, we demonstrated such feasibility where Artificial Neural Network (ANN) based pattern recognition techniques could be implemented for species identification in the context of food safety. In this study, we present a Support Vector Machine (SVM) model which improved the average accuracy up to 85%. Contrary to this, the ANN method yielded ~80% accuracy after extensive parameter optimization. Both methods showed excellent genus level identification, but SVM showed slightly better accuracy  for most species. Highly accurate species level identification remains a challenge, especially in distinguishing between species from the same genus which may require improvements in both imaging and machine learning techniques. In summary, our work does illustrate a new SVM based technique and provides a good comparison with the ANN model in our context. We believe such insights will pave better way forward for the application of machine learning towards species identification and food safety.

The Inverse Problem for Confined Aquifer Flow: Identification and Estimation With Extensions

NASA Astrophysics Data System (ADS)

Loaiciga, Hugo A.; MariñO, Miguel A.

1987-01-01

The contributions of this work are twofold. First, a methodology for estimating the elements of parameter matrices in the governing equation of flow in a confined aquifer is developed. The estimation techniques for the distributed-parameter inverse problem pertain to linear least squares and generalized least squares methods. The linear relationship among the known heads and unknown parameters of the flow equation provides the background for developing criteria for determining the identifiability status of unknown parameters. Under conditions of exact or overidentification it is possible to develop statistically consistent parameter estimators and their asymptotic distributions. The estimation techniques, namely, two-stage least squares and three stage least squares, are applied to a specific groundwater inverse problem and compared between themselves and with an ordinary least squares estimator. The three-stage estimator provides the closer approximation to the actual parameter values, but it also shows relatively large standard errors as compared to the ordinary and two-stage estimators. The estimation techniques provide the parameter matrices required to simulate the unsteady groundwater flow equation. Second, a nonlinear maximum likelihood estimation approach to the inverse problem is presented. The statistical properties of maximum likelihood estimators are derived, and a procedure to construct confidence intervals and do hypothesis testing is given. The relative merits of the linear and maximum likelihood estimators are analyzed. Other topics relevant to the identification and estimation methodologies, i.e., a continuous-time solution to the flow equation, coping with noise-corrupted head measurements, and extension of the developed theory to nonlinear cases are also discussed. A simulation study is used to evaluate the methods developed in this study.

Challenging Aerospace Problems for Intelligent Systems

DTIC Science & Technology

2003-06-01

importance of each rule. Techniques such as logarithmic regression or Saaty’s AHP may be employed to apply the weights on to the fuzzy rules. 15-9 Given u...at which designs could be evaluated. This implies that modeling techniques such as neural networks, fuzzy systems and so on can play an important role...failure conditions [4-6]. These approaches apply techniques, such as neural networks, fuzzy logic, and parameter identification, to improve aircraft

Airway and tissue loading in postinterrupter response of the respiratory system - an identification algorithm construction.

PubMed

Jablonski, Ireneusz; Mroczka, Janusz

2010-01-01

The paper offers an enhancement of the classical interrupter technique algorithm dedicated to respiratory mechanics measurements. Idea consists in exploitation of information contained in postocclusional transient states during indirect measurement of parameter characteristics by model identification. It needs the adequacy of an inverse analogue to general behavior of the real system and a reliable algorithm of parameter estimation. The second one was a subject of reported works, which finally showed the potential of the approach to separation of airway and tissue response in a case of short-term excitation by interrupter valve operation. Investigations were conducted in a regime of forward-inverse computer experiment.

Identification of handwriting by using the genetic algorithm (GA) and support vector machine (SVM)

NASA Astrophysics Data System (ADS)

Zhang, Qigui; Deng, Kai

2016-12-01

As portable digital camera and a camera phone comes more and more popular, and equally pressing is meeting the requirements of people to shoot at any time, to identify and storage handwritten character. In this paper, genetic algorithm(GA) and support vector machine(SVM)are used for identification of handwriting. Compare with parameters-optimized method, this technique overcomes two defects: first, it's easy to trap in the local optimum; second, finding the best parameters in the larger range will affects the efficiency of classification and prediction. As the experimental results suggest, GA-SVM has a higher recognition rate.

Explicit least squares system parameter identification for exact differential input/output models

NASA Technical Reports Server (NTRS)

Pearson, A. E.

1993-01-01

The equation error for a class of systems modeled by input/output differential operator equations has the potential to be integrated exactly, given the input/output data on a finite time interval, thereby opening up the possibility of using an explicit least squares estimation technique for system parameter identification. The paper delineates the class of models for which this is possible and shows how the explicit least squares cost function can be obtained in a way that obviates dealing with unknown initial and boundary conditions. The approach is illustrated by two examples: a second order chemical kinetics model and a third order system of Lorenz equations.

A two-thermocouple probe technique for estimating thermocouple time constants in flows with combustion: In situ parameter identification of a first-order lag system

NASA Astrophysics Data System (ADS)

Tagawa, M.; Shimoji, T.; Ohta, Y.

1998-09-01

A two-thermocouple probe, composed of two fine-wire thermocouples of unequal diameters, is a novel technique for estimating thermocouple time constants without any dynamic calibration of the thermocouple response. This technique is most suitable for measuring fluctuating temperatures in turbulent combustion. In the present study, the reliability and applicability of this technique are appraised in a turbulent wake of a heated cylinder (without combustion). A fine-wire resistance thermometer (cold wire) of fast response is simultaneously used to provide a reference temperature. A quantitative and detailed comparison between the cold-wire measurement and the compensated thermocouple ones shows that a previous estimation scheme gives thermocouple time constants smaller than appropriate values, unless the noise in the thermocouple signals is negligible and/or the spatial resolution of the two-thermocouple probe is sufficiently high. The scheme has been improved so as to maximize the correlation coefficient between the two compensated-thermocouple outputs. The improved scheme offers better compensation of the thermocouple response. The present approach is generally applicable to in situ parameter identification of a first-order lag system.

Polarimetric SAR Interferometry based modeling for tree height and aboveground biomass retrieval in a tropical deciduous forest

NASA Astrophysics Data System (ADS)

Kumar, Shashi; Khati, Unmesh G.; Chandola, Shreya; Agrawal, Shefali; Kushwaha, Satya P. S.

2017-08-01

The regulation of the carbon cycle is a critical ecosystem service provided by forests globally. It is, therefore, necessary to have robust techniques for speedy assessment of forest biophysical parameters at the landscape level. It is arduous and time taking to monitor the status of vast forest landscapes using traditional field methods. Remote sensing and GIS techniques are efficient tools that can monitor the health of forests regularly. Biomass estimation is a key parameter in the assessment of forest health. Polarimetric SAR (PolSAR) remote sensing has already shown its potential for forest biophysical parameter retrieval. The current research work focuses on the retrieval of forest biophysical parameters of tropical deciduous forest, using fully polarimetric spaceborne C-band data with Polarimetric SAR Interferometry (PolInSAR) techniques. PolSAR based Interferometric Water Cloud Model (IWCM) has been used to estimate aboveground biomass (AGB). Input parameters to the IWCM have been extracted from the decomposition modeling of SAR data as well as PolInSAR coherence estimation. The technique of forest tree height retrieval utilized PolInSAR coherence based modeling approach. Two techniques - Coherence Amplitude Inversion (CAI) and Three Stage Inversion (TSI) - for forest height estimation are discussed, compared and validated. These techniques allow estimation of forest stand height and true ground topography. The accuracy of the forest height estimated is assessed using ground-based measurements. PolInSAR based forest height models showed enervation in the identification of forest vegetation and as a result height values were obtained in river channels and plain areas. Overestimation in forest height was also noticed at several patches of the forest. To overcome this problem, coherence and backscatter based threshold technique is introduced for forest area identification and accurate height estimation in non-forested regions. IWCM based modeling for forest AGB retrieval showed R2 value of 0.5, RMSE of 62.73 (t ha-1) and a percent accuracy of 51%. TSI based PolInSAR inversion modeling showed the most accurate result for forest height estimation. The correlation between the field measured forest height and the estimated tree height using TSI technique is 62% with an average accuracy of 91.56% and RMSE of 2.28 m. The study suggested that PolInSAR coherence based modeling approach has significant potential for retrieval of forest biophysical parameters.

Flight Investigation of Prescribed Simultaneous Independent Surface Excitations for Real-Time Parameter Identification

NASA Technical Reports Server (NTRS)

Moes, Timothy R.; Smith, Mark S.; Morelli, Eugene A.

2003-01-01

Near real-time stability and control derivative extraction is required to support flight demonstration of Intelligent Flight Control System (IFCS) concepts being developed by NASA, academia, and industry. Traditionally, flight maneuvers would be designed and flown to obtain stability and control derivative estimates using a postflight analysis technique. The goal of the IFCS concept is to be able to modify the control laws in real time for an aircraft that has been damaged in flight. In some IFCS implementations, real-time parameter identification (PID) of the stability and control derivatives of the damaged aircraft is necessary for successfully reconfiguring the control system. This report investigates the usefulness of Prescribed Simultaneous Independent Surface Excitations (PreSISE) to provide data for rapidly obtaining estimates of the stability and control derivatives. Flight test data were analyzed using both equation-error and output-error PID techniques. The equation-error PID technique is known as Fourier Transform Regression (FTR) and is a frequency-domain real-time implementation. Selected results were compared with a time-domain output-error technique. The real-time equation-error technique combined with the PreSISE maneuvers provided excellent derivative estimation in the longitudinal axis. However, the PreSISE maneuvers as presently defined were not adequate for accurate estimation of the lateral-directional derivatives.

An adaptive technique for a redundant-sensor navigation system.

NASA Technical Reports Server (NTRS)

Chien, T.-T.

1972-01-01

An on-line adaptive technique is developed to provide a self-contained redundant-sensor navigation system with a capability to utilize its full potentiality in reliability and performance. This adaptive system is structured as a multistage stochastic process of detection, identification, and compensation. It is shown that the detection system can be effectively constructed on the basis of a design value, specified by mission requirements, of the unknown parameter in the actual system, and of a degradation mode in the form of a constant bias jump. A suboptimal detection system on the basis of Wald's sequential analysis is developed using the concept of information value and information feedback. The developed system is easily implemented, and demonstrates a performance remarkably close to that of the optimal nonlinear detection system. An invariant transformation is derived to eliminate the effect of nuisance parameters such that the ambiguous identification system can be reduced to a set of disjoint simple hypotheses tests. By application of a technique of decoupled bias estimation in the compensation system the adaptive system can be operated without any complicated reorganization.

Sparse feature learning for instrument identification: Effects of sampling and pooling methods.

PubMed

Han, Yoonchang; Lee, Subin; Nam, Juhan; Lee, Kyogu

2016-05-01

Feature learning for music applications has recently received considerable attention from many researchers. This paper reports on the sparse feature learning algorithm for musical instrument identification, and in particular, focuses on the effects of the frame sampling techniques for dictionary learning and the pooling methods for feature aggregation. To this end, two frame sampling techniques are examined that are fixed and proportional random sampling. Furthermore, the effect of using onset frame was analyzed for both of proposed sampling methods. Regarding summarization of the feature activation, a standard deviation pooling method is used and compared with the commonly used max- and average-pooling techniques. Using more than 47 000 recordings of 24 instruments from various performers, playing styles, and dynamics, a number of tuning parameters are experimented including the analysis frame size, the dictionary size, and the type of frequency scaling as well as the different sampling and pooling methods. The results show that the combination of proportional sampling and standard deviation pooling achieve the best overall performance of 95.62% while the optimal parameter set varies among the instrument classes.

Gene Identification Algorithms Using Exploratory Statistical Analysis of Periodicity

NASA Astrophysics Data System (ADS)

Mukherjee, Shashi Bajaj; Sen, Pradip Kumar

2010-10-01

Studying periodic pattern is expected as a standard line of attack for recognizing DNA sequence in identification of gene and similar problems. But peculiarly very little significant work is done in this direction. This paper studies statistical properties of DNA sequences of complete genome using a new technique. A DNA sequence is converted to a numeric sequence using various types of mappings and standard Fourier technique is applied to study the periodicity. Distinct statistical behaviour of periodicity parameters is found in coding and non-coding sequences, which can be used to distinguish between these parts. Here DNA sequences of Drosophila melanogaster were analyzed with significant accuracy.

X-31 aerodynamic characteristics determined from flight data

NASA Technical Reports Server (NTRS)

Kokolios, Alex

1993-01-01

The lateral aerodynamic characteristics of the X-31 were determined at angles of attack ranging from 20 to 45 deg. Estimates of the lateral stability and control parameters were obtained by applying two parameter estimation techniques, linear regression, and the extended Kalman filter to flight test data. An attempt to apply maximum likelihood to extract parameters from the flight data was also made but failed for the reasons presented. An overview of the System Identification process is given. The overview includes a listing of the more important properties of all three estimation techniques that were applied to the data. A comparison is given of results obtained from flight test data and wind tunnel data for four important lateral parameters. Finally, future research to be conducted in this area is discussed.

An Indirect System Identification Technique for Stable Estimation of Continuous-Time Parameters of the Vestibulo-Ocular Reflex (VOR)

NASA Technical Reports Server (NTRS)

Kukreja, Sunil L.; Wallin, Ragnar; Boyle, Richard D.

2013-01-01

The vestibulo-ocular reflex (VOR) is a well-known dual mode bifurcating system that consists of slow and fast modes associated with nystagmus and saccade, respectively. Estimation of continuous-time parameters of nystagmus and saccade models are known to be sensitive to estimation methodology, noise and sampling rate. The stable and accurate estimation of these parameters are critical for accurate disease modelling, clinical diagnosis, robotic control strategies, mission planning for space exploration and pilot safety, etc. This paper presents a novel indirect system identification method for the estimation of continuous-time parameters of VOR employing standardised least-squares with dual sampling rates in a sparse structure. This approach permits the stable and simultaneous estimation of both nystagmus and saccade data. The efficacy of this approach is demonstrated via simulation of a continuous-time model of VOR with typical parameters found in clinical studies and in the presence of output additive noise.

Recent developments in learning control and system identification for robots and structures

NASA Technical Reports Server (NTRS)

Phan, M.; Juang, J.-N.; Longman, R. W.

1990-01-01

This paper reviews recent results in learning control and learning system identification, with particular emphasis on discrete-time formulation, and their relation to adaptive theory. Related continuous-time results are also discussed. Among the topics presented are proportional, derivative, and integral learning controllers, time-domain formulation of discrete learning algorithms. Newly developed techniques are described including the concept of the repetition domain, and the repetition domain formulation of learning control by linear feedback, model reference learning control, indirect learning control with parameter estimation, as well as related basic concepts, recursive and non-recursive methods for learning identification.

Heart Sound Biometric System Based on Marginal Spectrum Analysis

PubMed Central

Zhao, Zhidong; Shen, Qinqin; Ren, Fangqin

2013-01-01

This work presents a heart sound biometric system based on marginal spectrum analysis, which is a new feature extraction technique for identification purposes. This heart sound identification system is comprised of signal acquisition, pre-processing, feature extraction, training, and identification. Experiments on the selection of the optimal values for the system parameters are conducted. The results indicate that the new spectrum coefficients result in a significant increase in the recognition rate of 94.40% compared with that of the traditional Fourier spectrum (84.32%) based on a database of 280 heart sounds from 40 participants. PMID:23429515

Direct modeling parameter signature analysis and failure mode prediction of physical systems using hybrid computer optimization

NASA Technical Reports Server (NTRS)

Drake, R. L.; Duvoisin, P. F.; Asthana, A.; Mather, T. W.

1971-01-01

High speed automated identification and design of dynamic systems, both linear and nonlinear, are discussed. Special emphasis is placed on developing hardware and techniques which are applicable to practical problems. The basic modeling experiment and new results are described. Using the improvements developed successful identification of several systems, including a physical example as well as simulated systems, was obtained. The advantages of parameter signature analysis over signal signature analysis in go-no go testing of operational systems were demonstrated. The feasibility of using these ideas in failure mode prediction in operating systems was also investigated. An improved digital controlled nonlinear function generator was developed, de-bugged, and completely documented.

Statistical analysis of texture in trunk images for biometric identification of tree species.

PubMed

Bressane, Adriano; Roveda, José A F; Martins, Antônio C G

2015-04-01

The identification of tree species is a key step for sustainable management plans of forest resources, as well as for several other applications that are based on such surveys. However, the present available techniques are dependent on the presence of tree structures, such as flowers, fruits, and leaves, limiting the identification process to certain periods of the year. Therefore, this article introduces a study on the application of statistical parameters for texture classification of tree trunk images. For that, 540 samples from five Brazilian native deciduous species were acquired and measures of entropy, uniformity, smoothness, asymmetry (third moment), mean, and standard deviation were obtained from the presented textures. Using a decision tree, a biometric species identification system was constructed and resulted to a 0.84 average precision rate for species classification with 0.83accuracy and 0.79 agreement. Thus, it can be considered that the use of texture presented in trunk images can represent an important advance in tree identification, since the limitations of the current techniques can be overcome.

NLSCIDNT user's guide maximum likehood parameter identification computer program with nonlinear rotorcraft model

NASA Technical Reports Server (NTRS)

1979-01-01

A nonlinear, maximum likelihood, parameter identification computer program (NLSCIDNT) is described which evaluates rotorcraft stability and control coefficients from flight test data. The optimal estimates of the parameters (stability and control coefficients) are determined (identified) by minimizing the negative log likelihood cost function. The minimization technique is the Levenberg-Marquardt method, which behaves like the steepest descent method when it is far from the minimum and behaves like the modified Newton-Raphson method when it is nearer the minimum. Twenty-one states and 40 measurement variables are modeled, and any subset may be selected. States which are not integrated may be fixed at an input value, or time history data may be substituted for the state in the equations of motion. Any aerodynamic coefficient may be expressed as a nonlinear polynomial function of selected 'expansion variables'.

Normalized inverse characterization of sound absorbing rigid porous media.

PubMed

Zieliński, Tomasz G

2015-06-01

This paper presents a methodology for the inverse characterization of sound absorbing rigid porous media, based on standard measurements of the surface acoustic impedance of a porous sample. The model parameters need to be normalized to have a robust identification procedure which fits the model-predicted impedance curves with the measured ones. Such a normalization provides a substitute set of dimensionless (normalized) parameters unambiguously related to the original model parameters. Moreover, two scaling frequencies are introduced, however, they are not additional parameters and for different, yet reasonable, assumptions of their values, the identification procedure should eventually lead to the same solution. The proposed identification technique uses measured and computed impedance curves for a porous sample not only in the standard configuration, that is, set to the rigid termination piston in an impedance tube, but also with air gaps of known thicknesses between the sample and the piston. Therefore, all necessary analytical formulas for sound propagation in double-layered media are provided. The methodology is illustrated by one numerical test and by two examples based on the experimental measurements of the acoustic impedance and absorption of porous ceramic samples of different thicknesses and a sample of polyurethane foam.

Adaptive/learning control of large space structures - System identification techniques. [for multi-configuration flexible spacecraft

NASA Technical Reports Server (NTRS)

Thau, F. E.; Montgomery, R. C.

1980-01-01

Techniques developed for the control of aircraft under changing operating conditions are used to develop a learning control system structure for a multi-configuration, flexible space vehicle. A configuration identification subsystem that is to be used with a learning algorithm and a memory and control process subsystem is developed. Adaptive gain adjustments can be achieved by this learning approach without prestoring of large blocks of parameter data and without dither signal inputs which will be suppressed during operations for which they are not compatible. The Space Shuttle Solar Electric Propulsion (SEP) experiment is used as a sample problem for the testing of adaptive/learning control system algorithms.

An Adaptive Technique for a Redundant-Sensor Navigation System. Ph.D. Thesis

NASA Technical Reports Server (NTRS)

Chien, T. T.

1972-01-01

An on-line adaptive technique is developed to provide a self-contained redundant-sensor navigation system with a capability to utilize its full potentiality in reliability and performance. The gyro navigation system is modeled as a Gauss-Markov process, with degradation modes defined as changes in characteristics specified by parameters associated with the model. The adaptive system is formulated as a multistage stochastic process: (1) a detection system, (2) an identification system and (3) a compensation system. It is shown that the sufficient statistics for the partially observable process in the detection and identification system is the posterior measure of the state of degradation, conditioned on the measurement history.

Modelling and identification for control of gas bearings

NASA Astrophysics Data System (ADS)

Theisen, Lukas R. S.; Niemann, Hans H.; Santos, Ilmar F.; Galeazzi, Roberto; Blanke, Mogens

2016-03-01

Gas bearings are popular for their high speed capabilities, low friction and clean operation, but suffer from poor damping, which poses challenges for safe operation in presence of disturbances. Feedback control can achieve enhanced damping but requires low complexity models of the dominant dynamics over its entire operating range. Models from first principles are complex and sensitive to parameter uncertainty. This paper presents an experimental technique for "in situ" identification of a low complexity model of a rotor-bearing-actuator system and demonstrates identification over relevant ranges of rotational speed and gas injection pressure. This is obtained using parameter-varying linear models that are found to capture the dominant dynamics. The approach is shown to be easily applied and to suit subsequent control design. Based on the identified models, decentralised proportional control is designed and shown to obtain the required damping in theory and in a laboratory test rig.

Automatic corn-soybean classification using Landsat MSS data. I - Near-harvest crop proportion estimation. II - Early season crop proportion estimation

NASA Technical Reports Server (NTRS)

Badhwar, G. D.

1984-01-01

The techniques used initially for the identification of cultivated crops from Landsat imagery depended greatly on the iterpretation of film products by a human analyst. This approach was not very effective and objective. Since 1978, new methods for crop identification are being developed. Badhwar et al. (1982) showed that multitemporal-multispectral data could be reduced to a simple feature space of alpha and beta and that these features would separate corn and soybean very well. However, there are disadvantages related to the use of alpha and beta parameters. The present investigation is concerned with a suitable method for extracting the required features. Attention is given to a profile model for crop discrimination, corn-soybean separation using profile parameters, and an automatic labeling (target recognition) method. The developed technique is extended to obtain a procedure which makes it possible to estimate the crop proportion of corn and soybean from Landsat data early in the growing season.

Aircraft applications of fault detection and isolation techniques

NASA Astrophysics Data System (ADS)

Marcos Esteban, Andres

In this thesis the problems of fault detection & isolation and fault tolerant systems are studied from the perspective of LTI frequency-domain, model-based techniques. Emphasis is placed on the applicability of these LTI techniques to nonlinear models, especially to aerospace systems. Two applications of Hinfinity LTI fault diagnosis are given using an open-loop (no controller) design approach: one for the longitudinal motion of a Boeing 747-100/200 aircraft, the other for a turbofan jet engine. An algorithm formalizing a robust identification approach based on model validation ideas is also given and applied to the previous jet engine. A general linear fractional transformation formulation is given in terms of the Youla and Dual Youla parameterizations for the integrated (control and diagnosis filter) approach. This formulation provides better insight into the trade-off between the control and the diagnosis objectives. It also provides the basic groundwork towards the development of nested schemes for the integrated approach. These nested structures allow iterative improvements on the control/filter Youla parameters based on successive identification of the system uncertainty (as given by the Dual Youla parameter). The thesis concludes with an application of Hinfinity LTI techniques to the integrated design for the longitudinal motion of the previous Boeing 747-100/200 model.

An Adaptive Kalman Filter Using a Simple Residual Tuning Method

NASA Technical Reports Server (NTRS)

Harman, Richard R.

1999-01-01

One difficulty in using Kalman filters in real world situations is the selection of the correct process noise, measurement noise, and initial state estimate and covariance. These parameters are commonly referred to as tuning parameters. Multiple methods have been developed to estimate these parameters. Most of those methods such as maximum likelihood, subspace, and observer Kalman Identification require extensive offline processing and are not suitable for real time processing. One technique, which is suitable for real time processing, is the residual tuning method. Any mismodeling of the filter tuning parameters will result in a non-white sequence for the filter measurement residuals. The residual tuning technique uses this information to estimate corrections to those tuning parameters. The actual implementation results in a set of sequential equations that run in parallel with the Kalman filter. A. H. Jazwinski developed a specialized version of this technique for estimation of process noise. Equations for the estimation of the measurement noise have also been developed. These algorithms are used to estimate the process noise and measurement noise for the Wide Field Infrared Explorer star tracker and gyro.

MXLKID: a maximum likelihood parameter identifier. [In LRLTRAN for CDC 7600

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gavel, D.T.

MXLKID (MaXimum LiKelihood IDentifier) is a computer program designed to identify unknown parameters in a nonlinear dynamic system. Using noisy measurement data from the system, the maximum likelihood identifier computes a likelihood function (LF). Identification of system parameters is accomplished by maximizing the LF with respect to the parameters. The main body of this report briefly summarizes the maximum likelihood technique and gives instructions and examples for running the MXLKID program. MXLKID is implemented LRLTRAN on the CDC7600 computer at LLNL. A detailed mathematical description of the algorithm is given in the appendices. 24 figures, 6 tables.

Hybrid parameter identification of a multi-modal underwater soft robot.

PubMed

Giorgio-Serchi, F; Arienti, A; Corucci, F; Giorelli, M; Laschi, C

2017-02-28

We introduce an octopus-inspired, underwater, soft-bodied robot capable of performing waterborne pulsed-jet propulsion and benthic legged-locomotion. This vehicle consists for as much as 80% of its volume of rubber-like materials so that structural flexibility is exploited as a key element during both modes of locomotion. The high bodily softness, the unconventional morphology and the non-stationary nature of its propulsion mechanisms require dynamic characterization of this robot to be dealt with by ad hoc techniques. We perform parameter identification by resorting to a hybrid optimization approach where the characterization of the dual ambulatory strategies of the robot is performed in a segregated fashion. A least squares-based method coupled with a genetic algorithm-based method is employed for the swimming and the crawling phases, respectively. The outcomes bring evidence that compartmentalized parameter identification represents a viable protocol for multi-modal vehicles characterization. However, the use of static thrust recordings as the input signal in the dynamic determination of shape-changing self-propelled vehicles is responsible for the critical underestimation of the quadratic drag coefficient.

In-Flight System Identification

NASA Technical Reports Server (NTRS)

Morelli, Eugene A.

1998-01-01

A method is proposed and studied whereby the system identification cycle consisting of experiment design and data analysis can be repeatedly implemented aboard a test aircraft in real time. This adaptive in-flight system identification scheme has many advantages, including increased flight test efficiency, adaptability to dynamic characteristics that are imperfectly known a priori, in-flight improvement of data quality through iterative input design, and immediate feedback of the quality of flight test results. The technique uses equation error in the frequency domain with a recursive Fourier transform for the real time data analysis, and simple design methods employing square wave input forms to design the test inputs in flight. Simulation examples are used to demonstrate that the technique produces increasingly accurate model parameter estimates resulting from sequentially designed and implemented flight test maneuvers. The method has reasonable computational requirements, and could be implemented aboard an aircraft in real time.

Modal identification of structures by a novel approach based on FDD-wavelet method

NASA Astrophysics Data System (ADS)

Tarinejad, Reza; Damadipour, Majid

2014-02-01

An important application of system identification in structural dynamics is the determination of natural frequencies, mode shapes and damping ratios during operation which can then be used for calibrating numerical models. In this paper, the combination of two advanced methods of Operational Modal Analysis (OMA) called Frequency Domain Decomposition (FDD) and Continuous Wavelet Transform (CWT) based on novel cyclic averaging of correlation functions (CACF) technique are used for identification of dynamic properties. By using this technique, the autocorrelation of averaged correlation functions is used instead of original signals. Integration of FDD and CWT methods is used to overcome their deficiency and take advantage of the unique capabilities of these methods. The FDD method is able to accurately estimate the natural frequencies and mode shapes of structures in the frequency domain. On the other hand, the CWT method is in the time-frequency domain for decomposition of a signal at different frequencies and determines the damping coefficients. In this paper, a new formulation applied to the wavelet transform of the averaged correlation function of an ambient response is proposed. This application causes to accurate estimation of damping ratios from weak (noise) or strong (earthquake) vibrations and long or short duration record. For this purpose, the modified Morlet wavelet having two free parameters is used. The optimum values of these two parameters are obtained by employing a technique which minimizes the entropy of the wavelet coefficients matrix. The capabilities of the novel FDD-Wavelet method in the system identification of various dynamic systems with regular or irregular distribution of mass and stiffness are illustrated. This combined approach is superior to classic methods and yields results that agree well with the exact solutions of the numerical models.

Estimation on nonlinear damping in second order distributed parameter systems

NASA Technical Reports Server (NTRS)

Banks, H. T.; Reich, Simeon; Rosen, I. G.

1989-01-01

An approximation and convergence theory for the identification of nonlinear damping in abstract wave equations is developed. It is assumed that the unknown dissipation mechanism to be identified can be described by a maximal monotone operator acting on the generalized velocity. The stiffness is assumed to be linear and symmetric. Functional analytic techniques are used to establish that solutions to a sequence of finite dimensional (Galerkin) approximating identification problems in some sense approximate a solution to the original infinite dimensional inverse problem.

The Shock and Vibration Bulletin. Part 3. Machinery Dynamics, System Identification and Structural Analysis

DTIC Science & Technology

1985-06-01

purposes. 55 15 525 + 2 225L L53 + L3 ) + 2G3(L 2 + L35L4 + L45)" For the present system identification + 2G(L45 2 + L45L5 + L5 "L 1 technique, the...orbital model is comprised of 257 nodes and 819 dynamic:"DOF’s. k; were compared to ITD results for a wide variety of TD input parameters. Overall, the

Optimization of a chemical identification algorithm

NASA Astrophysics Data System (ADS)

Chyba, Thomas H.; Fisk, Brian; Gunning, Christin; Farley, Kevin; Polizzi, Amber; Baughman, David; Simpson, Steven; Slamani, Mohamed-Adel; Almassy, Robert; Da Re, Ryan; Li, Eunice; MacDonald, Steve; Slamani, Ahmed; Mitchell, Scott A.; Pendell-Jones, Jay; Reed, Timothy L.; Emge, Darren

2010-04-01

A procedure to evaluate and optimize the performance of a chemical identification algorithm is presented. The Joint Contaminated Surface Detector (JCSD) employs Raman spectroscopy to detect and identify surface chemical contamination. JCSD measurements of chemical warfare agents, simulants, toxic industrial chemicals, interferents and bare surface backgrounds were made in the laboratory and under realistic field conditions. A test data suite, developed from these measurements, is used to benchmark algorithm performance throughout the improvement process. In any one measurement, one of many possible targets can be present along with interferents and surfaces. The detection results are expressed as a 2-category classification problem so that Receiver Operating Characteristic (ROC) techniques can be applied. The limitations of applying this framework to chemical detection problems are discussed along with means to mitigate them. Algorithmic performance is optimized globally using robust Design of Experiments and Taguchi techniques. These methods require figures of merit to trade off between false alarms and detection probability. Several figures of merit, including the Matthews Correlation Coefficient and the Taguchi Signal-to-Noise Ratio are compared. Following the optimization of global parameters which govern the algorithm behavior across all target chemicals, ROC techniques are employed to optimize chemical-specific parameters to further improve performance.

Virus detection and quantification using electrical parameters

NASA Astrophysics Data System (ADS)

Ahmad, Mahmoud Al; Mustafa, Farah; Ali, Lizna M.; Rizvi, Tahir A.

2014-10-01

Here we identify and quantitate two similar viruses, human and feline immunodeficiency viruses (HIV and FIV), suspended in a liquid medium without labeling, using a semiconductor technique. The virus count was estimated by calculating the impurities inside a defined volume by observing the change in electrical parameters. Empirically, the virus count was similar to the absolute value of the ratio of the change of the virus suspension dopant concentration relative to the mock dopant over the change in virus suspension Debye volume relative to mock Debye volume. The virus type was identified by constructing a concentration-mobility relationship which is unique for each kind of virus, allowing for a fast (within minutes) and label-free virus quantification and identification. For validation, the HIV and FIV virus preparations were further quantified by a biochemical technique and the results obtained by both approaches corroborated well. We further demonstrate that the electrical technique could be applied to accurately measure and characterize silica nanoparticles that resemble the virus particles in size. Based on these results, we anticipate our present approach to be a starting point towards establishing the foundation for label-free electrical-based identification and quantification of an unlimited number of viruses and other nano-sized particles.

Identification and feedback control in structures with piezoceramic actuators

NASA Technical Reports Server (NTRS)

Banks, H. T.; Ito, K.; Wang, Y.

1992-01-01

In this lecture we give fundamental well-posedness results for a variational formulation of a class of damped second order partial differential equations with unbounded input or control coefficients. Included as special cases in this class are structures with piezoceramic actuators. We consider approximation techniques leading to computational methods in the context of both parameter estimation and feedback control problems for these systems. Rigorous convergence results for parameter estimates and feedback gains are discussed.

Identification of laboratory techniques to optimize Superpave HMA surface friction characteristics : final report, April 2010.

DOT National Transportation Integrated Search

2010-04-15

Wet pavement friction is known to be one of the most important roadway safety parameters. In this : research, frictional properties of flexible (asphalt) pavements were investigated. : As a part of this study, a laboratory device to polish asphalt sp...

Extension of the Optimized Virtual Fields Method to estimate viscoelastic material parameters from 3D dynamic displacement fields

PubMed Central

Connesson, N.; Clayton, E.H.; Bayly, P.V.; Pierron, F.

2015-01-01

In-vivo measurement of the mechanical properties of soft tissues is essential to provide necessary data in biomechanics and medicine (early cancer diagnosis, study of traumatic brain injuries, etc.). Imaging techniques such as Magnetic Resonance Elastography (MRE) can provide 3D displacement maps in the bulk and in vivo, from which, using inverse methods, it is then possible to identify some mechanical parameters of the tissues (stiffness, damping etc.). The main difficulties in these inverse identification procedures consist in dealing with the pressure waves contained in the data and with the experimental noise perturbing the spatial derivatives required during the processing. The Optimized Virtual Fields Method (OVFM) [1], designed to be robust to noise, present natural and rigorous solution to deal with these problems. The OVFM has been adapted to identify material parameter maps from Magnetic Resonance Elastography (MRE) data consisting of 3-dimensional displacement fields in harmonically loaded soft materials. In this work, the method has been developed to identify elastic and viscoelastic models. The OVFM sensitivity to spatial resolution and to noise has been studied by analyzing 3D analytically simulated displacement data. This study evaluates and describes the OVFM identification performances: different biases on the identified parameters are induced by the spatial resolution and experimental noise. The well-known identification problems in the case of quasi-incompressible materials also find a natural solution in the OVFM. Moreover, an a posteriori criterion to estimate the local identification quality is proposed. The identification results obtained on actual experiments are briefly presented. PMID:26146416

Parametric system identification of catamaran for improving controller design

NASA Astrophysics Data System (ADS)

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.

Autonomous Modal Identification of the Space Shuttle Tail Rudder

NASA Technical Reports Server (NTRS)

Pappa, Richard S.; James, George H., III; Zimmerman, David C.

1997-01-01

Autonomous modal identification automates the calculation of natural vibration frequencies, damping, and mode shapes of a structure from experimental data. This technology complements damage detection techniques that use continuous or periodic monitoring of vibration characteristics. The approach shown in the paper incorporates the Eigensystem Realization Algorithm (ERA) as a data analysis engine and an autonomous supervisor to condense multiple estimates of modal parameters using ERA's Consistent-Mode Indicator and correlation of mode shapes. The procedure was applied to free-decay responses of a Space Shuttle tail rudder and successfully identified the seven modes of the structure below 250 Hz. The final modal parameters are a condensed set of results for 87 individual ERA cases requiring approximately five minutes of CPU time on a DEC Alpha computer.

A method to investigate the diffusion properties of nuclear calcium.

PubMed

Queisser, Gillian; Wittum, Gabriel

2011-10-01

Modeling biophysical processes in general requires knowledge about underlying biological parameters. The quality of simulation results is strongly influenced by the accuracy of these parameters, hence the identification of parameter values that the model includes is a major part of simulating biophysical processes. In many cases, secondary data can be gathered by experimental setups, which are exploitable by mathematical inverse modeling techniques. Here we describe a method for parameter identification of diffusion properties of calcium in the nuclei of rat hippocampal neurons. The method is based on a Gauss-Newton method for solving a least-squares minimization problem and was formulated in such a way that it is ideally implementable in the simulation platform uG. Making use of independently published space- and time-dependent calcium imaging data, generated from laser-assisted calcium uncaging experiments, here we could identify the diffusion properties of nuclear calcium and were able to validate a previously published model that describes nuclear calcium dynamics as a diffusion process.

Detection and identification of concealed weapons using matrix pencil

NASA Astrophysics Data System (ADS)

Adve, Raviraj S.; Thayaparan, Thayananthan

2011-06-01

The detection and identification of concealed weapons is an extremely hard problem due to the weak signature of the target buried within the much stronger signal from the human body. This paper furthers the automatic detection and identification of concealed weapons by proposing the use of an effective approach to obtain the resonant frequencies in a measurement. The technique, based on Matrix Pencil, a scheme for model based parameter estimation also provides amplitude information, hence providing a level of confidence in the results. Of specific interest is the fact that Matrix Pencil is based on a singular value decomposition, making the scheme robust against noise.

Parameter identification of material constants in a composite shell structure

NASA Technical Reports Server (NTRS)

Martinez, David R.; Carne, Thomas G.

1988-01-01

One of the basic requirements in engineering analysis is the development of a mathematical model describing the system. Frequently comparisons with test data are used as a measurement of the adequacy of the model. An attempt is typically made to update or improve the model to provide a test verified analysis tool. System identification provides a systematic procedure for accomplishing this task. The terms system identification, parameter estimation, and model correlation all refer to techniques that use test information to update or verify mathematical models. The goal of system identification is to improve the correlation of model predictions with measured test data, and produce accurate, predictive models. For nonmetallic structures the modeling task is often difficult due to uncertainties in the elastic constants. A finite element model of the shell was created, which included uncertain orthotropic elastic constants. A modal survey test was then performed on the shell. The resulting modal data, along with the finite element model of the shell, were used in a Bayes estimation algorithm. This permitted the use of covariance matrices to weight the confidence in the initial parameter values as well as confidence in the measured test data. The estimation procedure also employed the concept of successive linearization to obtain an approximate solution to the original nonlinear estimation problem.

INDES User's guide multistep input design with nonlinear rotorcraft modeling

NASA Technical Reports Server (NTRS)

1979-01-01

The INDES computer program, a multistep input design program used as part of a data processing technique for rotorcraft systems identification, is described. Flight test inputs base on INDES improve the accuracy of parameter estimates. The input design algorithm, program input, and program output are presented.

System identification principles in studies of forest dynamics.

Treesearch

Rolfe A. Leary

1970-01-01

Shows how it is possible to obtain governing equation parameter estimates on the basis of observed system states. The approach used represents a constructive alternative to regression techniques for models expressed as differential equations. This approach allows scientists to more completely quantify knowledge of forest development processes, to express theories in...

Intelligent Systems Approach for Automated Identification of Individual Control Behavior of a Human Operator

NASA Technical Reports Server (NTRS)

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.

System identification of timber masonry walls using shaking table test

NASA Astrophysics Data System (ADS)

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.

Rigid body mode identification of the PAH-2 helicopter using the eigensystem realization algorithm

NASA Technical Reports Server (NTRS)

Schenk, Axel; Pappa, Richard S.

1992-01-01

The rigid body modes of the PAH-2 'Tiger' helicopter were identified using the Eigensystem Realization Algorithm (ERA). This work complements ground vibration tests performed using DLR's traditional phase resonance technique and the ISSPA (Identification of Structural System Parameters) method. Rigid body modal parameters are important for ground resonance prediction. Time-domain data for ERA were obtained by inverse Fourier transformation of frequency response functions measured with stepped-sine excitation. Mode purity (based on the Phase Resonance Criterion) was generally equal to or greater than corresponding results obtained in the ground vibration tests. All identified natural frequencies and mode shapes correlate well with corresponding ground vibration test results. The modal identification approach discussed in this report has become increasingly attractive in recent years due to the steadily declining cost and increased performance of scientific computers. As illustrated in this application, modern time-domain methods can be successfully applied to data acquired using DLR's existing test equipment. Some suggestions are made for future applications of time domain modal identification in this manner.

System/observer/controller identification toolbox

NASA Technical Reports Server (NTRS)

Juang, Jer-Nan; Horta, Lucas G.; Phan, Minh

1992-01-01

System Identification is the process of constructing a mathematical model from input and output data for a system under testing, and characterizing the system uncertainties and measurement noises. The mathematical model structure can take various forms depending upon the intended use. The SYSTEM/OBSERVER/CONTROLLER IDENTIFICATION TOOLBOX (SOCIT) is a collection of functions, written in MATLAB language and expressed in M-files, that implements a variety of modern system identification techniques. For an open loop system, the central features of the SOCIT are functions for identification of a system model and its corresponding forward and backward observers directly from input and output data. The system and observers are represented by a discrete model. The identified model and observers may be used for controller design of linear systems as well as identification of modal parameters such as dampings, frequencies, and mode shapes. For a closed-loop system, an observer and its corresponding controller gain directly from input and output data.

On the selection of user-defined parameters in data-driven stochastic subspace identification

NASA Astrophysics Data System (ADS)

Priori, C.; De Angelis, M.; Betti, R.

2018-02-01

The paper focuses on the time domain output-only technique called Data-Driven Stochastic Subspace Identification (DD-SSI); in order to identify modal models (frequencies, damping ratios and mode shapes), the role of its user-defined parameters is studied, and rules to determine their minimum values are proposed. Such investigation is carried out using, first, the time histories of structural responses to stationary excitations, with a large number of samples, satisfying the hypothesis on the input imposed by DD-SSI. Then, the case of non-stationary seismic excitations with a reduced number of samples is considered. In this paper, partitions of the data matrix different from the one proposed in the SSI literature are investigated, together with the influence of different choices of the weighting matrices. The study is carried out considering two different applications: (1) data obtained from vibration tests on a scaled structure and (2) in-situ tests on a reinforced concrete building. Referring to the former, the identification of a steel frame structure tested on a shaking table is performed using its responses in terms of absolute accelerations to a stationary (white noise) base excitation and to non-stationary seismic excitations of low intensity. Black-box and modal models are identified in both cases and the results are compared with those from an input-output subspace technique. With regards to the latter, the identification of a complex hospital building is conducted using data obtained from ambient vibration tests.

A new technique for identification of minerals in hyperspectral images. Application to robust characterization of phyllosilicate deposits at Mawrth Vallis using CRISM images.

NASA Astrophysics Data System (ADS)

Parente, M.; Bishop, J. L.

2008-12-01

Mapping of Mars by MRO has revealed the presence of numerous small phyllosilicate outcrops. These are typically identified in CRISM images using "summary products" (Pelkey, 2007) that consist of band ratios, depths and spectral slopes around diagnostic wavelengths. The summary products are designed to capture spectral features related to both surface mineralogy and atmospheric gases and aerosols. Such products, as an analysis tool to characterize composition as well as a targeting tool to identify areas of mineralogical interest, have been successful in capturing the known diversity of the Martian surface, and in highlighting locations with strong spectral signatures. Here we present alternative mineral mapping technique that 1) aims to increase the robustness of mineral detections with respect to the specific CRISM artifacts, 2) takes advantage of the spatial context of each pixel and 3) develops new parameters for the discrimination of species in the phyllosilicates family. We include spatial context by evaluating spectral shapes, band depths and spectral slopes for the current pixel based on its spatial neighbors within the same geological unit. Furthermore, the parameters are based on estimates that are more robust to CRISM speckling noise that might alter the parameters and potentially the mineral interpretation. As an effort to distinguish between phyllosilicates species, we are augmenting the suite of existent parameters with a set of mineral parameters that involve the position, number and shapes of diagnostic phyllosilicate absorptions. We are comparing the effectiveness of this new approach to the summary product procedure. The study shows that homogeneous mineral maps and diagnostic spectral identifications are possible as a result of the application of such new parameters. We applied the technique to the discrimination of kaolinite in Mawrth Vallis. The experiments show several small kaolinite outcrops dispersed within the more extensive Al-rich phyllosilicates in regions around the MSL landing sites. Another test was the discrimination of montmorillonite and nontronite in Mawrth Vallis that can be successfully accomplished by band depths summary products near 2.2 and 2.3 μm. The new technique produces improved maps with lower noise levels and lower percentage of false detections.

Use of the Morlet mother wavelet in the frequency-scale domain decomposition technique for the modal identification of ambient vibration responses

NASA Astrophysics Data System (ADS)

Le, Thien-Phu

2017-10-01

The frequency-scale domain decomposition technique has recently been proposed for operational modal analysis. The technique is based on the Cauchy mother wavelet. In this paper, the approach is extended to the Morlet mother wavelet, which is very popular in signal processing due to its superior time-frequency localization. Based on the regressive form and an appropriate norm of the Morlet mother wavelet, the continuous wavelet transform of the power spectral density of ambient responses enables modes in the frequency-scale domain to be highlighted. Analytical developments first demonstrate the link between modal parameters and the local maxima of the continuous wavelet transform modulus. The link formula is then used as the foundation of the proposed modal identification method. Its practical procedure, combined with the singular value decomposition algorithm, is presented step by step. The proposition is finally verified using numerical examples and a laboratory test.

An Adaptive Kalman Filter using a Simple Residual Tuning Method

NASA Technical Reports Server (NTRS)

Harman, Richard R.

1999-01-01

One difficulty in using Kalman filters in real world situations is the selection of the correct process noise, measurement noise, and initial state estimate and covariance. These parameters are commonly referred to as tuning parameters. Multiple methods have been developed to estimate these parameters. Most of those methods such as maximum likelihood, subspace, and observer Kalman Identification require extensive offline processing and are not suitable for real time processing. One technique, which is suitable for real time processing, is the residual tuning method. Any mismodeling of the filter tuning parameters will result in a non-white sequence for the filter measurement residuals. The residual tuning technique uses this information to estimate corrections to those tuning parameters. The actual implementation results in a set of sequential equations that run in parallel with the Kalman filter. Equations for the estimation of the measurement noise have also been developed. These algorithms are used to estimate the process noise and measurement noise for the Wide Field Infrared Explorer star tracker and gyro.

Use of simulated experiments for material characterization of brittle materials subjected to high strain rate dynamic tension

PubMed Central

Saletti, Dominique

2017-01-01

Rapid progress in ultra-high-speed imaging has allowed material properties to be studied at high strain rates by applying full-field measurements and inverse identification methods. Nevertheless, the sensitivity of these techniques still requires a better understanding, since various extrinsic factors present during an actual experiment make it difficult to separate different sources of errors that can significantly affect the quality of the identified results. This study presents a methodology using simulated experiments to investigate the accuracy of the so-called spalling technique (used to study tensile properties of concrete subjected to high strain rates) by numerically simulating the entire identification process. The experimental technique uses the virtual fields method and the grid method. The methodology consists of reproducing the recording process of an ultra-high-speed camera by generating sequences of synthetically deformed images of a sample surface, which are then analysed using the standard tools. The investigation of the uncertainty of the identified parameters, such as Young's modulus along with the stress–strain constitutive response, is addressed by introducing the most significant user-dependent parameters (i.e. acquisition speed, camera dynamic range, grid sampling, blurring), proving that the used technique can be an effective tool for error investigation. This article is part of the themed issue ‘Experimental testing and modelling of brittle materials at high strain rates’. PMID:27956505

Comparison of data mining techniques applied to fetal heart rate parameters for the early identification of IUGR fetuses.

PubMed

Magenes, G; Bellazzi, R; Malovini, A; Signorini, M G

2016-08-01

The onset of fetal pathologies can be screened during pregnancy by means of Fetal Heart Rate (FHR) monitoring and analysis. Noticeable advances in understanding FHR variations were obtained in the last twenty years, thanks to the introduction of quantitative indices extracted from the FHR signal. This study searches for discriminating Normal and Intra Uterine Growth Restricted (IUGR) fetuses by applying data mining techniques to FHR parameters, obtained from recordings in a population of 122 fetuses (61 healthy and 61 IUGRs), through standard CTG non-stress test. We computed N=12 indices (N=4 related to time domain FHR analysis, N=4 to frequency domain and N=4 to non-linear analysis) and normalized them with respect to the gestational week. We compared, through a 10-fold crossvalidation procedure, 15 data mining techniques in order to select the more reliable approach for identifying IUGR fetuses. The results of this comparison highlight that two techniques (Random Forest and Logistic Regression) show the best classification accuracy and that both outperform the best single parameter in terms of mean AUROC on the test sets.

A Benchmark Problem for Development of Autonomous Structural Modal Identification

NASA Technical Reports Server (NTRS)

Pappa, Richard S.; Woodard, Stanley E.; Juang, Jer-Nan

1996-01-01

This paper summarizes modal identification results obtained using an autonomous version of the Eigensystem Realization Algorithm on a dynamically complex, laboratory structure. The benchmark problem uses 48 of 768 free-decay responses measured in a complete modal survey test. The true modal parameters of the structure are well known from two previous, independent investigations. Without user involvement, the autonomous data analysis identified 24 to 33 structural modes with good to excellent accuracy in 62 seconds of CPU time (on a DEC Alpha 4000 computer). The modal identification technique described in the paper is the baseline algorithm for NASA's Autonomous Dynamics Determination (ADD) experiment scheduled to fly on International Space Station assembly flights in 1997-1999.

Dataset for Testing Contamination Source Identification Methods for Water Distribution Networks

EPA Pesticide Factsheets

This dataset includes the results of a simulation study using the source inversion techniques available in the Water Security Toolkit. The data was created to test the different techniques for accuracy, specificity, false positive rate, and false negative rate. The tests examined different parameters including measurement error, modeling error, injection characteristics, time horizon, network size, and sensor placement. The water distribution system network models that were used in the study are also included in the dataset. This dataset is associated with the following publication:Seth, A., K. Klise, J. Siirola, T. Haxton , and C. Laird. Testing Contamination Source Identification Methods for Water Distribution Networks. Journal of Environmental Division, Proceedings of American Society of Civil Engineers. American Society of Civil Engineers (ASCE), Reston, VA, USA, ., (2016).

Free-decay time-domain modal identification for large space structures

NASA Technical Reports Server (NTRS)

Kim, Hyoung M.; Vanhorn, David A.; Doiron, Harold H.

1992-01-01

Concept definition studies for the Modal Identification Experiment (MIE), a proposed space flight experiment for the Space Station Freedom (SSF), have demonstrated advantages and compatibility of free-decay time-domain modal identification techniques with the on-orbit operational constraints of large space structures. Since practical experience with modal identification using actual free-decay responses of large space structures is very limited, several numerical and test data reduction studies were conducted. Major issues and solutions were addressed, including closely-spaced modes, wide frequency range of interest, data acquisition errors, sampling delay, excitation limitations, nonlinearities, and unknown disturbances during free-decay data acquisition. The data processing strategies developed in these studies were applied to numerical simulations of the MIE, test data from a deployable truss, and launch vehicle flight data. Results of these studies indicate free-decay time-domain modal identification methods can provide accurate modal parameters necessary to characterize the structural dynamics of large space structures.

A LEAST ABSOLUTE SHRINKAGE AND SELECTION OPERATOR (LASSO) FOR NONLINEAR SYSTEM IDENTIFICATION

NASA Technical Reports Server (NTRS)

Kukreja, Sunil L.; Lofberg, Johan; Brenner, Martin J.

2006-01-01

Identification of parametric nonlinear models involves estimating unknown parameters and detecting its underlying structure. Structure computation is concerned with selecting a subset of parameters to give a parsimonious description of the system which may afford greater insight into the functionality of the system or a simpler controller design. In this study, a least absolute shrinkage and selection operator (LASSO) technique is investigated for computing efficient model descriptions of nonlinear systems. The LASSO minimises the residual sum of squares by the addition of a 1 penalty term on the parameter vector of the traditional 2 minimisation problem. Its use for structure detection is a natural extension of this constrained minimisation approach to pseudolinear regression problems which produces some model parameters that are exactly zero and, therefore, yields a parsimonious system description. The performance of this LASSO structure detection method was evaluated by using it to estimate the structure of a nonlinear polynomial model. Applicability of the method to more complex systems such as those encountered in aerospace applications was shown by identifying a parsimonious system description of the F/A-18 Active Aeroelastic Wing using flight test data.

[Study on the automatic parameters identification of water pipe network model].

PubMed

Jia, Hai-Feng; Zhao, Qi-Feng

2010-01-01

Based on the problems analysis on development and application of water pipe network model, the model parameters automatic identification is regarded as a kernel bottleneck of model's application in water supply enterprise. The methodology of water pipe network model parameters automatic identification based on GIS and SCADA database is proposed. Then the kernel algorithm of model parameters automatic identification is studied, RSA (Regionalized Sensitivity Analysis) is used for automatic recognition of sensitive parameters, and MCS (Monte-Carlo Sampling) is used for automatic identification of parameters, the detail technical route based on RSA and MCS is presented. The module of water pipe network model parameters automatic identification is developed. At last, selected a typical water pipe network as a case, the case study on water pipe network model parameters automatic identification is conducted and the satisfied results are achieved.

Crop Identification Using Time Series of Landsat-8 and Radarsat-2 Images: Application in a Groundwater Irrigated Region, South India

NASA Astrophysics Data System (ADS)

Sharma, A. K.; Hubert-Moy, L.; Betbederet, J.; Ruiz, L.; Sekhar, M.; Corgne, S.

2016-08-01

Monitoring land use and land cover and more particularly irrigated cropland dynamics is of great importance for water resources management and land use planning. The objective of this study was to evaluate the combined use of multi-temporal optical and radar data with a high spatial resolution in order to improve the precision of irrigated crop identification by taking into account information on crop phenological stages. SAR and optical parameters were derived from time- series of seven quad-pol RADARSAT-2 and four Landsat-8 images which were acquired on the Berambadi catchment, South India, during the monsoon crop season at the growth stages of turmeric crop. To select the best parameter to discriminate turmeric crops, an analysis of covariance (ANCOVA) was applied on all the time-series parameters and the most discriminant ones were classified using the Support Vector Machine (SVM) technique. Results show that in absence of optical images, polarimetric parameters derived from SAR time-series can be used for the turmeric area estimates and that the combined use of SAR and optical parameters can improve the classification accuracy to identify turmeric.

Earth resources sensor data handling system: NASA JSC version

NASA Technical Reports Server (NTRS)

1974-01-01

The design of the NASA JSC data handling system is presented. Data acquisition parameters and computer display formats and the flow of image data through the system, with recommendations for improving system efficiency are discussed along with modifications to existing data handling procedures which will allow utilization of data duplication techniques and the accurate identification of imagery.

Frequency-Response Identification of XV-15 Tilt-Rotor Aircraft Dynamics

DTIC Science & Technology

1987-05-01

Sciences, Vol. 14, No. 9, Sept. 1947, pp. 493-499. 1 163 8. Greenberg , Harry, "A Survey of Methods for Determining Stabil- ity Parameters of an Airplane...Sweep Testing Technique using a Bell 214-ST Helicopter," NASA TM-89422, 1987. 21. Hilbert , K. B., Lebacqz, J. V., and Hindson, W. S., "Flight

Signal-Noise Identification of Magnetotelluric Signals Using Fractal-Entropy and Clustering Algorithm for Targeted De-Noising

NASA Astrophysics Data System (ADS)

Li, Jin; Zhang, Xian; Gong, Jinzhe; Tang, Jingtian; Ren, Zhengyong; Li, Guang; Deng, Yanli; Cai, Jin

A new technique is proposed for signal-noise identification and targeted de-noising of Magnetotelluric (MT) signals. This method is based on fractal-entropy and clustering algorithm, which automatically identifies signal sections corrupted by common interference (square, triangle and pulse waves), enabling targeted de-noising and preventing the loss of useful information in filtering. To implement the technique, four characteristic parameters — fractal box dimension (FBD), higuchi fractal dimension (HFD), fuzzy entropy (FuEn) and approximate entropy (ApEn) — are extracted from MT time-series. The fuzzy c-means (FCM) clustering technique is used to analyze the characteristic parameters and automatically distinguish signals with strong interference from the rest. The wavelet threshold (WT) de-noising method is used only to suppress the identified strong interference in selected signal sections. The technique is validated through signal samples with known interference, before being applied to a set of field measured MT/Audio Magnetotelluric (AMT) data. Compared with the conventional de-noising strategy that blindly applies the filter to the overall dataset, the proposed method can automatically identify and purposefully suppress the intermittent interference in the MT/AMT signal. The resulted apparent resistivity-phase curve is more continuous and smooth, and the slow-change trend in the low-frequency range is more precisely reserved. Moreover, the characteristic of the target-filtered MT/AMT signal is close to the essential characteristic of the natural field, and the result more accurately reflects the inherent electrical structure information of the measured site.

Identification of terpenes and essential oils by means of static headspace gas chromatography-ion mobility spectrometry.

PubMed

Rodríguez-Maecker, Roman; Vyhmeister, Eduardo; Meisen, Stefan; Rosales Martinez, Antonio; Kuklya, Andriy; Telgheder, Ursula

2017-11-01

Static headspace gas chromatography-ion mobility spectrometry (SHS GC-IMS) is a relatively new analytical technique that has considerable potential for analysis of volatile organic compounds (VOCs). In this study, SHS GC-IMS was used for the identification of the major terpene components of various essential oils (EOs). Based on the data obtained from 25 terpene standards and 50 EOs, a database for fingerprint identification of characteristic terpenes and EOs was generated utilizing SHS GC-IMS for authenticity testing of fragrances in foods, cosmetics, and personal care products. This database contains specific normalized IMS drift times and GC retention indices for 50 terpene components of EOs. Initially, the SHS GC-IMS parameters, e.g., drift gas and carrier gas flow rates, drift tube, and column temperatures, were evaluated to determine suitable operating conditions for terpene separation and identification. Gas chromatography-mass spectrometry (GC-MS) was used as a reference method for the identification of terpenes in EOs. The fingerprint pattern based on the normalized IMS drift times and retention indices of 50 terpenes is presented for 50 EOs. The applicability of the method was proven on examples of ten commercially available food, cosmetic, and personal care product samples. The results confirm the suitability of SHS GC-IMS as a powerful analytical technique for direct identification of terpene components in solid and liquid samples without any pretreatment. Graphical abstract Fingerprint pattern identification of terpenes and essential oils using static headspace gas chromatography-ion mobility spectrometry.

System identification of jet engines

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sugiyama, N.

2000-01-01

System identification plays an important role in advanced control systems for jet engines, in which controls are performed adaptively using data from the actual engine and the identified engine. An identification technique for jet engine using the Constant Gain Extended Kalman Filter (CGEKF) is described. The filter is constructed for a two-spool turbofan engine. The CGEKF filter developed here can recognize parameter change in engine components and estimate unmeasurable variables over whole flight conditions. These capabilities are useful for an advanced Full Authority Digital Electric Control (FADEC). Effects of measurement noise and bias, effects of operating point and unpredicted performancemore » change are discussed. Some experimental results using the actual engine are shown to evaluate the effectiveness of CGEKF filter.« less

Neural networks for function approximation in nonlinear control

NASA Technical Reports Server (NTRS)

Linse, Dennis J.; Stengel, Robert F.

1990-01-01

Two neural network architectures are compared with a classical spline interpolation technique for the approximation of functions useful in a nonlinear control system. A standard back-propagation feedforward neural network and a cerebellar model articulation controller (CMAC) neural network are presented, and their results are compared with a B-spline interpolation procedure that is updated using recursive least-squares parameter identification. Each method is able to accurately represent a one-dimensional test function. Tradeoffs between size requirements, speed of operation, and speed of learning indicate that neural networks may be practical for identification and adaptation in a nonlinear control environment.

Rock surface roughness measurement using CSI technique and analysis of surface characterization by qualitative and quantitative results

NASA Astrophysics Data System (ADS)

Mukhtar, Husneni; Montgomery, Paul; Gianto; Susanto, K.

2016-01-01

In order to develop image processing that is widely used in geo-processing and analysis, we introduce an alternative technique for the characterization of rock samples. The technique that we have used for characterizing inhomogeneous surfaces is based on Coherence Scanning Interferometry (CSI). An optical probe is first used to scan over the depth of the surface roughness of the sample. Then, to analyse the measured fringe data, we use the Five Sample Adaptive method to obtain quantitative results of the surface shape. To analyse the surface roughness parameters, Hmm and Rq, a new window resizing analysis technique is employed. The results of the morphology and surface roughness analysis show micron and nano-scale information which is characteristic of each rock type and its history. These could be used for mineral identification and studies in rock movement on different surfaces. Image processing is thus used to define the physical parameters of the rock surface.

Modeling and Parameter Estimation of Spacecraft Fuel Slosh with Diaphragms Using Pendulum Analogs

NASA Technical Reports Server (NTRS)

Chatman, Yadira; Gangadharan, Sathya; Schlee, Keith; Ristow, James; Suderman, James; Walker, Charles; Hubert, Carl

2007-01-01

Prediction and control of liquid slosh in moving containers is an important consideration in the design of spacecraft and launch vehicle control systems. Even with modern computing systems, CFD type simulations are not fast enough to allow for large scale Monte Carlo analyses of spacecraft and launch vehicle dynamic behavior with slosh included. It is still desirable to use some type of simplified mechanical analog for the slosh to shorten computation time. Analytic determination of the slosh analog parameters has met with mixed success and is made even more difficult by the introduction of propellant management devices such as elastomeric diaphragms. By subjecting full-sized fuel tanks with actual flight fuel loads to motion similar to that experienced in flight and measuring the forces experienced by the tanks, these parameters can be determined experimentally. Currently, the identification of the model parameters is a laborious trial-and-error process in which the hand-derived equations of motion for the mechanical analog are evaluated and their results compared with the experimental results. This paper will describe efforts by the university component of a team comprised of NASA's Launch Services Program, Embry Riddle Aeronautical University, Southwest Research Institute and Hubert Astronautics to improve the accuracy and efficiency of modeling techniques used to predict these types of motions. Of particular interest is the effect of diaphragms and bladders on the slosh dynamics and how best to model these devices. The previous research was an effort to automate the process of slosh model parameter identification using a MATLAB/SimMechanics-based computer simulation. These results are the first step in applying the same computer estimation to a full-size tank and vehicle propulsion system. The introduction of diaphragms to this experimental set-up will aid in a better and more complete prediction of fuel slosh characteristics and behavior. Automating the parameter identification process will save time and thus allow earlier identification of potential vehicle performance problems.

Characterization of the leachate in an urban landfill by physicochemical analysis and solid phase microextraction-GC/MS.

PubMed

Banar, Müfide; Ozkan, Aysun; Kürkçüoğlu, Mine

2006-10-01

The aim of this study is to evaluate extensively the characterization and identification of major pollutant parameters by paying attention to the organic chemical pollution for unregulated dumping site leachate in Eskişehir/Turkey. The study that is first and only one research has been very important data related with before new sanitary landfill site in Eskişehir city. For this purpose, in this study leachate samples were collected in-situ at monthly interval for a period of 8 months. Firstly, thirty three physicochemical parameters were monitored. Secondly, SPME technique was used for identification of organic pollutants. Meteorological data were also recorded for the same sampling period to correlate meteorological data and physicochemical parameters. Mean values are used in the correlation analysis. Correlation is shown only for the relationship between air temperature and NO(3) (-). No correlation has been found between rain and leachate quality parameters since the amount of rain was very low during the sampling period. However, analysis results were generally decreased in winter season when each parameter and each sampling point are examined separately. According to correlation between every parameter, especially solid content and dissolved oxygen concentration of leachate is affecting to other parameters. Also, sodium and potassium are changing proportionally with same parameters (suspended solids, fixed solids, dissolved oxygen) and high correlation between chloride and heavy metal concentration is showing. The results were statistically evaluated by use of SPSS 10.0 program. Second part of the study, the leachate was extracted by Solid Phase Microextraction (SPME) technique and then analyzed. Of the methodologies tested in this study, the best one selected was based on 100 micro m polydimethylsiloxane coated fiber (PDMS), headspace with heating (Delta HS) sampling mode and an extraction time of 15 min. at a temperature of 50 degrees C. Thirty three organic compounds in leachate were identified by GC/MS.

An intelligent signal processing and pattern recognition technique for defect identification using an active sensor network

NASA Astrophysics Data System (ADS)

Su, Zhongqing; Ye, Lin

2004-08-01

The practical utilization of elastic waves, e.g. Rayleigh-Lamb waves, in high-performance structural health monitoring techniques is somewhat impeded due to the complicated wave dispersion phenomena, the existence of multiple wave modes, the high susceptibility to diverse interferences, the bulky sampled data and the difficulty in signal interpretation. An intelligent signal processing and pattern recognition (ISPPR) approach using the wavelet transform and artificial neural network algorithms was developed; this was actualized in a signal processing package (SPP). The ISPPR technique comprehensively functions as signal filtration, data compression, characteristic extraction, information mapping and pattern recognition, capable of extracting essential yet concise features from acquired raw wave signals and further assisting in structural health evaluation. For validation, the SPP was applied to the prediction of crack growth in an alloy structural beam and construction of a damage parameter database for defect identification in CF/EP composite structures. It was clearly apparent that the elastic wave propagation-based damage assessment could be dramatically streamlined by introduction of the ISPPR technique.

Identification of Optimum Magnetic Behavior of NanoCrystalline CmFeAl Type Heusler Alloy Powders Using Response Surface Methodology

NASA Astrophysics Data System (ADS)

Srivastava, Y.; Srivastava, S.; Boriwal, L.

2016-09-01

Mechanical alloying is a novelistic solid state process that has received considerable attention due to many advantages over other conventional processes. In the present work, Co2FeAl healer alloy powder, prepared successfully from premix basic powders of Cobalt (Co), Iron (Fe) and Aluminum (Al) in stoichiometric of 60Co-26Fe-14Al (weight %) by novelistic mechano-chemical route. Magnetic properties of mechanically alloyed powders were characterized by vibrating sample magnetometer (VSM). 2 factor 5 level design matrix was applied to experiment process. Experimental results were used for response surface methodology. Interaction between the input process parameters and the response has been established with the help of regression analysis. Further analysis of variance technique was applied to check the adequacy of developed model and significance of process parameters. Test case study was performed with those parameters, which was not selected for main experimentation but range was same. Response surface methodology, the process parameters must be optimized to obtain improved magnetic properties. Further optimum process parameters were identified using numerical and graphical optimization techniques.

Identification of Low Order Equivalent System Models From Flight Test Data

NASA Technical Reports Server (NTRS)

Morelli, Eugene A.

2000-01-01

Identification of low order equivalent system dynamic models from flight test data was studied. Inputs were pilot control deflections, and outputs were aircraft responses, so the models characterized the total aircraft response including bare airframe and flight control system. Theoretical investigations were conducted and related to results found in the literature. Low order equivalent system modeling techniques using output error and equation error parameter estimation in the frequency domain were developed and validated on simulation data. It was found that some common difficulties encountered in identifying closed loop low order equivalent system models from flight test data could be overcome using the developed techniques. Implications for data requirements and experiment design were discussed. The developed methods were demonstrated using realistic simulation cases, then applied to closed loop flight test data from the NASA F-18 High Alpha Research Vehicle.

On the problem of modeling for parameter identification in distributed structures

NASA Technical Reports Server (NTRS)

Norris, Mark A.; Meirovitch, Leonard

1988-01-01

Structures are often characterized by parameters, such as mass and stiffness, that are spatially distributed. Parameter identification of distributed structures is subject to many of the difficulties involved in the modeling problem, and the choice of the model can greatly affect the results of the parameter identification process. Analogously to control spillover in the control of distributed-parameter systems, identification spillover is shown to exist as well and its effect is to degrade the parameter estimates. Moreover, as in modeling by the Rayleigh-Ritz method, it is shown that, for a Rayleigh-Ritz type identification algorithm, an inclusion principle exists in the identification of distributed-parameter systems as well, so that the identified natural frequencies approach the actual natural frequencies monotonically from above.

Nonparametric identification of nonlinear dynamic systems using a synchronisation-based method

NASA Astrophysics Data System (ADS)

Kenderi, Gábor; Fidlin, Alexander

2014-12-01

The present study proposes an identification method for highly nonlinear mechanical systems that does not require a priori knowledge of the underlying nonlinearities to reconstruct arbitrary restoring force surfaces between degrees of freedom. This approach is based on the master-slave synchronisation between a dynamic model of the system as the slave and the real system as the master using measurements of the latter. As the model synchronises to the measurements, it becomes an observer of the real system. The optimal observer algorithm in a least-squares sense is given by the Kalman filter. Using the well-known state augmentation technique, the Kalman filter can be turned into a dual state and parameter estimator to identify parameters of a priori characterised nonlinearities. The paper proposes an extension of this technique towards nonparametric identification. A general system model is introduced by describing the restoring forces as bilateral spring-dampers with time-variant coefficients, which are estimated as augmented states. The estimation procedure is followed by an a posteriori statistical analysis to reconstruct noise-free restoring force characteristics using the estimated states and their estimated variances. Observability is provided using only one measured mechanical quantity per degree of freedom, which makes this approach less demanding in the number of necessary measurement signals compared with truly nonparametric solutions, which typically require displacement, velocity and acceleration signals. Additionally, due to the statistical rigour of the procedure, it successfully addresses signals corrupted by significant measurement noise. In the present paper, the method is described in detail, which is followed by numerical examples of one degree of freedom (1DoF) and 2DoF mechanical systems with strong nonlinearities of vibro-impact type to demonstrate the effectiveness of the proposed technique.

Flight instrumentation specification for parameter identification: Program user's guide. [instrument errors/error analysis

NASA Technical Reports Server (NTRS)

Mohr, R. L.

1975-01-01

A set of four digital computer programs is presented which can be used to investigate the effects of instrumentation errors on the accuracy of aircraft and helicopter stability-and-control derivatives identified from flight test data. The programs assume that the differential equations of motion are linear and consist of small perturbations about a quasi-steady flight condition. It is also assumed that a Newton-Raphson optimization technique is used for identifying the estimates of the parameters. Flow charts and printouts are included.

A new adaptive algorithm for automated feature extraction in exponentially damped signals for health monitoring of smart structures

NASA Astrophysics Data System (ADS)

Qarib, Hossein; Adeli, Hojjat

2015-12-01

In this paper authors introduce a new adaptive signal processing technique for feature extraction and parameter estimation in noisy exponentially damped signals. The iterative 3-stage method is based on the adroit integration of the strengths of parametric and nonparametric methods such as multiple signal categorization, matrix pencil, and empirical mode decomposition algorithms. The first stage is a new adaptive filtration or noise removal scheme. The second stage is a hybrid parametric-nonparametric signal parameter estimation technique based on an output-only system identification technique. The third stage is optimization of estimated parameters using a combination of the primal-dual path-following interior point algorithm and genetic algorithm. The methodology is evaluated using a synthetic signal and a signal obtained experimentally from transverse vibrations of a steel cantilever beam. The method is successful in estimating the frequencies accurately. Further, it estimates the damping exponents. The proposed adaptive filtration method does not include any frequency domain manipulation. Consequently, the time domain signal is not affected as a result of frequency domain and inverse transformations.

Identification of the Quality Spot Welding used Non Destructive Test-Ultrasonic Testing: (Effect of Welding Time)

NASA Astrophysics Data System (ADS)

Sifa, A.; Endramawan, T.; Badruzzaman

2017-03-01

Resistance Spot Welding (RSW) is frequently used as one way of welding is used in the manufacturing process, especially in the automotive industry [4][5][6][7]. Several parameters influence the process of welding points. To determine the quality of a welding job needs to be tested, either by damaging or testing without damage, in this study conducted experimental testing the quality of welding or identify quality of the nugget by using Non-Destructive Test (NDT) -Ultrasonic Testing (UT), in which the identification of the quality of the welding is done with parameter thickness of worksheet after welding using NDT-UT with use same material worksheet and have more thickness of worksheet, the thickness of the worksheet single plate 1mm, with the capability of propagation Ultrasonic Testing (UT) standard limited> 3 mm [1], welding process parameters such as the time difference between 1-10s and the welding current of 8 KV, visually Heat Affected Zone ( HAZ ) have different results due to the length of time of welding. UT uses a probe that is used with a frequency of 4 MHz, diameter 10 mm, range 100 and the couplant used is oil. Identification techniques using drop 6dB, with sound velocity 2267 m / s of Fe, with the result that the effect of the Welding time affect the size of the HAZ, identification with the lowest time 1s show results capable identified joined through NDT - UT.

Relationships between digital signal processing and control and estimation theory

NASA Technical Reports Server (NTRS)

Willsky, A. S.

1978-01-01

Research directions in the fields of digital signal processing and modern control and estimation theory are discussed. Stability theory, linear prediction and parameter identification, system synthesis and implementation, two-dimensional filtering, decentralized control and estimation, and image processing are considered in order to uncover some of the basic similarities and differences in the goals, techniques, and philosophy of the disciplines.

A program to form a multidisciplinary data base and analysis for dynamic systems

NASA Technical Reports Server (NTRS)

Taylor, L. W.; Suit, W. T.; Mayo, M. H.

1984-01-01

Diverse sets of experimental data and analysis programs have been assembled for the purpose of facilitating research in systems identification, parameter estimation and state estimation techniques. The data base analysis programs are organized to make it easy to compare alternative approaches. Additional data and alternative forms of analysis will be included as they become available.

Chemical composition of shale oil. 1; Dependence on oil shale origin

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kesavan, S.; Lee, S.; Polasky, M.E.

1991-01-01

This paper reports on shale oils obtained by nitrogen retorting of North Carolina, Cleveland, Ohio, Colorado, Rundle, Stuart, and Condor oil shales that have been chemically characterized by g.c.-m.s. techniques. After species identification, chemical compositions of the shale oils have been related to the geological origins of the parent shales. Based on the characteristics observed in the chromatograms, eight semi-quantitative parameters have been used to describe the chromatograms. Six of these parameters describe the chromatograms. Six of these parameters describe the relative abundance and distribution of straight chain alkanes and alkenes in the chromatograms. The other two parameters represent themore » abundance, relative to the total amount of volatiles in the oil, of alkylbenzenes and alkylphenols.« less

[Evaluation of the efficacy of sentinel node detection in breast cancer: chronological course and influence of the incorporation of an intra-operative portable gamma camera].

PubMed

Goñi Gironés, E; Vicente García, F; Serra Arbeloa, P; Estébanez Estébanez, C; Calvo Benito, A; Rodrigo Rincón, I; Camarero Salazar, A; Martínez Lozano, M E

2013-01-01

To define the sentinel node identification rate in breast cancer, the chronological evolution of this parameter and the influence of the introduction of a portable gamma camera. A retrospective study was conducted using a prospective database of 754 patients who had undergone a sentinel lymph node biopsy between January 2003 and December 2011. The technique was mixed in the starting period and subsequently was performed with radiotracer intra-peritumorally administered the day before of the surgery. Until October 2009, excision of the sentinel node was guided by a probe. After that date, a portable gamma camera was introduced for intrasurgical detection. The SN was biopsied in 725 out of the 754 patients studied. The resulting technique global effectiveness was 96.2%. In accordance with the year of the surgical intervention, the identification percentage was 93.5% in 2003, 88.7% in 2004, 94.3% in 2005, 95.7% in 2006, 93.3% in 2007, 98.8% in 2008, 97.1% in 2009 and 99.1% in 2010 and 2011. There was a significant difference in the proportion of identification before and after the incorporation of the portable gamma camera of 4.6% (95% CI of the difference 2-7.2%, P = 0.0037). The percentage of global identification exceeds the recommended level following the current guidelines. Chronologically, the improvement for this parameter during the study period has been observed. These data suggest that the incorporation of a portable gamma camera had an important role. Copyright © 2013 Elsevier España, S.L. and SEMNIM. All rights reserved.

[A peak recognition algorithm designed for chromatographic peaks of transformer oil].

PubMed

Ou, Linjun; Cao, Jian

2014-09-01

In the field of the chromatographic peak identification of the transformer oil, the traditional first-order derivative requires slope threshold to achieve peak identification. In terms of its shortcomings of low automation and easy distortion, the first-order derivative method was improved by applying the moving average iterative method and the normalized analysis techniques to identify the peaks. Accurate identification of the chromatographic peaks was realized through using multiple iterations of the moving average of signal curves and square wave curves to determine the optimal value of the normalized peak identification parameters, combined with the absolute peak retention times and peak window. The experimental results show that this algorithm can accurately identify the peaks and is not sensitive to the noise, the chromatographic peak width or the peak shape changes. It has strong adaptability to meet the on-site requirements of online monitoring devices of dissolved gases in transformer oil.

Attaining insight into interactions between hydrologic model parameters and geophysical attributes for national-scale model parameter estimation

NASA Astrophysics Data System (ADS)

Mizukami, N.; Clark, M. P.; Newman, A. J.; Wood, A.; Gutmann, E. D.

2017-12-01

Estimating spatially distributed model parameters is a grand challenge for large domain hydrologic modeling, especially in the context of hydrologic model applications such as streamflow forecasting. Multi-scale Parameter Regionalization (MPR) is a promising technique that accounts for the effects of fine-scale geophysical attributes (e.g., soil texture, land cover, topography, climate) on model parameters and nonlinear scaling effects on model parameters. MPR computes model parameters with transfer functions (TFs) that relate geophysical attributes to model parameters at the native input data resolution and then scales them using scaling functions to the spatial resolution of the model implementation. One of the biggest challenges in the use of MPR is identification of TFs for each model parameter: both functional forms and geophysical predictors. TFs used to estimate the parameters of hydrologic models typically rely on previous studies or were derived in an ad-hoc, heuristic manner, potentially not utilizing maximum information content contained in the geophysical attributes for optimal parameter identification. Thus, it is necessary to first uncover relationships among geophysical attributes, model parameters, and hydrologic processes (i.e., hydrologic signatures) to obtain insight into which and to what extent geophysical attributes are related to model parameters. We perform multivariate statistical analysis on a large-sample catchment data set including various geophysical attributes as well as constrained VIC model parameters at 671 unimpaired basins over the CONUS. We first calibrate VIC model at each catchment to obtain constrained parameter sets. Additionally, parameter sets sampled during the calibration process are used for sensitivity analysis using various hydrologic signatures as objectives to understand the relationships among geophysical attributes, parameters, and hydrologic processes.

Modal vector estimation for closely spaced frequency modes

NASA Technical Reports Server (NTRS)

Craig, R. R., Jr.; Chung, Y. T.; Blair, M.

1982-01-01

Techniques for obtaining improved modal vector estimates for systems with closely spaced frequency modes are discussed. In describing the dynamical behavior of a complex structure modal parameters are often analyzed: undamped natural frequency, mode shape, modal mass, modal stiffness and modal damping. From both an analytical standpoint and an experimental standpoint, identification of modal parameters is more difficult if the system has repeated frequencies or even closely spaced frequencies. The more complex the structure, the more likely it is to have closely spaced frequencies. This makes it difficult to determine valid mode shapes using single shaker test methods. By employing band selectable analysis (zoom) techniques and by employing Kennedy-Pancu circle fitting or some multiple degree of freedom (MDOF) curve fit procedure, the usefulness of the single shaker approach can be extended.

Individually Identifiable Surface Acoustic Wave Sensors, Tags and Systems

NASA Technical Reports Server (NTRS)

Hines, Jacqueline H. (Inventor); Solie, Leland P. (Inventor); Tucker, Dana Y. G. (Inventor); Hines, Andrew T. (Inventor)

2017-01-01

A surface-launched acoustic wave sensor tag system for remotely sensing and/or providing identification information using sets of surface acoustic wave (SAW) sensor tag devices is characterized by acoustic wave device embodiments that include coding and other diversity techniques to produce groups of sensors that interact minimally, reducing or alleviating code collision problems typical of prior art coded SAW sensors and tags, and specific device embodiments of said coded SAW sensor tags and systems. These sensor/tag devices operate in a system which consists of one or more uniquely identifiable sensor/tag devices and a wireless interrogator. The sensor device incorporates an antenna for receiving incident RF energy and re-radiating the tag identification information and the sensor measured parameter(s). Since there is no power source in or connected to the sensor, it is a passive sensor. The device is wirelessly interrogated by the interrogator.

Identification of visual evoked response parameters sensitive to pilot mental state

NASA Technical Reports Server (NTRS)

Zacharias, G. L.

1988-01-01

Systems analysis techniques were developed and demonstrated for modeling the electroencephalographic (EEG) steady state visual evoked response (ssVER), for use in EEG data compression and as an indicator of mental workload. The study focused on steady state frequency domain stimulation and response analysis, implemented with a sum-of-sines (SOS) stimulus generator and an off-line describing function response analyzer. Three major tasks were conducted: (1) VER related systems identification material was reviewed; (2) Software for experiment control and data analysis was developed and implemented; and (3) ssVER identification and modeling was demonstrated, via a mental loading experiment. It was found that a systems approach to ssVER functional modeling can serve as the basis for eventual development of a mental workload indicator. The review showed how transient visual evoked response (tVER) and ssVER research are related at the functional level, the software development showed how systems techniques can be used for ssVER characterization, and the pilot experiment showed how a simple model can be used to capture the basic dynamic response of the ssVER, under varying loads.

An efficient recursive least square-based condition monitoring approach for a rail vehicle suspension system

NASA Astrophysics Data System (ADS)

Liu, X. Y.; Alfi, S.; Bruni, S.

2016-06-01

A model-based condition monitoring strategy for the railway vehicle suspension is proposed in this paper. This approach is based on recursive least square (RLS) algorithm focusing on the deterministic 'input-output' model. RLS has Kalman filtering feature and is able to identify the unknown parameters from a noisy dynamic system by memorising the correlation properties of variables. The identification of suspension parameter is achieved by machine learning of the relationship between excitation and response in a vehicle dynamic system. A fault detection method for the vertical primary suspension is illustrated as an instance of this condition monitoring scheme. Simulation results from the rail vehicle dynamics software 'ADTreS' are utilised as 'virtual measurements' considering a trailer car of Italian ETR500 high-speed train. The field test data from an E464 locomotive are also employed to validate the feasibility of this strategy for the real application. Results of the parameter identification performed indicate that estimated suspension parameters are consistent or approximate with the reference values. These results provide the supporting evidence that this fault diagnosis technique is capable of paving the way for the future vehicle condition monitoring system.

Review: Optimization methods for groundwater modeling and management

NASA Astrophysics Data System (ADS)

Yeh, William W.-G.

2015-09-01

Optimization methods have been used in groundwater modeling as well as for the planning and management of groundwater systems. This paper reviews and evaluates the various optimization methods that have been used for solving the inverse problem of parameter identification (estimation), experimental design, and groundwater planning and management. Various model selection criteria are discussed, as well as criteria used for model discrimination. The inverse problem of parameter identification concerns the optimal determination of model parameters using water-level observations. In general, the optimal experimental design seeks to find sampling strategies for the purpose of estimating the unknown model parameters. A typical objective of optimal conjunctive-use planning of surface water and groundwater is to minimize the operational costs of meeting water demand. The optimization methods include mathematical programming techniques such as linear programming, quadratic programming, dynamic programming, stochastic programming, nonlinear programming, and the global search algorithms such as genetic algorithms, simulated annealing, and tabu search. Emphasis is placed on groundwater flow problems as opposed to contaminant transport problems. A typical two-dimensional groundwater flow problem is used to explain the basic formulations and algorithms that have been used to solve the formulated optimization problems.

Parameter identification of partially covered piezoelectric cantilever power scavenger based on the coupled distributed parameter solution

NASA Astrophysics Data System (ADS)

Hosseini; Hamedi; Ebrahimi Mamaghani; Kim; Kim; Dayou

2017-07-01

Among the various techniques of power scavenging, piezoelectric energy harvesting usually has more power density. Although piezoceramics are usually more efficient than other piezoelectric materials, since they are very brittle and fragile, researchers are looking for alternative materials. Recently Cellulose Electro-active paper (EAPap) has been recognized as a smart material with piezoelectric behavior that can be used in energy scavenging systems. The majority of researches in energy harvesting area, use unimorph piezoelectric cantilever beams. This paper presents an analytical solution based on distributed parameter model for partially covered pieoelectric cantilever energy harvester. The purpose of the paper is to describe the changes in generated power with damping and the load resistance using analytical calculations. The analytical data are verified using experiment on a vibrating cantilever substrate that is partially covered by EAPap films. The results are very close to each other. Also asymptotic trends of the voltage, current and power outputs are investigated and expressions are obtained for the extreme conditions of the load resistance. These new findings provide guidelines for identification and manipulation of effective parameters in order to achieve the efficient performance in different ambient source conditions.

Airframe Icing Research Gaps: NASA Perspective

NASA Technical Reports Server (NTRS)

Potapczuk, Mark

2009-01-01

qCurrent Airframe Icing Technology Gaps: Development of a full 3D ice accretion simulation model. Development of an improved simulation model for SLD conditions. CFD modeling of stall behavior for ice-contaminated wings/tails. Computational methods for simulation of stability and control parameters. Analysis of thermal ice protection system performance. Quantification of 3D ice shape geometric characteristics Development of accurate ground-based simulation of SLD conditions. Development of scaling methods for SLD conditions. Development of advanced diagnostic techniques for assessment of tunnel cloud conditions. Identification of critical ice shapes for aerodynamic performance degradation. Aerodynamic scaling issues associated with testing scale model ice shape geometries. Development of altitude scaling methods for thermal ice protections systems. Development of accurate parameter identification methods. Measurement of stability and control parameters for an ice-contaminated swept wing aircraft. Creation of control law modifications to prevent loss of control during icing encounters. 3D ice shape geometries. Collection efficiency data for ice shape geometries. SLD ice shape data, in-flight and ground-based, for simulation verification. Aerodynamic performance data for 3D geometries and various icing conditions. Stability and control parameter data for iced aircraft configurations. Thermal ice protection system data for simulation validation.

Multi-Innovation Gradient Iterative Locally Weighted Learning Identification for A Nonlinear Ship Maneuvering System

NASA Astrophysics Data System (ADS)

Bai, Wei-wei; Ren, Jun-sheng; Li, Tie-shan

2018-06-01

This paper explores a highly accurate identification modeling approach for the ship maneuvering motion with fullscale trial. A multi-innovation gradient iterative (MIGI) approach is proposed to optimize the distance metric of locally weighted learning (LWL), and a novel non-parametric modeling technique is developed for a nonlinear ship maneuvering system. This proposed method's advantages are as follows: first, it can avoid the unmodeled dynamics and multicollinearity inherent to the conventional parametric model; second, it eliminates the over-learning or underlearning and obtains the optimal distance metric; and third, the MIGI is not sensitive to the initial parameter value and requires less time during the training phase. These advantages result in a highly accurate mathematical modeling technique that can be conveniently implemented in applications. To verify the characteristics of this mathematical model, two examples are used as the model platforms to study the ship maneuvering.

Nonlinear and Digital Man-machine Control Systems Modeling

NASA Technical Reports Server (NTRS)

Mekel, R.

1972-01-01

An adaptive modeling technique is examined by which controllers can be synthesized to provide corrective dynamics to a human operator's mathematical model in closed loop control systems. The technique utilizes a class of Liapunov functions formulated for this purpose, Liapunov's stability criterion and a model-reference system configuration. The Liapunov function is formulated to posses variable characteristics to take into consideration the identification dynamics. The time derivative of the Liapunov function generate the identification and control laws for the mathematical model system. These laws permit the realization of a controller which updates the human operator's mathematical model parameters so that model and human operator produce the same response when subjected to the same stimulus. A very useful feature is the development of a digital computer program which is easily implemented and modified concurrent with experimentation. The program permits the modeling process to interact with the experimentation process in a mutually beneficial way.

A stochastic global identification framework for aerospace structures operating under varying flight states

NASA Astrophysics Data System (ADS)

Kopsaftopoulos, Fotis; Nardari, Raphael; Li, Yu-Hung; Chang, Fu-Kuo

2018-01-01

In this work, a novel data-based stochastic "global" identification framework is introduced for aerospace structures operating under varying flight states and uncertainty. In this context, the term "global" refers to the identification of a model that is capable of representing the structure under any admissible flight state based on data recorded from a sample of these states. The proposed framework is based on stochastic time-series models for representing the structural dynamics and aeroelastic response under multiple flight states, with each state characterized by several variables, such as the airspeed, angle of attack, altitude and temperature, forming a flight state vector. The method's cornerstone lies in the new class of Vector-dependent Functionally Pooled (VFP) models which allow the explicit analytical inclusion of the flight state vector into the model parameters and, hence, system dynamics. This is achieved via the use of functional data pooling techniques for optimally treating - as a single entity - the data records corresponding to the various flight states. In this proof-of-concept study the flight state vector is defined by two variables, namely the airspeed and angle of attack of the vehicle. The experimental evaluation and assessment is based on a prototype bio-inspired self-sensing composite wing that is subjected to a series of wind tunnel experiments under multiple flight states. Distributed micro-sensors in the form of stretchable sensor networks are embedded in the composite layup of the wing in order to provide the sensing capabilities. Experimental data collected from piezoelectric sensors are employed for the identification of a stochastic global VFP model via appropriate parameter estimation and model structure selection methods. The estimated VFP model parameters constitute two-dimensional functions of the flight state vector defined by the airspeed and angle of attack. The identified model is able to successfully represent the wing's aeroelastic response under the admissible flight states via a minimum number of estimated parameters compared to standard identification approaches. The obtained results demonstrate the high accuracy and effectiveness of the proposed global identification framework, thus constituting a first step towards the next generation of "fly-by-feel" aerospace vehicles with state awareness capabilities.

Automatic measurement of images on astrometric plates

NASA Astrophysics Data System (ADS)

Ortiz Gil, A.; Lopez Garcia, A.; Martinez Gonzalez, J. M.; Yershov, V.

1994-04-01

We present some results on the process of automatic detection and measurement of objects in overlapped fields of astrometric plates. The main steps of our algorithm are the following: determination of the Scale and Tilt between charge coupled devices (CCD) and microscope coordinate systems and estimation of signal-to-noise ratio in each field;--image identification and improvement of its position and size;--image final centering;--image selection and storage. Several parameters allow the use of variable criteria for image identification, characterization and selection. Problems related with faint images and crowded fields will be approached by special techniques (morphological filters, histogram properties and fitting models).

Modeling global vector fields of chaotic systems from noisy time series with the aid of structure-selection techniques.

PubMed

Xu, Daolin; Lu, Fangfang

2006-12-01

We address the problem of reconstructing a set of nonlinear differential equations from chaotic time series. A method that combines the implicit Adams integration and the structure-selection technique of an error reduction ratio is proposed for system identification and corresponding parameter estimation of the model. The structure-selection technique identifies the significant terms from a pool of candidates of functional basis and determines the optimal model through orthogonal characteristics on data. The technique with the Adams integration algorithm makes the reconstruction available to data sampled with large time intervals. Numerical experiment on Lorenz and Rossler systems shows that the proposed strategy is effective in global vector field reconstruction from noisy time series.

Dynamic parameter identification of robot arms with servo-controlled electrical motors

NASA Astrophysics Data System (ADS)

Jiang, Zhao-Hui; Senda, Hiroshi

2005-12-01

This paper addresses the issue of dynamic parameter identification of the robot manipulator with servo-controlled electrical motors. An assumption is made that all kinematical parameters, such as link lengths, are known, and only dynamic parameters containing mass, moment of inertia, and their functions need to be identified. First, we derive dynamics of the robot arm with a linear form of the unknown dynamic parameters by taking dynamic characteristics of the motor and servo unit into consideration. Then, we implement the parameter identification approach to identify the unknown parameters with respect to individual link separately. A pseudo-inverse matrix is used for formulation of the parameter identification. The optimal solution is guaranteed in a sense of least-squares of the mean errors. A Direct Drive (DD) SCARA type industrial robot arm AdeptOne is used as an application example of the parameter identification. Simulations and experiments for both open loop and close loop controls are carried out. Comparison of the results confirms the correctness and usefulness of the parameter identification and the derived dynamic model.

Plan View Pattern Control for Steel Plates through Constrained Locally Weighted Regression

NASA Astrophysics Data System (ADS)

Shigemori, Hiroyasu; Nambu, Koji; Nagao, Ryo; Araki, Tadashi; Mizushima, Narihito; Kano, Manabu; Hasebe, Shinji

A technique for performing parameter identification in a locally weighted regression model using foresight information on the physical properties of the object of interest as constraints was proposed. This method was applied to plan view pattern control of steel plates, and a reduction of shape nonconformity (crop) at the plate head end was confirmed by computer simulation based on real operation data.

Identification and control of plasma vertical position using neural network in Damavand tokamak.

PubMed

Rasouli, H; Rasouli, C; Koohi, A

2013-02-01

In this work, a nonlinear model is introduced to determine the vertical position of the plasma column in Damavand tokamak. Using this model as a simulator, a nonlinear neural network controller has been designed. In the first stage, the electronic drive and sensory circuits of Damavand tokamak are modified. These circuits can control the vertical position of the plasma column inside the vacuum vessel. Since the vertical position of plasma is an unstable parameter, a direct closed loop system identification algorithm is performed. In the second stage, a nonlinear model is identified for plasma vertical position, based on the multilayer perceptron (MLP) neural network (NN) structure. Estimation of simulator parameters has been performed by back-propagation error algorithm using Levenberg-Marquardt gradient descent optimization technique. The model is verified through simulation of the whole closed loop system using both simulator and actual plant in similar conditions. As the final stage, a MLP neural network controller is designed for simulator model. In the last step, online training is performed to tune the controller parameters. Simulation results justify using of the NN controller for the actual plant.

RMB identification based on polarization parameters inversion imaging

NASA Astrophysics Data System (ADS)

Liu, Guoyan; Gao, Kun; Liu, Xuefeng; Ni, Guoqiang

2016-10-01

Social order is threatened by counterfeit money. Conventional anti-counterfeit technology is much too old to identify its authenticity or not. The intrinsic difference between genuine notes and counterfeit notes is its paper tissue. In this paper a new technology of detecting RMB is introduced, the polarization parameter indirect microscopic imaging technique. A conventional reflection microscopic system is used as the basic optical system, and inserting into it with polarization-modulation mechanics. The near-field structural characteristics can be delivered by optical wave and material coupling. According to coupling and conduction physics, calculate the changes of optical wave parameters, then get the curves of the intensity of the image. By analyzing near-field polarization parameters in nanoscale, finally calculate indirect polarization parameter imaging of the fiber of the paper tissue in order to identify its authenticity.

Reduced-order model for underwater target identification using proper orthogonal decomposition

NASA Astrophysics Data System (ADS)

Ramesh, Sai Sudha; Lim, Kian Meng

2017-03-01

Research on underwater acoustics has seen major development over the past decade due to its widespread applications in domains such as underwater communication/navigation (SONAR), seismic exploration and oceanography. In particular, acoustic signatures from partially or fully buried targets can be used in the identification of buried mines for mine counter measures (MCM). Although there exist several techniques to identify target properties based on SONAR images and acoustic signatures, these methods first employ a feature extraction method to represent the dominant characteristics of a data set, followed by the use of an appropriate classifier based on neural networks or the relevance vector machine. The aim of the present study is to demonstrate the applications of proper orthogonal decomposition (POD) technique in capturing dominant features of a set of scattered pressure signals, and subsequent use of the POD modes and coefficients in the identification of partially buried underwater target parameters such as its location, size and material density. Several numerical examples are presented to demonstrate the performance of the system identification method based on POD. Although the present study is based on 2D acoustic model, the method can be easily extended to 3D models and thereby enables cost-effective representations of large-scale data.

An Empirical Bayes Approach to Spatial Analysis

NASA Technical Reports Server (NTRS)

Morris, C. N.; Kostal, H.

1983-01-01

Multi-channel LANDSAT data are collected in several passes over agricultural areas during the growing season. How empirical Bayes modeling can be used to develop crop identification and discrimination techniques that account for spatial correlation in such data is considered. The approach models the unobservable parameters and the data separately, hoping to take advantage of the fact that the bulk of spatial correlation lies in the parameter process. The problem is then framed in terms of estimating posterior probabilities of crop types for each spatial area. Some empirical Bayes spatial estimation methods are used to estimate the logits of these probabilities.

Comparison of Nerve Stimulation-guided Axillary Brachial Plexus Block, Single Injection versus Four Injections: A Prospective Randomized Double-blind Study.

PubMed

Badiger, Santoshi V; Desai, Sameer N

2017-01-01

A variety of techniques have been described for the axillary block using nerve stimulator, either with single injection, two, three, or four separate injections. Identification of all the four nerves is more difficult and time-consuming than other methods. Aim of the present study is to compare success rate, onset, and duration of sensory and motor anesthesia of axillary block using nerve stimulator, either with single injection after identification of any one of the four nerves or four separate injections following identification of each of nerve. Prospective, randomized, double-blind study. Patients undergoing forearm and hand surgeries under axillary block. One hundred patients, aged 18-75 years, were randomly allocated into two groups of 50 each. Axillary block was performed under the guidance of nerve stimulator with a mixture of 18 ml of 1.5% lignocaine and 18 ml of 0.5% bupivacaine. In the first group ( n = 50), all 36 ml of local anesthetic was injected after the identification of motor response to any one of the nerves and in Group 2, all the four nerves were identified by the motor response, and 9 ml of local anesthetic was injected at each of the nerves. The success rate of the block, onset, and duration of sensory and motor block was assessed. Categorical variables were compared using the Chi-square test, and continuous variables were compared using independent t -test. The success rate of the block with four injection technique was higher compared to single-injection technique (84% vs. 56%, P = 0.02). Four injection groups had a faster onset of sensory and motor block and prolonged duration of analgesia compared to single-injection group ( P < 0.001). There were no significant differences in the incidence of accidental arterial puncture and hemodynamic parameter between the groups. Identification of all the four nerves produced higher success rate and better quality of the block when compared to single-injection technique.

Real-Time Stability and Control Derivative Extraction From F-15 Flight Data

NASA Technical Reports Server (NTRS)

Smith, Mark S.; Moes, Timothy R.; Morelli, Eugene A.

2003-01-01

A real-time, frequency-domain, equation-error parameter identification (PID) technique was used to estimate stability and control derivatives from flight data. This technique is being studied to support adaptive control system concepts currently being developed by NASA (National Aeronautics and Space Administration), academia, and industry. This report describes the basic real-time algorithm used for this study and implementation issues for onboard usage as part of an indirect-adaptive control system. A confidence measures system for automated evaluation of PID results is discussed. Results calculated using flight data from a modified F-15 aircraft are presented. Test maneuvers included pilot input doublets and automated inputs at several flight conditions. Estimated derivatives are compared to aerodynamic model predictions. Data indicate that the real-time PID used for this study performs well enough to be used for onboard parameter estimation. For suitable test inputs, the parameter estimates converged rapidly to sufficient levels of accuracy. The devised confidence measures used were moderately successful.

Investigation into discretization methods of the six-parameter Iwan model

NASA Astrophysics Data System (ADS)

Li, Yikun; Hao, Zhiming; Feng, Jiaquan; Zhang, Dingguo

2017-02-01

Iwan model is widely applied for the purpose of describing nonlinear mechanisms of jointed structures. In this paper, parameter identification procedures of the six-parameter Iwan model based on joint experiments with different preload techniques are performed. Four kinds of discretization methods deduced from stiffness equation of the six-parameter Iwan model are provided, which can be used to discretize the integral-form Iwan model into a sum of finite Jenkins elements. In finite element simulation, the influences of discretization methods and numbers of Jenkins elements on computing accuracy are discussed. Simulation results indicate that a higher accuracy can be obtained with larger numbers of Jenkins elements. It is also shown that compared with other three kinds of discretization methods, the geometric series discretization based on stiffness provides the highest computing accuracy.

New results on finite-time parameter identification and synchronization of uncertain complex dynamical networks with perturbation

NASA Astrophysics Data System (ADS)

Zhao, Hui; Zheng, Mingwen; Li, Shudong; Wang, Weiping

2018-03-01

Some existing papers focused on finite-time parameter identification and synchronization, but provided incomplete theoretical analyses. Such works incorporated conflicting constraints for parameter identification, therefore, the practical significance could not be fully demonstrated. To overcome such limitations, the underlying paper presents new results of parameter identification and synchronization for uncertain complex dynamical networks with impulsive effect and stochastic perturbation based on finite-time stability theory. Novel results of parameter identification and synchronization control criteria are obtained in a finite time by utilizing Lyapunov function and linear matrix inequality respectively. Finally, numerical examples are presented to illustrate the effectiveness of our theoretical results.

Identification of integrated airframe: Propulsion effects on an F-15 aircraft for application to drag minimization

NASA Technical Reports Server (NTRS)

Schkolnik, Gerard S.

1993-01-01

The application of an adaptive real-time measurement-based performance optimization technique is being explored for a future flight research program. The key technical challenge of the approach is parameter identification, which uses a perturbation-search technique to identify changes in performance caused by forced oscillations of the controls. The controls on the NASA F-15 highly integrated digital electronic control (HIDEC) aircraft were perturbed using inlet cowl rotation steps at various subsonic and supersonic flight conditions to determine the effect on aircraft performance. The feasibility of the perturbation-search technique for identifying integrated airframe-propulsion system performance effects was successfully shown through flight experiments and postflight data analysis. Aircraft response and control data were analyzed postflight to identify gradients and to determine the minimum drag point. Changes in longitudinal acceleration as small as 0.004 g were measured, and absolute resolution was estimated to be 0.002 g or approximately 50 lbf of drag. Two techniques for identifying performance gradients were compared: a least-squares estimation algorithm and a modified maximum likelihood estimator algorithm. A complementary filter algorithm was used with the least squares estimator.

Identification of integrated airframe-propulsion effects on an F-15 aircraft for application to drag minimization

NASA Technical Reports Server (NTRS)

Schkolnik, Gerald S.

1993-01-01

The application of an adaptive real-time measurement-based performance optimization technique is being explored for a future flight research program. The key technical challenge of the approach is parameter identification, which uses a perturbation-search technique to identify changes in performance caused by forced oscillations of the controls. The controls on the NASA F-15 highly integrated digital electronic control (HIDEC) aircraft were perturbed using inlet cowl rotation steps at various subsonic and supersonic flight conditions to determine the effect on aircraft performance. The feasibility of the perturbation-search technique for identifying integrated airframe-propulsion system performance effects was successfully shown through flight experiments and postflight data analysis. Aircraft response and control data were analyzed postflight to identify gradients and to determine the minimum drag point. Changes in longitudinal acceleration as small as 0.004 g were measured, and absolute resolution was estimated to be 0.002 g or approximately 50 lbf of drag. Two techniques for identifying performance gradients were compared: a least-squares estimation algorithm and a modified maximum likelihood estimator algorithm. A complementary filter algorithm was used with the least squares estimator.

Adaptive infinite impulse response system identification using modified-interior search algorithm with Lèvy flight.

PubMed

Kumar, Manjeet; Rawat, Tarun Kumar; Aggarwal, Apoorva

2017-03-01

In this paper, a new meta-heuristic optimization technique, called interior search algorithm (ISA) with Lèvy flight is proposed and applied to determine the optimal parameters of an unknown infinite impulse response (IIR) system for the system identification problem. ISA is based on aesthetics, which is commonly used in interior design and decoration processes. In ISA, composition phase and mirror phase are applied for addressing the nonlinear and multimodal system identification problems. System identification using modified-ISA (M-ISA) based method involves faster convergence, single parameter tuning and does not require derivative information because it uses a stochastic random search using the concepts of Lèvy flight. A proper tuning of control parameter has been performed in order to achieve a balance between intensification and diversification phases. In order to evaluate the performance of the proposed method, mean square error (MSE), computation time and percentage improvement are considered as the performance measure. To validate the performance of M-ISA based method, simulations has been carried out for three benchmarked IIR systems using same order and reduced order system. Genetic algorithm (GA), particle swarm optimization (PSO), cat swarm optimization (CSO), cuckoo search algorithm (CSA), differential evolution using wavelet mutation (DEWM), firefly algorithm (FFA), craziness based particle swarm optimization (CRPSO), harmony search (HS) algorithm, opposition based harmony search (OHS) algorithm, hybrid particle swarm optimization-gravitational search algorithm (HPSO-GSA) and ISA are also used to model the same examples and simulation results are compared. Obtained results confirm the efficiency of the proposed method. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Robust Library Building for Autonomous Classification of Downhole Geophysical Logs Using Gaussian Processes

NASA Astrophysics Data System (ADS)

Silversides, Katherine L.; Melkumyan, Arman

2017-03-01

Machine learning techniques such as Gaussian Processes can be used to identify stratigraphically important features in geophysical logs. The marker shales in the banded iron formation hosted iron ore deposits of the Hamersley Ranges, Western Australia, form distinctive signatures in the natural gamma logs. The identification of these marker shales is important for stratigraphic identification of unit boundaries for the geological modelling of the deposit. Machine learning techniques each have different unique properties that will impact the results. For Gaussian Processes (GPs), the output values are inclined towards the mean value, particularly when there is not sufficient information in the library. The impact that these inclinations have on the classification can vary depending on the parameter values selected by the user. Therefore, when applying machine learning techniques, care must be taken to fit the technique to the problem correctly. This study focuses on optimising the settings and choices for training a GPs system to identify a specific marker shale. We show that the final results converge even when different, but equally valid starting libraries are used for the training. To analyse the impact on feature identification, GP models were trained so that the output was inclined towards a positive, neutral or negative output. For this type of classification, the best results were when the pull was towards a negative output. We also show that the GP output can be adjusted by using a standard deviation coefficient that changes the balance between certainty and accuracy in the results.

A Multi-Resolution Nonlinear Mapping Technique for Design and Analysis Applications

NASA Technical Reports Server (NTRS)

Phan, Minh Q.

1998-01-01

This report describes a nonlinear mapping technique where the unknown static or dynamic system is approximated by a sum of dimensionally increasing functions (one-dimensional curves, two-dimensional surfaces, etc.). These lower dimensional functions are synthesized from a set of multi-resolution basis functions, where the resolutions specify the level of details at which the nonlinear system is approximated. The basis functions also cause the parameter estimation step to become linear. This feature is taken advantage of to derive a systematic procedure to determine and eliminate basis functions that are less significant for the particular system under identification. The number of unknown parameters that must be estimated is thus reduced and compact models obtained. The lower dimensional functions (identified curves and surfaces) permit a kind of "visualization" into the complexity of the nonlinearity itself.

A Multi-Resolution Nonlinear Mapping Technique for Design and Analysis Application

NASA Technical Reports Server (NTRS)

Phan, Minh Q.

1997-01-01

This report describes a nonlinear mapping technique where the unknown static or dynamic system is approximated by a sum of dimensionally increasing functions (one-dimensional curves, two-dimensional surfaces, etc.). These lower dimensional functions are synthesized from a set of multi-resolution basis functions, where the resolutions specify the level of details at which the nonlinear system is approximated. The basis functions also cause the parameter estimation step to become linear. This feature is taken advantage of to derive a systematic procedure to determine and eliminate basis functions that are less significant for the particular system under identification. The number of unknown parameters that must be estimated is thus reduced and compact models obtained. The lower dimensional functions (identified curves and surfaces) permit a kind of "visualization" into the complexity of the nonlinearity itself.

Chemistry of MOS-LSI radiation hardening

NASA Technical Reports Server (NTRS)

Grunthaner, P.

1985-01-01

The objective of this task was to obtain chemical information on MOS test samples. Toward this end, high resolution X-ray photoemission spectroscopy (XPS) has been the primary techniques used to characterize the chemistry and structure of the SiO2/Si interface for a variety of MOS structures with differing degrees of susceptibility to damage by ionizing radiation. The major accomplishments of this program are: (1) the identification of a structurally distinct region of SiO2 in the near-interfacial region of thermal SiO2 on Si; (2) the identification in the near-interfacial region of SiO2 structural differences between radiation hard and soft gate oxides; (3) the direct observation of radiation-induced damage sites in thermal SiO2 with XPS using in situ electron stress; (4) the correlation of suboxide state distributions at the SiO2/Si interface with processing parameters and radiation susceptibility; (5) the development of a chemical mechanism for radiation-induced interface state generation in SiO2/Si structures; and (6) the development benign chemical profiling techniques which permit the investigation of oxide/semiconductor structures using surface sensitive electron spectroscopic techniques.

Flight data identification of six degree-of-freedom stability and control derivatives of a large crane type helicopter

NASA Technical Reports Server (NTRS)

Tomaine, R. L.

1976-01-01

Flight test data from a large 'crane' type helicopter were collected and processed for the purpose of identifying vehicle rigid body stability and control derivatives. The process consisted of using digital and Kalman filtering techniques for state estimation and Extended Kalman filtering for parameter identification, utilizing a least squares algorithm for initial derivative and variance estimates. Data were processed for indicated airspeeds from 0 m/sec to 152 m/sec. Pulse, doublet and step control inputs were investigated. Digital filter frequency did not have a major effect on the identification process, while the initial derivative estimates and the estimated variances had an appreciable effect on many derivative estimates. The major derivatives identified agreed fairly well with analytical predictions and engineering experience. Doublet control inputs provided better results than pulse or step inputs.

An AI-based approach to structural damage identification by modal analysis

NASA Technical Reports Server (NTRS)

Glass, B. J.; Hanagud, S.

1990-01-01

Flexible-structure damage is presently addressed by a combined model- and parameter-identification approach which employs the AI methodologies of classification, heuristic search, and object-oriented model knowledge representation. The conditions for model-space search convergence to the best model are discussed in terms of search-tree organization and initial model parameter error. In the illustrative example of a truss structure presented, the use of both model and parameter identification is shown to lead to smaller parameter corrections than would be required by parameter identification alone.

Survey of adaptive control using Liapunov design

NASA Technical Reports Server (NTRS)

Lindorff, D. P.; Carroll, R. L.

1972-01-01

A survey was made of the literature devoted to the synthesis of model-tracking adaptive systems based on application of Liapunov's second method. The basic synthesis procedure is introduced and a critical review of extensions made to the theory since 1966 is made. The extensions relate to design for relative stability, reduction of order techniques, design with disturbance, design with time variable parameters, multivariable systems, identification, and an adaptive observer.

A historical overview of flight flutter testing

NASA Technical Reports Server (NTRS)

Kehoe, Michael W.

1995-01-01

This paper reviews the test techniques developed over the last several decades for flight flutter testing of aircraft. Structural excitation systems, instrumentation systems, digital data preprocessing, and parameter identification algorithms (for frequency and damping estimates from the response data) are described. Practical experiences and example test programs illustrate the combined, integrated effectiveness of the various approaches used. Finally, comments regarding the direction of future developments and needs are presented.

Methods for Aircraft State and Parameter Identification

DTIC Science & Technology

1975-05-01

fast heat exchange with the air. The isolation of the flask was improved by plastic foam. See Fig. 5. A small electric heater mounted inside the...flight tests is necessary to have a check on possible changes in the caracteristics of the transducers. If Kalman filtering or corresponding techniques...negates the need for transparent plastic overlays of the flight data. thereby eliminating the coneiderable amount of parallax and distortion which is

Integration of Online Parameter Identification and Neural Network for In-Flight Adaptive Control

NASA Technical Reports Server (NTRS)

Hageman, Jacob J.; Smith, Mark S.; Stachowiak, Susan

2003-01-01

An indirect adaptive system has been constructed for robust control of an aircraft with uncertain aerodynamic characteristics. This system consists of a multilayer perceptron pre-trained neural network, online stability and control derivative identification, a dynamic cell structure online learning neural network, and a model following control system based on the stochastic optimal feedforward and feedback technique. The pre-trained neural network and model following control system have been flight-tested, but the online parameter identification and online learning neural network are new additions used for in-flight adaptation of the control system model. A description of the modification and integration of these two stand-alone software packages into the complete system in preparation for initial flight tests is presented. Open-loop results using both simulation and flight data, as well as closed-loop performance of the complete system in a nonlinear, six-degree-of-freedom, flight validated simulation, are analyzed. Results show that this online learning system, in contrast to the nonlearning system, has the ability to adapt to changes in aerodynamic characteristics in a real-time, closed-loop, piloted simulation, resulting in improved flying qualities.

Aeroservoelastic Uncertainty Model Identification from Flight Data

NASA Technical Reports Server (NTRS)

Brenner, Martin J.

2001-01-01

Uncertainty modeling is a critical element in the estimation of robust stability margins for stability boundary prediction and robust flight control system development. There has been a serious deficiency to date in aeroservoelastic data analysis with attention to uncertainty modeling. Uncertainty can be estimated from flight data using both parametric and nonparametric identification techniques. The model validation problem addressed in this paper is to identify aeroservoelastic models with associated uncertainty structures from a limited amount of controlled excitation inputs over an extensive flight envelope. The challenge to this problem is to update analytical models from flight data estimates while also deriving non-conservative uncertainty descriptions consistent with the flight data. Multisine control surface command inputs and control system feedbacks are used as signals in a wavelet-based modal parameter estimation procedure for model updates. Transfer function estimates are incorporated in a robust minimax estimation scheme to get input-output parameters and error bounds consistent with the data and model structure. Uncertainty estimates derived from the data in this manner provide an appropriate and relevant representation for model development and robust stability analysis. This model-plus-uncertainty identification procedure is applied to aeroservoelastic flight data from the NASA Dryden Flight Research Center F-18 Systems Research Aircraft.

Moving microphone arrays to reduce spatial aliasing in the beamforming technique: theoretical background and numerical investigation.

PubMed

Cigada, Alfredo; Lurati, Massimiliano; Ripamonti, Francesco; Vanali, Marcello

2008-12-01

This paper introduces a measurement technique aimed at reducing or possibly eliminating the spatial aliasing problem in the beamforming technique. Beamforming main disadvantages are a poor spatial resolution, at low frequency, and the spatial aliasing problem, at higher frequency, leading to the identification of false sources. The idea is to move the microphone array during the measurement operation. In this paper, the proposed approach is theoretically and numerically investigated by means of simple sound propagation models, proving its efficiency in reducing the spatial aliasing. A number of different array configurations are numerically investigated together with the most important parameters governing this measurement technique. A set of numerical results concerning the case of a planar rotating array is shown, together with a first experimental validation of the method.

PARTICLE FILTERING WITH SEQUENTIAL PARAMETER LEARNING FOR NONLINEAR BOLD fMRI SIGNALS.

PubMed

Xia, Jing; Wang, Michelle Yongmei

Analyzing the blood oxygenation level dependent (BOLD) effect in the functional magnetic resonance imaging (fMRI) is typically based on recent ground-breaking time series analysis techniques. This work represents a significant improvement over existing approaches to system identification using nonlinear hemodynamic models. It is important for three reasons. First, instead of using linearized approximations of the dynamics, we present a nonlinear filtering based on the sequential Monte Carlo method to capture the inherent nonlinearities in the physiological system. Second, we simultaneously estimate the hidden physiological states and the system parameters through particle filtering with sequential parameter learning to fully take advantage of the dynamic information of the BOLD signals. Third, during the unknown static parameter learning, we employ the low-dimensional sufficient statistics for efficiency and avoiding potential degeneration of the parameters. The performance of the proposed method is validated using both the simulated data and real BOLD fMRI data.

Development and validation of an automated operational modal analysis algorithm for vibration-based monitoring and tensile load estimation

NASA Astrophysics Data System (ADS)

Rainieri, Carlo; Fabbrocino, Giovanni

2015-08-01

In the last few decades large research efforts have been devoted to the development of methods for automated detection of damage and degradation phenomena at an early stage. Modal-based damage detection techniques are well-established methods, whose effectiveness for Level 1 (existence) and Level 2 (location) damage detection is demonstrated by several studies. The indirect estimation of tensile loads in cables and tie-rods is another attractive application of vibration measurements. It provides interesting opportunities for cheap and fast quality checks in the construction phase, as well as for safety evaluations and structural maintenance over the structure lifespan. However, the lack of automated modal identification and tracking procedures has been for long a relevant drawback to the extensive application of the above-mentioned techniques in the engineering practice. An increasing number of field applications of modal-based structural health and performance assessment are appearing after the development of several automated output-only modal identification procedures in the last few years. Nevertheless, additional efforts are still needed to enhance the robustness of automated modal identification algorithms, control the computational efforts and improve the reliability of modal parameter estimates (in particular, damping). This paper deals with an original algorithm for automated output-only modal parameter estimation. Particular emphasis is given to the extensive validation of the algorithm based on simulated and real datasets in view of continuous monitoring applications. The results point out that the algorithm is fairly robust and demonstrate its ability to provide accurate and precise estimates of the modal parameters, including damping ratios. As a result, it has been used to develop systems for vibration-based estimation of tensile loads in cables and tie-rods. Promising results have been achieved for non-destructive testing as well as continuous monitoring purposes. They are documented in the last sections of the paper.

Mathematical correlation of modal-parameter-identification methods via system-realization theory

NASA Technical Reports Server (NTRS)

Juang, Jer-Nan

1987-01-01

A unified approach is introduced using system-realization theory to derive and correlate modal-parameter-identification methods for flexible structures. Several different time-domain methods are analyzed and treated. A basic mathematical foundation is presented which provides insight into the field of modal-parameter identification for comparison and evaluation. The relation among various existing methods is established and discussed. This report serves as a starting point to stimulate additional research toward the unification of the many possible approaches for modal-parameter identification.

Decoupling Identification for Serial Two-Link Two-Inertia System

NASA Astrophysics Data System (ADS)

Oaki, Junji; Adachi, Shuichi

The purpose of our study is to develop a precise model by applying the technique of system identification for the model-based control of a nonlinear robot arm, under taking joint-elasticity into consideration. We previously proposed a systematic identification method, called “decoupling identification,” for a “SCARA-type” planar two-link robot arm with elastic joints caused by the Harmonic-drive® reduction gears. The proposed method serves as an extension of the conventional rigid-joint-model-based identification. The robot arm is treated as a serial two-link two-inertia system with nonlinearity. The decoupling identification method using link-accelerometer signals enables the serial two-link two-inertia system to be divided into two linear one-link two-inertia systems. The MATLAB®'s commands for state-space model estimation are utilized in the proposed method. Physical parameters such as motor inertias, link inertias, joint-friction coefficients, and joint-spring coefficients are estimated through the identified one-link two-inertia systems using a gray-box approach. This paper describes accuracy evaluations using the two-link arm for the decoupling identification method under introducing closed-loop-controlled elements and varying amplitude-setup of identification-input. Experimental results show that the identification method also works with closed-loop-controlled elements. Therefore, the identification method is applicable to a “PUMA-type” vertical robot arm under gravity.

Parametric output-only identification of time-varying structures using a kernel recursive extended least squares TARMA approach

NASA Astrophysics Data System (ADS)

Ma, Zhi-Sai; Liu, Li; Zhou, Si-Da; Yu, Lei; Naets, Frank; Heylen, Ward; Desmet, Wim

2018-01-01

The problem of parametric output-only identification of time-varying structures in a recursive manner is considered. A kernelized time-dependent autoregressive moving average (TARMA) model is proposed by expanding the time-varying model parameters onto the basis set of kernel functions in a reproducing kernel Hilbert space. An exponentially weighted kernel recursive extended least squares TARMA identification scheme is proposed, and a sliding-window technique is subsequently applied to fix the computational complexity for each consecutive update, allowing the method to operate online in time-varying environments. The proposed sliding-window exponentially weighted kernel recursive extended least squares TARMA method is employed for the identification of a laboratory time-varying structure consisting of a simply supported beam and a moving mass sliding on it. The proposed method is comparatively assessed against an existing recursive pseudo-linear regression TARMA method via Monte Carlo experiments and shown to be capable of accurately tracking the time-varying dynamics. Furthermore, the comparisons demonstrate the superior achievable accuracy, lower computational complexity and enhanced online identification capability of the proposed kernel recursive extended least squares TARMA approach.

Moment Method and Pixel-by-Pixel Method: Complementary Mode Identification I. Testing FG Vir-like pulsation modes

NASA Astrophysics Data System (ADS)

Zima, W.; Kolenberg, K.; Briquet, M.; Breger, M.

2004-06-01

We have carried out a Hare-and-Hound test to determine the reliability of the Moment Method (Briquet & Aerts 2003) and the Pixel-by-Pixel Method (Mantegazza 2000) for the identification of pulsation modes in Delta Scuti stars. For this purpose we calculated synthetic line profiles, exhibiting six pulsation modes of low degree and with input parameters initially unknown to us. The aim was to test and increase the quality of the mode identification by applying both methods independently and by using a combined technique. Our results show that, whereas the azimuthal order m and its sign can be fixed by both methods, the degree l is not determined unambiguously. Both identification methods show a better reliability if multiple modes are fitted simultaneously. In particular, the inclination angle is better determined. We have to emphasize that the outcome of this test is only meaningful for stars having pulsational velocities below 0.2 vsini. This is the first part of a series of articles, in which we will test these spectroscopic identification methods.

Using a tracer technique to identify the extent of non-ideal flows in the continuous mixing of non-Newtonian fluids

NASA Astrophysics Data System (ADS)

Patel, D.; Ein-Mozaffari, F.; Mehrvar, M.

2013-05-01

The identification of non-ideal flows in a continuous-flow mixing of non-Newtonian fluids is a challenging task for various chemical industries: plastic manufacturing, water and wastewater treatment, and pulp and paper manufacturing. Non-ideal flows such as channelling, recirculation, and dead zones significantly affect the performance of continuous-flow mixing systems. Therefore, the main objective of this paper was to develop an identification protocol to measure non-ideal flows in the continuous-flow mixing system. The extent of non-ideal flows was quantified using a dynamic model that incorporated channelling, recirculation, and dead volume in the mixing vessel. To estimate the dynamic model parameters, the system was excited using a frequency-modulated random binary input by injecting the saline solution (as a tracer) into the fresh feed stream prior to being pumped into the mixing vessel. The injection of the tracer was controlled by a computer-controlled on-off solenoid valve. Using the trace technique, the extent of channelling and the effective mixed volume were successfully determined and used as mixing quality criteria. Such identification procedures can be applied at various areas of chemical engineering in order to improve the mixing quality.

Stochastic filtering for damage identification through nonlinear structural finite element model updating

NASA Astrophysics Data System (ADS)

Astroza, Rodrigo; Ebrahimian, Hamed; Conte, Joel P.

2015-03-01

This paper describes a novel framework that combines advanced mechanics-based nonlinear (hysteretic) finite element (FE) models and stochastic filtering techniques to estimate unknown time-invariant parameters of nonlinear inelastic material models used in the FE model. Using input-output data recorded during earthquake events, the proposed framework updates the nonlinear FE model of the structure. The updated FE model can be directly used for damage identification and further used for damage prognosis. To update the unknown time-invariant parameters of the FE model, two alternative stochastic filtering methods are used: the extended Kalman filter (EKF) and the unscented Kalman filter (UKF). A three-dimensional, 5-story, 2-by-1 bay reinforced concrete (RC) frame is used to verify the proposed framework. The RC frame is modeled using fiber-section displacement-based beam-column elements with distributed plasticity and is subjected to the ground motion recorded at the Sylmar station during the 1994 Northridge earthquake. The results indicate that the proposed framework accurately estimate the unknown material parameters of the nonlinear FE model. The UKF outperforms the EKF when the relative root-mean-square error of the recorded responses are compared. In addition, the results suggest that the convergence of the estimate of modeling parameters is smoother and faster when the UKF is utilized.

On Markov parameters in system identification

NASA Technical Reports Server (NTRS)

Phan, Minh; Juang, Jer-Nan; Longman, Richard W.

1991-01-01

A detailed discussion of Markov parameters in system identification is given. Different forms of input-output representation of linear discrete-time systems are reviewed and discussed. Interpretation of sampled response data as Markov parameters is presented. Relations between the state-space model and particular linear difference models via the Markov parameters are formulated. A generalization of Markov parameters to observer and Kalman filter Markov parameters for system identification is explained. These extended Markov parameters play an important role in providing not only a state-space realization, but also an observer/Kalman filter for the system of interest.

Large Modal Survey Testing Using the Ibrahim Time Domain Identification Technique

NASA Technical Reports Server (NTRS)

Ibrahim, S. R.; Pappa, R. S.

1985-01-01

The ability of the ITD identification algorithm in identifying a complete set of structural modal parameters using a large number of free-response time histories simultaneously in one analysis, assuming a math model with a high number of degrees-of-freedom, has been studied. Identification results using simulated free responses of a uniform rectangular plate, with 225 measurement stations, and experimental responses from a ground vibration test of the Long Duration Exposure Facility (LDEF) Space Shuttle payload, with 142 measurement stations, are presented. As many as 300 degrees-of-freedom were allowed in analyzing these data. In general, the use of a significantly oversized math model in the identification process was found to maintain or increase identification accuracy and to identify modes of low response level that are not identified with smaller math model sizes. The concept of a Mode Shape Correlation Constant is introduced for use when more than one identification analysis of the same structure are conducted. This constant quantifies the degree of correlation between any two sets of complex mode shapes identified using different excitation conditions, different user-selectable algorithm constants, or overlapping sets of measurements.

Large modal survey testing using the Ibrahim time domain /ITD/ identification technique

NASA Technical Reports Server (NTRS)

Ibrahim, S. R.; Pappa, R. S.

1981-01-01

The ability of the ITD identification algorithm in identifying a complete set of structural modal parameters using a large number of free-response time histories simultaneously in one analysis, assuming a math model with a high number of degrees-of-freedom, has been studied. Identification results using simulated free responses of a uniform rectangular plate, with 225 measurement stations, and experimental responses from a ground vibration test of the Long Duration Exposure Facility (LDEF) Space Shuttle payload, with 142 measurement stations, are presented. As many as 300 degrees-of-freedom were allowed in analyzing these data. In general, the use of a significantly oversized math model in the identification process was found to maintain or increase identification accuracy and to identify modes of low response level that are not identified with smaller math model sizes. The concept of a Mode Shape Correlation Constant is introduced for use when more than one identification analysis of the same structure are conducted. This constant quantifies the degree of correlation between any two sets of complex mode shapes identified using different excitation conditions, different user-selectable algorithm constants, or overlapping sets of measurements.

High-resolution X-ray diffraction with no sample preparation

PubMed Central

Turner, S. M. R.; Degryse, P.; Shortland, A. J.

2017-01-01

It is shown that energy-dispersive X-ray diffraction (EDXRD) implemented in a back-reflection geometry is extremely insensitive to sample morphology and positioning even in a high-resolution configuration. This technique allows high-quality X-ray diffraction analysis of samples that have not been prepared and is therefore completely non-destructive. The experimental technique was implemented on beamline B18 at the Diamond Light Source synchrotron in Oxfordshire, UK. The majority of the experiments in this study were performed with pre-characterized geological materials in order to elucidate the characteristics of this novel technique and to develop the analysis methods. Results are presented that demonstrate phase identification, the derivation of precise unit-cell parameters and extraction of microstructural information on unprepared rock samples and other sample types. A particular highlight was the identification of a specific polytype of a muscovite in an unprepared mica schist sample, avoiding the time-consuming and difficult preparation steps normally required to make this type of identification. The technique was also demonstrated in application to a small number of fossil and archaeological samples. Back-reflection EDXRD implemented in a high-resolution configuration shows great potential in the crystallographic analysis of cultural heritage artefacts for the purposes of scientific research such as provenancing, as well as contributing to the formulation of conservation strategies. Possibilities for moving the technique from the synchrotron into museums are discussed. The avoidance of the need to extract samples from high-value and rare objects is a highly significant advantage, applicable also in other potential research areas such as palaeontology, and the study of meteorites and planetary materials brought to Earth by sample-return missions. PMID:28660862

On parameters identification of computational models of vibrations during quiet standing of humans

NASA Astrophysics Data System (ADS)

Barauskas, R.; Krušinskienė, R.

2007-12-01

Vibration of the center of pressure (COP) of human body on the base of support during quiet standing is a very popular clinical research, which provides useful information about the physical and health condition of an individual. In this work, vibrations of COP of a human body in forward-backward direction during still standing are generated using controlled inverted pendulum (CIP) model with a single degree of freedom (dof) supplied with proportional, integral and differential (PID) controller, which represents the behavior of the central neural system of a human body and excited by cumulative disturbance vibration, generated within the body due to breathing or any other physical condition. The identification of the model and disturbance parameters is an important stage while creating a close-to-reality computational model able to evaluate features of disturbance. The aim of this study is to present the CIP model parameters identification approach based on the information captured by time series of the COP signal. The identification procedure is based on an error function minimization. Error function is formulated in terms of time laws of computed and experimentally measured COP vibrations. As an alternative, error function is formulated in terms of the stabilogram diffusion function (SDF). The minimization of error functions is carried out by employing methods based on sensitivity functions of the error with respect to model and excitation parameters. The sensitivity functions are obtained by using the variational techniques. The inverse dynamic problem approach has been employed in order to establish the properties of the disturbance time laws ensuring the satisfactory coincidence of measured and computed COP vibration laws. The main difficulty of the investigated problem is encountered during the model validation stage. Generally, neither the PID controller parameter set nor the disturbance time law are known in advance. In this work, an error function formulated in terms of time derivative of disturbance torque has been proposed in order to obtain PID controller parameters, as well as the reference time law of the disturbance. The disturbance torque is calculated from experimental data using the inverse dynamic approach. Experiments presented in this study revealed that vibrations of disturbance torque and PID controller parameters identified by the method may be qualified as feasible in humans. Presented approach may be easily extended to structural models with any number of dof or higher structural complexity.

Optimization Parameters of Air-conditioning and Heat Insulation Systems of a Pressurized Cabins of Long-distance Airplanes

NASA Astrophysics Data System (ADS)

Gusev, Sergey A.; Nikolaev, Vladimir N.

2018-01-01

The method for determination of an aircraft compartment thermal condition, based on a mathematical model of a compartment thermal condition was developed. Development of solution techniques for solving heat exchange direct and inverse problems and for determining confidence intervals of parametric identification estimations was carried out. The required performance of air-conditioning, ventilation systems and heat insulation depth of crew and passenger cabins were received.

Survey of adaptive control using Liapunov design

NASA Technical Reports Server (NTRS)

Lindorff, D. P.; Carroll, R. L.

1973-01-01

A survey of the literature in which Liapunov's second method is used in determining the control law is presented, with emphasis placed on the model-tracking adaptive control problem. Forty references are listed. Following a brief tutorial exposition of the adaptive control problem, the techniques for treating reduction of order, disturbance and time-varying parameters, multivariable systems, identification, and adaptive observers are discussed. The method is critically evaluated, particularly with respect to possibilities for application.

Computational Difficulties in the Identification and Optimization of Control Systems.

DTIC Science & Technology

1980-01-01

necessary and Identify by block number) - -. 3. iABSTRACT (Continue on revers, side It necessary and Identify by block number) As more realistic models ...Island 02912 ABSTRACT As more realistic models for resource management are developed, the need for efficient computational techniques for parameter...optimization (optimal control) in "state" models which This research was supported in part by ttfe National Science Foundation under grant NSF-MCS 79-05774

Identification of superficial defects in reconstructed 3D objects using phase-shifting fringe projection

NASA Astrophysics Data System (ADS)

Madrigal, Carlos A.; Restrepo, Alejandro; Branch, John W.

2016-09-01

3D reconstruction of small objects is used in applications of surface analysis, forensic analysis and tissue reconstruction in medicine. In this paper, we propose a strategy for the 3D reconstruction of small objects and the identification of some superficial defects. We applied a technique of projection of structured light patterns, specifically sinusoidal fringes and an algorithm of phase unwrapping. A CMOS camera was used to capture images and a DLP digital light projector for synchronous projection of the sinusoidal pattern onto the objects. We implemented a technique based on a 2D flat pattern as calibration process, so the intrinsic and extrinsic parameters of the camera and the DLP were defined. Experimental tests were performed in samples of artificial teeth, coal particles, welding defects and surfaces tested with Vickers indentation. Areas less than 5cm were studied. The objects were reconstructed in 3D with densities of about one million points per sample. In addition, the steps of 3D description, identification of primitive, training and classification were implemented to recognize defects, such as: holes, cracks, roughness textures and bumps. We found that pattern recognition strategies are useful, when quality supervision of surfaces has enough quantities of points to evaluate the defective region, because the identification of defects in small objects is a demanding activity of the visual inspection.

Identification of spilled oils by NIR spectroscopy technology based on KPCA and LSSVM

NASA Astrophysics Data System (ADS)

Tan, Ailing; Bi, Weihong

2011-08-01

Oil spills on the sea surface are seen relatively often with the development of the petroleum exploitation and transportation of the sea. Oil spills are great threat to the marine environment and the ecosystem, thus the oil pollution in the ocean becomes an urgent topic in the environmental protection. To develop the oil spill accident treatment program and track the source of the spilled oils, a novel qualitative identification method combined Kernel Principal Component Analysis (KPCA) and Least Square Support Vector Machine (LSSVM) was proposed. The proposed method adapt Fourier transform NIR spectrophotometer to collect the NIR spectral data of simulated gasoline, diesel fuel and kerosene oil spills samples and do some pretreatments to the original spectrum. We use the KPCA algorithm which is an extension of Principal Component Analysis (PCA) using techniques of kernel methods to extract nonlinear features of the preprocessed spectrum. Support Vector Machines (SVM) is a powerful methodology for solving spectral classification tasks in chemometrics. LSSVM are reformulations to the standard SVMs which lead to solving a system of linear equations. So a LSSVM multiclass classification model was designed which using Error Correcting Output Code (ECOC) method borrowing the idea of error correcting codes used for correcting bit errors in transmission channels. The most common and reliable approach to parameter selection is to decide on parameter ranges, and to then do a grid search over the parameter space to find the optimal model parameters. To test the proposed method, 375 spilled oil samples of unknown type were selected to study. The optimal model has the best identification capabilities with the accuracy of 97.8%. Experimental results show that the proposed KPCA plus LSSVM qualitative analysis method of near infrared spectroscopy has good recognition result, which could work as a new method for rapid identification of spilled oils.

Dynamic neural networks based on-line identification and control of high performance motor drives

NASA Technical Reports Server (NTRS)

Rubaai, Ahmed; Kotaru, Raj

1995-01-01

In the automated and high-tech industries of the future, there wil be a need for high performance motor drives both in the low-power range and in the high-power range. To meet very straight demands of tracking and regulation in the two quadrants of operation, advanced control technologies are of a considerable interest and need to be developed. In response a dynamics learning control architecture is developed with simultaneous on-line identification and control. the feature of the proposed approach, to efficiently combine the dual task of system identification (learning) and adaptive control of nonlinear motor drives into a single operation is presented. This approach, therefore, not only adapts to uncertainties of the dynamic parameters of the motor drives but also learns about their inherent nonlinearities. In fact, most of the neural networks based adaptive control approaches in use have an identification phase entirely separate from the control phase. Because these approaches separate the identification and control modes, it is not possible to cope with dynamic changes in a controlled process. Extensive simulation studies have been conducted and good performance was observed. The robustness characteristics of neuro-controllers to perform efficiently in a noisy environment is also demonstrated. With this initial success, the principal investigator believes that the proposed approach with the suggested neural structure can be used successfully for the control of high performance motor drives. Two identification and control topologies based on the model reference adaptive control technique are used in this present analysis. No prior knowledge of load dynamics is assumed in either topology while the second topology also assumes no knowledge of the motor parameters.

Identification of a parametric, discrete-time model of ankle stiffness.

PubMed

Guarin, Diego L; Jalaleddini, Kian; Kearney, Robert E

2013-01-01

Dynamic ankle joint stiffness defines the relationship between the position of the ankle and the torque acting about it and can be separated into intrinsic and reflex components. Under stationary conditions, intrinsic stiffness can described by a linear second order system while reflex stiffness is described by Hammerstein system whose input is delayed velocity. Given that reflex and intrinsic torque cannot be measured separately, there has been much interest in the development of system identification techniques to separate them analytically. To date, most methods have been nonparametric and as a result there is no direct link between the estimated parameters and those of the stiffness model. This paper presents a novel algorithm for identification of a discrete-time model of ankle stiffness. Through simulations we show that the algorithm gives unbiased results even in the presence of large, non-white noise. Application of the method to experimental data demonstrates that it produces results consistent with previous findings.

Identification of diffusive transport properties of poly(vinyl alcohol) hydrogels from reservoir test.

PubMed

Kazimierska-Drobny, Katarzyna; Kaczmarek, Mariusz

2013-12-01

In this paper the identification of diffusion coefficient, retardation factor and surface distribution coefficient for selected salts in poly(vinyl alcohol) hydrogels is performed. The identification of the transport parameters is based on the previously developed inverse problem technique using experimental data from the reservoir test and the solution of the diffusive transport equation with linear equilibrium sorption. The estimated values of diffusion coefficient are: for physiological fluid (6.30±0.10)×10(-10) m(2)/s, for 1 M NaCl (6.42±0.39)×10(-10) m(2)/s, and for 1 M KCl (7.94±0.38)×10(-10) m(2)/s. The retardation factor for all tested materials and salts is equal or close to one. The average value of the effective surface distribution coefficient is equal to 0.5. © 2013 Elsevier B.V. All rights reserved.

Mathematical correlation of modal parameter identification methods via system realization theory

NASA Technical Reports Server (NTRS)

Juang, J. N.

1986-01-01

A unified approach is introduced using system realization theory to derive and correlate modal parameter identification methods for flexible structures. Several different time-domain and frequency-domain methods are analyzed and treated. A basic mathematical foundation is presented which provides insight into the field of modal parameter identification for comparison and evaluation. The relation among various existing methods is established and discussed. This report serves as a starting point to stimulate additional research towards the unification of the many possible approaches for modal parameter identification.

Inverse analysis of aerodynamic loads from strain information using structural models and neural networks

NASA Astrophysics Data System (ADS)

Wada, Daichi; Sugimoto, Yohei

2017-04-01

Aerodynamic loads on aircraft wings are one of the key parameters to be monitored for reliable and effective aircraft operations and management. Flight data of the aerodynamic loads would be used onboard to control the aircraft and accumulated data would be used for the condition-based maintenance and the feedback for the fatigue and critical load modeling. The effective sensing techniques such as fiber optic distributed sensing have been developed and demonstrated promising capability of monitoring structural responses, i.e., strains on the surface of the aircraft wings. By using the developed techniques, load identification methods for structural health monitoring are expected to be established. The typical inverse analysis for load identification using strains calculates the loads in a discrete form of concentrated forces, however, the distributed form of the loads is essential for the accurate and reliable estimation of the critical stress at structural parts. In this study, we demonstrate an inverse analysis to identify the distributed loads from measured strain information. The introduced inverse analysis technique calculates aerodynamic loads not in a discrete but in a distributed manner based on a finite element model. In order to verify the technique through numerical simulations, we apply static aerodynamic loads on a flat panel model, and conduct the inverse identification of the load distributions. We take two approaches to build the inverse system between loads and strains. The first one uses structural models and the second one uses neural networks. We compare the performance of the two approaches, and discuss the effect of the amount of the strain sensing information.

Optical characterization of agricultural pest insects: a methodological study in the spectral and time domains

NASA Astrophysics Data System (ADS)

Li, Y. Y.; Zhang, H.; Duan, Z.; Lian, M.; Zhao, G. Y.; Sun, X. H.; Hu, J. D.; Gao, L. N.; Feng, H. Q.; Svanberg, S.

2016-08-01

Identification of agricultural pest insects is an important aspect in insect research and agricultural monitoring. We have performed a methodological study of how spectroscopic techniques and wing-beat frequency analysis might provide relevant information. An optical system based on the combination of close-range remote sensing and reflectance spectroscopy was developed to study the optical characteristics of different flying insects, collected in Southern China. The results demonstrate that the combination of wing-beat frequency assessment and reflectance spectral analysis has the potential to successfully differentiate between insect species. Further, studies of spectroscopic characteristics of fixed specimen of insects, also from Central China, showed the possibility of refined agricultural pest identification. Here, in addition to reflectance recordings also laser-induced fluorescence spectra were investigated for all the species of insects under study and found to provide complementary information to optically distinguish insects. In order to prove the practicality of the techniques explored, clearly fieldwork aiming at elucidating the variability of parameters, even within species, must be performed.

Resistance Curves in the Tensile and Compressive Longitudinal Failure of Composites

NASA Technical Reports Server (NTRS)

Camanho, Pedro P.; Catalanotti, Giuseppe; Davila, Carlos G.; Lopes, Claudio S.; Bessa, Miguel A.; Xavier, Jose C.

2010-01-01

This paper presents a new methodology to measure the crack resistance curves associated with fiber-dominated failure modes in polymer-matrix composites. These crack resistance curves not only characterize the fracture toughness of the material, but are also the basis for the identification of the parameters of the softening laws used in the analytical and numerical simulation of fracture in composite materials. The method proposed is based on the identification of the crack tip location by the use of Digital Image Correlation and the calculation of the J-integral directly from the test data using a simple expression derived for cross-ply composite laminates. It is shown that the results obtained using the proposed methodology yield crack resistance curves similar to those obtained using FEM-based methods in compact tension carbon-epoxy specimens. However, it is also shown that the Digital Image Correlation based technique can be used to extract crack resistance curves in compact compression tests for which FEM-based techniques are inadequate.

Technical note: Simultaneous fully dynamic characterization of multiple input–output relationships in climate models

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kravitz, Ben; MacMartin, Douglas G.; Rasch, Philip J.

We introduce system identification techniques to climate science wherein multiple dynamic input–output relationships can be simultaneously characterized in a single simulation. This method, involving multiple small perturbations (in space and time) of an input field while monitoring output fields to quantify responses, allows for identification of different timescales of climate response to forcing without substantially pushing the climate far away from a steady state. We use this technique to determine the steady-state responses of low cloud fraction and latent heat flux to heating perturbations over 22 regions spanning Earth's oceans. We show that the response characteristics are similar to thosemore » of step-change simulations, but in this new method the responses for 22 regions can be characterized simultaneously. Moreover, we can estimate the timescale over which the steady-state response emerges. The proposed methodology could be useful for a wide variety of purposes in climate science, including characterization of teleconnections and uncertainty quantification to identify the effects of climate model tuning parameters.« less

Technical note: Simultaneous fully dynamic characterization of multiple input–output relationships in climate models

DOE PAGES

Kravitz, Ben; MacMartin, Douglas G.; Rasch, Philip J.; ...

2017-02-17

We introduce system identification techniques to climate science wherein multiple dynamic input–output relationships can be simultaneously characterized in a single simulation. This method, involving multiple small perturbations (in space and time) of an input field while monitoring output fields to quantify responses, allows for identification of different timescales of climate response to forcing without substantially pushing the climate far away from a steady state. We use this technique to determine the steady-state responses of low cloud fraction and latent heat flux to heating perturbations over 22 regions spanning Earth's oceans. We show that the response characteristics are similar to thosemore » of step-change simulations, but in this new method the responses for 22 regions can be characterized simultaneously. Moreover, we can estimate the timescale over which the steady-state response emerges. The proposed methodology could be useful for a wide variety of purposes in climate science, including characterization of teleconnections and uncertainty quantification to identify the effects of climate model tuning parameters.« less

New techniques for the analysis of manual control systems. [mathematical models of human operator behavior

NASA Technical Reports Server (NTRS)

Bekey, G. A.

1971-01-01

Studies are summarized on the application of advanced analytical and computational methods to the development of mathematical models of human controllers in multiaxis manual control systems. Specific accomplishments include the following: (1) The development of analytical and computer methods for the measurement of random parameters in linear models of human operators. (2) Discrete models of human operator behavior in a multiple display situation were developed. (3) Sensitivity techniques were developed which make possible the identification of unknown sampling intervals in linear systems. (4) The adaptive behavior of human operators following particular classes of vehicle failures was studied and a model structure proposed.

Removing Visual Bias in Filament Identification: A New Goodness-of-fit Measure

NASA Astrophysics Data System (ADS)

Green, C.-E.; Cunningham, M. R.; Dawson, J. R.; Jones, P. A.; Novak, G.; Fissel, L. M.

2017-05-01

Different combinations of input parameters to filament identification algorithms, such as disperse and filfinder, produce numerous different output skeletons. The skeletons are a one-pixel-wide representation of the filamentary structure in the original input image. However, these output skeletons may not necessarily be a good representation of that structure. Furthermore, a given skeleton may not be as good of a representation as another. Previously, there has been no mathematical “goodness-of-fit” measure to compare output skeletons to the input image. Thus far this has been assessed visually, introducing visual bias. We propose the application of the mean structural similarity index (MSSIM) as a mathematical goodness-of-fit measure. We describe the use of the MSSIM to find the output skeletons that are the most mathematically similar to the original input image (the optimum, or “best,” skeletons) for a given algorithm, and independently of the algorithm. This measure makes possible systematic parameter studies, aimed at finding the subset of input parameter values returning optimum skeletons. It can also be applied to the output of non-skeleton-based filament identification algorithms, such as the Hessian matrix method. The MSSIM removes the need to visually examine thousands of output skeletons, and eliminates the visual bias, subjectivity, and limited reproducibility inherent in that process, representing a major improvement upon existing techniques. Importantly, it also allows further automation in the post-processing of output skeletons, which is crucial in this era of “big data.”

Optimization of injection molding process parameters for a plastic cell phone housing component

NASA Astrophysics Data System (ADS)

Rajalingam, Sokkalingam; Vasant, Pandian; Khe, Cheng Seong; Merican, Zulkifli; Oo, Zeya

2016-11-01

To produce thin-walled plastic items, injection molding process is one of the most widely used application tools. However, to set optimal process parameters is difficult as it may cause to produce faulty items on injected mold like shrinkage. This study aims at to determine such an optimum injection molding process parameters which can reduce the fault of shrinkage on a plastic cell phone cover items. Currently used setting of machines process produced shrinkage and mis-specified length and with dimensions below the limit. Thus, for identification of optimum process parameters, maintaining closer targeted length and width setting magnitudes with minimal variations, more experiments are needed. The mold temperature, injection pressure and screw rotation speed are used as process parameters in this research. For optimal molding process parameters the Response Surface Methods (RSM) is applied. The major contributing factors influencing the responses were identified from analysis of variance (ANOVA) technique. Through verification runs it was found that the shrinkage defect can be minimized with the optimal setting found by RSM.

Minimization of model representativity errors in identification of point source emission from atmospheric concentration measurements

NASA Astrophysics Data System (ADS)

Sharan, Maithili; Singh, Amit Kumar; Singh, Sarvesh Kumar

2017-11-01

Estimation of an unknown atmospheric release from a finite set of concentration measurements is considered an ill-posed inverse problem. Besides ill-posedness, the estimation process is influenced by the instrumental errors in the measured concentrations and model representativity errors. The study highlights the effect of minimizing model representativity errors on the source estimation. This is described in an adjoint modelling framework and followed in three steps. First, an estimation of point source parameters (location and intensity) is carried out using an inversion technique. Second, a linear regression relationship is established between the measured concentrations and corresponding predicted using the retrieved source parameters. Third, this relationship is utilized to modify the adjoint functions. Further, source estimation is carried out using these modified adjoint functions to analyse the effect of such modifications. The process is tested for two well known inversion techniques, called renormalization and least-square. The proposed methodology and inversion techniques are evaluated for a real scenario by using concentrations measurements from the Idaho diffusion experiment in low wind stable conditions. With both the inversion techniques, a significant improvement is observed in the retrieval of source estimation after minimizing the representativity errors.

Systems identification using a modified Newton-Raphson method: A FORTRAN program

NASA Technical Reports Server (NTRS)

Taylor, L. W., Jr.; Iliff, K. W.

1972-01-01

A FORTRAN program is offered which computes a maximum likelihood estimate of the parameters of any linear, constant coefficient, state space model. For the case considered, the maximum likelihood estimate can be identical to that which minimizes simultaneously the weighted mean square difference between the computed and measured response of a system and the weighted square of the difference between the estimated and a priori parameter values. A modified Newton-Raphson or quasilinearization method is used to perform the minimization which typically requires several iterations. A starting technique is used which insures convergence for any initial values of the unknown parameters. The program and its operation are described in sufficient detail to enable the user to apply the program to his particular problem with a minimum of difficulty.

Constitutive error based parameter estimation technique for plate structures using free vibration signatures

NASA Astrophysics Data System (ADS)

Guchhait, Shyamal; Banerjee, Biswanath

2018-04-01

In this paper, a variant of constitutive equation error based material parameter estimation procedure for linear elastic plates is developed from partially measured free vibration sig-natures. It has been reported in many research articles that the mode shape curvatures are much more sensitive compared to mode shape themselves to localize inhomogeneity. Complying with this idea, an identification procedure is framed as an optimization problem where the proposed cost function measures the error in constitutive relation due to incompatible curvature/strain and moment/stress fields. Unlike standard constitutive equation error based procedure wherein a solution of a couple system is unavoidable in each iteration, we generate these incompatible fields via two linear solves. A simple, yet effective, penalty based approach is followed to incorporate measured data. The penalization parameter not only helps in incorporating corrupted measurement data weakly but also acts as a regularizer against the ill-posedness of the inverse problem. Explicit linear update formulas are then developed for anisotropic linear elastic material. Numerical examples are provided to show the applicability of the proposed technique. Finally, an experimental validation is also provided.

Application of gray level mapping in computed tomographic colonography: a pilot study to compare with traditional surface rendering method for identification and differentiation of endoluminal lesions

PubMed Central

Chen, Lih-Shyang; Hsu, Ta-Wen; Chang, Shu-Han; Lin, Chih-Wen; Chen, Yu-Ruei; Hsieh, Chin-Chiang; Han, Shu-Chen; Chang, Ku-Yaw; Hou, Chun-Ju

2017-01-01

Objective: In traditional surface rendering (SR) computed tomographic endoscopy, only the shape of endoluminal lesion is depicted without gray-level information unless the volume rendering technique is used. However, volume rendering technique is relatively slow and complex in terms of computation time and parameter setting. We use computed tomographic colonography (CTC) images as examples and report a new visualization technique by three-dimensional gray level mapping (GM) to better identify and differentiate endoluminal lesions. Methods: There are 33 various endoluminal cases from 30 patients evaluated in this clinical study. These cases were segmented using gray-level threshold. The marching cube algorithm was used to detect isosurfaces in volumetric data sets. GM is applied using the surface gray level of CTC. Radiologists conducted the clinical evaluation of the SR and GM images. The Wilcoxon signed-rank test was used for data analysis. Results: Clinical evaluation confirms GM is significantly superior to SR in terms of gray-level pattern and spatial shape presentation of endoluminal cases (p < 0.01) and improves the confidence of identification and clinical classification of endoluminal lesions significantly (p < 0.01). The specificity and diagnostic accuracy of GM is significantly better than those of SR in diagnostic performance evaluation (p < 0.01). Conclusion: GM can reduce confusion in three-dimensional CTC and well correlate CTC with sectional images by the location as well as gray-level value. Hence, GM increases identification and differentiation of endoluminal lesions, and facilitates diagnostic process. Advances in knowledge: GM significantly improves the traditional SR method by providing reliable gray-level information for the surface points and is helpful in identification and differentiation of endoluminal lesions according to their shape and density. PMID:27925483

Dynamic identification of axial force and boundary restraints in tie rods and cables with uncertainty quantification using Set Inversion Via Interval Analysis

NASA Astrophysics Data System (ADS)

Kernicky, Timothy; Whelan, Matthew; Al-Shaer, Ehab

2018-06-01

A methodology is developed for the estimation of internal axial force and boundary restraints within in-service, prismatic axial force members of structural systems using interval arithmetic and contractor programming. The determination of the internal axial force and end restraints in tie rods and cables using vibration-based methods has been a long standing problem in the area of structural health monitoring and performance assessment. However, for structural members with low slenderness where the dynamics are significantly affected by the boundary conditions, few existing approaches allow for simultaneous identification of internal axial force and end restraints and none permit for quantifying the uncertainties in the parameter estimates due to measurement uncertainties. This paper proposes a new technique for approaching this challenging inverse problem that leverages the Set Inversion Via Interval Analysis algorithm to solve for the unknown axial forces and end restraints using natural frequency measurements. The framework developed offers the ability to completely enclose the feasible solutions to the parameter identification problem, given specified measurement uncertainties for the natural frequencies. This ability to propagate measurement uncertainty into the parameter space is critical towards quantifying the confidence in the individual parameter estimates to inform decision-making within structural health diagnosis and prognostication applications. The methodology is first verified with simulated data for a case with unknown rotational end restraints and then extended to a case with unknown translational and rotational end restraints. A laboratory experiment is then presented to demonstrate the application of the methodology to an axially loaded rod with progressively increased end restraint at one end.

Input design for identification of aircraft stability and control derivatives

NASA Technical Reports Server (NTRS)

Gupta, N. K.; Hall, W. E., Jr.

1975-01-01

An approach for designing inputs to identify stability and control derivatives from flight test data is presented. This approach is based on finding inputs which provide the maximum possible accuracy of derivative estimates. Two techniques of input specification are implemented for this objective - a time domain technique and a frequency domain technique. The time domain technique gives the control input time history and can be used for any allowable duration of test maneuver, including those where data lengths can only be of short duration. The frequency domain technique specifies the input frequency spectrum, and is best applied for tests where extended data lengths, much longer than the time constants of the modes of interest, are possible. These technqiues are used to design inputs to identify parameters in longitudinal and lateral linear models of conventional aircraft. The constraints of aircraft response limits, such as on structural loads, are realized indirectly through a total energy constraint on the input. Tests with simulated data and theoretical predictions show that the new approaches give input signals which can provide more accurate parameter estimates than can conventional inputs of the same total energy. Results obtained indicate that the approach has been brought to the point where it should be used on flight tests for further evaluation.

Galerkin approximation for inverse problems for nonautonomous nonlinear distributed systems

NASA Technical Reports Server (NTRS)

Banks, H. T.; Reich, Simeon; Rosen, I. G.

1988-01-01

An abstract framework and convergence theory is developed for Galerkin approximation for inverse problems involving the identification of nonautonomous nonlinear distributed parameter systems. A set of relatively easily verified conditions is provided which are sufficient to guarantee the existence of optimal solutions and their approximation by a sequence of solutions to a sequence of approximating finite dimensional identification problems. The approach is based on the theory of monotone operators in Banach spaces and is applicable to a reasonably broad class of nonlinear distributed systems. Operator theoretic and variational techniques are used to establish a fundamental convergence result. An example involving evolution systems with dynamics described by nonstationary quasilinear elliptic operators along with some applications are presented and discussed.

New encoded single-indicator sequences based on physico-chemical parameters for efficient exon identification.

PubMed

Meher, J K; Meher, P K; Dash, G N; Raval, M K

2012-01-01

The first step in gene identification problem based on genomic signal processing is to convert character strings into numerical sequences. These numerical sequences are then analysed spectrally or using digital filtering techniques for the period-3 peaks, which are present in exons (coding areas) and absent in introns (non-coding areas). In this paper, we have shown that single-indicator sequences can be generated by encoding schemes based on physico-chemical properties. Two new methods are proposed for generating single-indicator sequences based on hydration energy and dipole moments. The proposed methods produce high peak at exon locations and effectively suppress false exons (intron regions having greater peak than exon regions) resulting in high discriminating factor, sensitivity and specificity.

Control Law Design in a Computational Aeroelasticity Environment

NASA Technical Reports Server (NTRS)

Newsom, Jerry R.; Robertshaw, Harry H.; Kapania, Rakesh K.

2003-01-01

A methodology for designing active control laws in a computational aeroelasticity environment is given. The methodology involves employing a systems identification technique to develop an explicit state-space model for control law design from the output of a computational aeroelasticity code. The particular computational aeroelasticity code employed in this paper solves the transonic small disturbance aerodynamic equation using a time-accurate, finite-difference scheme. Linear structural dynamics equations are integrated simultaneously with the computational fluid dynamics equations to determine the time responses of the structure. These structural responses are employed as the input to a modern systems identification technique that determines the Markov parameters of an "equivalent linear system". The Eigensystem Realization Algorithm is then employed to develop an explicit state-space model of the equivalent linear system. The Linear Quadratic Guassian control law design technique is employed to design a control law. The computational aeroelasticity code is modified to accept control laws and perform closed-loop simulations. Flutter control of a rectangular wing model is chosen to demonstrate the methodology. Various cases are used to illustrate the usefulness of the methodology as the nonlinearity of the aeroelastic system is increased through increased angle-of-attack changes.

Material parameter estimation with terahertz time-domain spectroscopy.

PubMed

Dorney, T D; Baraniuk, R G; Mittleman, D M

2001-07-01

Imaging systems based on terahertz (THz) time-domain spectroscopy offer a range of unique modalities owing to the broad bandwidth, subpicosecond duration, and phase-sensitive detection of the THz pulses. Furthermore, the possibility exists for combining spectroscopic characterization or identification with imaging because the radiation is broadband in nature. To achieve this, we require novel methods for real-time analysis of THz waveforms. This paper describes a robust algorithm for extracting material parameters from measured THz waveforms. Our algorithm simultaneously obtains both the thickness and the complex refractive index of an unknown sample under certain conditions. In contrast, most spectroscopic transmission measurements require knowledge of the sample's thickness for an accurate determination of its optical parameters. Our approach relies on a model-based estimation, a gradient descent search, and the total variation measure. We explore the limits of this technique and compare the results with literature data for optical parameters of several different materials.

Proceedings of the Conference on the Stability and Dynamic Response of Rotors with Squeeze Film Bearings, 8-10 May 79.

DTIC Science & Technology

1979-01-01

oil films, the effects of squeeze film bearings on the dynamic response of rotor-bearing systems , design techniques and methods of analyzing complicated...rotor-bearing systems including squeeze film bearings. The consensus of the participants was that further research is needed to more fully understand...176 BEARING PARAMETER IDENTIFICATION, E. Woomer, W. D. Pilkey .... 189 TRANSIENT DYNAMICS OF SQUEEZE FILM BEARING SYSTEMS , A. J

A response surface methodology based damage identification technique

NASA Astrophysics Data System (ADS)

Fang, S. E.; Perera, R.

2009-06-01

Response surface methodology (RSM) is a combination of statistical and mathematical techniques to represent the relationship between the inputs and outputs of a physical system by explicit functions. This methodology has been widely employed in many applications such as design optimization, response prediction and model validation. But so far the literature related to its application in structural damage identification (SDI) is scarce. Therefore this study attempts to present a systematic SDI procedure comprising four sequential steps of feature selection, parameter screening, primary response surface (RS) modeling and updating, and reference-state RS modeling with SDI realization using the factorial design (FD) and the central composite design (CCD). The last two steps imply the implementation of inverse problems by model updating in which the RS models substitute the FE models. The proposed method was verified against a numerical beam, a tested reinforced concrete (RC) frame and an experimental full-scale bridge with the modal frequency being the output responses. It was found that the proposed RSM-based method performs well in predicting the damage of both numerical and experimental structures having single and multiple damage scenarios. The screening capacity of the FD can provide quantitative estimation of the significance levels of updating parameters. Meanwhile, the second-order polynomial model established by the CCD provides adequate accuracy in expressing the dynamic behavior of a physical system.

Metamodel-based inverse method for parameter identification: elastic-plastic damage model

NASA Astrophysics Data System (ADS)

Huang, Changwu; El Hami, Abdelkhalak; Radi, Bouchaïb

2017-04-01

This article proposed a metamodel-based inverse method for material parameter identification and applies it to elastic-plastic damage model parameter identification. An elastic-plastic damage model is presented and implemented in numerical simulation. The metamodel-based inverse method is proposed in order to overcome the disadvantage in computational cost of the inverse method. In the metamodel-based inverse method, a Kriging metamodel is constructed based on the experimental design in order to model the relationship between material parameters and the objective function values in the inverse problem, and then the optimization procedure is executed by the use of a metamodel. The applications of the presented material model and proposed parameter identification method in the standard A 2017-T4 tensile test prove that the presented elastic-plastic damage model is adequate to describe the material's mechanical behaviour and that the proposed metamodel-based inverse method not only enhances the efficiency of parameter identification but also gives reliable results.

Parameter identification for structural dynamics based on interval analysis algorithm

NASA Astrophysics Data System (ADS)

Yang, Chen; Lu, Zixing; Yang, Zhenyu; Liang, Ke

2018-04-01

A parameter identification method using interval analysis algorithm for structural dynamics is presented in this paper. The proposed uncertain identification method is investigated by using central difference method and ARMA system. With the help of the fixed memory least square method and matrix inverse lemma, a set-membership identification technology is applied to obtain the best estimation of the identified parameters in a tight and accurate region. To overcome the lack of insufficient statistical description of the uncertain parameters, this paper treats uncertainties as non-probabilistic intervals. As long as we know the bounds of uncertainties, this algorithm can obtain not only the center estimations of parameters, but also the bounds of errors. To improve the efficiency of the proposed method, a time-saving algorithm is presented by recursive formula. At last, to verify the accuracy of the proposed method, two numerical examples are applied and evaluated by three identification criteria respectively.

The Design and Development of Test Platform for Wheat Precision Seeding Based on Image Processing Techniques

NASA Astrophysics Data System (ADS)

Li, Qing; Lin, Haibo; Xiu, Yu-Feng; Wang, Ruixue; Yi, Chuijie

The test platform of wheat precision seeding based on image processing techniques is designed to develop the wheat precision seed metering device with high efficiency and precision. Using image processing techniques, this platform gathers images of seeds (wheat) on the conveyer belt which are falling from seed metering device. Then these data are processed and analyzed to calculate the qualified rate, reseeding rate and leakage sowing rate, etc. This paper introduces the whole structure, design parameters of the platform and hardware & software of the image acquisition system were introduced, as well as the method of seed identification and seed-space measurement using image's threshold and counting the seed's center. By analyzing the experimental result, the measurement error is less than ± 1mm.

A facial reconstruction and identification technique for seriously devastating head wounds.

PubMed

Joukal, Marek; Frišhons, Jan

2015-07-01

Many authors have focused on facial identification techniques, and facial reconstructions for cases when skulls have been found are especially well known. However, a standardized facial identification technique for an unknown body with seriously devastating head injuries has not yet been developed. A reconstruction and identification technique was used in 7 cases of accidents involving trains striking pedestrians. This identification technique is based on the removal of skull bone fragments, subsequent fixation of soft tissue onto a universal commercial polystyrene head model, precise suture of dermatomuscular flaps, and definitive adjustment using cosmetic treatments. After reconstruction, identifying marks such as scars, eyebrows, facial lines, facial hair and partly hairstyle become evident. It is then possible to present a modified picture of the reconstructed face to relatives. After comparing the results with photos of the person before death, this technique has proven to be very useful for identifying unknown bodies when other identification techniques are not available. This technique is useful for its being rather quick and especially for its results. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Evaluation of the automatic optical authentication technologies for control systems of objects

NASA Astrophysics Data System (ADS)

Averkin, Vladimir V.; Volegov, Peter L.; Podgornov, Vladimir A.

2000-03-01

The report considers the evaluation of the automatic optical authentication technologies for the automated integrated system of physical protection, control and accounting of nuclear materials at RFNC-VNIITF, and for providing of the nuclear materials nonproliferation regime. The report presents the nuclear object authentication objectives and strategies, the methodology of the automatic optical authentication and results of the development of pattern recognition techniques carried out under the ISTC project #772 with the purpose of identification of unique features of surface structure of a controlled object and effects of its random treatment. The current decision of following functional control tasks is described in the report: confirmation of the item authenticity (proof of the absence of its substitution by an item of similar shape), control over unforeseen change of item state, control over unauthorized access to the item. The most important distinctive feature of all techniques is not comprehensive description of some properties of controlled item, but unique identification of item using minimum necessary set of parameters, properly comprising identification attribute of the item. The main emphasis in the technical approach is made on the development of rather simple technological methods for the first time intended for use in the systems of physical protection, control and accounting of nuclear materials. The developed authentication devices and system are described.

Improving needle tip identification during ultrasound-guided procedures in anaesthetic practice.

PubMed

Scholten, H J; Pourtaherian, A; Mihajlovic, N; Korsten, H H M; A Bouwman, R

2017-07-01

Ultrasound guidance is becoming standard practice for needle-based interventions in anaesthetic practice, such as vascular access and peripheral nerve blocks. However, difficulties in aligning the needle and the transducer can lead to incorrect identification of the needle tip, possibly damaging structures not visible on the ultrasound screen. Additional techniques specifically developed to aid alignment of needle and probe or identification of the needle tip are now available. In this scoping review, advantages and limitations of the following categories of those solutions are presented: needle guides; alterations to needle or needle tip; three- and four-dimensional ultrasound; magnetism, electromagnetic or GPS systems; optical tracking; augmented (virtual) reality; robotic assistance; and automated (computerised) needle detection. Most evidence originates from phantom studies, case reports and series, with few randomised clinical trials. Improved first-pass success and reduced performance time are the most frequently cited benefits, whereas the need for additional and often expensive hardware is the greatest limitation to widespread adoption. Novice ultrasound users seem to benefit most and great potential lies in education. Future research should focus on reporting relevant clinical parameters to learn which technique will benefit patients most in terms of success and safety. © 2017 The Association of Anaesthetists of Great Britain and Ireland.

Markov-random-field-based super-resolution mapping for identification of urban trees in VHR images

NASA Astrophysics Data System (ADS)

Ardila, Juan P.; Tolpekin, Valentyn A.; Bijker, Wietske; Stein, Alfred

2011-11-01

Identification of tree crowns from remote sensing requires detailed spectral information and submeter spatial resolution imagery. Traditional pixel-based classification techniques do not fully exploit the spatial and spectral characteristics of remote sensing datasets. We propose a contextual and probabilistic method for detection of tree crowns in urban areas using a Markov random field based super resolution mapping (SRM) approach in very high resolution images. Our method defines an objective energy function in terms of the conditional probabilities of panchromatic and multispectral images and it locally optimizes the labeling of tree crown pixels. Energy and model parameter values are estimated from multiple implementations of SRM in tuning areas and the method is applied in QuickBird images to produce a 0.6 m tree crown map in a city of The Netherlands. The SRM output shows an identification rate of 66% and commission and omission errors in small trees and shrub areas. The method outperforms tree crown identification results obtained with maximum likelihood, support vector machines and SRM at nominal resolution (2.4 m) approaches.

Independent component analysis-based algorithm for automatic identification of Raman spectra applied to artistic pigments and pigment mixtures.

PubMed

González-Vidal, Juan José; Pérez-Pueyo, Rosanna; Soneira, María José; Ruiz-Moreno, Sergio

2015-03-01

A new method has been developed to automatically identify Raman spectra, whether they correspond to single- or multicomponent spectra. The method requires no user input or judgment. There are thus no parameters to be tweaked. Furthermore, it provides a reliability factor on the resulting identification, with the aim of becoming a useful support tool for the analyst in the decision-making process. The method relies on the multivariate techniques of principal component analysis (PCA) and independent component analysis (ICA), and on some metrics. It has been developed for the application of automated spectral analysis, where the analyzed spectrum is provided by a spectrometer that has no previous knowledge of the analyzed sample, meaning that the number of components in the sample is unknown. We describe the details of this method and demonstrate its efficiency by identifying both simulated spectra and real spectra. The method has been applied to artistic pigment identification. The reliable and consistent results that were obtained make the methodology a helpful tool suitable for the identification of pigments in artwork or in paint in general.

Multi-level damage identification with response reconstruction

NASA Astrophysics Data System (ADS)

Zhang, Chao-Dong; Xu, You-Lin

2017-10-01

Damage identification through finite element (FE) model updating usually forms an inverse problem. Solving the inverse identification problem for complex civil structures is very challenging since the dimension of potential damage parameters in a complex civil structure is often very large. Aside from enormous computation efforts needed in iterative updating, the ill-condition and non-global identifiability features of the inverse problem probably hinder the realization of model updating based damage identification for large civil structures. Following a divide-and-conquer strategy, a multi-level damage identification method is proposed in this paper. The entire structure is decomposed into several manageable substructures and each substructure is further condensed as a macro element using the component mode synthesis (CMS) technique. The damage identification is performed at two levels: the first is at macro element level to locate the potentially damaged region and the second is over the suspicious substructures to further locate as well as quantify the damage severity. In each level's identification, the damage searching space over which model updating is performed is notably narrowed down, not only reducing the computation amount but also increasing the damage identifiability. Besides, the Kalman filter-based response reconstruction is performed at the second level to reconstruct the response of the suspicious substructure for exact damage quantification. Numerical studies and laboratory tests are both conducted on a simply supported overhanging steel beam for conceptual verification. The results demonstrate that the proposed multi-level damage identification via response reconstruction does improve the identification accuracy of damage localization and quantization considerably.

Flight test evaluation of predicted light aircraft drag, performance, and stability

NASA Technical Reports Server (NTRS)

Smetana, F. O.; Fox, S. R.

1979-01-01

A technique was developed which permits simultaneous extraction of complete lift, drag, and thrust power curves from time histories of a single aircraft maneuver such as a pull up (from V max to V stall) and pushover (to V max for level flight). The technique, which is an extension of nonlinear equations of motion of the parameter identification methods of Iliff and Taylor and includes provisions for internal data compatibility improvement as well, was shown to be capable of correcting random errors in the most sensitive data channel and yielding highly accurate results. Flow charts, listings, sample inputs and outputs for the relevant routines are provided as appendices. This technique was applied to flight data taken on the ATLIT aircraft. Lack of adequate knowledge of the correct full throttle thrust horsepower true airspeed variation and considerable internal data inconsistency made it impossible to apply the trajectory matching features of the technique.

Label-Free Imaging and Biochemical Characterization of Bovine Sperm Cells

PubMed Central

Ferrara, Maria Antonietta; Di Caprio, Giuseppe; Managò, Stefano; De Angelis, Annalisa; Sirleto, Luigi; Coppola, Giuseppe; De Luca, Anna Chiara

2015-01-01

A full label-free morphological and biochemical characterization is desirable to select spermatozoa during preparation for artificial insemination. In order to study these fundamental parameters, we take advantage of two attractive techniques: digital holography (DH) and Raman spectroscopy (RS). DH presents new opportunities for studying morphological aspect of cells and tissues non-invasively, quantitatively and without the need for staining or tagging, while RS is a very specific technique allowing the biochemical analysis of cellular components with a spatial resolution in the sub-micrometer range. In this paper, morphological and biochemical bovine sperm cell alterations were studied using these techniques. In addition, a complementary DH and RS study was performed to identify X- and Y-chromosome-bearing sperm cells. We demonstrate that the two techniques together are a powerful and highly efficient tool elucidating some important criterions for sperm morphological selection and sex-identification, overcoming many of the limitations associated with existing protocols. PMID:25836358

Analysis and application of minimum variance discrete time system identification

NASA Technical Reports Server (NTRS)

Kaufman, H.; Kotob, S.

1975-01-01

An on-line minimum variance parameter identifier is developed which embodies both accuracy and computational efficiency. The formulation results in a linear estimation problem with both additive and multiplicative noise. The resulting filter which utilizes both the covariance of the parameter vector itself and the covariance of the error in identification is proven to be mean square convergent and mean square consistent. The MV parameter identification scheme is then used to construct a stable state and parameter estimation algorithm.

Identification and stochastic control of helicopter dynamic modes

NASA Technical Reports Server (NTRS)

Molusis, J. A.; Bar-Shalom, Y.

1983-01-01

A general treatment of parameter identification and stochastic control for use on helicopter dynamic systems is presented. Rotor dynamic models, including specific applications to rotor blade flapping and the helicopter ground resonance problem are emphasized. Dynamic systems which are governed by periodic coefficients as well as constant coefficient models are addressed. The dynamic systems are modeled by linear state variable equations which are used in the identification and stochastic control formulation. The pure identification problem as well as the stochastic control problem which includes combined identification and control for dynamic systems is addressed. The stochastic control problem includes the effect of parameter uncertainty on the solution and the concept of learning and how this is affected by the control's duel effect. The identification formulation requires algorithms suitable for on line use and thus recursive identification algorithms are considered. The applications presented use the recursive extended kalman filter for parameter identification which has excellent convergence for systems without process noise.

Low Angle-of-Attack Longitudinal Aerodynamic Parameters of Navy T-2 Trainer Aircraft Extracted from Flight Data: A Comparison of Identification Techniques. Volume I. Data Acquisition and Modified Newton-Raphson Analysis

DTIC Science & Technology

1975-06-23

SYSTEM • The numbunng o» tec^nic«! pioject (ep<.iii muert My the N<»v-«l AN Development Center is ariaogert (or specific identitf.ition onrposti E«i.h...chord (W.P. + 73,92) Av Sweepback (257. chord) Airfoil Section lv Tall length (.25 cw to .25 cv) VERTICAL FIN Sf Area (including 2.14 ft 2

Digital signal processing and control and estimation theory -- Points of tangency, area of intersection, and parallel directions

NASA Technical Reports Server (NTRS)

Willsky, A. S.

1976-01-01

A number of current research directions in the fields of digital signal processing and modern control and estimation theory were studied. Topics such as stability theory, linear prediction and parameter identification, system analysis and implementation, two-dimensional filtering, decentralized control and estimation, image processing, and nonlinear system theory were examined in order to uncover some of the basic similarities and differences in the goals, techniques, and philosophy of the two disciplines. An extensive bibliography is included.

D-Optimal Experimental Design for Contaminant Source Identification

NASA Astrophysics Data System (ADS)

Sai Baba, A. K.; Alexanderian, A.

2016-12-01

Contaminant source identification seeks to estimate the release history of a conservative solute given point concentration measurements at some time after the release. This can be mathematically expressed as an inverse problem, with a linear observation operator or a parameter-to-observation map, which we tackle using a Bayesian approach. Acquisition of experimental data can be laborious and expensive. The goal is to control the experimental parameters - in our case, the sparsity of the sensors, to maximize the information gain subject to some physical or budget constraints. This is known as optimal experimental design (OED). D-optimal experimental design seeks to maximize the expected information gain, and has long been considered the gold standard in the statistics community. Our goal is to develop scalable methods for D-optimal experimental designs involving large-scale PDE constrained problems with high-dimensional parameter fields. A major challenge for the OED, is that a nonlinear optimization algorithm for the D-optimality criterion requires repeated evaluation of objective function and gradient involving the determinant of large and dense matrices - this cost can be prohibitively expensive for applications of interest. We propose novel randomized matrix techniques that bring down the computational costs of the objective function and gradient evaluations by several orders of magnitude compared to the naive approach. The effect of randomized estimators on the accuracy and the convergence of the optimization solver will be discussed. The features and benefits of our new approach will be demonstrated on a challenging model problem from contaminant source identification involving the inference of the initial condition from spatio-temporal observations in a time-dependent advection-diffusion problem.

Listening to the Noise: Random Fluctuations Reveal Gene Network Parameters

NASA Astrophysics Data System (ADS)

Munsky, Brian; Trinh, Brooke; Khammash, Mustafa

2010-03-01

The cellular environment is abuzz with noise originating from the inherent random motion of reacting molecules in the living cell. In this noisy environment, clonal cell populations exhibit cell-to-cell variability that can manifest significant prototypical differences. Noise induced stochastic fluctuations in cellular constituents can be measured and their statistics quantified using flow cytometry, single molecule fluorescence in situ hybridization, time lapse fluorescence microscopy and other single cell and single molecule measurement techniques. We show that these random fluctuations carry within them valuable information about the underlying genetic network. Far from being a nuisance, the ever-present cellular noise acts as a rich source of excitation that, when processed through a gene network, carries its distinctive fingerprint that encodes a wealth of information about that network. We demonstrate that in some cases the analysis of these random fluctuations enables the full identification of network parameters, including those that may otherwise be difficult to measure. We use theoretical investigations to establish experimental guidelines for the identification of gene regulatory networks, and we apply these guideline to experimentally identify predictive models for different regulatory mechanisms in bacteria and yeast.

Design and parameter estimation of hybrid magnetic bearings for blood pump applications

NASA Astrophysics Data System (ADS)

Lim, Tau Meng; Zhang, Dongsheng; Yang, Juanjuan; Cheng, Shanbao; Low, Sze Hsien; Chua, Leok Poh; Wu, Xiaowei

2009-10-01

This paper discusses the design and parameter estimation of the dynamics characteristics of a high-speed hybrid magnetic bearings (HMBs) system for axial flow blood pump applications. The rotor/impeller of the pump is driven by a three-phase permanent magnet (PM) brushless and sensorless DC motor. It is levitated by two HMBs at both ends in five-degree-of-freedom with proportional-integral-derivative (PID) controllers; among which four radial directions are actively controlled and one axial direction is passively controlled. Test results show that the rotor can be stably supported to speeds of 14,000 rpm. The frequency domain parameter estimation technique with statistical analysis is adopted to validate the stiffness and damping coefficients of the HMBs system. A specially designed test rig facilitated the estimation of the bearing's coefficients in air—in both the radial and axial directions. The radial stiffness of the HMBs is compared to the Ansoft's Maxwell 2D/3D finite element magnetostatic results. Experimental estimation showed that the dynamics characteristics of the HMBs system are dominated by the frequency-dependent stiffness coefficients. The actuator gain was also successfully calibrated and may potentially extend the parameter estimation technique developed in the study of identification and monitoring of the pump's dynamics properties under normal operating conditions with fluid.

Linear and non-linear systems identification for adaptive control in mechanical applications vibration suppression

NASA Astrophysics Data System (ADS)

Cazzulani, Gabriele; Resta, Ferruccio; Ripamonti, Francesco

2012-04-01

During the last years, more and more mechanical applications saw the introduction of active control strategies. In particular, the need of improving the performances and/or the system health is very often associated to vibration suppression. This goal can be achieved considering both passive and active solutions. In this sense, many active control strategies have been developed, such as the Independent Modal Space Control (IMSC) or the resonant controllers (PPF, IRC, . . .). In all these cases, in order to tune and optimize the control strategy, the knowledge of the system dynamic behaviour is very important and it can be achieved both considering a numerical model of the system or through an experimental identification process. Anyway, dealing with non-linear or time-varying systems, a tool able to online identify the system parameters becomes a key-point for the control logic synthesis. The aim of the present work is the definition of a real-time technique, based on ARMAX models, that estimates the system parameters starting from the measurements of piezoelectric sensors. These parameters are returned to the control logic, that automatically adapts itself to the system dynamics. The problem is numerically investigated considering a carbon-fiber plate model forced through a piezoelectric patch.

A simplified fractional order impedance model and parameter identification method for lithium-ion batteries

PubMed Central

Yang, Qingxia; Xu, Jun; Cao, Binggang; Li, Xiuqing

2017-01-01

Identification of internal parameters of lithium-ion batteries is a useful tool to evaluate battery performance, and requires an effective model and algorithm. Based on the least square genetic algorithm, a simplified fractional order impedance model for lithium-ion batteries and the corresponding parameter identification method were developed. The simplified model was derived from the analysis of the electrochemical impedance spectroscopy data and the transient response of lithium-ion batteries with different states of charge. In order to identify the parameters of the model, an equivalent tracking system was established, and the method of least square genetic algorithm was applied using the time-domain test data. Experiments and computer simulations were carried out to verify the effectiveness and accuracy of the proposed model and parameter identification method. Compared with a second-order resistance-capacitance (2-RC) model and recursive least squares method, small tracing voltage fluctuations were observed. The maximum battery voltage tracing error for the proposed model and parameter identification method is within 0.5%; this demonstrates the good performance of the model and the efficiency of the least square genetic algorithm to estimate the internal parameters of lithium-ion batteries. PMID:28212405

Combining Raman spectroscopy and digital holographic microscopy for label-free classification of human immune cells (Conference Presentation)

NASA Astrophysics Data System (ADS)

McReynolds, Naomi; Cooke, Fiona G. M.; Chen, Mingzhou; Powis, Simon J.; Dholakia, Kishan

2017-02-01

Moving towards label-free techniques for cell identification is essential for many clinical and research applications. Raman spectroscopy and digital holographic microscopy (DHM) are both label-free, non-destructive optical techniques capable of providing complimentary information. We demonstrate a multi-modal system which may simultaneously take Raman spectra and DHM images to provide both a molecular and a morphological description of our sample. In this study we use Raman spectroscopy and DHM to discriminate between three immune cell populations CD4+ T cells, B cells, and monocytes, which together comprise key functional immune cell subsets in immune responses to invading pathogens. Various parameters that may be used to describe the phase images are also examined such as pixel value histograms or texture analysis. Using our system it is possible to consider each technique individually or in combination. Principal component analysis is used on the data set to discriminate between cell types and leave-one-out cross-validation is used to estimate the efficiency of our method. Raman spectroscopy provides specific chemical information but requires relatively long acquisition times, combining this with a faster modality such as DHM could help achieve faster throughput rates. The combination of these two complimentary optical techniques provides a wealth of information for cell characterisation which is a step towards achieving label free technology for the identification of human immune cells.

Critical and subcritical damage monitoring of bonded composite repairs using innovative non-destructive techniques

NASA Astrophysics Data System (ADS)

Grammatikos, S. A.; Kordatos, E. Z.; Aggelis, D. G.; Matikas, T. E.; Paipetis, A. S.

2012-04-01

Infrared Thermography (IrT) has been shown to be capable of detecting and monitoring service induced damage of repair composite structures. Full-field imaging, along with portability are the primary benefits of the thermographic technique. On-line lock-in thermography has been reported to successfully monitor damage propagation or/and stress concentration in composite coupons, as mechanical stresses in structures induce heat concentration phenomena around flaws. During mechanical fatigue, cyclic loading plays the role of the heating source and this allows for critical and subcritical damage identification and monitoring using thermography. The Electrical Potential Change Technique (EPCT) is a new method for damage identification and monitoring during loading. The measurement of electrical potential changes at specific points of Carbon Fiber Reinforced Polymers (CFRPs) under load are reported to enable the monitoring of strain or/and damage accumulation. Along with the aforementioned techniques Finally, Acoustic Emission (AE) method is well known to provide information about the location and type of damage. Damage accumulation due to cyclic loading imposes differentiation of certain parameters of AE like duration and energy. Within the scope of this study, infrared thermography is employed along with AE and EPCT methods in order to assess the integrity of bonded repair patches on composite substrates and to monitor critical and subcritical damage induced by the mechanical loading. The combined methodologies were effective in identifying damage initiation and propagation of bonded composite repairs.

Initial planetary base construction techniques and machine implementation

NASA Technical Reports Server (NTRS)

Crockford, William W.

1987-01-01

Conceptual designs of (1) initial planetary base structures, and (2) an unmanned machine to perform the construction of these structures using materials local to the planet are presented. Rock melting is suggested as a possible technique to be used by the machine in fabricating roads, platforms, and interlocking bricks. Identification of problem areas in machine design and materials processing is accomplished. The feasibility of the designs is contingent upon favorable results of an analysis of the engineering behavior of the product materials. The analysis requires knowledge of several parameters for solution of the constitutive equations of the theory of elasticity. An initial collection of these parameters is presented which helps to define research needed to perform a realistic feasibility study. A qualitative approach to estimating power and mass lift requirements for the proposed machine is used which employs specifications of currently available equipment. An initial, unmanned mission scenario is discussed with emphasis on identifying uncompleted tasks and suggesting design considerations for vehicles and primitive structures which use the products of the machine processing.

Recognition of speaker-dependent continuous speech with KEAL

NASA Astrophysics Data System (ADS)

Mercier, G.; Bigorgne, D.; Miclet, L.; Le Guennec, L.; Querre, M.

1989-04-01

A description of the speaker-dependent continuous speech recognition system KEAL is given. An unknown utterance, is recognized by means of the followng procedures: acoustic analysis, phonetic segmentation and identification, word and sentence analysis. The combination of feature-based, speaker-independent coarse phonetic segmentation with speaker-dependent statistical classification techniques is one of the main design features of the acoustic-phonetic decoder. The lexical access component is essentially based on a statistical dynamic programming technique which aims at matching a phonemic lexical entry containing various phonological forms, against a phonetic lattice. Sentence recognition is achieved by use of a context-free grammar and a parsing algorithm derived from Earley's parser. A speaker adaptation module allows some of the system parameters to be adjusted by matching known utterances with their acoustical representation. The task to be performed, described by its vocabulary and its grammar, is given as a parameter of the system. Continuously spoken sentences extracted from a 'pseudo-Logo' language are analyzed and results are presented.

Array magnetics modal analysis for the DIII-D tokamak based on localized time-series modelling

DOE PAGES

Olofsson, K. Erik J.; Hanson, Jeremy M.; Shiraki, Daisuke; ...

2014-07-14

Here, time-series analysis of magnetics data in tokamaks is typically done using block-based fast Fourier transform methods. This work presents the development and deployment of a new set of algorithms for magnetic probe array analysis. The method is based on an estimation technique known as stochastic subspace identification (SSI). Compared with the standard coherence approach or the direct singular value decomposition approach, the new technique exhibits several beneficial properties. For example, the SSI method does not require that frequencies are orthogonal with respect to the timeframe used in the analysis. Frequencies are obtained directly as parameters of localized time-series models.more » The parameters are extracted by solving small-scale eigenvalue problems. Applications include maximum-likelihood regularized eigenmode pattern estimation, detection of neoclassical tearing modes, including locked mode precursors, and automatic clustering of modes, and magnetics-pattern characterization of sawtooth pre- and postcursors, edge harmonic oscillations and fishbones.« less

Forensic identification of resampling operators: A semi non-intrusive approach.

PubMed

Cao, Gang; Zhao, Yao; Ni, Rongrong

2012-03-10

Recently, several new resampling operators have been proposed and successfully invalidate the existing resampling detectors. However, the reliability of such anti-forensic techniques is unaware and needs to be investigated. In this paper, we focus on the forensic identification of digital image resampling operators including the traditional type and the anti-forensic type which hides the trace of traditional resampling. Various resampling algorithms involving geometric distortion (GD)-based, dual-path-based and postprocessing-based are investigated. The identification is achieved in the manner of semi non-intrusive, supposing the resampling software could be accessed. Given an input pattern of monotone signal, polarity aberration of GD-based resampled signal's first derivative is analyzed theoretically and measured by effective feature metric. Dual-path-based and postprocessing-based resampling can also be identified by feeding proper test patterns. Experimental results on various parameter settings demonstrate the effectiveness of the proposed approach. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

Writers Identification Based on Multiple Windows Features Mining

NASA Astrophysics Data System (ADS)

Fadhil, Murad Saadi; Alkawaz, Mohammed Hazim; Rehman, Amjad; Saba, Tanzila

2016-03-01

Now a days, writer identification is at high demand to identify the original writer of the script at high accuracy. The one of the main challenge in writer identification is how to extract the discriminative features of different authors' scripts to classify precisely. In this paper, the adaptive division method on the offline Latin script has been implemented using several variant window sizes. Fragments of binarized text a set of features are extracted and classified into clusters in the form of groups or classes. Finally, the proposed approach in this paper has been tested on various parameters in terms of text division and window sizes. It is observed that selection of the right window size yields a well positioned window division. The proposed approach is tested on IAM standard dataset (IAM, Institut für Informatik und angewandte Mathematik, University of Bern, Bern, Switzerland) that is a constraint free script database. Finally, achieved results are compared with several techniques reported in the literature.

Quantum Biometrics with Retinal Photon Counting

NASA Astrophysics Data System (ADS)

Loulakis, M.; Blatsios, G.; Vrettou, C. S.; Kominis, I. K.

2017-10-01

It is known that the eye's scotopic photodetectors, rhodopsin molecules, and their associated phototransduction mechanism leading to light perception, are efficient single-photon counters. We here use the photon-counting principles of human rod vision to propose a secure quantum biometric identification based on the quantum-statistical properties of retinal photon detection. The photon path along the human eye until its detection by rod cells is modeled as a filter having a specific transmission coefficient. Precisely determining its value from the photodetection statistics registered by the conscious observer is a quantum parameter estimation problem that leads to a quantum secure identification method. The probabilities for false-positive and false-negative identification of this biometric technique can readily approach 10-10 and 10-4, respectively. The security of the biometric method can be further quantified by the physics of quantum measurements. An impostor must be able to perform quantum thermometry and quantum magnetometry with energy resolution better than 10-9ℏ , in order to foil the device by noninvasively monitoring the biometric activity of a user.

Steps Toward Unveiling the True Population of AGN: Photometric Selection of Broad-Line AGN

NASA Astrophysics Data System (ADS)

Schneider, Evan; Impey, C.

2012-01-01

We present an AGN selection technique that enables identification of broad-line AGN using only photometric data. An extension of infrared selection techniques, our method involves fitting a given spectral energy distribution with a model consisting of three physically motivated components: infrared power law emission, optical accretion disk emission, and host galaxy emission. Each component can be varied in intensity, and a reduced chi-square minimization routine is used to determine the optimum parameters for each object. Using this model, both broad- and narrow-line AGN are seen to fall within discrete ranges of parameter space that have plausible bounds, allowing physical trends with luminosity and redshift to be determined. Based on a fiducial sample of AGN from the catalog of Trump et al. (2009), we find the region occupied by broad-line AGN to be distinct from that of quiescent or star-bursting galaxies. Because this technique relies only on photometry, it will allow us to find AGN at fainter magnitudes than are accessible in spectroscopic surveys, and thus probe a population of less luminous and/or higher redshift objects. With the vast availability of photometric data in large surveys, this technique should have broad applicability and result in large samples that will complement X-ray AGN catalogs.

Dual ant colony operational modal analysis parameter estimation method

NASA Astrophysics Data System (ADS)

Sitarz, Piotr; Powałka, Bartosz

2018-01-01

Operational Modal Analysis (OMA) is a common technique used to examine the dynamic properties of a system. Contrary to experimental modal analysis, the input signal is generated in object ambient environment. Operational modal analysis mainly aims at determining the number of pole pairs and at estimating modal parameters. Many methods are used for parameter identification. Some methods operate in time while others in frequency domain. The former use correlation functions, the latter - spectral density functions. However, while some methods require the user to select poles from a stabilisation diagram, others try to automate the selection process. Dual ant colony operational modal analysis parameter estimation method (DAC-OMA) presents a new approach to the problem, avoiding issues involved in the stabilisation diagram. The presented algorithm is fully automated. It uses deterministic methods to define the interval of estimated parameters, thus reducing the problem to optimisation task which is conducted with dedicated software based on ant colony optimisation algorithm. The combination of deterministic methods restricting parameter intervals and artificial intelligence yields very good results, also for closely spaced modes and significantly varied mode shapes within one measurement point.

Comprehensive MR imaging of acute gynecologic diseases.

PubMed

Dohke, M; Watanabe, Y; Okumura, A; Amoh, Y; Hayashi, T; Yoshizako, T; Yasui, M; Nakashita, S; Nakanishi, J; Dodo, Y

2000-01-01

Rapid advances in techniques of magnetic resonance (MR) imaging have enabled diagnosis of acute gynecologic conditions, which are characterized by sudden onset of lower abdominal pain, fever, genital bleeding, intraperitoneal bleeding, or symptoms of shock. The chemical-selective fat-suppression technique not only helps establish the characteristics of lesions that contain fat components but also increases the conspicuity of inflammatory lesions. When a T2-weighted image is obtained with a very long effective echo time (>250 msec), even a small amount of ascites can be easily identified and the contrast between urine and complex fluid becomes more conspicuous. T2*-weighted images are useful for identification of hemorrhagic lesions by demonstrating deoxyhemoglobin and hemosiderin. Contrast material-enhanced dynamic subtraction MR imaging performed with a three-dimensional fast field-echo sequence and a rapid bolus injection of gadopentetate dimeglumine allows evaluation of lesion vascularity and the anatomic relationship between pelvic vessels and a lesion and allows identification of the bleeding point by demonstrating extravasation of contrast material. To optimize the MR imaging examination, attention should be given to the parameters of each pulse sequence and proper combination of the sequences.

Fast identification of the conduction-type of nanomaterials by field emission technique.

PubMed

Yang, Xun; Gan, Haibo; Tian, Yan; Peng, Luxi; Xu, Ningsheng; Chen, Jun; Chen, Huanjun; Deng, Shaozhi; Liang, Shi-Dong; Liu, Fei

2017-10-12

There are more or less dopants or defects existing in nanomaterials, so they usually have different conduct-types even for the same substrate. Therefore, fast identification of the conduction-type of nanomaterials is very essential for their practical application in functional nanodevices. Here we use the field emission (FE) technique to research nanomaterials and establish a generalized Schottky-Nordheim (SN) model, in which an important parameter λ (the image potential factor) is first introduced to describe the effective image potential. By regarding λ as the criterion, their energy-band structure can be identified: (a) λ = 1: metal; (b) 0.5 < λ < 1: n-type semiconductor; (c) 0 < λ < 0.5: p-type semiconductor. Moreover, this method can be utilized to qualitatively evaluate the doping-degree for a given semiconductor. We test numerically and experimentally a group of nanomaterial emitters and all results agree with our theoretical results very well, which suggests that our method based on FE measurements should be an ideal and powerful tool to fast ascertain the conduction-type of nanomaterials.

Battery management system with distributed wireless sensors

DOEpatents

Farmer, Joseph C.; Bandhauer, Todd M.

2016-02-23

A system for monitoring parameters of an energy storage system having a multiplicity of individual energy storage cells. A radio frequency identification and sensor unit is connected to each of the individual energy storage cells. The radio frequency identification and sensor unit operates to sense the parameter of each individual energy storage cell and provides radio frequency transmission of the parameters of each individual energy storage cell. A management system monitors the radio frequency transmissions from the radio frequency identification and sensor units for monitoring the parameters of the energy storage system.

Energy awareness for supercapacitors using Kalman filter state-of-charge tracking

NASA Astrophysics Data System (ADS)

Nadeau, Andrew; Hassanalieragh, Moeen; Sharma, Gaurav; Soyata, Tolga

2015-11-01

Among energy buffering alternatives, supercapacitors can provide unmatched efficiency and durability. Additionally, the direct relation between a supercapacitor's terminal voltage and stored energy can improve energy awareness. However, a simple capacitive approximation cannot adequately represent the stored energy in a supercapacitor. It is shown that the three branch equivalent circuit model provides more accurate energy awareness. This equivalent circuit uses three capacitances and associated resistances to represent the supercapacitor's internal SOC (state-of-charge). However, the SOC cannot be determined from one observation of the terminal voltage, and must be tracked over time using inexact measurements. We present: 1) a Kalman filtering solution for tracking the SOC; 2) an on-line system identification procedure to efficiently estimate the equivalent circuit's parameters; and 3) experimental validation of both parameter estimation and SOC tracking for 5 F, 10 F, 50 F, and 350 F supercapacitors. Validation is done within the operating range of a solar powered application and the associated power variability due to energy harvesting. The proposed techniques are benchmarked against the simple capacitive model and prior parameter estimation techniques, and provide a 67% reduction in root-mean-square error for predicting usable buffered energy.

Utility of a novel error-stepping method to improve gradient-based parameter identification by increasing the smoothness of the local objective surface: a case-study of pulmonary mechanics.

PubMed

Docherty, Paul D; Schranz, Christoph; Chase, J Geoffrey; Chiew, Yeong Shiong; Möller, Knut

2014-05-01

Accurate model parameter identification relies on accurate forward model simulations to guide convergence. However, some forward simulation methodologies lack the precision required to properly define the local objective surface and can cause failed parameter identification. The role of objective surface smoothness in identification of a pulmonary mechanics model was assessed using forward simulation from a novel error-stepping method and a proprietary Runge-Kutta method. The objective surfaces were compared via the identified parameter discrepancy generated in a Monte Carlo simulation and the local smoothness of the objective surfaces they generate. The error-stepping method generated significantly smoother error surfaces in each of the cases tested (p<0.0001) and more accurate model parameter estimates than the Runge-Kutta method in three of the four cases tested (p<0.0001) despite a 75% reduction in computational cost. Of note, parameter discrepancy in most cases was limited to a particular oblique plane, indicating a non-intuitive multi-parameter trade-off was occurring. The error-stepping method consistently improved or equalled the outcomes of the Runge-Kutta time-integration method for forward simulations of the pulmonary mechanics model. This study indicates that accurate parameter identification relies on accurate definition of the local objective function, and that parameter trade-off can occur on oblique planes resulting prematurely halted parameter convergence. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Application of meta-analysis methods for identifying proteomic expression level differences.

PubMed

Amess, Bob; Kluge, Wolfgang; Schwarz, Emanuel; Haenisch, Frieder; Alsaif, Murtada; Yolken, Robert H; Leweke, F Markus; Guest, Paul C; Bahn, Sabine

2013-07-01

We present new statistical approaches for identification of proteins with expression levels that are significantly changed when applying meta-analysis to two or more independent experiments. We showed that the Euclidean distance measure has reduced risk of false positives compared to the rank product method. Our Ψ-ranking method has advantages over the traditional fold-change approach by incorporating both the fold-change direction as well as the p-value. In addition, the second novel method, Π-ranking, considers the ratio of the fold-change and thus integrates all three parameters. We further improved the latter by introducing our third technique, Σ-ranking, which combines all three parameters in a balanced nonparametric approach. © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

A modified NARMAX model-based self-tuner with fault tolerance for unknown nonlinear stochastic hybrid systems with an input-output direct feed-through term.

PubMed

Tsai, Jason S-H; Hsu, Wen-Teng; Lin, Long-Guei; Guo, Shu-Mei; Tann, Joseph W

2014-01-01

A modified nonlinear autoregressive moving average with exogenous inputs (NARMAX) model-based state-space self-tuner with fault tolerance is proposed in this paper for the unknown nonlinear stochastic hybrid system with a direct transmission matrix from input to output. Through the off-line observer/Kalman filter identification method, one has a good initial guess of modified NARMAX model to reduce the on-line system identification process time. Then, based on the modified NARMAX-based system identification, a corresponding adaptive digital control scheme is presented for the unknown continuous-time nonlinear system, with an input-output direct transmission term, which also has measurement and system noises and inaccessible system states. Besides, an effective state space self-turner with fault tolerance scheme is presented for the unknown multivariable stochastic system. A quantitative criterion is suggested by comparing the innovation process error estimated by the Kalman filter estimation algorithm, so that a weighting matrix resetting technique by adjusting and resetting the covariance matrices of parameter estimate obtained by the Kalman filter estimation algorithm is utilized to achieve the parameter estimation for faulty system recovery. Consequently, the proposed method can effectively cope with partially abrupt and/or gradual system faults and input failures by the fault detection. Copyright © 2013 ISA. Published by Elsevier Ltd. All rights reserved.

A comparative assessment of different frequency based damage detection in unidirectional composite plates using MFC sensors

NASA Astrophysics Data System (ADS)

de Medeiros, Ricardo; Sartorato, Murilo; Vandepitte, Dirk; Tita, Volnei

2016-11-01

The basic concept of the vibration based damage identification methods is that the dynamic behaviour of a structure can change if damage occurs. Damage in a structure can alter the structural integrity, and therefore, the physical properties like stiffness, mass and/or damping may change. The dynamic behaviour of a structure is a function of these physical properties and will, therefore, directly be affected by the damage. The dynamic behaviour can be described in terms of time, frequency and modal domain parameters. The changes in these parameters (or properties derived from these parameters) are used as indicators of damage. Hence, this work has two main objectives. The first one is to provide an overview of the structural vibration based damage identification methods. For this purpose, a fundamental description of the structural vibration based damage identification problem is given, followed by a short literature overview of the damage features, which are commonly addressed. The second objective is to create a damage identification method for detection of the damage in composite structures. To aid in this process, two basic principles are discussed, namely the effect of the potential damage case on the dynamic behaviour, and the consequences involved with the information reduction in the signal processing. Modal properties from the structural dynamic output response are obtained. In addition, experimental and computational results are presented for the application of modal analysis techniques applied to composite specimens with and without damage. The excitation of the structures is performed using an impact hammer and, for measuring the output data, accelerometers as well as piezoelectric sensors. Finite element models are developed by shell elements, and numerical results are compared to experimental data, showing good correlation for the response of the specimens in some specific frequency range. Finally, FRFs are analysed using suitable metrics, including a new one, which are compared in terms of their capability for damage identification. The experimental and numerical results show that the vibration-based damage methods combined to the metrics can be used in Structural Health Monitoring (SHM) systems to identify the damage in the structure.

Studies of the micromorphology of sputtered TiN thin films by autocorrelation techniques

NASA Astrophysics Data System (ADS)

Smagoń, Kamil; Stach, Sebastian; Ţălu, Ştefan; Arman, Ali; Achour, Amine; Luna, Carlos; Ghobadi, Nader; Mardani, Mohsen; Hafezi, Fatemeh; Ahmadpourian, Azin; Ganji, Mohsen; Grayeli Korpi, Alireza

2017-12-01

Autocorrelation techniques are crucial tools for the study of the micromorphology of surfaces: They provide the description of anisotropic properties and the identification of repeated patterns on the surface, facilitating the comparison of samples. In the present investigation, some fundamental concepts of these techniques including the autocorrelation function and autocorrelation length have been reviewed and applied in the study of titanium nitride thin films by atomic force microscopy (AFM). The studied samples were grown on glass substrates by reactive magnetron sputtering at different substrate temperatures (from 25 {}°C to 400 {}°C , and their micromorphology was studied by AFM. The obtained AFM data were analyzed using MountainsMap Premium software obtaining the correlation function, the structure of isotropy and the spatial parameters according to ISO 25178 and EUR 15178N. These studies indicated that the substrate temperature during the deposition process is an important parameter to modify the micromorphology of sputtered TiN thin films and to find optimized surface properties. For instance, the autocorrelation length exhibited a maximum value for the sample prepared at a substrate temperature of 300 {}°C , and the sample obtained at 400 {}°C presented a maximum angle of the direction of the surface structure.

Detection, Identification, Location, and Remote Sensing Using SAW RFID Sensor Tags

NASA Technical Reports Server (NTRS)

Barton, Richard J.; Kennedy, Timothy F.; Williams, Robert M.; Fink, Patrick W.; Ngo, Phong H.

2009-01-01

The Electromagnetic Systems Branch (EV4) of the Avionic Systems Division at NASA Johnson Space Center in Houston, TX is studying the utility of surface acoustic wave (SAW) radiofrequency identification (RFID) tags for multiple wireless applications including detection, identification, tracking, and remote sensing of objects on the lunar surface, monitoring of environmental test facilities, structural shape and health monitoring, and nondestructive test and evaluation of assets. For all of these applications, it is anticipated that the system utilized to interrogate the SAW RFID tags may need to operate at fairly long range and in the presence of considerable multipath and multiple-access interference. Towards that end, EV4 is developing a prototype SAW RFID wireless interrogation system for use in such environments called the Passive Adaptive RFID Sensor Equipment (PARSED) system. The system utilizes a digitally beam-formed planar receiving antenna array to extend range and provide direction-of-arrival information coupled with an approximate maximum-likelihood signal processing algorithm to provide near-optimal estimation of both range and temperature. The system is capable of forming a large number of beams within the field of view and resolving the information from several tags within each beam. The combination of both spatial and waveform discrimination provides the capability to track and monitor telemetry from a large number of objects appearing simultaneously within the field of view of the receiving array. In this paper, we will consider the application of the PARSEQ system to the problem of simultaneous detection, identification, localization, and temperature estimation for multiple objects. We will summarize the overall design of the PARSEQ system and present a detailed description of the design and performance of the signal detection and estimation algorithms incorporated in the system. The system is currently configured only to measure temperature (jointly with range and tag ID), but future versions will be revised to measure parameters other than temperature as SAW tags capable of interfacing with external sensors become available. It is anticipated that the estimation of arbitrary parameters measured using SAW-based sensors will be based on techniques very similar to the joint range and temperature estimation techniques described in this paper.

Identifying mechanical property parameters of planetary soil using in-situ data obtained from exploration rovers

NASA Astrophysics Data System (ADS)

Ding, Liang; Gao, Haibo; Liu, Zhen; Deng, Zongquan; Liu, Guangjun

2015-12-01

Identifying the mechanical property parameters of planetary soil based on terramechanics models using in-situ data obtained from autonomous planetary exploration rovers is both an important scientific goal and essential for control strategy optimization and high-fidelity simulations of rovers. However, identifying all the terrain parameters is a challenging task because of the nonlinear and coupling nature of the involved functions. Three parameter identification methods are presented in this paper to serve different purposes based on an improved terramechanics model that takes into account the effects of slip, wheel lugs, etc. Parameter sensitivity and coupling of the equations are analyzed, and the parameters are grouped according to their sensitivity to the normal force, resistance moment and drawbar pull. An iterative identification method using the original integral model is developed first. In order to realize real-time identification, the model is then simplified by linearizing the normal and shearing stresses to derive decoupled closed-form analytical equations. Each equation contains one or two groups of soil parameters, making step-by-step identification of all the unknowns feasible. Experiments were performed using six different types of single-wheels as well as a four-wheeled rover moving on planetary soil simulant. All the unknown model parameters were identified using the measured data and compared with the values obtained by conventional experiments. It is verified that the proposed iterative identification method provides improved accuracy, making it suitable for scientific studies of soil properties, whereas the step-by-step identification methods based on simplified models require less calculation time, making them more suitable for real-time applications. The models have less than 10% margin of error comparing with the measured results when predicting the interaction forces and moments using the corresponding identified parameters.

Parameter de-correlation and model-identification in hybrid-style terrestrial laser scanner self-calibration

NASA Astrophysics Data System (ADS)

Lichti, Derek D.; Chow, Jacky; Lahamy, Hervé

One of the important systematic error parameters identified in terrestrial laser scanners is the collimation axis error, which models the non-orthogonality between two instrumental axes. The quality of this parameter determined by self-calibration, as measured by its estimated precision and its correlation with the tertiary rotation angle κ of the scanner exterior orientation, is strongly dependent on instrument architecture. While the quality is generally very high for panoramic-type scanners, it is comparably poor for hybrid-style instruments. Two methods for improving the quality of the collimation axis error in hybrid instrument self-calibration are proposed herein: (1) the inclusion of independent observations of the tertiary rotation angle κ; and (2) the use of a new collimation axis error model. Five real datasets were captured with two different hybrid-style scanners to test each method's efficacy. While the first method achieves the desired outcome of complete decoupling of the collimation axis error from κ, it is shown that the high correlation is simply transferred to other model variables. The second method achieves partial parameter de-correlation to acceptable levels. Importantly, it does so without any adverse, secondary correlations and is therefore the method recommended for future use. Finally, systematic error model identification has been greatly aided in previous studies by graphical analyses of self-calibration residuals. This paper presents results showing the architecture dependence of this technique, revealing its limitations for hybrid scanners.

Quantitative evaluation of redox ratio and collagen characteristics during breast cancer chemotherapy using two-photon intrinsic imaging.

PubMed

Wu, Shulian; Huang, Yudian; Tang, Qinggong; Li, Zhifang; Horng, Hannah; Li, Jiatian; Wu, Zaihua; Chen, Yu; Li, Hui

2018-03-01

Preoperative neoadjuvant treatment in locally advanced breast cancer is recognized as an effective adjuvant therapy, as it improves treatment outcomes. However, the potential complications remain a threat, so there is an urgent clinical need to assess both the tumor response and changes in its microenvironment using non-invasive and precise identification techniques. Here, two-photon microscopy was employed to detect morphological alterations in breast cancer progression and recession throughout chemotherapy. The changes in structure were analyzed based on the autofluorescence and collagen of differing statuses. Parameters, including optical redox ratio, the ratio of second harmonic generation and auto-fluorescence signal, collagen density, and collagen shape orientation, were studied. Results indicate that these parameters are potential indicators for evaluating breast tumors and their microenvironment changes during progression and chemotherapy. Combined analyses of these parameters could provide a quantitative, novel method for monitoring tumor therapy.

Multiple concurrent recursive least squares identification with application to on-line spacecraft mass-property identification

NASA Technical Reports Server (NTRS)

Wilson, Edward (Inventor)

2006-01-01

The present invention is a method for identifying unknown parameters in a system having a set of governing equations describing its behavior that cannot be put into regression form with the unknown parameters linearly represented. In this method, the vector of unknown parameters is segmented into a plurality of groups where each individual group of unknown parameters may be isolated linearly by manipulation of said equations. Multiple concurrent and independent recursive least squares identification of each said group run, treating other unknown parameters appearing in their regression equation as if they were known perfectly, with said values provided by recursive least squares estimation from the other groups, thereby enabling the use of fast, compact, efficient linear algorithms to solve problems that would otherwise require nonlinear solution approaches. This invention is presented with application to identification of mass and thruster properties for a thruster-controlled spacecraft.

Identification of modal parameters including unmeasured forces and transient effects

NASA Astrophysics Data System (ADS)

Cauberghe, B.; Guillaume, P.; Verboven, P.; Parloo, E.

2003-08-01

In this paper, a frequency-domain method to estimate modal parameters from short data records with known input (measured) forces and unknown input forces is presented. The method can be used for an experimental modal analysis, an operational modal analysis (output-only data) and the combination of both. A traditional experimental and operational modal analysis in the frequency domain starts respectively, from frequency response functions and spectral density functions. To estimate these functions accurately sufficient data have to be available. The technique developed in this paper estimates the modal parameters directly from the Fourier spectra of the outputs and the known input. Instead of using Hanning windows on these short data records the transient effects are estimated simultaneously with the modal parameters. The method is illustrated, tested and validated by Monte Carlo simulations and experiments. The presented method to process short data sequences leads to unbiased estimates with a small variance in comparison to the more traditional approaches.

Identification of cracks in thick beams with a cracked beam element model

NASA Astrophysics Data System (ADS)

Hou, Chuanchuan; Lu, Yong

2016-12-01

The effect of a crack on the vibration of a beam is a classical problem, and various models have been proposed, ranging from the basic stiffness reduction method to the more sophisticated model involving formulation based on the additional flexibility due to a crack. However, in the damage identification or finite element model updating applications, it is still common practice to employ a simple stiffness reduction factor to represent a crack in the identification process, whereas the use of a more realistic crack model is rather limited. In this paper, the issues with the simple stiffness reduction method, particularly concerning thick beams, are highlighted along with a review of several other crack models. A robust finite element model updating procedure is then presented for the detection of cracks in beams. The description of the crack parameters is based on the cracked beam flexibility formulated by means of the fracture mechanics, and it takes into consideration of shear deformation and coupling between translational and longitudinal vibrations, and thus is particularly suitable for thick beams. The identification procedure employs a global searching technique using Genetic Algorithms, and there is no restriction on the location, severity and the number of cracks to be identified. The procedure is verified to yield satisfactory identification for practically any configurations of cracks in a beam.

Real-time Crystal Growth Visualization and Quantification by Energy-Resolved Neutron Imaging.

PubMed

Tremsin, Anton S; Perrodin, Didier; Losko, Adrian S; Vogel, Sven C; Bourke, Mark A M; Bizarri, Gregory A; Bourret, Edith D

2017-04-20

Energy-resolved neutron imaging is investigated as a real-time diagnostic tool for visualization and in-situ measurements of "blind" processes. This technique is demonstrated for the Bridgman-type crystal growth enabling remote and direct measurements of growth parameters crucial for process optimization. The location and shape of the interface between liquid and solid phases are monitored in real-time, concurrently with the measurement of elemental distribution within the growth volume and with the identification of structural features with a ~100 μm spatial resolution. Such diagnostics can substantially reduce the development time between exploratory small scale growth of new materials and their subsequent commercial production. This technique is widely applicable and is not limited to crystal growth processes.

Real-time Crystal Growth Visualization and Quantification by Energy-Resolved Neutron Imaging

NASA Astrophysics Data System (ADS)

Tremsin, Anton S.; Perrodin, Didier; Losko, Adrian S.; Vogel, Sven C.; Bourke, Mark A. M.; Bizarri, Gregory A.; Bourret, Edith D.

2017-04-01

Energy-resolved neutron imaging is investigated as a real-time diagnostic tool for visualization and in-situ measurements of “blind” processes. This technique is demonstrated for the Bridgman-type crystal growth enabling remote and direct measurements of growth parameters crucial for process optimization. The location and shape of the interface between liquid and solid phases are monitored in real-time, concurrently with the measurement of elemental distribution within the growth volume and with the identification of structural features with a ~100 μm spatial resolution. Such diagnostics can substantially reduce the development time between exploratory small scale growth of new materials and their subsequent commercial production. This technique is widely applicable and is not limited to crystal growth processes.

Parameter Estimation for a Hybrid Adaptive Flight Controller

NASA Technical Reports Server (NTRS)

Campbell, Stefan F.; Nguyen, Nhan T.; Kaneshige, John; Krishnakumar, Kalmanje

2009-01-01

This paper expands on the hybrid control architecture developed at the NASA Ames Research Center by addressing issues related to indirect adaptation using the recursive least squares (RLS) algorithm. Specifically, the hybrid control architecture is an adaptive flight controller that features both direct and indirect adaptation techniques. This paper will focus almost exclusively on the modifications necessary to achieve quality indirect adaptive control. Additionally this paper will present results that, using a full non -linear aircraft model, demonstrate the effectiveness of the hybrid control architecture given drastic changes in an aircraft s dynamics. Throughout the development of this topic, a thorough discussion of the RLS algorithm as a system identification technique will be provided along with results from seven well-known modifications to the popular RLS algorithm.

Real-time Crystal Growth Visualization and Quantification by Energy-Resolved Neutron Imaging

PubMed Central

Tremsin, Anton S.; Perrodin, Didier; Losko, Adrian S.; Vogel, Sven C.; Bourke, Mark A.M.; Bizarri, Gregory A.; Bourret, Edith D.

2017-01-01

Energy-resolved neutron imaging is investigated as a real-time diagnostic tool for visualization and in-situ measurements of “blind” processes. This technique is demonstrated for the Bridgman-type crystal growth enabling remote and direct measurements of growth parameters crucial for process optimization. The location and shape of the interface between liquid and solid phases are monitored in real-time, concurrently with the measurement of elemental distribution within the growth volume and with the identification of structural features with a ~100 μm spatial resolution. Such diagnostics can substantially reduce the development time between exploratory small scale growth of new materials and their subsequent commercial production. This technique is widely applicable and is not limited to crystal growth processes. PMID:28425461

Study on Material Parameters Identification of Brain Tissue Considering Uncertainty of Friction Coefficient

NASA Astrophysics Data System (ADS)

Guan, Fengjiao; Zhang, Guanjun; Liu, Jie; Wang, Shujing; Luo, Xu; Zhu, Feng

2017-10-01

Accurate material parameters are critical to construct the high biofidelity finite element (FE) models. However, it is hard to obtain the brain tissue parameters accurately because of the effects of irregular geometry and uncertain boundary conditions. Considering the complexity of material test and the uncertainty of friction coefficient, a computational inverse method for viscoelastic material parameters identification of brain tissue is presented based on the interval analysis method. Firstly, the intervals are used to quantify the friction coefficient in the boundary condition. And then the inverse problem of material parameters identification under uncertain friction coefficient is transformed into two types of deterministic inverse problem. Finally the intelligent optimization algorithm is used to solve the two types of deterministic inverse problems quickly and accurately, and the range of material parameters can be easily acquired with no need of a variety of samples. The efficiency and convergence of this method are demonstrated by the material parameters identification of thalamus. The proposed method provides a potential effective tool for building high biofidelity human finite element model in the study of traffic accident injury.

The application of artificial intelligence for the identification of the maceral groups and mineral components of coal

NASA Astrophysics Data System (ADS)

Mlynarczuk, Mariusz; Skiba, Marta

2017-06-01

The correct and consistent identification of the petrographic properties of coal is an important issue for researchers in the fields of mining and geology. As part of the study described in this paper, investigations concerning the application of artificial intelligence methods for the identification of the aforementioned characteristics were carried out. The methods in question were used to identify the maceral groups of coal, i.e. vitrinite, inertinite, and liptinite. Additionally, an attempt was made to identify some non-organic minerals. The analyses were performed using pattern recognition techniques (NN, kNN), as well as artificial neural network techniques (a multilayer perceptron - MLP). The classification process was carried out using microscopy images of polished sections of coals. A multidimensional feature space was defined, which made it possible to classify the discussed structures automatically, based on the methods of pattern recognition and algorithms of the artificial neural networks. Also, from the study we assessed the impact of the parameters for which the applied methods proved effective upon the final outcome of the classification procedure. The result of the analyses was a high percentage (over 97%) of correct classifications of maceral groups and mineral components. The paper discusses also an attempt to analyze particular macerals of the inertinite group. It was demonstrated that using artificial neural networks to this end makes it possible to classify the macerals properly in over 91% of cases. Thus, it was proved that artificial intelligence methods can be successfully applied for the identification of selected petrographic features of coal.

DNA Barcoding of Zooplankton in the Hampton Roads Area: A Biodiversity Assessment

NASA Astrophysics Data System (ADS)

Salcedo, A.; Rodríguez, Á. E.; Gibson, D. M.

2016-02-01

The study of zooplankton biodiversity and distribution is crucial to understand oceanic ecosystems and anticipate the effects of climate change. Previously, identification of zooplankton relied in morphological identification employed by expert taxonomists. DNA barcoding, a technique that uses the mitochondrial DNA (mtDNA) Cytochrome Oxidase 1 (CO1) gene is widely used for taxonomic identification. Thus, this molecular technique will be used to begin a detailed characterization of zooplankton diversity, abundance and community structure in the Hampton Roads Area (HRA). Stations 1 (Jones Creek) and 3 (lower Chesapeake Bay) were sampled in June 19, 2015. Stations 1, 2 (James River), and 3 were sampled in September 2015. Zooplankton samples were collected in triplicates with a 0.5m, 200 µm mesh net. Physical parameters (dissolved oxygen, salinity, temperature and, water transparency) were measured. Species identified as Opistonema oglinum (Atlantic Thread Herring) and Paracalanus parvus copepods were found at station 3; Anchoa mitchilli and Acartia tonsa copepods were found at stations 1 and 3. This study indicates that mtDNA-CO1 barcoding is suitable to identify zooplankton to the species level and helps validate DNA barcoding as a faster, more accurate taxonomic approach. The long term objective of this project is to provide a comprehensive assessment of zooplankton in the HRA and to generate a reference record for broad monitoring programs; vital for a better understanding and management of ecologically and commercially important species.

Mining MaNGA for Merging Galaxies: A New Imaging and Kinematic Technique from Hydrodynamical Simulations

NASA Astrophysics Data System (ADS)

Nevin, Becky; Comerford, Julia M.; Blecha, Laura

2018-06-01

Merging galaxies play a key role in galaxy evolution, and progress in our understanding of galaxy evolution is slowed by the difficulty of making accurate galaxy merger identifications. Mergers are typically identified using imaging alone, which has its limitations and biases. With the growing popularity of integral field spectroscopy (IFS), it is now possible to use kinematic signatures to improve galaxy merger identifications. I use GADGET-3 hydrodynamical simulations of merging galaxies with the radiative transfer code SUNRISE, the later of which enables me to apply the same analysis to simulations and observations. From the simulated galaxies, I have developed the first merging galaxy classification scheme that is based on kinematics and imaging. Utilizing a Linear Discriminant Analysis tool, I have determined which kinematic and imaging predictors are most useful for identifying mergers of various merger parameters (such as orientation, mass ratio, gas fraction, and merger stage). I will discuss the strengths and limitations of the classification technique and then my initial results for applying the classification to the >10,000 observed galaxies in the MaNGA (Mapping Nearby Galaxies at Apache Point) IFS survey. Through accurate identification of merging galaxies in the MaNGA survey, I will advance our understanding of supermassive black hole growth in galaxy mergers and other open questions related to galaxy evolution.

[Advances of studies on new technology and method for identifying traditional Chinese medicinal materials].

PubMed

Chen, Shilin; Guo, Baolin; Zhang, Guijun; Yan, Zhuyun; Luo, Guangming; Sun, Suqin; Wu, Hezhen; Huang, Linfang; Pang, Xiaohui; Chen, Jianbo

2012-04-01

In this review, the authors summarized the new technologies and methods for identifying traditional Chinese medicinal materials, including molecular identification, chemical identification, morphological identification, microscopic identification and identification based on biological effects. The authors introduced the principle, characteristics, application and prospect on each new technology or method and compared their advantages and disadvantages. In general, new methods make the result more objective and accurate. DNA barcoding technique and spectroscopy identification have their owner obvious strongpoint in universality and digitalization. In the near future, the two techniques are promising to be the main trend for identifying traditional Chinese medicinal materials. The identification techniques based on microscopy, liquid chromatography, PCR, biological effects and DNA chip will be indispensable supplements. However, the bionic identification technology is just placed in the developing stage at present.

Selection of regularization parameter for l1-regularized damage detection

NASA Astrophysics Data System (ADS)

Hou, Rongrong; Xia, Yong; Bao, Yuequan; Zhou, Xiaoqing

2018-06-01

The l1 regularization technique has been developed for structural health monitoring and damage detection through employing the sparsity condition of structural damage. The regularization parameter, which controls the trade-off between data fidelity and solution size of the regularization problem, exerts a crucial effect on the solution. However, the l1 regularization problem has no closed-form solution, and the regularization parameter is usually selected by experience. This study proposes two strategies of selecting the regularization parameter for the l1-regularized damage detection problem. The first method utilizes the residual and solution norms of the optimization problem and ensures that they are both small. The other method is based on the discrepancy principle, which requires that the variance of the discrepancy between the calculated and measured responses is close to the variance of the measurement noise. The two methods are applied to a cantilever beam and a three-story frame. A range of the regularization parameter, rather than one single value, can be determined. When the regularization parameter in this range is selected, the damage can be accurately identified even for multiple damage scenarios. This range also indicates the sensitivity degree of the damage identification problem to the regularization parameter.

Global identification of stochastic dynamical systems under different pseudo-static operating conditions: The functionally pooled ARMAX case

NASA Astrophysics Data System (ADS)

Sakellariou, J. S.; Fassois, S. D.

2017-01-01

The identification of a single global model for a stochastic dynamical system operating under various conditions is considered. Each operating condition is assumed to have a pseudo-static effect on the dynamics and be characterized by a single measurable scheduling variable. Identification is accomplished within a recently introduced Functionally Pooled (FP) framework, which offers a number of advantages over Linear Parameter Varying (LPV) identification techniques. The focus of the work is on the extension of the framework to include the important FP-ARMAX model case. Compared to their simpler FP-ARX counterparts, FP-ARMAX models are much more general and offer improved flexibility in describing various types of stochastic noise, but at the same time lead to a more complicated, non-quadratic, estimation problem. Prediction Error (PE), Maximum Likelihood (ML), and multi-stage estimation methods are postulated, and the PE estimator optimality, in terms of consistency and asymptotic efficiency, is analytically established. The postulated estimators are numerically assessed via Monte Carlo experiments, while the effectiveness of the approach and its superiority over its FP-ARX counterpart are demonstrated via an application case study pertaining to simulated railway vehicle suspension dynamics under various mass loading conditions.

Stochastic global identification of a bio-inspired self-sensing composite UAV wing via wind tunnel experiments

NASA Astrophysics Data System (ADS)

Kopsaftopoulos, Fotios; Nardari, Raphael; Li, Yu-Hung; Wang, Pengchuan; Chang, Fu-Kuo

2016-04-01

In this work, the system design, integration, and wind tunnel experimental evaluation are presented for a bioinspired self-sensing intelligent composite unmanned aerial vehicle (UAV) wing. A total of 148 micro-sensors, including piezoelectric, strain, and temperature sensors, in the form of stretchable sensor networks are embedded in the layup of a composite wing in order to enable its self-sensing capabilities. Novel stochastic system identification techniques based on time series models and statistical parameter estimation are employed in order to accurately interpret the sensing data and extract real-time information on the coupled air flow-structural dynamics. Special emphasis is given to the wind tunnel experimental assessment under various flight conditions defined by multiple airspeeds and angles of attack. A novel modeling approach based on the recently introduced Vector-dependent Functionally Pooled (VFP) model structure is employed for the stochastic identification of the "global" coupled airflow-structural dynamics of the wing and their correlation with dynamic utter and stall. The obtained results demonstrate the successful system-level integration and effectiveness of the stochastic identification approach, thus opening new perspectives for the state sensing and awareness capabilities of the next generation of "fly-by-fee" UAVs.

Analysis of pilot control strategy

NASA Technical Reports Server (NTRS)

Heffley, R. K.; Hanson, G. D.; Jewell, W. F.; Clement, W. F.

1983-01-01

Methods for nonintrusive identification of pilot control strategy and task execution dynamics are presented along with examples based on flight data. The specific analysis technique is Nonintrusive Parameter Identification Procedure (NIPIP), which is described in a companion user's guide (NASA CR-170398). Quantification of pilot control strategy and task execution dynamics is discussed in general terms followed by a more detailed description of how NIPIP can be applied. The examples are based on flight data obtained from the NASA F-8 digital fly by wire airplane. These examples involve various piloting tasks and control axes as well as a demonstration of how the dynamics of the aircraft itself are identified using NIPIP. Application of NIPIP to the AFTI/F-16 flight test program is discussed. Recommendations are made for flight test applications in general and refinement of NIPIP to include interactive computer graphics.

Remote sensing based on hyperspectral data analysis

NASA Astrophysics Data System (ADS)

Sharifahmadian, Ershad

In remote sensing, accurate identification of far objects, especially concealed objects is difficult. In this study, to improve object detection from a distance, the hyperspecral imaging and wideband technology are employed with the emphasis on wideband radar. As the wideband data includes a broad range of frequencies, it can reveal information about both the surface of the object and its content. Two main contributions are made in this study: 1) Developing concept of return loss for target detection: Unlike typical radar detection methods which uses radar cross section to detect an object, it is possible to enhance the process of detection and identification of concealed targets using the wideband radar based on the electromagnetic characteristics --conductivity, permeability, permittivity, and return loss-- of materials. During the identification process, collected wideband data is evaluated with information from wideband signature library which has already been built. In fact, several classes (e.g. metal, wood, etc.) and subclasses (ex. metals with high conductivity) have been defined based on their electromagnetic characteristics. Materials in a scene are then classified based on these classes. As an example, materials with high electrical conductivity can be conveniently detected. In fact, increasing relative conductivity leads to a reduction in the return loss. Therefore, metals with high conductivity (ex. copper) shows stronger radar reflections compared with metals with low conductivity (ex. stainless steel). Thus, it is possible to appropriately discriminate copper from stainless steel. 2) Target recognition techniques: To detect and identify targets, several techniques have been proposed, in particular the Multi-Spectral Wideband Radar Image (MSWRI) which is able to localize and identify concealed targets. The MSWRI is based on the theory of robust capon beamformer. During identification process, information from wideband signature library is utilized. The WB signature library includes such parameters as conductivity, permeability, permittivity, and return loss at different frequencies for possible materials related to a target. In the MSWRI approach, identification procedure is performed by calculating the RLs at different selected frequencies. Based on similarity of the calculated RLs and RL from WB signature library, targets are detected and identified. Based on the simulation and experimental results, it is concluded that the MSWRI technique is a promising approach for standoff target detection.

Identification of Bouc-Wen hysteretic parameters based on enhanced response sensitivity approach

NASA Astrophysics Data System (ADS)

Wang, Li; Lu, Zhong-Rong

2017-05-01

This paper aims to identify parameters of Bouc-Wen hysteretic model using time-domain measured data. It follows a general inverse identification procedure, that is, identifying model parameters is treated as an optimization problem with the nonlinear least squares objective function. Then, the enhanced response sensitivity approach, which has been shown convergent and proper for such kind of problems, is adopted to solve the optimization problem. Numerical tests are undertaken to verify the proposed identification approach.

Manufacturing of hybrid aluminum copper joints by electromagnetic pulse welding - Identification of quantitative process windows

NASA Astrophysics Data System (ADS)

Psyk, Verena; Scheffler, Christian; Linnemann, Maik; Landgrebe, Dirk

2017-10-01

Compared to conventional joining techniques, electromagnetic pulse welding offers important advantages especially when it comes to dissimilar material connections as e.g. copper aluminum welds. However, due to missing guidelines and tools for process design, the process has not been widely implemented in industrial production, yet. In order to contribute to overcoming this obstacle, a combined numerical and experimental process analysis for electromagnetic pulse welding of Cu-DHP and EN AW-1050 was carried out and the results were consolidated in a quantitative collision parameter based process window.

Multishaker modal testing

NASA Technical Reports Server (NTRS)

Craig, R. R., Jr.

1985-01-01

A component mode synthesis method for damped structures was developed and modal test methods were explored which could be employed to determine the relevant parameters required by the component mode synthesis method. Research was conducted on the following topics: (1) Development of a generalized time-domain component mode synthesis technique for damped systems; (2) Development of a frequency-domain component mode synthesis method for damped systems; and (3) Development of a system identification algorithm applicable to general damped systems. Abstracts are presented of the major publications which have been previously issued on these topics.

Adaptive Modal Identification for Flutter Suppression Control

NASA Technical Reports Server (NTRS)

Nguyen, Nhan T.; Drew, Michael; Swei, Sean S.

2016-01-01

In this paper, we will develop an adaptive modal identification method for identifying the frequencies and damping of a flutter mode based on model-reference adaptive control (MRAC) and least-squares methods. The least-squares parameter estimation will achieve parameter convergence in the presence of persistent excitation whereas the MRAC parameter estimation does not guarantee parameter convergence. Two adaptive flutter suppression control approaches are developed: one based on MRAC and the other based on the least-squares method. The MRAC flutter suppression control is designed as an integral part of the parameter estimation where the feedback signal is used to estimate the modal information. On the other hand, the separation principle of control and estimation is applied to the least-squares method. The least-squares modal identification is used to perform parameter estimation.

Methodological optimization of tinnitus assessment using prepulse inhibition of the acoustic startle reflex.

PubMed

Longenecker, R J; Galazyuk, A V

2012-11-16

Recently prepulse inhibition of the acoustic startle reflex (ASR) became a popular technique for tinnitus assessment in laboratory animals. This method confers a significant advantage over the previously used time-consuming behavioral approaches utilizing basic mechanisms of conditioning. Although this technique has been successfully used to assess tinnitus in different laboratory animals, many of the finer details of this methodology have not been described enough to be replicated, but are critical for tinnitus assessment. Here we provide detail description of key procedures and methodological issues that provide guidance for newcomers with the process of learning to correctly apply gap detection techniques for tinnitus assessment in laboratory animals. The major categories of these issues include: refinement of hardware for best performance, optimization of stimulus parameters, behavioral considerations, and identification of optimal strategies for data analysis. This article is part of a Special Issue entitled: Tinnitus Neuroscience. Copyright © 2012. Published by Elsevier B.V.

On the Use of Machine Learning Techniques for the Mechanical Characterization of Soft Biological Tissues.

PubMed

Cilla, M; Pérez-Rey, I; Martínez, M A; Peña, Estefania; Martínez, Javier

2018-06-23

Motivated by the search for new strategies for fitting a material model, a new approach is explored in the present work. The use of numerical and complex algorithms based on machine learning techniques such as support vector machines for regression, bagged decision trees and artificial neural networks is proposed for solving the parameter identification of constitutive laws for soft biological tissues. First, the mathematical tools were trained with analytical uniaxial data (circumferential and longitudinal directions) as inputs, and their corresponding material parameters of the Gasser, Ogden and Holzapfel strain energy function as outputs. The train and test errors show great efficiency during the training process in finding correlations between inputs and outputs; besides, the correlation coefficients were very close to 1. Second, the tool was validated with unseen observations of analytical circumferential and longitudinal uniaxial data. The results show an excellent agreement between the prediction of the material parameters of the SEF and the analytical curves. Finally, data from real circumferential and longitudinal uniaxial tests on different cardiovascular tissues were fitted, thus the material model of these tissues was predicted. We found that the method was able to consistently identify model parameters, and we believe that the use of these numerical tools could lead to an improvement in the characterization of soft biological tissues. This article is protected by copyright. All rights reserved.

Goldstone radio spectrum signal identification, March 1980 - March 1982

NASA Technical Reports Server (NTRS)

Gaudian, B. A.

1982-01-01

The signal identification process is described. The Goldstone radio spectrum environment contains signals that are a potential source of electromagnetic interference to the Goldstone tracking receivers. The identification of these signals is accomplished by the use of signal parameters and environment parameters. Statistical data on the Goldstone radio spectrum environment from 2285 to 2305 MHz are provided.

Assimilation of concentration measurements for retrieving multiple point releases in atmosphere: A least-squares approach to inverse modelling

NASA Astrophysics Data System (ADS)

Singh, Sarvesh Kumar; Rani, Raj

2015-10-01

The study addresses the identification of multiple point sources, emitting the same tracer, from their limited set of merged concentration measurements. The identification, here, refers to the estimation of locations and strengths of a known number of simultaneous point releases. The source-receptor relationship is described in the framework of adjoint modelling by using an analytical Gaussian dispersion model. A least-squares minimization framework, free from an initialization of the release parameters (locations and strengths), is presented to estimate the release parameters. This utilizes the distributed source information observable from the given monitoring design and number of measurements. The technique leads to an exact retrieval of the true release parameters when measurements are noise free and exactly described by the dispersion model. The inversion algorithm is evaluated using the real data from multiple (two, three and four) releases conducted during Fusion Field Trials in September 2007 at Dugway Proving Ground, Utah. The release locations are retrieved, on average, within 25-45 m of the true sources with the distance from retrieved to true source ranging from 0 to 130 m. The release strengths are also estimated within a factor of three to the true release rates. The average deviations in retrieval of source locations are observed relatively large in two release trials in comparison to three and four release trials.

Seismic Hazard Assessment for a Characteristic Earthquake Scenario: Probabilistic-Deterministic Method

NASA Astrophysics Data System (ADS)

mouloud, Hamidatou

2016-04-01

The objective of this paper is to analyze the seismic activity and the statistical treatment of seismicity catalog the Constantine region between 1357 and 2014 with 7007 seismic event. Our research is a contribution to improving the seismic risk management by evaluating the seismic hazard in the North-East Algeria. In the present study, Earthquake hazard maps for the Constantine region are calculated. Probabilistic seismic hazard analysis (PSHA) is classically performed through the Cornell approach by using a uniform earthquake distribution over the source area and a given magnitude range. This study aims at extending the PSHA approach to the case of a characteristic earthquake scenario associated with an active fault. The approach integrates PSHA with a high-frequency deterministic technique for the prediction of peak and spectral ground motion parameters in a characteristic earthquake. The method is based on the site-dependent evaluation of the probability of exceedance for the chosen strong-motion parameter. We proposed five sismotectonique zones. Four steps are necessary: (i) identification of potential sources of future earthquakes, (ii) assessment of their geological, geophysical and geometric, (iii) identification of the attenuation pattern of seismic motion, (iv) calculation of the hazard at a site and finally (v) hazard mapping for a region. In this study, the procedure of the earthquake hazard evaluation recently developed by Kijko and Sellevoll (1992) is used to estimate seismic hazard parameters in the northern part of Algeria.

Selection of noisy measurement locations for error reduction in static parameter identification

NASA Astrophysics Data System (ADS)

Sanayei, Masoud; Onipede, Oladipo; Babu, Suresh R.

1992-09-01

An incomplete set of noisy static force and displacement measurements is used for parameter identification of structures at the element level. Measurement location and the level of accuracy in the measured data can drastically affect the accuracy of the identified parameters. A heuristic method is presented to select a limited number of degrees of freedom (DOF) to perform a successful parameter identification and to reduce the impact of measurement errors on the identified parameters. This pretest simulation uses an error sensitivity analysis to determine the effect of measurement errors on the parameter estimates. The selected DOF can be used for nondestructive testing and health monitoring of structures. Two numerical examples, one for a truss and one for a frame, are presented to demonstrate that using the measurements at the selected subset of DOF can limit the error in the parameter estimates.

In-theater piracy: finding where the pirate was

NASA Astrophysics Data System (ADS)

Chupeau, Bertrand; Massoudi, Ayoub; Lefèbvre, Frédéric

2008-02-01

Pirate copies of feature films are proliferating on the Internet. DVD rip or screener recording methods involve the duplication of officially distributed media whereas 'cam' versions are illicitly captured with handheld camcorders in movie theaters. Several, complementary, multimedia forensic techniques such as copy identification, forensic tracking marks or sensor forensics can deter those clandestine recordings. In the case of camcorder capture in a theater, the image is often geometrically distorted, the main artifact being the trapezoidal effect, also known as 'keystoning', due to a capture viewing axis not being perpendicular to the screen. In this paper we propose to analyze the geometric distortions in a pirate copy to determine the camcorder viewing angle to the screen perpendicular and derive the approximate position of the pirate in the theater. The problem is first of all geometrically defined, by describing the general projection and capture setup, and by identifying unknown parameters and estimates. The estimation approach based on the identification of an eight-parameter homographic model of the 'keystoning' effect is then presented. A validation experiment based on ground truth collected in a real movie theater is reported, and the accuracy of the proposed method is assessed.

XRF map identification problems based on a PDE electrodeposition model

NASA Astrophysics Data System (ADS)

Sgura, Ivonne; Bozzini, Benedetto

2017-04-01

In this paper we focus on the following map identification problem (MIP): given a morphochemical reaction-diffusion (RD) PDE system modeling an electrodepostion process, we look for a time t *, belonging to the transient dynamics and a set of parameters \\mathbf{p} , such that the PDE solution, for the morphology h≤ft(x,y,{{t}\\ast};\\mathbf{p}\\right) and for the chemistry θ ≤ft(x,y,{{t}\\ast};\\mathbf{p}\\right) approximates a given experimental map M *. Towards this aim, we introduce a numerical algorithm using singular value decomposition (SVD) and Frobenius norm to give a measure of error distance between experimental maps for h and θ and simulated solutions of the RD-PDE system on a fixed time integration interval. The technique proposed allows quantitative use of microspectroscopy images, such as XRF maps. Specifically, in this work we have modelled the morphology and manganese distributions of nanostructured components of innovative batteries and we have followed their changes resulting from ageing under operating conditions. The availability of quantitative information on space-time evolution of active materials in terms of model parameters will allow dramatic improvements in knowledge-based optimization of battery fabrication and operation.

An IT-enabled supply chain model: a simulation study

NASA Astrophysics Data System (ADS)

Cannella, Salvatore; Framinan, Jose M.; Barbosa-Póvoa, Ana

2014-11-01

During the last decades, supply chain collaboration practices and the underlying enabling technologies have evolved from the classical electronic data interchange (EDI) approach to a web-based and radio frequency identification (RFID)-enabled collaboration. In this field, most of the literature has focused on the study of optimal parameters for reducing the total cost of suppliers, by adopting operational research (OR) techniques. Herein we are interested in showing that the considered information technology (IT)-enabled structure is resilient, that is, it works well across a reasonably broad range of parameter settings. By adopting a methodological approach based on system dynamics, we study a multi-tier collaborative supply chain. Results show that the IT-enabled supply chain improves operational performance and customer service level. Nonetheless, benefits for geographically dispersed networks are of minor entity.

Operational stability prediction in milling based on impact tests

NASA Astrophysics Data System (ADS)

Kiss, Adam K.; Hajdu, David; Bachrathy, Daniel; Stepan, Gabor

2018-03-01

Chatter detection is usually based on the analysis of measured signals captured during cutting processes. These techniques, however, often give ambiguous results close to the stability boundaries, which is a major limitation in industrial applications. In this paper, an experimental chatter detection method is proposed based on the system's response for perturbations during the machining process, and no system parameter identification is required. The proposed method identifies the dominant characteristic multiplier of the periodic dynamical system that models the milling process. The variation of the modulus of the largest characteristic multiplier can also be monitored, the stability boundary can precisely be extrapolated, while the manufacturing parameters are still kept in the chatter-free region. The method is derived in details, and also verified experimentally in laboratory environment.

Identification of drought in Dhalai river watershed using MCDM and ANN models

NASA Astrophysics Data System (ADS)

Aher, Sainath; Shinde, Sambhaji; Guha, Shantamoy; Majumder, Mrinmoy

2017-03-01

An innovative approach for drought identification is developed using Multi-Criteria Decision Making (MCDM) and Artificial Neural Network (ANN) models from surveyed drought parameter data around the Dhalai river watershed in Tripura hinterlands, India. Total eight drought parameters, i.e., precipitation, soil moisture, evapotranspiration, vegetation canopy, cropping pattern, temperature, cultivated land, and groundwater level were obtained from expert, literature and cultivator survey. Then, the Analytic Hierarchy Process (AHP) and Analytic Network Process (ANP) were used for weighting of parameters and Drought Index Identification (DII). Field data of weighted parameters in the meso scale Dhalai River watershed were collected and used to train the ANN model. The developed ANN model was used in the same watershed for identification of drought. Results indicate that the Limited-Memory Quasi-Newton algorithm was better than the commonly used training method. Results obtained from the ANN model shows the drought index developed from the study area ranges from 0.32 to 0.72. Overall analysis revealed that, with appropriate training, the ANN model can be used in the areas where the model is calibrated, or other areas where the range of input parameters is similar to the calibrated region for drought identification.

Vehicle dynamic prediction systems with on-line identification of vehicle parameters and road conditions.

PubMed

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.

Vehicle Dynamic Prediction Systems with On-Line Identification of Vehicle Parameters and Road Conditions

PubMed Central

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

Surface defect detection in tiling Industries using digital image processing methods: analysis and evaluation.

PubMed

Karimi, Mohammad H; Asemani, Davud

2014-05-01

Ceramic and tile industries should indispensably include a grading stage to quantify the quality of products. Actually, human control systems are often used for grading purposes. An automatic grading system is essential to enhance the quality control and marketing of the products. Since there generally exist six different types of defects originating from various stages of tile manufacturing lines with distinct textures and morphologies, many image processing techniques have been proposed for defect detection. In this paper, a survey has been made on the pattern recognition and image processing algorithms which have been used to detect surface defects. Each method appears to be limited for detecting some subgroup of defects. The detection techniques may be divided into three main groups: statistical pattern recognition, feature vector extraction and texture/image classification. The methods such as wavelet transform, filtering, morphology and contourlet transform are more effective for pre-processing tasks. Others including statistical methods, neural networks and model-based algorithms can be applied to extract the surface defects. Although, statistical methods are often appropriate for identification of large defects such as Spots, but techniques such as wavelet processing provide an acceptable response for detection of small defects such as Pinhole. A thorough survey is made in this paper on the existing algorithms in each subgroup. Also, the evaluation parameters are discussed including supervised and unsupervised parameters. Using various performance parameters, different defect detection algorithms are compared and evaluated. Copyright © 2013 ISA. Published by Elsevier Ltd. All rights reserved.

Active Aeroelastic Wing Aerodynamic Model Development and Validation for a Modified F/A-18A Airplane

NASA Technical Reports Server (NTRS)

Cumming, Stephen B.; Diebler, Corey G.

2005-01-01

A new aerodynamic model has been developed and validated for a modified F/A-18A airplane used for the Active Aeroelastic Wing (AAW) research program. The goal of the program was to demonstrate the advantages of using the inherent flexibility of an aircraft to enhance its performance. The research airplane was an F/A-18A with wings modified to reduce stiffness and a new control system to increase control authority. There have been two flight phases. Data gathered from the first flight phase were used to create the new aerodynamic model. A maximum-likelihood output-error parameter estimation technique was used to obtain stability and control derivatives. The derivatives were incorporated into the National Aeronautics and Space Administration F-18 simulation, validated, and used to develop new AAW control laws. The second phase of flights was used to evaluate the handling qualities of the AAW airplane and the control law design process, and to further test the accuracy of the new model. The flight test envelope covered Mach numbers between 0.85 and 1.30 and dynamic pressures from 600 to 1250 pound-force per square foot. The results presented in this report demonstrate that a thorough parameter identification analysis can be used to improve upon models that were developed using other means. This report describes the parameter estimation technique used, details the validation techniques, discusses differences between previously existing F/A-18 models, and presents results from the second phase of research flights.

Three-Dimensional Aeroelastic and Aerothermoelastic Behavior in Hypersonic Flow

NASA Technical Reports Server (NTRS)

McNamara, Jack J.; Friedmann, Peretz P.; Powell, Kenneth G.; Thuruthimattam, Biju J.; Bartels, Robert E.

2005-01-01

The aeroelastic and aerothermoelastic behavior of three-dimensional configurations in hypersonic flow regime are studied. The aeroelastic behavior of a low aspect ratio wing, representative of a fin or control surface on a generic hypersonic vehicle, is examined using third order piston theory, Euler and Navier-Stokes aerodynamics. The sensitivity of the aeroelastic behavior generated using Euler and Navier-Stokes aerodynamics to parameters governing temporal accuracy is also examined. Also, a refined aerothermoelastic model, which incorporates the heat transfer between the fluid and structure using CFD generated aerodynamic heating, is used to examine the aerothermoelastic behavior of the low aspect ratio wing in the hypersonic regime. Finally, the hypersonic aeroelastic behavior of a generic hypersonic vehicle with a lifting-body type fuselage and canted fins is studied using piston theory and Euler aerodynamics for the range of 2.5 less than or equal to M less than or equal to 28, at altitudes ranging from 10,000 feet to 80,000 feet. This analysis includes a study on optimal mesh selection for use with Euler aerodynamics. In addition to the aeroelastic and aerothermoelastic results presented, three time domain flutter identification techniques are compared, namely the moving block approach, the least squares curve fitting method, and a system identification technique using an Auto-Regressive model of the aeroelastic system. In general, the three methods agree well. The system identification technique, however, provided quick damping and frequency estimations with minimal response record length, and therefore o ers significant reductions in computational cost. In the present case, the computational cost was reduced by 75%. The aeroelastic and aerothermoelastic results presented illustrate the applicability of the CFL3D code for the hypersonic flight regime.

Conceptural Study of Gyroscopic Damping Systems for Structural Indentification

NASA Astrophysics Data System (ADS)

Furuya, H.; Senba, A.

2002-01-01

System identification of the adaptive gyroscopic damper system (AGDS) is treated in this paper. The adaptive gyroscopic damper system was proposed as the extension of the conventional gyroscopic damper under the concept of intelligent adaptive structure systems [1]. The conventional gyroscopic damper has passive characteristics similar to a tuned mass damper (TMD). Because the conventional gyroscopic damper has one natural frequency, several applications to the ground structures have been studied to suppress the fundamental vibration mode (e.g. [2]). On the other hand, as the AGDS has a property of adjusting the natural frequency of the gimbal to that of the structural system by controlling the moment of inertia around its gimbal axis, the performance for suppressing the vibration of one-DOF system was improved. In addition, by extending this property, suppression of multiple modes vibration by quasi-static control for the AGDS was demonstrated [3]. To realize the high performance for suppressing the structural vibration, the identification of characteristics of the structural system with AGDS is significant, because the adaptability of the AGDS to the natural frequency of the system reflects to the performance. By using a capability of AGDS as changing its moment of inertia around its gimbals axis by controlling appendage mass, the system identification is also possible. A sensitivity analysis for the change of the response amplitude and the natural frequency with modal parameters is applied to the method. The errors included in the identification results of modal parameters for cantilevered beam model is examined. The numerical demonstrations were performed to investigate the identification errors of system parameters by the response amplitude and the natural frequency with modal parameters, respectively. The results show that the technique used in the study can identify the structural system and the identification errors occur for near the natural frequency of the system by using the response amplitude, and for the optimum momentum inertia by using the natural frequency. References [1] Hiroshi FURUYA, Masanori TAKAHASHI, and Tatsuo OHMACHI: Concept of Adaptive Gyroscopic Damper and Vibration Suppression of Flexible Structures, 8th International Conference on Adaptive Structures and Technologies, Wakayama, Oct. 29-31, 1997, eds. Y. Murotsu, C.A. Rogers, P. Santini, and H. Okubo, Technomic Publishing, pp.247-254, 1998. [2] Hiroshi FURUYA, Masanori TAKAHASHI, and Tatsuo OHMACHI: Pseudo Feedback Control of Adaptive Gyroscopic Damper for Vibration Suppression, 39th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Material Conference, AIAA 98-1796, Long Beach, CA, April 20-23, pp.830-834, 1998. [3] Hiroshi FURUYA and Atsuo KOBORI: Suppression of Multiple Modes Vibration of Flexible Structures with Adaptive Gyroscopic Damper System, 10th International Conference on Adaptive Structures and Technologies, Paris, Oct. 13-15, 1999, eds. R. Ohayon, and M. Bernadou, Technomic Publishing, pp. 127-134, 1999.

Identifying High-Rate Flows Based on Sequential Sampling

NASA Astrophysics Data System (ADS)

Zhang, Yu; Fang, Binxing; Luo, Hao

We consider the problem of fast identification of high-rate flows in backbone links with possibly millions of flows. Accurate identification of high-rate flows is important for active queue management, traffic measurement and network security such as detection of distributed denial of service attacks. It is difficult to directly identify high-rate flows in backbone links because tracking the possible millions of flows needs correspondingly large high speed memories. To reduce the measurement overhead, the deterministic 1-out-of-k sampling technique is adopted which is also implemented in Cisco routers (NetFlow). Ideally, a high-rate flow identification method should have short identification time, low memory cost and processing cost. Most importantly, it should be able to specify the identification accuracy. We develop two such methods. The first method is based on fixed sample size test (FSST) which is able to identify high-rate flows with user-specified identification accuracy. However, since FSST has to record every sampled flow during the measurement period, it is not memory efficient. Therefore the second novel method based on truncated sequential probability ratio test (TSPRT) is proposed. Through sequential sampling, TSPRT is able to remove the low-rate flows and identify the high-rate flows at the early stage which can reduce the memory cost and identification time respectively. According to the way to determine the parameters in TSPRT, two versions of TSPRT are proposed: TSPRT-M which is suitable when low memory cost is preferred and TSPRT-T which is suitable when short identification time is preferred. The experimental results show that TSPRT requires less memory and identification time in identifying high-rate flows while satisfying the accuracy requirement as compared to previously proposed methods.

Morphology of sustentaculum tali: Biomechanical importance and correlation with angular dimensions of the talus.

PubMed

Mahato, Niladri Kumar

2011-12-01

The talus and the calcaneus share the bulk of load transmitted from the leg to the skeleton of the foot. The present study analyses the inter-relationship between the superior articular surface and the angular dimensions of the talus with the morphology of the sustentaculum tali. Identification of possible relationships between different angular parameters of the talus morphology and the sustentaculum tali in context of load transmission through the foot. One articular surface and three angular parameters at the junction of the head and the body were measured from dried human talar bones. Corresponding calcaneal samples were measured for four dimensions at the sustentaculum tali. Correlation and regression statistical values between parameters were worked out and analysed. Several parameters within the talus demonstrated significant correlations amongst themselves. The neck vertical angle showed a strong correlation with the articulating surface area below the head of the talus. The inter-relationship between articular and angular parameters within the talus demonstrates strong correlation for certain parameters. Data presented in the study may be helpful to adjust calcaneal and talar screw placement techniques, prosthesis designing and bio-mechanical studies at this important region. Copyright © 2011 Elsevier Ltd. All rights reserved.

Comparative study of surrogate models for groundwater contamination source identification at DNAPL-contaminated sites

NASA Astrophysics Data System (ADS)

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.

Load compensation in a lean burn natural gas vehicle

NASA Astrophysics Data System (ADS)

Gangopadhyay, Anupam

A new multivariable PI tuning technique is developed in this research that is primarily developed for regulation purposes. Design guidelines are developed based on closed-loop stability. The new multivariable design is applied in a natural gas vehicle to combine idle and A/F ratio control loops. This results in better recovery during low idle operation of a vehicle under external step torques. A powertrain model of a natural gas engine is developed and validated for steady-state and transient operation. The nonlinear model has three states: engine speed, intake manifold pressure and fuel fraction in the intake manifold. The model includes the effect of fuel partial pressure in the intake manifold filling and emptying dynamics. Due to the inclusion of fuel fraction as a state, fuel flow rate into the cylinders is also accurately modeled. A linear system identification is performed on the nonlinear model. The linear model structure is predicted analytically from the nonlinear model and the coefficients of the predicted transfer function are shown to be functions of key physical parameters in the plant. Simulations of linear system and model parameter identification is shown to converge to the predicted values of the model coefficients. The multivariable controller developed in this research could be designed in an algebraic fashion once the plant model is known. It is thus possible to implement the multivariable PI design in an adaptive fashion combining the controller with identified plant model on-line. This will result in a self-tuning regulator (STR) type controller where the underlying design criteria is the multivariable tuning technique designed in this research.

Multidirectional In Vivo Characterization of Skin Using Wiener Nonlinear Stochastic System Identification Techniques.

PubMed

Parker, Matthew D; Jones, Lynette A; Hunter, Ian W; Taberner, A J; Nash, M P; Nielsen, P M F

2017-01-01

A triaxial force-sensitive microrobot was developed to dynamically perturb skin in multiple deformation modes, in vivo. Wiener static nonlinear identification was used to extract the linear dynamics and static nonlinearity of the force-displacement behavior of skin. Stochastic input forces were applied to the volar forearm and thenar eminence of the hand, producing probe tip perturbations in indentation and tangential extension. Wiener static nonlinear approaches reproduced the resulting displacements with variances accounted for (VAF) ranging 94-97%, indicating a good fit to the data. These approaches provided VAF improvements of 0.1-3.4% over linear models. Thenar eminence stiffness measures were approximately twice those measured on the forearm. Damping was shown to be significantly higher on the palm, whereas the perturbed mass typically was lower. Coefficients of variation (CVs) for nonlinear parameters were assessed within and across individuals. Individual CVs ranged from 2% to 11% for indentation and from 2% to 19% for extension. Stochastic perturbations with incrementally increasing mean amplitudes were applied to the same test areas. Differences between full-scale and incremental reduced-scale perturbations were investigated. Different incremental preloading schemes were investigated. However, no significant difference in parameters was found between different incremental preloading schemes. Incremental schemes provided depth-dependent estimates of stiffness and damping, ranging from 300 N/m and 2 Ns/m, respectively, at the surface to 5 kN/m and 50 Ns/m at greater depths. The device and techniques used in this research have potential applications in areas, such as evaluating skincare products, assessing skin hydration, or analyzing wound healing.

Distributed Parallel Processing and Dynamic Load Balancing Techniques for Multidisciplinary High Speed Aircraft Design

NASA Technical Reports Server (NTRS)

Krasteva, Denitza T.

1998-01-01

Multidisciplinary design optimization (MDO) for large-scale engineering problems poses many challenges (e.g., the design of an efficient concurrent paradigm for global optimization based on disciplinary analyses, expensive computations over vast data sets, etc.) This work focuses on the application of distributed schemes for massively parallel architectures to MDO problems, as a tool for reducing computation time and solving larger problems. The specific problem considered here is configuration optimization of a high speed civil transport (HSCT), and the efficient parallelization of the embedded paradigm for reasonable design space identification. Two distributed dynamic load balancing techniques (random polling and global round robin with message combining) and two necessary termination detection schemes (global task count and token passing) were implemented and evaluated in terms of effectiveness and scalability to large problem sizes and a thousand processors. The effect of certain parameters on execution time was also inspected. Empirical results demonstrated stable performance and effectiveness for all schemes, and the parametric study showed that the selected algorithmic parameters have a negligible effect on performance.

Scalability of surrogate-assisted multi-objective optimization of antenna structures exploiting variable-fidelity electromagnetic simulation models

NASA Astrophysics Data System (ADS)

Koziel, Slawomir; Bekasiewicz, Adrian

2016-10-01

Multi-objective optimization of antenna structures is a challenging task owing to the high computational cost of evaluating the design objectives as well as the large number of adjustable parameters. Design speed-up can be achieved by means of surrogate-based optimization techniques. In particular, a combination of variable-fidelity electromagnetic (EM) simulations, design space reduction techniques, response surface approximation models and design refinement methods permits identification of the Pareto-optimal set of designs within a reasonable timeframe. Here, a study concerning the scalability of surrogate-assisted multi-objective antenna design is carried out based on a set of benchmark problems, with the dimensionality of the design space ranging from six to 24 and a CPU cost of the EM antenna model from 10 to 20 min per simulation. Numerical results indicate that the computational overhead of the design process increases more or less quadratically with the number of adjustable geometric parameters of the antenna structure at hand, which is a promising result from the point of view of handling even more complex problems.

Experimental Investigation of the Acoustic Nonlinear Behavior in Granular Polymer Bonded Explosives with Progressive Fatigue Damage

PubMed Central

Yang, Zhanfeng; Tian, Yong; Li, Weibin; Zhou, Haiqiang; Zhang, Weibin; Li, Jingming

2017-01-01

The measurement of acoustic nonlinear response is known as a promising technique to characterize material micro-damages. In this paper, nonlinear ultrasonic approach is used to characterize the evolution of fatigue induced micro-cracks in polymer bonded explosives. The variations of acoustic nonlinearity with respect to fatigue cycles in the specimens are obtained in this investigation. The present results show a significant increase of acoustic nonlinearity with respect to fatigue cycles. The experimental observation of the correlation between the acoustic nonlinearity and fatigue cycles in carbon/epoxy laminates, verifies that an acoustic nonlinear response can be used to evaluate the progressive fatigue damage in the granular polymer bonded explosives. The sensitivity comparison of nonlinear and linear parameters of ultrasonic waves in the specimens shows that nonlinear acoustic parameters are more promising indicators to fatigue induced micro-damage than linear ones. The feasibility study of the micro-damage assessment of polymer bonded explosives by nonlinear ultrasonic technique in this work can be applied to damage identification, material degradation monitoring, and lifetime prediction of the explosive parts. PMID:28773017

X-ray topographic studies and measurement of lattice parameter differences within synthetic diamonds grown by the reconstitution technique

NASA Astrophysics Data System (ADS)

Wierzchowski, W.; Moore, M.; Makepeace, A. P. W.; Yacoot, A.

1991-10-01

A 4 x 4 x 1.5 cu mm cuboctahedral diamond and two 0.7 mm thick slabs cut from a truncated octahedral diamond grown by the reconstitution technique were studied in different double-crystal arrangements with both conventional and synchrotron X-ray sources. The back-reflection double crystal topographs of large polished 001-plane-oriented faces intersecting different growth sectors, together with cathodoluminescence patterns, allowed identification of these sectors. A double-crystal arrangement, employing the -3 2 5 quartz reflection matching the symmetrical 004 diamond reflection in CuK(alpha 1) radiation, was used for measurement of lattice parameter differences with an accuracy of one and a half parts per million. The simultaneous investigation by means of Lang projection and section topography provided complementary information about the crystallographic defects and internal structures of growth sectors. Observation of the cuboctahedral diamond with a filter of peak transmittance at 430 nm revealed a 'Maltese cross' growth feature in the central (001) growth sector, which also affected the birefringence pattern. However, this feature only very slightly affected the double-crystal topographs.

Experimental Investigation of the Acoustic Nonlinear Behavior in Granular Polymer Bonded Explosives with Progressive Fatigue Damage.

PubMed

Yang, Zhanfeng; Tian, Yong; Li, Weibin; Zhou, Haiqiang; Zhang, Weibin; Li, Jingming

2017-06-16

The measurement of acoustic nonlinear response is known as a promising technique to characterize material micro-damages. In this paper, nonlinear ultrasonic approach is used to characterize the evolution of fatigue induced micro-cracks in polymer bonded explosives. The variations of acoustic nonlinearity with respect to fatigue cycles in the specimens are obtained in this investigation. The present results show a significant increase of acoustic nonlinearity with respect to fatigue cycles. The experimental observation of the correlation between the acoustic nonlinearity and fatigue cycles in carbon/epoxy laminates, verifies that an acoustic nonlinear response can be used to evaluate the progressive fatigue damage in the granular polymer bonded explosives. The sensitivity comparison of nonlinear and linear parameters of ultrasonic waves in the specimens shows that nonlinear acoustic parameters are more promising indicators to fatigue induced micro-damage than linear ones. The feasibility study of the micro-damage assessment of polymer bonded explosives by nonlinear ultrasonic technique in this work can be applied to damage identification, material degradation monitoring, and lifetime prediction of the explosive parts.

Application of identification techniques to remote manipulator system flight data

NASA Technical Reports Server (NTRS)

Shepard, G. D.; Lepanto, J. A.; Metzinger, R. W.; Fogel, E.

1983-01-01

This paper addresses the application of identification techniques to flight data from the Space Shuttle Remote Manipulator System (RMS). A description of the remote manipulator, including structural and control system characteristics, sensors, and actuators is given. A brief overview of system identification procedures is presented, and the practical aspects of implementing system identification algorithms are discussed. In particular, the problems posed by desampling rate, numerical error, and system nonlinearities are considered. Simulation predictions of damping, frequency, and system order are compared with values identified from flight data to support an evaluation of RMS structural and control system models. Finally, conclusions are drawn regarding the application of identification techniques to flight data obtained from a flexible space structure.

Image processing for IMRT QA dosimetry.

PubMed

Zaini, Mehran R; Forest, Gary J; Loshek, David D

2005-01-01

We have automated the determination of the placement location of the dosimetry ion chamber within intensity-modulated radiotherapy (IMRT) fields, as part of streamlining the entire IMRT quality assurance process. This paper describes the mathematical image-processing techniques to arrive at the appropriate measurement locations within the planar dose maps of the IMRT fields. A specific spot within the found region is identified based on its flatness, radiation magnitude, location, area, and the avoidance of the interleaf spaces. The techniques used include applying a Laplacian, dilation, erosion, region identification, and measurement point selection based on three parameters: the size of the erosion operator, the gradient, and the importance of the area of a region versus its magnitude. These three parameters are adjustable by the user. However, the first one requires tweaking in extremely rare occasions, the gradient requires rare adjustments, and the last parameter needs occasional fine-tuning. This algorithm has been tested in over 50 cases. In about 5% of cases, the algorithm does not find a measurement point due to the extremely steep and narrow regions within the fluence maps. In such cases, manual selection of a point is allowed by our code, which is also difficult to ascertain, since the fluence map does not yield itself to an appropriate measurement point selection.

Using Space Syntax to Assess Safety in Public Areas - Case Study of Tarbiat Pedestrian Area, Tabriz-Iran

NASA Astrophysics Data System (ADS)

Cihangir Çamur, Kübra; Roshani, Mehdi; Pirouzi, Sania

2017-10-01

In studying the urban complex issues, simulation and modelling of public space use considerably helps in determining and measuring factors such as urban safety. Depth map software for determining parameters of the spatial layout techniques; and Statistical Package for Social Sciences (SPSS) software for analysing and evaluating the views of the pedestrians on public safety were used in this study. Connectivity, integration, and depth of the area in the Tarbiat city blocks were measured using the Space Syntax Method, and these parameters are presented as graphical and mathematical data. The combination of the results obtained from the questionnaire and statistical analysis with the results of spatial arrangement technique represents the appropriate and inappropriate spaces for pedestrians. This method provides a useful and effective instrument for decision makers, planners, urban designers and programmers in order to evaluate public spaces in the city. Prior to physical modification of urban public spaces, space syntax simulates the pedestrian safety to be used as an analytical tool by the city management. Finally, regarding the modelled parameters and identification of different characteristics of the case, this study represents the strategies and policies in order to increase the safety of the pedestrians of Tarbiat in Tabriz.

STEPS: a grid search methodology for optimized peptide identification filtering of MS/MS database search results.

PubMed

Piehowski, Paul D; Petyuk, Vladislav A; Sandoval, John D; Burnum, Kristin E; Kiebel, Gary R; Monroe, Matthew E; Anderson, Gordon A; Camp, David G; Smith, Richard D

2013-03-01

For bottom-up proteomics, there are wide variety of database-searching algorithms in use for matching peptide sequences to tandem MS spectra. Likewise, there are numerous strategies being employed to produce a confident list of peptide identifications from the different search algorithm outputs. Here we introduce a grid-search approach for determining optimal database filtering criteria in shotgun proteomics data analyses that is easily adaptable to any search. Systematic Trial and Error Parameter Selection--referred to as STEPS--utilizes user-defined parameter ranges to test a wide array of parameter combinations to arrive at an optimal "parameter set" for data filtering, thus maximizing confident identifications. The benefits of this approach in terms of numbers of true-positive identifications are demonstrated using datasets derived from immunoaffinity-depleted blood serum and a bacterial cell lysate, two common proteomics sample types. © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Performance of basic kinematic thresholds in the identification of crash and near-crash events within naturalistic driving data.

PubMed

Perez, Miguel A; Sudweeks, Jeremy D; Sears, Edie; Antin, Jonathan; Lee, Suzanne; Hankey, Jonathan M; Dingus, Thomas A

2017-06-01

Understanding causal factors for traffic safety-critical events (e.g., crashes and near-crashes) is an important step in reducing their frequency and severity. Naturalistic driving data offers unparalleled insight into these factors, but requires identification of situations where crashes are present within large volumes of data. Sensitivity and specificity of these identification approaches are key to minimizing the resources required to validate candidate crash events. This investigation used data from the Second Strategic Highway Research Program Naturalistic Driving Study (SHRP 2 NDS) and the Canada Naturalistic Driving Study (CNDS) to develop and validate different kinematic thresholds that can be used to detect crash events. Results indicate that the sensitivity of many of these approaches can be quite low, but can be improved by selecting particular threshold levels based on detection performance. Additional improvements in these approaches are possible, and may involve leveraging combinations of different detection approaches, including advanced statistical techniques and artificial intelligence approaches, additional parameter modifications, and automation of validation processes. Copyright © 2017 Elsevier Ltd. All rights reserved.

Signal Prediction With Input Identification

NASA Technical Reports Server (NTRS)

Juang, Jer-Nan; Chen, Ya-Chin

1999-01-01

A novel coding technique is presented for signal prediction with applications including speech coding, system identification, and estimation of input excitation. The approach is based on the blind equalization method for speech signal processing in conjunction with the geometric subspace projection theory to formulate the basic prediction equation. The speech-coding problem is often divided into two parts, a linear prediction model and excitation input. The parameter coefficients of the linear predictor and the input excitation are solved simultaneously and recursively by a conventional recursive least-squares algorithm. The excitation input is computed by coding all possible outcomes into a binary codebook. The coefficients of the linear predictor and excitation, and the index of the codebook can then be used to represent the signal. In addition, a variable-frame concept is proposed to block the same excitation signal in sequence in order to reduce the storage size and increase the transmission rate. The results of this work can be easily extended to the problem of disturbance identification. The basic principles are outlined in this report and differences from other existing methods are discussed. Simulations are included to demonstrate the proposed method.

Speaker gender identification based on majority vote classifiers

NASA Astrophysics Data System (ADS)

Mezghani, Eya; Charfeddine, Maha; Nicolas, Henri; Ben Amar, Chokri

2017-03-01

Speaker gender identification is considered among the most important tools in several multimedia applications namely in automatic speech recognition, interactive voice response systems and audio browsing systems. Gender identification systems performance is closely linked to the selected feature set and the employed classification model. Typical techniques are based on selecting the best performing classification method or searching optimum tuning of one classifier parameters through experimentation. In this paper, we consider a relevant and rich set of features involving pitch, MFCCs as well as other temporal and frequency-domain descriptors. Five classification models including decision tree, discriminant analysis, nave Bayes, support vector machine and k-nearest neighbor was experimented. The three best perming classifiers among the five ones will contribute by majority voting between their scores. Experimentations were performed on three different datasets spoken in three languages: English, German and Arabic in order to validate language independency of the proposed scheme. Results confirm that the presented system has reached a satisfying accuracy rate and promising classification performance thanks to the discriminating abilities and diversity of the used features combined with mid-level statistics.

Computerized Liquid Crystal Phase Identification by Neural Networks Analysis of Polarizing Microscopy Textures

NASA Astrophysics Data System (ADS)

Karaszi, Zoltan; Konya, Andrew; Dragan, Feodor; Jakli, Antal; CPIP/LCI; CS Dept. of Kent State University Collaboration

Polarizing optical microscopy (POM) is traditionally the best-established method of studying liquid crystals, and using POM started already with Otto Lehman in 1890. An expert, who is familiar with the science of optics of anisotropic materials and typical textures of liquid crystals, can identify phases with relatively large confidence. However, for unambiguous identification usually other expensive and time-consuming experiments are needed. Replacement of the subjective and qualitative human eye-based liquid crystal texture analysis with quantitative computerized image analysis technique started only recently and were used to enhance the detection of smooth phase transitions, determine order parameter and birefringence of specific liquid crystal phases. We investigate if the computer can recognize and name the phase where the texture was taken. To judge the potential of reliable image recognition based on this procedure, we used 871 images of liquid crystal textures belonging to five main categories: Nematic, Smectic A, Smectic C, Cholesteric and Crystal, and used a Neural Network Clustering Technique included in the data mining software package in Java ``WEKA''. A neural network trained on a set of 827 LC textures classified the remaining 44 textures with 80% accuracy.

Comparison of the techniques for the identification of the epidural space using the loss-of-resistance technique or an automated syringe - results of a randomized double-blind study.

PubMed

Duniec, Larysa; Nowakowski, Piotr; Sieczko, Jakub; Chlebus, Marcin; Łazowski, Tomasz

2016-01-01

The conventional, loss of resistance technique for identification of the epidural space is highly dependent on the anaesthetist's personal experience and is susceptible to technical errors. Therefore, an alternative, automated technique was devised to overcome the drawbacks of the traditional method. The aim of the study was to compare the efficacy of epidural space identification and the complication rate between the two groups - the automatic syringe and conventional loss of resistance methods. 47 patients scheduled for orthopaedic and gynaecology procedures under epidural anaesthesia were enrolled into the study. The number of attempts, ease of epidural space identification, complication rate and the patients' acceptance regarding the two techniques were evaluated. The majority of blocks were performed by trainee anaesthetists (91.5%). No statistical difference was found between the number of needle insertion attempts (1 vs. 2), the efficacy of epidural anaesthesia or the number of complications between the groups. The ease of epidural space identification, as assessed by an anaesthetist, was significantly better (P = 0.011) in the automated group (87.5% vs. 52.4%). A similar number of patients (92% vs. 94%) in both groups stated they would accept epidural anaesthesia in the future. The automated and loss of resistance methods of epidural space identification were proved to be equivalent in terms of efficacy and safety. Since the use of the automated technique may facilitate epidural space identification, it may be regarded as useful technique for anaesthetists inexperienced in epidural anaesthesia, or for trainees.

Chemical vapor deposition growth

NASA Technical Reports Server (NTRS)

Ruth, R. P.; Manasevit, H. M.; Kenty, J. L.; Moudy, L. A.; Simpson, W. I.; Yang, J. J.

1976-01-01

The chemical vapor deposition (CVD) method for the growth of Si sheet on inexpensive substrate materials is investigated. The objective is to develop CVD techniques for producing large areas of Si sheet on inexpensive substrate materials, with sheet properties suitable for fabricating solar cells meeting the technical goals of the Low Cost Silicon Solar Array Project. Specific areas covered include: (1) modification and test of existing CVD reactor system; (2) identification and/or development of suitable inexpensive substrate materials; (3) experimental investigation of CVD process parameters using various candidate substrate materials; (4) preparation of Si sheet samples for various special studies, including solar cell fabrication; (5) evaluation of the properties of the Si sheet material produced by the CVD process; and (6) fabrication and evaluation of experimental solar cell structures, using standard and near-standard processing techniques.

Real-time Crystal Growth Visualization and Quantification by Energy-Resolved Neutron Imaging

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tremsin, Anton S.; Perrodin, Didier; Losko, Adrian S.

Energy-resolved neutron imaging is investigated as a real-time diagnostic tool for visualization and in-situ measurements of "blind" processes. This technique is demonstrated for the Bridgman-type crystal growth enabling remote and direct measurements of growth parameters crucial for process optimization. The location and shape of the interface between liquid and solid phases are monitored in real-time, concurrently with the measurement of elemental distribution within the growth volume and with the identification of structural features with a ~100 μm spatial resolution. Such diagnostics can substantially reduce the development time between exploratory small scale growth of new materials and their subsequent commercial production.more » This technique is widely applicable and is not limited to crystal growth processes.« less

Real-time Crystal Growth Visualization and Quantification by Energy-Resolved Neutron Imaging

DOE PAGES

Tremsin, Anton S.; Perrodin, Didier; Losko, Adrian S.; ...

2017-04-20

Energy-resolved neutron imaging is investigated as a real-time diagnostic tool for visualization and in-situ measurements of "blind" processes. This technique is demonstrated for the Bridgman-type crystal growth enabling remote and direct measurements of growth parameters crucial for process optimization. The location and shape of the interface between liquid and solid phases are monitored in real-time, concurrently with the measurement of elemental distribution within the growth volume and with the identification of structural features with a ~100 μm spatial resolution. Such diagnostics can substantially reduce the development time between exploratory small scale growth of new materials and their subsequent commercial production.more » This technique is widely applicable and is not limited to crystal growth processes.« less

A study of parameter identification

NASA Technical Reports Server (NTRS)

Herget, C. J.; Patterson, R. E., III

1978-01-01

A set of definitions for deterministic parameter identification ability were proposed. Deterministic parameter identificability properties are presented based on four system characteristics: direct parameter recoverability, properties of the system transfer function, properties of output distinguishability, and uniqueness properties of a quadratic cost functional. Stochastic parameter identifiability was defined in terms of the existence of an estimation sequence for the unknown parameters which is consistent in probability. Stochastic parameter identifiability properties are presented based on the following characteristics: convergence properties of the maximum likelihood estimate, properties of the joint probability density functions of the observations, and properties of the information matrix.

Model-based damage evaluation of layered CFRP structures

NASA Astrophysics Data System (ADS)

Munoz, Rafael; Bochud, Nicolas; Rus, Guillermo; Peralta, Laura; Melchor, Juan; Chiachío, Juan; Chiachío, Manuel; Bond, Leonard J.

2015-03-01

An ultrasonic evaluation technique for damage identification of layered CFRP structures is presented. This approach relies on a model-based estimation procedure that combines experimental data and simulation of ultrasonic damage-propagation interactions. The CFPR structure, a [0/90]4s lay-up, has been tested in an immersion through transmission experiment, where a scan has been performed on a damaged specimen. Most ultrasonic techniques in industrial practice consider only a few features of the received signals, namely, time of flight, amplitude, attenuation, frequency contents, and so forth. In this case, once signals are captured, an algorithm is used to reconstruct the complete signal waveform and extract the unknown damage parameters by means of modeling procedures. A linear version of the data processing has been performed, where only Young modulus has been monitored and, in a second nonlinear version, the first order nonlinear coefficient β was incorporated to test the possibility of detection of early damage. The aforementioned physical simulation models are solved by the Transfer Matrix formalism, which has been extended from linear to nonlinear harmonic generation technique. The damage parameter search strategy is based on minimizing the mismatch between the captured and simulated signals in the time domain in an automated way using Genetic Algorithms. Processing all scanned locations, a C-scan of the parameter of each layer can be reconstructed, obtaining the information describing the state of each layer and each interface. Damage can be located and quantified in terms of changes in the selected parameter with a measurable extension. In the case of the nonlinear coefficient of first order, evidence of higher sensitivity to damage than imaging the linearly estimated Young Modulus is provided.

JuPOETs: a constrained multiobjective optimization approach to estimate biochemical model ensembles in the Julia programming language.

PubMed

Bassen, David M; Vilkhovoy, Michael; Minot, Mason; Butcher, Jonathan T; Varner, Jeffrey D

2017-01-25

Ensemble modeling is a promising approach for obtaining robust predictions and coarse grained population behavior in deterministic mathematical models. Ensemble approaches address model uncertainty by using parameter or model families instead of single best-fit parameters or fixed model structures. Parameter ensembles can be selected based upon simulation error, along with other criteria such as diversity or steady-state performance. Simulations using parameter ensembles can estimate confidence intervals on model variables, and robustly constrain model predictions, despite having many poorly constrained parameters. In this software note, we present a multiobjective based technique to estimate parameter or models ensembles, the Pareto Optimal Ensemble Technique in the Julia programming language (JuPOETs). JuPOETs integrates simulated annealing with Pareto optimality to estimate ensembles on or near the optimal tradeoff surface between competing training objectives. We demonstrate JuPOETs on a suite of multiobjective problems, including test functions with parameter bounds and system constraints as well as for the identification of a proof-of-concept biochemical model with four conflicting training objectives. JuPOETs identified optimal or near optimal solutions approximately six-fold faster than a corresponding implementation in Octave for the suite of test functions. For the proof-of-concept biochemical model, JuPOETs produced an ensemble of parameters that gave both the mean of the training data for conflicting data sets, while simultaneously estimating parameter sets that performed well on each of the individual objective functions. JuPOETs is a promising approach for the estimation of parameter and model ensembles using multiobjective optimization. JuPOETs can be adapted to solve many problem types, including mixed binary and continuous variable types, bilevel optimization problems and constrained problems without altering the base algorithm. JuPOETs is open source, available under an MIT license, and can be installed using the Julia package manager from the JuPOETs GitHub repository.

System Identification of Mistuned Bladed Disks from Traveling Wave Response Measurements

NASA Technical Reports Server (NTRS)

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.

Identification of PARMA Models and Their Application to the Modeling of River flows

NASA Astrophysics Data System (ADS)

Tesfaye, Y. G.; Meerschaert, M. M.; Anderson, P. L.

2004-05-01

The generation of synthetic river flow samples that can reproduce the essential statistical features of historical river flows is essential to the planning, design and operation of water resource systems. Most river flow series are periodically stationary; that is, their mean and covariance functions are periodic with respect to time. We employ a periodic ARMA (PARMA) model. The innovation algorithm can be used to obtain parameter estimates for PARMA models with finite fourth moment as well as infinite fourth moment but finite variance. Anderson and Meerschaert (2003) provide a method for model identification when the time series has finite fourth moment. This article, an extension of the previous work by Anderson and Meerschaert, demonstrates the effectiveness of the technique using simulated data. An application to monthly flow data for the Frazier River in British Columbia is also included to illustrate the use of these methods.

Identification and simulation evaluation of an AH-64 helicopter hover math model

NASA Technical Reports Server (NTRS)

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.

Semi-automated identification of cones in the human retina using circle Hough transform

PubMed Central

Bukowska, Danuta M.; Chew, Avenell L.; Huynh, Emily; Kashani, Irwin; Wan, Sue Ling; Wan, Pak Ming; Chen, Fred K

2015-01-01

A large number of human retinal diseases are characterized by a progressive loss of cones, the photoreceptors critical for visual acuity and color perception. Adaptive Optics (AO) imaging presents a potential method to study these cells in vivo. However, AO imaging in ophthalmology is a relatively new phenomenon and quantitative analysis of these images remains difficult and tedious using manual methods. This paper illustrates a novel semi-automated quantitative technique enabling registration of AO images to macular landmarks, cone counting and its radius quantification at specified distances from the foveal center. The new cone counting approach employs the circle Hough transform (cHT) and is compared to automated counting methods, as well as arbitrated manual cone identification. We explore the impact of varying the circle detection parameter on the validity of cHT cone counting and discuss the potential role of using this algorithm in detecting both cones and rods separately. PMID:26713186

A knowledge-based approach to identification and adaptation in dynamical systems control

NASA Technical Reports Server (NTRS)

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.

Simulation study into the identification of nuclear materials in cargo containers using cosmic rays

NASA Astrophysics Data System (ADS)

Blackwell, T. B.; Kudryavtsev, V. A.

2015-04-01

Muon tomography represents a new type of imaging technique that can be used in detecting high-Z materials. Monte Carlo simulations for muon scattering in different types of target materials are presented. The dependence of the detector capability to identify high-Z targets on spatial resolution has been studied. Muon tracks are reconstructed using a basic point of closest approach (PoCA) algorithm. In this article we report the development of a secondary analysis algorithm that is applied to the reconstructed PoCA points. This algorithm efficiently ascertains clusters of voxels with high average scattering angles to identify `areas of interest' within the inspected volume. Using this approach the effect of other parameters, such as the distance between detectors and the number of detectors per set, on material identification is also presented. Finally, false positive and false negative rates for detecting shielded HEU in realistic scenarios with low-Z clutter are presented.

Classification of rice grain varieties arranged in scattered and heap fashion using image processing

NASA Astrophysics Data System (ADS)

Bhat, Sudhanva; Panat, Sreedath; N, Arunachalam

2017-03-01

Inspection and classification of food grains is a manual process in many of the food grain processing industries. Automation of such a process is going to be beneficial for industries facing shortage of skilled workforce. Machine Vision techniques are some of the popular approaches for developing such automations. Most of the existing works on the topic deal with identification of the rice variety by analyzing images of well separated and isolated rice grains from which a lot of geometrical features can be extracted. This paper proposes techniques to estimate geometrical parameters from the images of scattered as well as heaped rice grains where the grain boundaries are not clearly identifiable. A methodology based on convexity is proposed to separate touching rice grains in the scattered rice grain images and get their geometrical parameters. And in case of heaped arrangement a Pixel-Distance Contribution Function is defined and is used to get points inside rice grains and then to find the boundary points of rice grains. These points are fit with the equation of an ellipse to estimate their lengths and breadths. The proposed techniques are applied on images of scattered and heaped rice grains of different varieties. It is shown that each variety gives a unique set of results.

Satellite angular velocity estimation based on star images and optical flow techniques.

PubMed

Fasano, Giancarmine; Rufino, Giancarlo; Accardo, Domenico; Grassi, Michele

2013-09-25

An optical flow-based technique is proposed to estimate spacecraft angular velocity based on sequences of star-field images. It does not require star identification and can be thus used to also deliver angular rate information when attitude determination is not possible, as during platform de tumbling or slewing. Region-based optical flow calculation is carried out on successive star images preprocessed to remove background. Sensor calibration parameters, Poisson equation, and a least-squares method are then used to estimate the angular velocity vector components in the sensor rotating frame. A theoretical error budget is developed to estimate the expected angular rate accuracy as a function of camera parameters and star distribution in the field of view. The effectiveness of the proposed technique is tested by using star field scenes generated by a hardware-in-the-loop testing facility and acquired by a commercial-off-the shelf camera sensor. Simulated cases comprise rotations at different rates. Experimental results are presented which are consistent with theoretical estimates. In particular, very accurate angular velocity estimates are generated at lower slew rates, while in all cases the achievable accuracy in the estimation of the angular velocity component along boresight is about one order of magnitude worse than the other two components.

Satellite Angular Velocity Estimation Based on Star Images and Optical Flow Techniques

PubMed Central

Fasano, Giancarmine; Rufino, Giancarlo; Accardo, Domenico; Grassi, Michele

2013-01-01

An optical flow-based technique is proposed to estimate spacecraft angular velocity based on sequences of star-field images. It does not require star identification and can be thus used to also deliver angular rate information when attitude determination is not possible, as during platform de tumbling or slewing. Region-based optical flow calculation is carried out on successive star images preprocessed to remove background. Sensor calibration parameters, Poisson equation, and a least-squares method are then used to estimate the angular velocity vector components in the sensor rotating frame. A theoretical error budget is developed to estimate the expected angular rate accuracy as a function of camera parameters and star distribution in the field of view. The effectiveness of the proposed technique is tested by using star field scenes generated by a hardware-in-the-loop testing facility and acquired by a commercial-off-the shelf camera sensor. Simulated cases comprise rotations at different rates. Experimental results are presented which are consistent with theoretical estimates. In particular, very accurate angular velocity estimates are generated at lower slew rates, while in all cases the achievable accuracy in the estimation of the angular velocity component along boresight is about one order of magnitude worse than the other two components. PMID:24072023

Comparison of photon correlation spectroscopy with photosedimentation analysis for the determination of aqueous colloid size distributions

USGS Publications Warehouse

Rees, Terry F.

1990-01-01

Colloidal materials, dispersed phases with dimensions between 0.001 and 1 μm, are potential transport media for a variety of contaminants in surface and ground water. Characterization of these colloids, and identification of the parameters that control their movement, are necessary before transport simulations can be attempted. Two techniques that can be used to determine the particle-size distribution of colloidal materials suspended in natural waters are compared. Photon correlation Spectroscopy (PCS) utilizes the Doppler frequency shift of photons scattered off particles undergoing Brownian motion to determine the size of colloids suspended in water. Photosedimentation analysis (PSA) measures the time-dependent change in optical density of a suspension of colloidal particles undergoing centrifugation. A description of both techniques, important underlying assumptions, and limitations are given. Results for a series of river water samples show that the colloid-size distribution means are statistically identical as determined by both techniques. This also is true of the mass median diameter (MMD), even though MMD values determined by PSA are consistently smaller than those determined by PCS. Because of this small negative bias, the skew parameters for the distributions are generally smaller for the PCS-determined distributions than for the PSA-determined distributions. Smaller polydispersity indices for the distributions are also determined by PCS.

Identification of internal properties of fibres and micro-swimmers

NASA Astrophysics Data System (ADS)

Plouraboué, Franck; Thiam, E. Ibrahima; Delmotte, Blaise; Climent, Eric

2017-01-01

In this paper, we address the identifiability of constitutive parameters of passive or active micro-swimmers. We first present a general framework for describing fibres or micro-swimmers using a bead-model description. Using a kinematic constraint formulation to describe fibres, flagellum or cilia, we find explicit linear relationship between elastic constitutive parameters and generalized velocities from computing contact forces. This linear formulation then permits one to address explicitly identifiability conditions and solve for parameter identification. We show that both active forcing and passive parameters are both identifiable independently but not simultaneously. We also provide unbiased estimators for generalized elastic parameters in the presence of Langevin-like forcing with Gaussian noise using a Bayesian approach. These theoretical results are illustrated in various configurations showing the efficiency of the proposed approach for direct parameter identification. The convergence of the proposed estimators is successfully tested numerically.

JUPITER PROJECT - JOINT UNIVERSAL PARAMETER IDENTIFICATION AND EVALUATION OF RELIABILITY

EPA Science Inventory

The JUPITER (Joint Universal Parameter IdenTification and Evaluation of Reliability) project builds on the technology of two widely used codes for sensitivity analysis, data assessment, calibration, and uncertainty analysis of environmental models: PEST and UCODE.

Laser micro-etching of metal prostheses for personal identification

PubMed Central

Ganapathy, Dhanraj; Sivaswamy, Vinay; Sekhar, Prathap

2017-01-01

Denture marking techniques play a vital role in establishing personal identification in suitable clinical and forensic situations. The denture marking techniques are categorized broadly into additive and ablative methods. Additive methods involve embedding or impregnation of markers for establishing personal identity. Ablative methods involve partial removal of the denture surface thereby providing a marking for identification. Engraving and etching methods are the commonly used ablative methods. Ablative methods can be of contact and noncontact subtypes. Laser micro-etching is a precise noncontact ablative denture marking technique that could be used for prostheses-guided personal identification. PMID:28584473

Laser micro-etching of metal prostheses for personal identification.

PubMed

Ganapathy, Dhanraj; Sivaswamy, Vinay; Sekhar, Prathap

2017-01-01

Denture marking techniques play a vital role in establishing personal identification in suitable clinical and forensic situations. The denture marking techniques are categorized broadly into additive and ablative methods. Additive methods involve embedding or impregnation of markers for establishing personal identity. Ablative methods involve partial removal of the denture surface thereby providing a marking for identification. Engraving and etching methods are the commonly used ablative methods. Ablative methods can be of contact and noncontact subtypes. Laser micro-etching is a precise noncontact ablative denture marking technique that could be used for prostheses-guided personal identification.

An overview of recent advances in system identification

NASA Technical Reports Server (NTRS)

Juang, Jer-Nan

1994-01-01

This paper presents an overview of the recent advances in system identification for modal testing and control of large flexible structures. Several techniques are discussed including the Observer/Kalman Filter Identification, the Observer/Controller Identification, and the State-Space System Identification in the Frequency Domain. The System/Observer/Controller Toolbox developed at NASA Langley Research Center is used to show the applications of these techniques to real aerospace structures such as the Hubble spacecraft telescope and the active flexible aircraft wing.

Nonlinear finite element model updating for damage identification of civil structures using batch Bayesian estimation

NASA Astrophysics Data System (ADS)

Ebrahimian, Hamed; Astroza, Rodrigo; Conte, Joel P.; de Callafon, Raymond A.

2017-02-01

This paper presents a framework for structural health monitoring (SHM) and damage identification of civil structures. This framework integrates advanced mechanics-based nonlinear finite element (FE) modeling and analysis techniques with a batch Bayesian estimation approach to estimate time-invariant model parameters used in the FE model of the structure of interest. The framework uses input excitation and dynamic response of the structure and updates a nonlinear FE model of the structure to minimize the discrepancies between predicted and measured response time histories. The updated FE model can then be interrogated to detect, localize, classify, and quantify the state of damage and predict the remaining useful life of the structure. As opposed to recursive estimation methods, in the batch Bayesian estimation approach, the entire time history of the input excitation and output response of the structure are used as a batch of data to estimate the FE model parameters through a number of iterations. In the case of non-informative prior, the batch Bayesian method leads to an extended maximum likelihood (ML) estimation method to estimate jointly time-invariant model parameters and the measurement noise amplitude. The extended ML estimation problem is solved efficiently using a gradient-based interior-point optimization algorithm. Gradient-based optimization algorithms require the FE response sensitivities with respect to the model parameters to be identified. The FE response sensitivities are computed accurately and efficiently using the direct differentiation method (DDM). The estimation uncertainties are evaluated based on the Cramer-Rao lower bound (CRLB) theorem by computing the exact Fisher Information matrix using the FE response sensitivities with respect to the model parameters. The accuracy of the proposed uncertainty quantification approach is verified using a sampling approach based on the unscented transformation. Two validation studies, based on realistic structural FE models of a bridge pier and a moment resisting steel frame, are performed to validate the performance and accuracy of the presented nonlinear FE model updating approach and demonstrate its application to SHM. These validation studies show the excellent performance of the proposed framework for SHM and damage identification even in the presence of high measurement noise and/or way-out initial estimates of the model parameters. Furthermore, the detrimental effects of the input measurement noise on the performance of the proposed framework are illustrated and quantified through one of the validation studies.

Accurate Identification of Cancerlectins through Hybrid Machine Learning Technology.

PubMed

Zhang, Jieru; Ju, Ying; Lu, Huijuan; Xuan, Ping; Zou, Quan

2016-01-01

Cancerlectins are cancer-related proteins that function as lectins. They have been identified through computational identification techniques, but these techniques have sometimes failed to identify proteins because of sequence diversity among the cancerlectins. Advanced machine learning identification methods, such as support vector machine and basic sequence features (n-gram), have also been used to identify cancerlectins. In this study, various protein fingerprint features and advanced classifiers, including ensemble learning techniques, were utilized to identify this group of proteins. We improved the prediction accuracy of the original feature extraction methods and classification algorithms by more than 10% on average. Our work provides a basis for the computational identification of cancerlectins and reveals the power of hybrid machine learning techniques in computational proteomics.

New approach to the differentiation of marble samples using thermal analysis and chemometrics in order to identify provenance

PubMed Central

2014-01-01

Background The possibility of applying a novel chemometric approach which could allow the differentiation of marble samples, all from different quarries located in the Mediterranean basin and frequently used in ancient times for artistic purposes, was investigated. By suggesting tentative or allowing to rule out unlikely attributions, this kind of differentiation could, indeed, be of valuable support to restorers and other professionals in the field of cultural heritage. Experimental data were obtained only using thermal analytical techniques: Thermogravimetry (TG), Derivative Thermogravimetry (DTG) and Differential Thermal Analysis (DTA). Results The extraction of kinetic parameters from the curves obtained using these thermal analytical techniques allowed Activation Energy values to be evaluated together with the logarithm of the Arrhenius pre-exponential factor of the main TG-DTG process. The main data thus obtained after subsequent chemometric evaluation (using Principal Components Analysis) have already proved useful in the identification the original quarry of a small number of archaeological marble finds. Conclusion One of the most evident advantages of the thermoanalytical – chemometric approach adopted seems to be that it allows the certain identification of an unknown find composed of a marble known to be present among the reference samples considered, that is, contained in the reference file. On the other hand with equal certainty it prevents the occurrence of erroneous or highly uncertain identification if the find being tested does not belong to the reference file considered. PMID:24982691

Estimation of Stability and Control Derivatives of an F-15

NASA Technical Reports Server (NTRS)

Smith, Mark; Moes, Tim

2006-01-01

A technique for real-time estimation of stability and control derivatives (derivatives of moment coefficients with respect to control-surface deflection angles) was used to support a flight demonstration of a concept of an indirect-adaptive intelligent flight control system (IFCS). Traditionally, parameter identification, including estimation of stability and control derivatives, is done post-flight. However, for the indirect-adaptive IFCS concept, parameter identification is required during flight so that the system can modify control laws for a damaged aircraft. The flight demonstration was carried out on a highly modified F-15 airplane (see Figure 1). The main objective was to estimate the stability and control derivatives of the airplane in nearly real time. A secondary goal was to develop a system to automatically assess the quality of the results, so as to be able to tell a learning neural network which data to use. Parameter estimation was performed by use of Fourier-transform regression (FTR) a technique developed at NASA Langley Research Center. FTR is an equation- error technique that operates in the frequency domain. Data are put into the frequency domain by use of a recursive Fourier transform for a discrete frequency set. This calculation simplifies many subsequent calculations, removes biases, and automatically filters out data beyond the chosen frequency range. FTR as applied here was tailored to work with pilot inputs, which produce correlated surface positions that prevent accurate parameter estimates, by replacing half the derivatives with predicted values. FTR was also set up to work only on a recent window of data, to accommodate changes in flight condition. A system of confidence measures was developed to identify quality-parameter estimates that a learning neural network could use. This system judged the estimates primarily on the basis of their estimated variances and of the level of aircraft response. The resulting FTR system was implemented in the Simulink software system and auto-coded in the C programming language for use on the Airborne Research Test System (ARTS II) computer installed in the F-15 airplane. The Simulink model was also used in a control room that utilizes the Ring Buffered Network Bus hardware and software, making it possible to evaluate test points during flights. In-flight parameter estimation was done for piloted and automated maneuvers, primarily at three test conditions. Figure 2 shows results for pitching moment due to symmetric stabilator actuations for a series of three pitch doublet maneuvers (in a doublet maneuver, a command to change attitude in a given direction by a given amount is followed immediately by a command to change attitude in the opposite direction by the same amount). A time window of 5 seconds was used. The portions of the curves shown in red are those that passed the confidence tests. The technique showed good convergence for most derivatives for both kinds of maneuvers - typically within a few seconds. The confidence tests were marginally successful, and it would be necessary to refine them for use in an IFCS.

Intelligent systems approach for automated identification of individual control behavior of a human operator

NASA Astrophysics Data System (ADS)

Zaychik, Kirill B.

Acceptable results have been obtained using conventional techniques to model the generic human operator's control behavior. However, little research has been done in an attempt to identify an individual based on his/her control behavior. The main hypothesis investigated in this dissertation is that different operators exhibit different control behavior when performing a given control task. Furthermore, inter-person differences are manifested in the amplitude and frequency content of the non-linear component of the control behavior. Two enhancements to the existing models of the human operator, which allow personalization of the modeled control behavior, are presented in this dissertation. One of the proposed enhancements accounts for the "testing" control signals, which are introduced by an operator for more accurate control of the system and/or to adjust his/her control strategy. Such enhancement uses the Artificial Neural Network (ANN), which can be fine-tuned to model the "testing" control behavior of a given individual. The other model enhancement took the form of an equiripple filter (EF), which conditions the power spectrum of the control signal before it is passed through the plant dynamics block. The filter design technique uses Parks-McClellan algorithm, which allows parameterization of the desired levels of power at certain frequencies. A novel automated parameter identification technique (APID) was developed to facilitate the identification process of the parameters of the selected models of the human operator. APID utilizes a Genetic Algorithm (GA) based optimization engine called the Bit-climbing Algorithm (BCA). Proposed model enhancements were validated using the experimental data obtained at three different sources: the Manual Control Laboratory software experiments, Unmanned Aerial Vehicle simulation, and NASA Langley Research Center Visual Motion Simulator studies. Validation analysis involves comparison of the actual and simulated control activity signals. Validation criteria used in this dissertation is based on comparing Power Spectral Densities of the control signals against that of the Precision model of the human operator. This dissertation also addresses the issue of applying the proposed human operator model augmentation to evaluate the effectiveness of the motion feedback when simulating the actual pilot control behavior in a flight simulator. The proposed modeling methodology allows for quantitative assessments and prediction of the need for platform motion, while performing aircraft/pilot simulation studies.

Inertial parameter identification using contact force information for an unknown object captured by a space manipulator

NASA Astrophysics Data System (ADS)

Chu, Zhongyi; Ma, Ye; Hou, Yueyang; Wang, Fengwen

2017-02-01

This paper presents a novel identification method for the intact inertial parameters of an unknown object in space captured by a manipulator in a space robotic system. With strong dynamic and kinematic coupling existing in the robotic system, the inertial parameter identification of the unknown object is essential for the ideal control strategy based on changes in the attitude and trajectory of the space robot via capturing operations. Conventional studies merely refer to the principle and theory of identification, and an error analysis process of identification is deficient for a practical scenario. To solve this issue, an analysis of the effect of errors on identification is illustrated first, and the accumulation of measurement or estimation errors causing poor identification precision is demonstrated. Meanwhile, a modified identification equation incorporating the contact force, as well as the force/torque of the end-effector, is proposed to weaken the accumulation of errors and improve the identification accuracy. Furthermore, considering a severe disturbance condition caused by various measured noises, the hybrid immune algorithm, Recursive Least Squares and Affine Projection Sign Algorithm (RLS-APSA), is employed to decode the modified identification equation to ensure a stable identification property. Finally, to verify the validity of the proposed identification method, the co-simulation of ADAMS-MATLAB is implemented by multi-degree of freedom models of a space robotic system, and the numerical results show a precise and stable identification performance, which is able to guarantee the execution of aerospace operations and prevent failed control strategies.

Inverse design of bulk morphologies in block copolymers using particle swarm optimization

NASA Astrophysics Data System (ADS)

Khadilkar, Mihir; Delaney, Kris; Fredrickson, Glenn

Multiblock polymers are a versatile platform for creating a large range of nanostructured materials with novel morphologies and properties. However, achieving desired structures or property combinations is difficult due to a vast design space comprised of parameters including monomer species, block sequence, block molecular weights and dispersity, copolymer architecture, and binary interaction parameters. Navigating through such vast design spaces to achieve an optimal formulation for a target structure or property set requires an efficient global optimization tool wrapped around a forward simulation technique such as self-consistent field theory (SCFT). We report on such an inverse design strategy utilizing particle swarm optimization (PSO) as the global optimizer and SCFT as the forward prediction engine. To avoid metastable states in forward prediction, we utilize pseudo-spectral variable cell SCFT initiated from a library of defect free seeds of known block copolymer morphologies. We demonstrate that our approach allows for robust identification of block copolymers and copolymer alloys that self-assemble into a targeted structure, optimizing parameters such as block fractions, blend fractions, and Flory chi parameters.

Direct system parameter identification of mechanical structures with application to modal analysis

NASA Technical Reports Server (NTRS)

Leuridan, J. M.; Brown, D. L.; Allemang, R. J.

1982-01-01

In this paper a method is described to estimate mechanical structure characteristics in terms of mass, stiffness and damping matrices using measured force input and response data. The estimated matrices can be used to calculate a consistent set of damped natural frequencies and damping values, mode shapes and modal scale factors for the structure. The proposed technique is attractive as an experimental modal analysis method since the estimation of the matrices does not require previous estimation of frequency responses and since the method can be used, without any additional complications, for multiple force input structure testing.

A predictor-corrector scheme for vortex identification

NASA Technical Reports Server (NTRS)

Singer, Bart A.; Banks, David C.

1994-01-01

A new algorithm for identifying and characterizing vortices in complex flows is presented. The scheme uses both the vorticity and pressure fields. A skeleton line along the center of a vortex is produced by a two-step predictor-corrector scheme. The technique uses the vector field to move in the direction of the skeleton line and the scalar field to correct the location in the plane perpendicular to the skeleton line. A general vortex cross section can be concisely defined with five parameters at each point along the skeleton line. The details of the method and examples of its use are discussed.

Input reconstruction of chaos sensors.

PubMed

Yu, Dongchuan; Liu, Fang; Lai, Pik-Yin

2008-06-01

Although the sensitivity of sensors can be significantly enhanced using chaotic dynamics due to its extremely sensitive dependence on initial conditions and parameters, how to reconstruct the measured signal from the distorted sensor response becomes challenging. In this paper we suggest an effective method to reconstruct the measured signal from the distorted (chaotic) response of chaos sensors. This measurement signal reconstruction method applies the neural network techniques for system structure identification and therefore does not require the precise information of the sensor's dynamics. We discuss also how to improve the robustness of reconstruction. Some examples are presented to illustrate the measurement signal reconstruction method suggested.

Computational inverse methods of heat source in fatigue damage problems

NASA Astrophysics Data System (ADS)

Chen, Aizhou; Li, Yuan; Yan, Bo

2018-04-01

Fatigue dissipation energy is the research focus in field of fatigue damage at present. It is a new idea to solve the problem of calculating fatigue dissipation energy by introducing inverse method of heat source into parameter identification of fatigue dissipation energy model. This paper introduces the research advances on computational inverse method of heat source and regularization technique to solve inverse problem, as well as the existing heat source solution method in fatigue process, prospects inverse method of heat source applying in fatigue damage field, lays the foundation for further improving the effectiveness of fatigue dissipation energy rapid prediction.

A refined Frequency Domain Decomposition tool for structural modal monitoring in earthquake engineering

NASA Astrophysics Data System (ADS)

Pioldi, Fabio; Rizzi, Egidio

2017-07-01

Output-only structural identification is developed by a refined Frequency Domain Decomposition ( rFDD) approach, towards assessing current modal properties of heavy-damped buildings (in terms of identification challenge), under strong ground motions. Structural responses from earthquake excitations are taken as input signals for the identification algorithm. A new dedicated computational procedure, based on coupled Chebyshev Type II bandpass filters, is outlined for the effective estimation of natural frequencies, mode shapes and modal damping ratios. The identification technique is also coupled with a Gabor Wavelet Transform, resulting in an effective and self-contained time-frequency analysis framework. Simulated response signals generated by shear-type frames (with variable structural features) are used as a necessary validation condition. In this context use is made of a complete set of seismic records taken from the FEMA P695 database, i.e. all 44 "Far-Field" (22 NS, 22 WE) earthquake signals. The modal estimates are statistically compared to their target values, proving the accuracy of the developed algorithm in providing prompt and accurate estimates of all current strong ground motion modal parameters. At this stage, such analysis tool may be employed for convenient application in the realm of Earthquake Engineering, towards potential Structural Health Monitoring and damage detection purposes.

F-15B Quiet Spike(TradeMark) Aeroservoelastic Flight-Test Data Analysis

NASA Technical Reports Server (NTRS)

Kukreja, Sunil L.

2007-01-01

System identification is utilized in the aerospace community for development of simulation models for robust control law design. These models are often described as linear, time-invariant processes and assumed to be uniform throughout the flight envelope. Nevertheless, it is well known that the underlying process is inherently nonlinear. Over the past several decades the controls and biomedical communities have made great advances in developing tools for the identification of nonlin ear systems. In this report, we show the application of one such nonlinear system identification technique, structure detection, for the an alysis of Quiet Spike(TradeMark)(Gulfstream Aerospace Corporation, Savannah, Georgia) aeroservoelastic flight-test data. Structure detectio n is concerned with the selection of a subset of candidate terms that best describe the observed output. Structure computation as a tool fo r black-box modeling may be of critical importance for the development of robust, parsimonious models for the flight-test community. The ob jectives of this study are to demonstrate via analysis of Quiet Spike(TradeMark) aeroservoelastic flight-test data for several flight conditions that: linear models are inefficient for modelling aeroservoelast ic data, nonlinear identification provides a parsimonious model description whilst providing a high percent fit for cross-validated data an d the model structure and parameters vary as the flight condition is altered.

Reduction of low frequency vibration of truck driver and seating system through system parameter identification, sensitivity analysis and active control

NASA Astrophysics Data System (ADS)

Wang, Xu; Bi, Fengrong; Du, Haiping

2018-05-01

This paper aims to develop an 5-degree-of-freedom driver and seating system model for optimal vibration control. A new method for identification of the driver seating system parameters from experimental vibration measurement has been developed. The parameter sensitivity analysis has been conducted considering the random excitation frequency and system parameter uncertainty. The most and least sensitive system parameters for the transmissibility ratio have been identified. The optimised PID controllers have been developed to reduce the driver's body vibration.

Finite-time master-slave synchronization and parameter identification for uncertain Lurie systems.

PubMed

Wang, Tianbo; Zhao, Shouwei; Zhou, Wuneng; Yu, Weiqin

2014-07-01

This paper investigates the finite-time master-slave synchronization and parameter identification problem for uncertain Lurie systems based on the finite-time stability theory and the adaptive control method. The finite-time master-slave synchronization means that the state of a slave system follows with that of a master system in finite time, which is more reasonable than the asymptotical synchronization in applications. The uncertainties include the unknown parameters and noise disturbances. An adaptive controller and update laws which ensures the synchronization and parameter identification to be realized in finite time are constructed. Finally, two numerical examples are given to show the effectiveness of the proposed method. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

Improving substructure identification accuracy of shear structures using virtual control system

NASA Astrophysics Data System (ADS)

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.

Design of satellite flexibility experiments

NASA Technical Reports Server (NTRS)

Kaplan, M. H.; Hillard, S. E.

1977-01-01

A preliminary study has been completed to begin development of a flight experiment to measure spacecraft control/flexible structure interaction. The work reported consists of two phases: identification of appropriate structural parameters which can be associated with flexibility phenomena, and suggestions for the development of an experiment for a satellite configuration typical of near-future vehicles which are sensitive to such effects. Recommendations are made with respect to the type of data to be collected and instrumentation associated with these data. The approach consists of developing the equations of motion for a vehicle possessing a flexible solar array, then linearizing about some nominal motion of the craft. A set of solutions are assumed for array deflection using a continuous normal mode method and important parameters are exposed. Inflight and ground based measurements are distinguished. Interrelationships between these parameters, measurement techniques, and input requirements are discussed which assure minimization of special vehicle maneuvers and optimization of data to be obtained during the normal flight sequence.

Identification of time-varying structural dynamic systems - An artificial intelligence approach

NASA Technical Reports Server (NTRS)

Glass, B. J.; Hanagud, S.

1992-01-01

An application of the artificial intelligence-derived methodologies of heuristic search and object-oriented programming to the problem of identifying the form of the model and the associated parameters of a time-varying structural dynamic system is presented in this paper. Possible model variations due to changes in boundary conditions or configurations of a structure are organized into a taxonomy of models, and a variant of best-first search is used to identify the model whose simulated response best matches that of the current physical structure. Simulated model responses are verified experimentally. An output-error approach is used in a discontinuous model space, and an equation-error approach is used in the parameter space. The advantages of the AI methods used, compared with conventional programming techniques for implementing knowledge structuring and inheritance, are discussed. Convergence conditions and example problems have been discussed. In the example problem, both the time-varying model and its new parameters have been identified when changes occur.

Modeling and parameter identification of impulse response matrix of mechanical systems

NASA Astrophysics Data System (ADS)

Bordatchev, Evgueni V.

1998-12-01

A method for studying the problem of modeling, identification and analysis of mechanical system dynamic characteristic in view of the impulse response matrix for the purpose of adaptive control is developed here. Two types of the impulse response matrices are considered: (i) on displacement, which describes the space-coupled relationship between vectors of the force and simulated displacement, which describes the space-coupled relationship between vectors of the force and simulated displacement and (ii) on acceleration, which also describes the space-coupled relationship between the vectors of the force and measured acceleration. The idea of identification consists of: (a) the practical obtaining of the impulse response matrix on acceleration by 'impact-response' technique; (b) the modeling and parameter estimation of the each impulse response function on acceleration through the fundamental representation of the impulse response function on displacement as a sum of the damped sine curves applying linear and non-linear least square methods; (c) simulating the impulse provides the additional possibility to calculate masses, damper and spring constants. The damped natural frequencies are used as a priori information and are found through the standard FFT analysis. The problem of double numerical integration is avoided by taking two derivations of the fundamental dynamic model of a mechanical system as linear combination of the mass-damper-spring subsystems. The identified impulse response matrix on displacement represents the dynamic properties of the mechanical system. From the engineering point of view, this matrix can be also understood as a 'dynamic passport' of the mechanical system and can be used for dynamic certification and analysis of the dynamic quality. In addition, the suggested approach mathematically reproduces amplitude-frequency response matrix in a low-frequency band and on zero frequency. This allows the possibility of determining the matrix of the static stiffness due to dynamic testing over the time of 10- 15 minutes. As a practical example, the dynamic properties in view of the impulse and frequency response matrices of the lathe spindle are obtained, identified and investigated. The developed approach for modeling and parameter identification appears promising for a wide range o industrial applications; for example, rotary systems.

Guided wave mode selection for inhomogeneous elastic waveguides using frequency domain finite element approach.

PubMed

Chillara, Vamshi Krishna; Ren, Baiyang; Lissenden, Cliff J

2016-04-01

This article describes the use of the frequency domain finite element (FDFE) technique for guided wave mode selection in inhomogeneous waveguides. Problems with Rayleigh-Lamb and Shear-Horizontal mode excitation in isotropic homogeneous plates are first studied to demonstrate the application of the approach. Then, two specific cases of inhomogeneous waveguides are studied using FDFE. Finally, an example of guided wave mode selection for inspecting disbonds in composites is presented. Identification of sensitive and insensitive modes for defect inspection is demonstrated. As the discretization parameters affect the accuracy of the results obtained from FDFE, effect of spatial discretization and the length of the domain used for the spatial fast Fourier transform are studied. Some recommendations with regard to the choice of the above parameters are provided. Copyright © 2015 Elsevier B.V. All rights reserved.

Quantitative segmentation of fluorescence microscopy images of heterogeneous tissue: Approach for tuning algorithm parameters

NASA Astrophysics Data System (ADS)

Mueller, Jenna L.; Harmany, Zachary T.; Mito, Jeffrey K.; Kennedy, Stephanie A.; Kim, Yongbaek; Dodd, Leslie; Geradts, Joseph; Kirsch, David G.; Willett, Rebecca M.; Brown, J. Quincy; Ramanujam, Nimmi

2013-02-01

The combination of fluorescent contrast agents with microscopy is a powerful technique to obtain real time images of tissue histology without the need for fixing, sectioning, and staining. The potential of this technology lies in the identification of robust methods for image segmentation and quantitation, particularly in heterogeneous tissues. Our solution is to apply sparse decomposition (SD) to monochrome images of fluorescently-stained microanatomy to segment and quantify distinct tissue types. The clinical utility of our approach is demonstrated by imaging excised margins in a cohort of mice after surgical resection of a sarcoma. Representative images of excised margins were used to optimize the formulation of SD and tune parameters associated with the algorithm. Our results demonstrate that SD is a robust solution that can advance vital fluorescence microscopy as a clinically significant technology.

Nondestructive damage evaluation in ceramic matrix composites for aerospace applications.

PubMed

Dassios, Konstantinos G; Kordatos, Evangelos Z; Aggelis, Dimitrios G; Matikas, Theodore E

2013-01-01

Infrared thermography (IRT) and acoustic emission (AE) are the two major nondestructive methodologies for evaluating damage in ceramic matrix composites (CMCs) for aerospace applications. The two techniques are applied herein to assess and monitor damage formation and evolution in a SiC-fiber reinforced CMC loaded under cyclic and fatigue loading. The paper explains how IRT and AE can be used for the assessment of the material's performance under fatigue. IRT and AE parameters are specifically used for the characterization of the complex damage mechanisms that occur during CMC fracture, and they enable the identification of the micromechanical processes that control material failure, mainly crack formation and propagation. Additionally, these nondestructive parameters help in early prediction of the residual life of the material and in establishing the fatigue limit of materials rapidly and accurately.

Identification of the contribution of contact and aerial biomechanical parameters in acrobatic performance

PubMed Central

Haering, Diane; Huchez, Aurore; Barbier, Franck; Holvoët, Patrice; Begon, Mickaël

2017-01-01

Introduction Teaching acrobatic skills with a minimal amount of repetition is a major challenge for coaches. Biomechanical, statistical or computer simulation tools can help them identify the most determinant factors of performance. Release parameters, change in moment of inertia and segmental momentum transfers were identified in the prediction of acrobatics success. The purpose of the present study was to evaluate the relative contribution of these parameters in performance throughout expertise or optimisation based improvements. The counter movement forward in flight (CMFIF) was chosen for its intrinsic dichotomy between the accessibility of its attempt and complexity of its mastery. Methods Three repetitions of the CMFIF performed by eight novice and eight advanced female gymnasts were recorded using a motion capture system. Optimal aerial techniques that maximise rotation potential at regrasp were also computed. A 14-segment-multibody-model defined through the Rigid Body Dynamics Library was used to compute recorded and optimal kinematics, and biomechanical parameters. A stepwise multiple linear regression was used to determine the relative contribution of these parameters in novice recorded, novice optimised, advanced recorded and advanced optimised trials. Finally, fixed effects of expertise and optimisation were tested through a mixed-effects analysis. Results and discussion Variation in release state only contributed to performances in novice recorded trials. Moment of inertia contribution to performance increased from novice recorded, to novice optimised, advanced recorded, and advanced optimised trials. Contribution to performance of momentum transfer to the trunk during the flight prevailed in all recorded trials. Although optimisation decreased transfer contribution, momentum transfer to the arms appeared. Conclusion Findings suggest that novices should be coached on both contact and aerial technique. Inversely, mainly improved aerial technique helped advanced gymnasts increase their performance. For both, reduction of the moment of inertia should be focused on. The method proposed in this article could be generalized to any aerial skill learning investigation. PMID:28422954

The Statistical Analysis Techniques to Support the NGNP Fuel Performance Experiments

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bihn T. Pham; Jeffrey J. Einerson

2010-06-01

This paper describes the development and application of statistical analysis techniques to support the AGR experimental program on NGNP fuel performance. The experiments conducted in the Idaho National Laboratory’s Advanced Test Reactor employ fuel compacts placed in a graphite cylinder shrouded by a steel capsule. The tests are instrumented with thermocouples embedded in graphite blocks and the target quantity (fuel/graphite temperature) is regulated by the He-Ne gas mixture that fills the gap volume. Three techniques for statistical analysis, namely control charting, correlation analysis, and regression analysis, are implemented in the SAS-based NGNP Data Management and Analysis System (NDMAS) for automatedmore » processing and qualification of the AGR measured data. The NDMAS also stores daily neutronic (power) and thermal (heat transfer) code simulation results along with the measurement data, allowing for their combined use and comparative scrutiny. The ultimate objective of this work includes (a) a multi-faceted system for data monitoring and data accuracy testing, (b) identification of possible modes of diagnostics deterioration and changes in experimental conditions, (c) qualification of data for use in code validation, and (d) identification and use of data trends to support effective control of test conditions with respect to the test target. Analysis results and examples given in the paper show the three statistical analysis techniques providing a complementary capability to warn of thermocouple failures. It also suggests that the regression analysis models relating calculated fuel temperatures and thermocouple readings can enable online regulation of experimental parameters (i.e. gas mixture content), to effectively maintain the target quantity (fuel temperature) within a given range.« less

Oligonucleotide (GTG)5 as a marker for Mycobacterium tuberculosis strain identification.

PubMed Central

Wiid, I J; Werely, C; Beyers, N; Donald, P; van Helden, P D

1994-01-01

Culture of Mycobacterium tuberculosis provides no information on the identity of a strain or the distribution of such a strain in the community. Strain identification of M. tuberculosis can help to address important epidemiological questions, e.g., the origin of an infection in a patient's household or community, whether reactivation of infection is endogenous or exogenous in origin, and the spread and early detection of organisms with acquired antibiotic resistance. To research this problem, strain identification must be reliable and accurate. Although genetic identification techniques already exist, it is valuable to have genetic identification techniques based on a number of genetic markers to improve the accurate identification of M. tuberculosis strains. We show that oligonucleotide (GTG)5 can be successfully applied to the identification of M. tuberculosis strains. This technique may be particularly useful in cases in which M. tuberculosis strains have few or no insertion elements (e.g., IS6110) or in identifying other strains of mycobacteria when informative probes are lacking. Images PMID:7914207

BUILDING MODEL ANALYSIS APPLICATIONS WITH THE JOINT UNIVERSAL PARAMETER IDENTIFICATION AND EVALUATION OF RELIABILITY (JUPITER) API

EPA Science Inventory

The open-source, public domain JUPITER (Joint Universal Parameter IdenTification and Evaluation of Reliability) API (Application Programming Interface) provides conventions and Fortran-90 modules to develop applications (computer programs) for analyzing process models. The input ...

A Comparison of Direction Finding Results From an FFT Peak Identification Technique With Those From the Music Algorithm

DTIC Science & Technology

1991-07-01

MUSIC ALGORITHM (U) by L.E. Montbrland go I July 1991 CRC REPORT NO. 1438 Ottawa I* Government of Canada Gouvsrnweient du Canada I o DParunnt of...FINDING RESULTS FROM AN FFT PEAK IDENTIFICATION TECHNIQUE WITH THOSE FROM THE MUSIC ALGORITHM (U) by L.E. Montbhrand CRC REPORT NO. 1438 July 1991...Ottawa A Comparison of Direction Finding Results From an FFT Peak Identification Technique With Those From the Music Algorithm L.E. Montbriand Abstract A

Post-acquisition data mining techniques for LC-MS/MS-acquired data in drug metabolite identification.

PubMed

Dhurjad, Pooja Sukhdev; Marothu, Vamsi Krishna; Rathod, Rajeshwari

2017-08-01

Metabolite identification is a crucial part of the drug discovery process. LC-MS/MS-based metabolite identification has gained widespread use, but the data acquired by the LC-MS/MS instrument is complex, and thus the interpretation of data becomes troublesome. Fortunately, advancements in data mining techniques have simplified the process of data interpretation with improved mass accuracy and provide a potentially selective, sensitive, accurate and comprehensive way for metabolite identification. In this review, we have discussed the targeted (extracted ion chromatogram, mass defect filter, product ion filter, neutral loss filter and isotope pattern filter) and untargeted (control sample comparison, background subtraction and metabolomic approaches) post-acquisition data mining techniques, which facilitate the drug metabolite identification. We have also discussed the importance of integrated data mining strategy.

Methods for using a biometric parameter in the identification of persons

DOEpatents

Hively, Lee M [Philadelphia, TN

2011-11-22

Brain waves are used as a biometric parameter to provide for authentication and identification of personnel. The brain waves are sampled using EEG equipment and are processed using phase-space distribution functions to compare digital signature data from enrollment of authorized individuals to data taken from a test subject to determine if the data from the test subject matches the signature data to a degree to support positive identification.

Integrated identification and control for nanosatellites reclaiming failed satellite

NASA Astrophysics Data System (ADS)

Han, Nan; Luo, Jianjun; Ma, Weihua; Yuan, Jianping

2018-05-01

Using nanosatellites to reclaim a failed satellite needs nanosatellites to attach to its surface to take over its attitude control function. This is challenging, since parameters including the inertia matrix of the combined spacecraft and the relative attitude information of attached nanosatellites with respect to the given body-fixed frame of the failed satellite are all unknown after the attachment. Besides, if the total control capacity needs to be increased during the reclaiming process by new nanosatellites, real-time parameters updating will be necessary. For these reasons, an integrated identification and control method is proposed in this paper, which enables the real-time parameters identification and attitude takeover control to be conducted concurrently. Identification of the inertia matrix of the combined spacecraft and the relative attitude information of attached nanosatellites are both considered. To guarantee sufficient excitation for the identification of the inertia matrix, a modified identification equation is established by filtering out sample points leading to ill-conditioned identification, and the identification performance of the inertia matrix is improved. Based on the real-time estimated inertia matrix, an attitude takeover controller is designed, the stability of the controller is analysed using Lyapunov method. The commanded control torques are allocated to each nanosatellite while the control saturation constraint being satisfied using the Quadratic Programming (QP) method. Numerical simulations are carried out to demonstrate the feasibility and effectiveness of the proposed integrated identification and control method.

Parameter identification for nonlinear aerodynamic systems

NASA Technical Reports Server (NTRS)

Pearson, Allan E.

1990-01-01

Parameter identification for nonlinear aerodynamic systems is examined. It is presumed that the underlying model can be arranged into an input/output (I/O) differential operator equation of a generic form. The algorithm estimation is especially efficient since the equation error can be integrated exactly given any I/O pair to obtain an algebraic function of the parameters. The algorithm for parameter identification was extended to the order determination problem for linear differential system. The degeneracy in a least squares estimate caused by feedback was addressed. A method of frequency analysis for determining the transfer function G(j omega) from transient I/O data was formulated using complex valued Fourier based modulating functions in contrast with the trigonometric modulating functions for the parameter estimation problem. A simulation result of applying the algorithm is given under noise-free conditions for a system with a low pass transfer function.

Biometric Authentication for Gender Classification Techniques: A Review

NASA Astrophysics Data System (ADS)

Mathivanan, P.; Poornima, K.

2017-12-01

One of the challenging biometric authentication applications is gender identification and age classification, which captures gait from far distance and analyze physical information of the subject such as gender, race and emotional state of the subject. It is found that most of the gender identification techniques have focused only with frontal pose of different human subject, image size and type of database used in the process. The study also classifies different feature extraction process such as, Principal Component Analysis (PCA) and Local Directional Pattern (LDP) that are used to extract the authentication features of a person. This paper aims to analyze different gender classification techniques that help in evaluating strength and weakness of existing gender identification algorithm. Therefore, it helps in developing a novel gender classification algorithm with less computation cost and more accuracy. In this paper, an overview and classification of different gender identification techniques are first presented and it is compared with other existing human identification system by means of their performance.

Identification Of Natural Dyes On Archaeological Textile Objects Using Laser Induced Fluorescent Technique

DOE Office of Scientific and Technical Information (OSTI.GOV)

Abdel-Kareem, O.; Eltokhy, A.; Harith, M. A.

2011-09-22

This study aims to evaluate the use of Laser Fluorescent as a non-destructive technique for identification of natural dyes on archaeological textile objects. In this study wool textile samples were dyed with 10 natural dyes such as cochineal, cutch, henna, indigo, Lac, madder, safflower, saffron, sumac and turmeric. These dyes common present on archaeological textile objects to be used as standard dyed textile samples. These selected natural dyes will be used as known references that can be used a guide to identify unknown archaeological dyes. The dyed textile samples were investigated with laser radiation in different wavelengths to detect themore » best wavelengths for identification each dye. This study confirms that Laser Florescent is very useful and a rapid technique can be used as a non-destructive technique for identification of natural dyes on archaeological textile objects. The results obtained with this study can be a guide for all conservators in identification of natural organic dyes on archaeological textile objects.« less

Identification Of Natural Dyes On Archaeological Textile Objects Using Laser Induced Fluorescent Technique

NASA Astrophysics Data System (ADS)

Abdel-Kareem, O.; Eltokhy, A.; Harith, M. A.

2011-09-01

This study aims to evaluate the use of Laser Fluorescent as a non-destructive technique for identification of natural dyes on archaeological textile objects. In this study wool textile samples were dyed with 10 natural dyes such as cochineal, cutch, henna, indigo, Lac, madder, safflower, saffron, sumac and turmeric. These dyes common present on archaeological textile objects to be used as standard dyed textile samples. These selected natural dyes will be used as known references that can be used a guide to identify unknown archaeological dyes. The dyed textile samples were investigated with laser radiation in different wavelengths to detect the best wavelengths for identification each dye. This study confirms that Laser Florescent is very useful and a rapid technique can be used as a non-destructive technique for identification of natural dyes on archaeological textile objects. The results obtained with this study can be a guide for all conservators in identification of natural organic dyes on archaeological textile objects.

P. falciparum in vitro killing rates allow to discriminate between different antimalarial mode-of-action.

PubMed

Sanz, Laura M; Crespo, Benigno; De-Cózar, Cristina; Ding, Xavier C; Llergo, Jose L; Burrows, Jeremy N; García-Bustos, Jose F; Gamo, Francisco-Javier

2012-01-01

Chemotherapy is still the cornerstone for malaria control. Developing drugs against Plasmodium parasites and monitoring their efficacy requires methods to accurately determine the parasite killing rate in response to treatment. Commonly used techniques essentially measure metabolic activity as a proxy for parasite viability. However, these approaches are susceptible to artefacts, as viability and metabolism are two parameters that are coupled during the parasite life cycle but can be differentially affected in response to drug actions. Moreover, traditional techniques do not allow to measure the speed-of-action of compounds on parasite viability, which is an essential efficacy determinant. We present here a comprehensive methodology to measure in vitro the direct effect of antimalarial compounds over the parasite viability, which is based on limiting serial dilution of treated parasites and re-growth monitoring. This methodology allows to precisely determine the killing rate of antimalarial compounds, which can be quantified by the parasite reduction ratio and parasite clearance time, which are key mode-of-action parameters. Importantly, we demonstrate that this technique readily permits to determine compound killing activities that might be otherwise missed by traditional, metabolism-based techniques. The analysis of a large set of antimalarial drugs reveals that this viability-based assay allows to discriminate compounds based on their antimalarial mode-of-action. This approach has been adapted to perform medium throughput screening, facilitating the identification of fast-acting antimalarial compounds, which are crucially needed for the control and possibly the eradication of malaria.

P. falciparum In Vitro Killing Rates Allow to Discriminate between Different Antimalarial Mode-of-Action

PubMed Central

Sanz, Laura M.; Crespo, Benigno; De-Cózar, Cristina; Ding, Xavier C.; Llergo, Jose L.; Burrows, Jeremy N.; García-Bustos, Jose F.; Gamo, Francisco-Javier

2012-01-01

Chemotherapy is still the cornerstone for malaria control. Developing drugs against Plasmodium parasites and monitoring their efficacy requires methods to accurately determine the parasite killing rate in response to treatment. Commonly used techniques essentially measure metabolic activity as a proxy for parasite viability. However, these approaches are susceptible to artefacts, as viability and metabolism are two parameters that are coupled during the parasite life cycle but can be differentially affected in response to drug actions. Moreover, traditional techniques do not allow to measure the speed-of-action of compounds on parasite viability, which is an essential efficacy determinant. We present here a comprehensive methodology to measure in vitro the direct effect of antimalarial compounds over the parasite viability, which is based on limiting serial dilution of treated parasites and re-growth monitoring. This methodology allows to precisely determine the killing rate of antimalarial compounds, which can be quantified by the parasite reduction ratio and parasite clearance time, which are key mode-of-action parameters. Importantly, we demonstrate that this technique readily permits to determine compound killing activities that might be otherwise missed by traditional, metabolism-based techniques. The analysis of a large set of antimalarial drugs reveals that this viability-based assay allows to discriminate compounds based on their antimalarial mode-of-action. This approach has been adapted to perform medium throughput screening, facilitating the identification of fast-acting antimalarial compounds, which are crucially needed for the control and possibly the eradication of malaria. PMID:22383983

Identification and calibration of the structural model of historical masonry building damaged during the 2016 Italian earthquakes: The case study of Palazzo del Podestà in Montelupone

NASA Astrophysics Data System (ADS)

Catinari, Federico; Pierdicca, Alessio; Clementi, Francesco; Lenci, Stefano

2017-11-01

The results of an ambient-vibration based investigation conducted on the "Palazzo del Podesta" in Montelupone (Italy) is presented. The case study was damaged during the 20I6 Italian earthquakes that stroke the central part of the Italy. The assessment procedure includes full-scale ambient vibration testing, modal identification from ambient vibration responses, finite element modeling and dynamic-based identification of the uncertain structural parameters of the model. A very good match between theoretical and experimental modal parameters was reached and the model updating has been performed identifying some structural parameters.

Load Modeling – A Review

DOE PAGES

Arif, Anmar; Wang, Zhaoyu; Wang, Jianhui; ...

2017-05-02

Load modeling has significant impact on power system studies. This paper presents a review on load modeling and identification techniques. Load models can be classified into two broad categories: static and dynamic models, while there are two types of approaches to identify model parameters: measurement-based and component-based. Load modeling has received more attention in recent years because of the renewable integration, demand-side management, and smart metering devices. However, the commonly used load models are outdated, and cannot represent emerging loads. There is a need to systematically review existing load modeling techniques and suggest future research directions to meet the increasingmore » interests from industry and academia. In this study, we provide a thorough survey on the academic research progress and industry practices, and highlight existing issues and new trends in load modeling.« less

Load Modeling – A Review

DOE Office of Scientific and Technical Information (OSTI.GOV)

Arif, Anmar; Wang, Zhaoyu; Wang, Jianhui

Load modeling has significant impact on power system studies. This paper presents a review on load modeling and identification techniques. Load models can be classified into two broad categories: static and dynamic models, while there are two types of approaches to identify model parameters: measurement-based and component-based. Load modeling has received more attention in recent years because of the renewable integration, demand-side management, and smart metering devices. However, the commonly used load models are outdated, and cannot represent emerging loads. There is a need to systematically review existing load modeling techniques and suggest future research directions to meet the increasingmore » interests from industry and academia. In this study, we provide a thorough survey on the academic research progress and industry practices, and highlight existing issues and new trends in load modeling.« less

Methods for the isolation and identification of polycyclic aromatic hydrocarbons found in complex mixtures and the determination of their possible toxicity by means of a host mediated bioassay technique. Progress report, July 1, 1976--February 1, 1977. [Cultured mouse leumemia cell bioassay system

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lipsky, S.R.; Alexander, G.; McMurray, W.

1977-02-01

Techniques were developed to produce excellent high performance glass capillary columns for gas chromatographic analyses of a wide range of complex mixtures of organic compounds, including those containing a wide array of polycyclic aromatic hydrocarbons (PAH) derived from a coal liquefaction process. Work was begun to assess the potential mutogenicity and/or carcinogenicity of the various isolated PAH fractions utilizing a unique host mediated bioassay system. Preliminary results indicate that further efforts will be required to determine dose response parameters of cultured mouse leukemia cells, as well as suitable vehicles for the satisfactory introduction of certain PAH fractions into this particularmore » bioassay system.« less

Identification of hydrological model parameter variation using ensemble Kalman filter

NASA Astrophysics Data System (ADS)

Deng, Chao; Liu, Pan; Guo, Shenglian; Li, Zejun; Wang, Dingbao

2016-12-01

Hydrological model parameters play an important role in the ability of model prediction. In a stationary context, parameters of hydrological models are treated as constants; however, model parameters may vary with time under climate change and anthropogenic activities. The technique of ensemble Kalman filter (EnKF) is proposed to identify the temporal variation of parameters for a two-parameter monthly water balance model (TWBM) by assimilating the runoff observations. Through a synthetic experiment, the proposed method is evaluated with time-invariant (i.e., constant) parameters and different types of parameter variations, including trend, abrupt change and periodicity. Various levels of observation uncertainty are designed to examine the performance of the EnKF. The results show that the EnKF can successfully capture the temporal variations of the model parameters. The application to the Wudinghe basin shows that the water storage capacity (SC) of the TWBM model has an apparent increasing trend during the period from 1958 to 2000. The identified temporal variation of SC is explained by land use and land cover changes due to soil and water conservation measures. In contrast, the application to the Tongtianhe basin shows that the estimated SC has no significant variation during the simulation period of 1982-2013, corresponding to the relatively stationary catchment properties. The evapotranspiration parameter (C) has temporal variations while no obvious change patterns exist. The proposed method provides an effective tool for quantifying the temporal variations of the model parameters, thereby improving the accuracy and reliability of model simulations and forecasts.

Identification of internal properties of fibers and micro-swimmers

NASA Astrophysics Data System (ADS)

Plouraboue, Franck; Thiam, Ibrahima; Delmotte, Blaise; Climent, Eric; PSC Collaboration

2016-11-01

In this presentation we discuss the identifiability of constitutive parameters of passive or active micro-swimmers. We first present a general framework for describing fibers or micro-swimmers using a bead-model description. Using a kinematic constraint formulation to describe fibers, flagellum or cilia, we find explicit linear relationship between elastic constitutive parameters and generalised velocities from computing contact forces. This linear formulation then permits to address explicitly identifiability conditions and solve for parameter identification. We show that both active forcing and passive parameters are both identifiable independently but not simultaneously. We also provide unbiased estimators for elastic parameters as well as active ones in the presence of Langevin-like forcing with Gaussian noise using normal linear regression models and maximum likelihood method. These theoretical results are illustrated in various configurations of relaxed or actuated passives fibers, and active filament of known passive properties, showing the efficiency of the proposed approach for direct parameter identification. The convergence of the proposed estimators is successfully tested numerically.

Trying to detect gas-phase ions? Understanding Ion Mobility Spectrometry

PubMed Central

Cumeras, R.; Figueras, E.; Davis, C.E.; Baumbach, J.I.; Gràcia, I.

2014-01-01

Ion Mobility Spectrometry (IMS) is a widely used and ‘well-known’ technique of ion separation in gaseous phase based on the differences of ion mobilities under an electric field. This technique has received increased interest over the last several decades as evidenced by the pace and advances of new IMS devices available. In this review we explore the hyphenated techniques that are used with IMS, especially mass spectrometry as identification approach and multi-capillary column as pre-separation approach. Also, we will pay special attention to the key figures of merit of the ion mobility spectrum and how data is treated, and the influences of the experimental parameters in both a conventional drift time IMS (DTIMS) and a miniaturized IMS also known as high Field Asymmetric IMS (FAIMS) in the planar configuration. The current review article is preceded by a companion review article which details the current instrumentation and to the sections that configures both a conventional DTIMS and FAIMS devices. Those reviews will give the reader an insightful view of the main characteristics and aspects of the IMS technique. PMID:25465248

Short Term Load Forecasting with Fuzzy Logic Systems for power system planning and reliability-A Review

NASA Astrophysics Data System (ADS)

Holmukhe, R. M.; Dhumale, Mrs. Sunita; Chaudhari, Mr. P. S.; Kulkarni, Mr. P. P.

2010-10-01

Load forecasting is very essential to the operation of Electricity companies. It enhances the energy efficient and reliable operation of power system. Forecasting of load demand data forms an important component in planning generation schedules in a power system. The purpose of this paper is to identify issues and better method for load foecasting. In this paper we focus on fuzzy logic system based short term load forecasting. It serves as overview of the state of the art in the intelligent techniques employed for load forecasting in power system planning and reliability. Literature review has been conducted and fuzzy logic method has been summarized to highlight advantages and disadvantages of this technique. The proposed technique for implementing fuzzy logic based forecasting is by Identification of the specific day and by using maximum and minimum temperature for that day and finally listing the maximum temperature and peak load for that day. The results show that Load forecasting where there are considerable changes in temperature parameter is better dealt with Fuzzy Logic system method as compared to other short term forecasting techniques.

Age determination by teeth examination: a comparison between different morphologic and quantitative analyses.

PubMed

Amariti, M L; Restori, M; De Ferrari, F; Paganelli, C; Faglia, R; Legnani, G

1999-06-01

Age determination by teeth examination is one of the main means of determining personal identification. Current studies have suggested different techniques for determining the age of a subject by means of the analysis of microscopic and macroscopic structural modifications of the tooth with ageing. The histological approach is useful among the various methodologies utilized for this purpose. It is still unclear as to what is the best technique, as almost all the authors suggest the use of the approach they themselves have tested. In the present study, age determination by means of microscopic techniques has been based on the quantitative analysis of three parameters, all well recognized in specialized literature: 1. dentinal tubules density/sclerosis 2. tooth translucency 3. analysis of the cementum thickness. After a description of the three methodologies (with automatic image processing of the dentinal sclerosis utilizing an appropriate computer program developed by the authors) the results obtained on cases using the three different approaches are presented, and the merits and failings of each technique are identified with the intention of identifying the one offering the least degree of error in age determination.

Construction and identification of a D-Vine model applied to the probability distribution of modal parameters in structural dynamics

NASA Astrophysics Data System (ADS)

Dubreuil, S.; Salaün, M.; Rodriguez, E.; Petitjean, F.

2018-01-01

This study investigates the construction and identification of the probability distribution of random modal parameters (natural frequencies and effective parameters) in structural dynamics. As these parameters present various types of dependence structures, the retained approach is based on pair copula construction (PCC). A literature review leads us to choose a D-Vine model for the construction of modal parameters probability distributions. Identification of this model is based on likelihood maximization which makes it sensitive to the dimension of the distribution, namely the number of considered modes in our context. To this respect, a mode selection preprocessing step is proposed. It allows the selection of the relevant random modes for a given transfer function. The second point, addressed in this study, concerns the choice of the D-Vine model. Indeed, D-Vine model is not uniquely defined. Two strategies are proposed and compared. The first one is based on the context of the study whereas the second one is purely based on statistical considerations. Finally, the proposed approaches are numerically studied and compared with respect to their capabilities, first in the identification of the probability distribution of random modal parameters and second in the estimation of the 99 % quantiles of some transfer functions.

Probing the chemistry of nickel/metal hydride battery cells using electrochemical impedance spectroscopy

NASA Technical Reports Server (NTRS)

Isaac, Bryan J.

1994-01-01

Electrochemical Impedance Spectroscopy (EIS) is a valuable tool for investigating the chemical and physical processes occurring at electrode surfaces. It offers information about electron transfer at interfaces, kinetics of reactions, and diffusion characteristics of the bulk phase between the electrodes. For battery cells, this technique offers another advantage in that it can be done without taking the battery apart. This non-destructive analysis technique can thus be used to gain a better understanding of the processes occurring within a battery cell. This also raises the possibility of improvements in battery design and identification or prediction of battery characteristics useful in industry and aerospace applications. EIS as a technique is powerful and capable of yielding significant information about the cell, but it also requires that the many parameters under investigation can be resolved. This implies an understanding of the processes occurring in a battery cell. Many battery types were surveyed in this work, but the main emphasis was on nickel/metal hydride batteries.

Cerebral microbleeds: a guide to detection and interpretation.

PubMed

Greenberg, Steven M; Vernooij, Meike W; Cordonnier, Charlotte; Viswanathan, Anand; Al-Shahi Salman, Rustam; Warach, Steven; Launer, Lenore J; Van Buchem, Mark A; Breteler, Monique Mb

2009-02-01

Cerebral microbleeds (CMBs) are increasingly recognised neuroimaging findings in individuals with cerebrovascular disease and dementia, and in normal ageing. There has been substantial progress in the understanding of CMBs in recent years, particularly in the development of newer MRI methods for the detection of CMBs and the application of these techniques to population-based samples of elderly people. In this Review, we focus on these recent developments and their effects on two main questions: how CMBs are detected, and how CMBs should be interpreted. The number of CMBs detected depends on MRI characteristics, such as pulse sequence, sequence parameters, spatial resolution, magnetic field strength, and image post-processing, emphasising the importance of taking into account MRI technique in the interpretation of study results. Recent investigations with sensitive MRI techniques have indicated a high prevalence of CMBs in community-dwelling elderly people. We propose a procedural guide for identification of CMBs and suggest possible future approaches for elucidating the role of these common lesions as markers for, and contributors to, small-vessel brain disease.

Towards large-scale FAME-based bacterial species identification using machine learning techniques.

PubMed

Slabbinck, Bram; De Baets, Bernard; Dawyndt, Peter; De Vos, Paul

2009-05-01

In the last decade, bacterial taxonomy witnessed a huge expansion. The swift pace of bacterial species (re-)definitions has a serious impact on the accuracy and completeness of first-line identification methods. Consequently, back-end identification libraries need to be synchronized with the List of Prokaryotic names with Standing in Nomenclature. In this study, we focus on bacterial fatty acid methyl ester (FAME) profiling as a broadly used first-line identification method. From the BAME@LMG database, we have selected FAME profiles of individual strains belonging to the genera Bacillus, Paenibacillus and Pseudomonas. Only those profiles resulting from standard growth conditions have been retained. The corresponding data set covers 74, 44 and 95 validly published bacterial species, respectively, represented by 961, 378 and 1673 standard FAME profiles. Through the application of machine learning techniques in a supervised strategy, different computational models have been built for genus and species identification. Three techniques have been considered: artificial neural networks, random forests and support vector machines. Nearly perfect identification has been achieved at genus level. Notwithstanding the known limited discriminative power of FAME analysis for species identification, the computational models have resulted in good species identification results for the three genera. For Bacillus, Paenibacillus and Pseudomonas, random forests have resulted in sensitivity values, respectively, 0.847, 0.901 and 0.708. The random forests models outperform those of the other machine learning techniques. Moreover, our machine learning approach also outperformed the Sherlock MIS (MIDI Inc., Newark, DE, USA). These results show that machine learning proves very useful for FAME-based bacterial species identification. Besides good bacterial identification at species level, speed and ease of taxonomic synchronization are major advantages of this computational species identification strategy.

Helicopter mathematical models and control law development for handling qualities research

NASA Technical Reports Server (NTRS)

Chen, Robert T. N.; Lebacqz, J. Victor; Aiken, Edwin W.; Tischler, Mark B.

1988-01-01

Progress made in joint NASA/Army research concerning rotorcraft flight-dynamics modeling, design methodologies for rotorcraft flight-control laws, and rotorcraft parameter identification is reviewed. Research into these interactive disciplines is needed to develop the analytical tools necessary to conduct flying qualities investigations using both the ground-based and in-flight simulators, and to permit an efficient means of performing flight test evaluation of rotorcraft flying qualities for specification compliance. The need for the research is particularly acute for rotorcraft because of their mathematical complexity, high order dynamic characteristics, and demanding mission requirements. The research in rotorcraft flight-dynamics modeling is pursued along two general directions: generic nonlinear models and nonlinear models for specific rotorcraft. In addition, linear models are generated that extend their utilization from 1-g flight to high-g maneuvers and expand their frequency range of validity for the design analysis of high-gain flight control systems. A variety of methods ranging from classical frequency-domain approaches to modern time-domain control methodology that are used in the design of rotorcraft flight control laws is reviewed. Also reviewed is a study conducted to investigate the design details associated with high-gain, digital flight control systems for combat rotorcraft. Parameter identification techniques developed for rotorcraft applications are reviewed.

Use of SDWBA predictions for acoustic volume backscattering and the Self-Organizing Map to discern frequencies identifying Meganyctiphanes norvegica from mesopelagic fish species

NASA Astrophysics Data System (ADS)

Peña, M.; Calise, L.

2016-04-01

To acoustically assess the biomass of multiple species or taxa within a survey region, the volume backscatter data should be apportioned to the constituent sound scatterers. Typically, measured backscatter is attributed to certain species using predictions at different frequencies, mostly based on the difference in scattering at the frequencies of 38 and 120 kHz (dual frequency method). We used the full version of the stochastic distortedwave Born approximation (SDWBA) model to predict backscatter spectra for Meganyctiphanes norvegica and to explore the sensitivities of ΔMVBS to the model parameters, e.g. acoustic frequency and incidence angle, and animal density and sound speed contrast, length, and shape. The orientation is almost the unique parameter responsible for variation, with fatness affecting longer lengths. We present a summary of ΔMVBS that can serve as the basis for identification algorithms. Next, we simulate the scenario encountered in the Balearic Sea (western Mediterranean) where Northern krill are mixed with mesopelagic fish species (bristlemouths and lanternfishes), which are modeled with a prolate spheroid model. Simulated numerical data are employed to emulate the discrimination process with the most common identification techniques and typical survey frequencies. The importance of using density-independent techniques for acoustic classification is highlighted. Finally, an unsupervised neural network, the Self-Organizing Map (SOM), is used to cluster these theoretical data and identify the frequencies that provide, in this case, the most classification potential. The simulation results confirm that pairs of frequencies spanning the Rayleigh and geometric scattering regimes of the targets are the most useful for clustering; a minimum of four frequencies are necessary to separate the three species, while three frequencies are able to differentiate krill from mesopelagic fish species.

Optimization of multilayer neural network parameters for speaker recognition

NASA Astrophysics Data System (ADS)

Tovarek, Jaromir; Partila, Pavol; Rozhon, Jan; Voznak, Miroslav; Skapa, Jan; Uhrin, Dominik; Chmelikova, Zdenka

2016-05-01

This article discusses the impact of multilayer neural network parameters for speaker identification. The main task of speaker identification is to find a specific person in the known set of speakers. It means that the voice of an unknown speaker (wanted person) belongs to a group of reference speakers from the voice database. One of the requests was to develop the text-independent system, which means to classify wanted person regardless of content and language. Multilayer neural network has been used for speaker identification in this research. Artificial neural network (ANN) needs to set parameters like activation function of neurons, steepness of activation functions, learning rate, the maximum number of iterations and a number of neurons in the hidden and output layers. ANN accuracy and validation time are directly influenced by the parameter settings. Different roles require different settings. Identification accuracy and ANN validation time were evaluated with the same input data but different parameter settings. The goal was to find parameters for the neural network with the highest precision and shortest validation time. Input data of neural networks are a Mel-frequency cepstral coefficients (MFCC). These parameters describe the properties of the vocal tract. Audio samples were recorded for all speakers in a laboratory environment. Training, testing and validation data set were split into 70, 15 and 15 %. The result of the research described in this article is different parameter setting for the multilayer neural network for four speakers.

Research on remote sensing identification of rural abandoned homesteads using multiparameter characteristics method

NASA Astrophysics Data System (ADS)

Xu, Saiping; Zhao, Qianjun; Yin, Kai; Cui, Bei; Zhang, Xiupeng

2016-10-01

Hollow village is a special phenomenon in the process of urbanization in China, which causes the waste of land resources. Therefore, it's imminent to carry out the hollow village recognition and renovation. However, there are few researches on the remote sensing identification of hollow village. In this context, in order to recognize the abandoned homesteads by remote sensing technique, the experiment was carried out as follows. Firstly, Gram-Schmidt transform method was utilized to complete the image fusion between multi-spectral images and panchromatic image of WorldView-2. Then the fusion images were made edge enhanced by high pass filtering. The multi-resolution segmentation and spectral difference segmentation were carried out to obtain the image objects. Secondly, spectral characteristic parameters were calculated, such as the normalized difference vegetation index (NDVI), the normalized difference water index (NDWI), the normalized difference Soil index (NDSI) etc. The shape feature parameters were extracted, such as Area, Length/Width Ratio and Rectangular Fit etc.. Thirdly, the SEaTH algorithm was used to determine the thresholds and optimize the feature space. Furthermore, the threshold classification method and the random forest classifier were combined, and the appropriate amount of samples were selected to train the classifier in order to determine the important feature parameters and the best classifier parameters involved in classification. Finally, the classification results was verified by computing the confusion matrix. The classification results were continuous and the phenomenon of salt and pepper using pixel classification was avoided effectively. In addition, the results showed that the extracted Abandoned Homesteads were in complete shapes, which could be distinguished from those confusing classes such as Homestead in Use and Roads.

Identification and assessment of hazardous compounds in drinking water.

PubMed

Fawell, J K; Fielding, M

1985-12-01

The identification of organic chemicals in drinking water and their assessment in terms of potential hazardous effects are two very different but closely associated tasks. In relation to both continuous low-level background contamination and specific, often high-level, contamination due to pollution incidents, the identification of contaminants is a pre-requisite to evaluation of significant hazards. Even in the case of the rapidly developing short-term bio-assays which are applied to water to indicate a potential genotoxic hazard (for example Ames tests), identification of the active chemicals is becoming a major factor in the further assessment of the response. Techniques for the identification of low concentrations of organic chemicals in drinking water have developed remarkably since the early 1970s and methods based upon gas chromatography-mass spectrometry (GC-MS) have revolutionised qualitative analysis of water. Such techniques are limited to "volatile" chemicals and these usually constitute a small fraction of the total organic material in water. However, in recent years there have been promising developments in techniques for "non-volatile" chemicals in water. Such techniques include combined high-performance liquid chromatography-mass spectrometry (HPLC-MS) and a variety of MS methods, involving, for example, field desorption, fast atom bombardment and thermospray ionisation techniques. In the paper identification techniques in general are reviewed and likely future developments outlined. The assessment of hazards associated with chemicals identified in drinking and related waters usually centres upon toxicology - an applied science which involves numerous disciplines. The paper examines the toxicological information needed, the quality and deployment of such information and discusses future research needs. Application of short-term bio-assays to drinking water is a developing area and one which is closely involved with, and to some extent dependent on, powerful methods of identification. Recent developments are discussed.

Signal and array processing techniques for RFID readers

NASA Astrophysics Data System (ADS)

Wang, Jing; Amin, Moeness; Zhang, Yimin

2006-05-01

Radio Frequency Identification (RFID) has recently attracted much attention in both the technical and business communities. It has found wide applications in, for example, toll collection, supply-chain management, access control, localization tracking, real-time monitoring, and object identification. Situations may arise where the movement directions of the tagged RFID items through a portal is of interest and must be determined. Doppler estimation may prove complicated or impractical to perform by RFID readers. Several alternative approaches, including the use of an array of sensors with arbitrary geometry, can be applied. In this paper, we consider direction-of-arrival (DOA) estimation techniques for application to near-field narrowband RFID problems. Particularly, we examine the use of a pair of RFID antennas to track moving RFID tagged items through a portal. With two antennas, the near-field DOA estimation problem can be simplified to a far-field problem, yielding a simple way for identifying the direction of the tag movement, where only one parameter, the angle, needs to be considered. In this case, tracking of the moving direction of the tag simply amounts to computing the spatial cross-correlation between the data samples received at the two antennas. It is pointed out that the radiation patterns of the reader and tag antennas, particularly their phase characteristics, have a significant effect on the performance of DOA estimation. Indoor experiments are conducted in the Radar Imaging and RFID Labs at Villanova University for validating the proposed technique for target movement direction estimations.

A methodology for airplane parameter estimation and confidence interval determination in nonlinear estimation problems. Ph.D. Thesis - George Washington Univ., Apr. 1985

NASA Technical Reports Server (NTRS)

Murphy, P. C.

1986-01-01

An algorithm for maximum likelihood (ML) estimation is developed with an efficient method for approximating the sensitivities. The ML algorithm relies on a new optimization method referred to as a modified Newton-Raphson with estimated sensitivities (MNRES). MNRES determines sensitivities by using slope information from local surface approximations of each output variable in parameter space. With the fitted surface, sensitivity information can be updated at each iteration with less computational effort than that required by either a finite-difference method or integration of the analytically determined sensitivity equations. MNRES eliminates the need to derive sensitivity equations for each new model, and thus provides flexibility to use model equations in any convenient format. A random search technique for determining the confidence limits of ML parameter estimates is applied to nonlinear estimation problems for airplanes. The confidence intervals obtained by the search are compared with Cramer-Rao (CR) bounds at the same confidence level. The degree of nonlinearity in the estimation problem is an important factor in the relationship between CR bounds and the error bounds determined by the search technique. Beale's measure of nonlinearity is developed in this study for airplane identification problems; it is used to empirically correct confidence levels and to predict the degree of agreement between CR bounds and search estimates.

A Program for the Identification of the Enterobacteriaceae for Use in Teaching the Principles of Computer Identification of Bacteria.

ERIC Educational Resources Information Center

Hammonds, S. J.

1990-01-01

A technique for the numerical identification of bacteria using normalized likelihoods calculated from a probabilistic database is described, and the principles of the technique are explained. The listing of the computer program is included. Specimen results from the program, and examples of how they should be interpreted, are given. (KR)

Reconstruction of Atmospheric Tracer Releases with Optimal Resolution Features: Concentration Data Assimilation

NASA Astrophysics Data System (ADS)

Singh, Sarvesh Kumar; Turbelin, Gregory; Issartel, Jean-Pierre; Kumar, Pramod; Feiz, Amir Ali

2015-04-01

The fast growing urbanization, industrialization and military developments increase the risk towards the human environment and ecology. This is realized in several past mortality incidents, for instance, Chernobyl nuclear explosion (Ukraine), Bhopal gas leak (India), Fukushima-Daichi radionuclide release (Japan), etc. To reduce the threat and exposure to the hazardous contaminants, a fast and preliminary identification of unknown releases is required by the responsible authorities for the emergency preparedness and air quality analysis. Often, an early detection of such contaminants is pursued by a distributed sensor network. However, identifying the origin and strength of unknown releases following the sensor reported concentrations is a challenging task. This requires an optimal strategy to integrate the measured concentrations with the predictions given by the atmospheric dispersion models. This is an inverse problem. The measured concentrations are insufficient and atmospheric dispersion models suffer from inaccuracy due to the lack of process understanding, turbulence uncertainties, etc. These lead to a loss of information in the reconstruction process and thus, affect the resolution, stability and uniqueness of the retrieved source. An additional well known issue is the numerical artifact arisen at the measurement locations due to the strong concentration gradient and dissipative nature of the concentration. Thus, assimilation techniques are desired which can lead to an optimal retrieval of the unknown releases. In general, this is facilitated within the Bayesian inference and optimization framework with a suitable choice of a priori information, regularization constraints, measurement and background error statistics. An inversion technique is introduced here for an optimal reconstruction of unknown releases using limited concentration measurements. This is based on adjoint representation of the source-receptor relationship and utilization of a weight function which exhibits a priori information about the unknown releases apparent to the monitoring network. The properties of the weight function provide an optimal data resolution and model resolution to the retrieved source estimates. The retrieved source estimates are proved theoretically to be stable against the random measurement errors and their reliability can be interpreted in terms of the distribution of the weight functions. Further, the same framework can be extended for the identification of the point type releases by utilizing the maximum of the retrieved source estimates. The inversion technique has been evaluated with the several diffusion experiments, like, Idaho low wind diffusion experiment (1974), IIT Delhi tracer experiment (1991), European Tracer Experiment (1994), Fusion Field Trials (2007), etc. In case of point release experiments, the source parameters are mostly retrieved close to the true source parameters with least error. Primarily, the proposed technique overcomes two major difficulties incurred in the source reconstruction: (i) The initialization of the source parameters as required by the optimization based techniques. The converged solution depends on their initialization. (ii) The statistical knowledge about the measurement and background errors as required by the Bayesian inference based techniques. These are hypothetically assumed in case of no prior knowledge.

Identification procedure for epistemic uncertainties using inverse fuzzy arithmetic

NASA Astrophysics Data System (ADS)

Haag, T.; Herrmann, J.; Hanss, M.

2010-10-01

For the mathematical representation of systems with epistemic uncertainties, arising, for example, from simplifications in the modeling procedure, models with fuzzy-valued parameters prove to be a suitable and promising approach. In practice, however, the determination of these parameters turns out to be a non-trivial problem. The identification procedure to appropriately update these parameters on the basis of a reference output (measurement or output of an advanced model) requires the solution of an inverse problem. Against this background, an inverse method for the computation of the fuzzy-valued parameters of a model with epistemic uncertainties is presented. This method stands out due to the fact that it only uses feedforward simulations of the model, based on the transformation method of fuzzy arithmetic, along with the reference output. An inversion of the system equations is not necessary. The advancement of the method presented in this paper consists of the identification of multiple input parameters based on a single reference output or measurement. An optimization is used to solve the resulting underdetermined problems by minimizing the uncertainty of the identified parameters. Regions where the identification procedure is reliable are determined by the computation of a feasibility criterion which is also based on the output data of the transformation method only. For a frequency response function of a mechanical system, this criterion allows a restriction of the identification process to some special range of frequency where its solution can be guaranteed. Finally, the practicability of the method is demonstrated by covering the measured output of a fluid-filled piping system by the corresponding uncertain FE model in a conservative way.

Identification of open quantum systems from observable time traces

DOE PAGES

Zhang, Jun; Sarovar, Mohan

2015-05-27

Estimating the parameters that dictate the dynamics of a quantum system is an important task for quantum information processing and quantum metrology, as well as fundamental physics. In our paper we develop a method for parameter estimation for Markovian open quantum systems using a temporal record of measurements on the system. Furthermore, the method is based on system realization theory and is a generalization of our previous work on identification of Hamiltonian parameters.

Application of physical parameter identification to finite-element models

NASA Technical Reports Server (NTRS)

Bronowicki, Allen J.; Lukich, Michael S.; Kuritz, Steven P.

1987-01-01

The time domain parameter identification method described previously is applied to TRW's Large Space Structure Truss Experiment. Only control sensors and actuators are employed in the test procedure. The fit of the linear structural model to the test data is improved by more than an order of magnitude using a physically reasonable parameter set. The electro-magnetic control actuators are found to contribute significant damping due to a combination of eddy current and back electro-motive force (EMF) effects. Uncertainties in both estimated physical parameters and modal behavior variables are given.

Parameter identification of civil engineering structures

NASA Technical Reports Server (NTRS)

Juang, J. N.; Sun, C. T.

1980-01-01

This paper concerns the development of an identification method required in determining structural parameter variations for systems subjected to an extended exposure to the environment. The concept of structural identifiability of a large scale structural system in the absence of damping is presented. Three criteria are established indicating that a large number of system parameters (the coefficient parameters of the differential equations) can be identified by a few actuators and sensors. An eight-bay-fifteen-story frame structure is used as example. A simple model is employed for analyzing the dynamic response of the frame structure.

Active learning of cortical connectivity from two-photon imaging data.

PubMed

Bertrán, Martín A; Martínez, Natalia L; Wang, Ye; Dunson, David; Sapiro, Guillermo; Ringach, Dario

2018-01-01

Understanding how groups of neurons interact within a network is a fundamental question in system neuroscience. Instead of passively observing the ongoing activity of a network, we can typically perturb its activity, either by external sensory stimulation or directly via techniques such as two-photon optogenetics. A natural question is how to use such perturbations to identify the connectivity of the network efficiently. Here we introduce a method to infer sparse connectivity graphs from in-vivo, two-photon imaging of population activity in response to external stimuli. A novel aspect of the work is the introduction of a recommended distribution, incrementally learned from the data, to optimally refine the inferred network. Unlike existing system identification techniques, this "active learning" method automatically focuses its attention on key undiscovered areas of the network, instead of targeting global uncertainty indicators like parameter variance. We show how active learning leads to faster inference while, at the same time, provides confidence intervals for the network parameters. We present simulations on artificial small-world networks to validate the methods and apply the method to real data. Analysis of frequency of motifs recovered show that cortical networks are consistent with a small-world topology model.

Design and Mechanical Evaluation of a Capacitive Sensor-Based Indexed Platform for Verification of Portable Coordinate Measuring Instruments

PubMed Central

Avila, Agustín Brau; Mazo, Jorge Santolaria; Martín, Juan José Aguilar

2014-01-01

During the last years, the use of Portable Coordinate Measuring Machines (PCMMs) in industry has increased considerably, mostly due to their flexibility for accomplishing in-line measuring tasks as well as their reduced costs and operational advantages as compared to traditional coordinate measuring machines (CMMs). However, their operation has a significant drawback derived from the techniques applied in the verification and optimization procedures of their kinematic parameters. These techniques are based on the capture of data with the measuring instrument from a calibrated gauge object, fixed successively in various positions so that most of the instrument measuring volume is covered, which results in time-consuming, tedious and expensive verification procedures. In this work the mechanical design of an indexed metrology platform (IMP) is presented. The aim of the IMP is to increase the final accuracy and to radically simplify the calibration, identification and verification of geometrical parameter procedures of PCMMs. The IMP allows us to fix the calibrated gauge object and move the measuring instrument in such a way that it is possible to cover most of the instrument working volume, reducing the time and operator fatigue to carry out these types of procedures. PMID:24451458

Design and mechanical evaluation of a capacitive sensor-based indexed platform for verification of portable coordinate measuring instruments.

PubMed

Avila, Agustín Brau; Mazo, Jorge Santolaria; Martín, Juan José Aguilar

2014-01-02

During the last years, the use of Portable Coordinate Measuring Machines (PCMMs) in industry has increased considerably, mostly due to their flexibility for accomplishing in-line measuring tasks as well as their reduced costs and operational advantages as compared to traditional coordinate measuring machines (CMMs). However, their operation has a significant drawback derived from the techniques applied in the verification and optimization procedures of their kinematic parameters. These techniques are based on the capture of data with the measuring instrument from a calibrated gauge object, fixed successively in various positions so that most of the instrument measuring volume is covered, which results in time-consuming, tedious and expensive verification procedures. In this work the mechanical design of an indexed metrology platform (IMP) is presented. The aim of the IMP is to increase the final accuracy and to radically simplify the calibration, identification and verification of geometrical parameter procedures of PCMMs. The IMP allows us to fix the calibrated gauge object and move the measuring instrument in such a way that it is possible to cover most of the instrument working volume, reducing the time and operator fatigue to carry out these types of procedures.

The Global Signature of Ocean Wave Spectra

NASA Astrophysics Data System (ADS)

Portilla-Yandún, Jesús

2018-01-01

A global atlas of ocean wave spectra is developed and presented. The development is based on a new technique for deriving wave spectral statistics, which is applied to the extensive ERA-Interim database from European Centre of Medium-Range Weather Forecasts. Spectral statistics is based on the idea of long-term wave systems, which are unique and distinct at every geographical point. The identification of those wave systems allows their separation from the overall spectrum using the partition technique. Their further characterization is made using standard integrated parameters, which turn out much more meaningful when applied to the individual components than to the total spectrum. The parameters developed include the density distribution of spectral partitions, which is the main descriptor; the identified wave systems; the individual distribution of the characteristic frequencies, directions, wave height, wave age, seasonal variability of wind and waves; return periods derived from extreme value analysis; and crossing-sea probabilities. This information is made available in web format for public use at http://www.modemat.epn.edu.ec/#/nereo. It is found that wave spectral statistics offers the possibility to synthesize data while providing a direct and comprehensive view of the local and regional wave conditions.

Active learning of cortical connectivity from two-photon imaging data

PubMed Central

Wang, Ye; Dunson, David; Sapiro, Guillermo; Ringach, Dario

2018-01-01

Understanding how groups of neurons interact within a network is a fundamental question in system neuroscience. Instead of passively observing the ongoing activity of a network, we can typically perturb its activity, either by external sensory stimulation or directly via techniques such as two-photon optogenetics. A natural question is how to use such perturbations to identify the connectivity of the network efficiently. Here we introduce a method to infer sparse connectivity graphs from in-vivo, two-photon imaging of population activity in response to external stimuli. A novel aspect of the work is the introduction of a recommended distribution, incrementally learned from the data, to optimally refine the inferred network. Unlike existing system identification techniques, this “active learning” method automatically focuses its attention on key undiscovered areas of the network, instead of targeting global uncertainty indicators like parameter variance. We show how active learning leads to faster inference while, at the same time, provides confidence intervals for the network parameters. We present simulations on artificial small-world networks to validate the methods and apply the method to real data. Analysis of frequency of motifs recovered show that cortical networks are consistent with a small-world topology model. PMID:29718955

Sources of hydrocarbons in urban road dust: Identification, quantification and prediction.

PubMed

Mummullage, Sandya; Egodawatta, Prasanna; Ayoko, Godwin A; Goonetilleke, Ashantha

2016-09-01

Among urban stormwater pollutants, hydrocarbons are a significant environmental concern due to their toxicity and relatively stable chemical structure. This study focused on the identification of hydrocarbon contributing sources to urban road dust and approaches for the quantification of pollutant loads to enhance the design of source control measures. The study confirmed the validity of the use of mathematical techniques of principal component analysis (PCA) and hierarchical cluster analysis (HCA) for source identification and principal component analysis/absolute principal component scores (PCA/APCS) receptor model for pollutant load quantification. Study outcomes identified non-combusted lubrication oils, non-combusted diesel fuels and tyre and asphalt wear as the three most critical urban hydrocarbon sources. The site specific variabilities of contributions from sources were replicated using three mathematical models. The models employed predictor variables of daily traffic volume (DTV), road surface texture depth (TD), slope of the road section (SLP), effective population (EPOP) and effective impervious fraction (EIF), which can be considered as the five governing parameters of pollutant generation, deposition and redistribution. Models were developed such that they can be applicable in determining hydrocarbon contributions from urban sites enabling effective design of source control measures. Copyright © 2016 Elsevier Ltd. All rights reserved.

Automated Detection of Clouds in Satellite Imagery

NASA Technical Reports Server (NTRS)

Jedlovec, Gary

2010-01-01

Many different approaches have been used to automatically detect clouds in satellite imagery. Most approaches are deterministic and provide a binary cloud - no cloud product used in a variety of applications. Some of these applications require the identification of cloudy pixels for cloud parameter retrieval, while others require only an ability to mask out clouds for the retrieval of surface or atmospheric parameters in the absence of clouds. A few approaches estimate a probability of the presence of a cloud at each point in an image. These probabilities allow a user to select cloud information based on the tolerance of the application to uncertainty in the estimate. Many automated cloud detection techniques develop sophisticated tests using a combination of visible and infrared channels to determine the presence of clouds in both day and night imagery. Visible channels are quite effective in detecting clouds during the day, as long as test thresholds properly account for variations in surface features and atmospheric scattering. Cloud detection at night is more challenging, since only courser resolution infrared measurements are available. A few schemes use just two infrared channels for day and night cloud detection. The most influential factor in the success of a particular technique is the determination of the thresholds for each cloud test. The techniques which perform the best usually have thresholds that are varied based on the geographic region, time of year, time of day and solar angle.

Parameter Identification and Uncertainty Analysis for Visual MODFLOW based Groundwater Flow Model in a Small River Basin, Eastern India

NASA Astrophysics Data System (ADS)

Jena, S.

2015-12-01

The overexploitation of groundwater resulted in abandoning many shallow tube wells in the river Basin in Eastern India. For the sustainability of groundwater resources, basin-scale modelling of groundwater flow is essential for the efficient planning and management of the water resources. The main intent of this study is to develope a 3-D groundwater flow model of the study basin using the Visual MODFLOW package and successfully calibrate and validate it using 17 years of observed data. The sensitivity analysis was carried out to quantify the susceptibility of aquifer system to the river bank seepage, recharge from rainfall and agriculture practices, horizontal and vertical hydraulic conductivities, and specific yield. To quantify the impact of parameter uncertainties, Sequential Uncertainty Fitting Algorithm (SUFI-2) and Markov chain Monte Carlo (MCMC) techniques were implemented. Results from the two techniques were compared and the advantages and disadvantages were analysed. Nash-Sutcliffe coefficient (NSE) and coefficient of determination (R2) were adopted as two criteria during calibration and validation of the developed model. NSE and R2 values of groundwater flow model for calibration and validation periods were in acceptable range. Also, the MCMC technique was able to provide more reasonable results than SUFI-2. The calibrated and validated model will be useful to identify the aquifer properties, analyse the groundwater flow dynamics and the change in groundwater levels in future forecasts.

Sentinel Lymph Node Detection Using Carbon Nanoparticles in Patients with Early Breast Cancer

PubMed Central

Lu, Jianping; Zeng, Yi; Chen, Xia; Yan, Jun

2015-01-01

Purpose Carbon nanoparticles have a strong affinity for the lymphatic system. The purpose of this study was to evaluate the feasibility of sentinel lymph node biopsy using carbon nanoparticles in early breast cancer and to optimize the application procedure. Methods Firstly, we performed a pilot study to demonstrate the optimized condition using carbon nanoparticles for sentinel lymph nodes (SLNs) detection by investigating 36 clinically node negative breast cancer patients. In subsequent prospective study, 83 patients with clinically node negative breast cancer were included to evaluate SLNs using carbon nanoparticles. Another 83 SLNs were detected by using blue dye. SLNs detection parameters were compared between the methods. All patients irrespective of the SLNs status underwent axillary lymph node dissection for verification of axillary node status after the SLN biopsy. Results In pilot study, a 1 ml carbon nanoparticles suspension used 10–15min before surgery was associated with the best detection rate. In subsequent prospective study, with carbon nanoparticles, the identification rate, accuracy, false negative rate was 100%, 96.4%, 11.1%, respectively. The identification rate and accuracy were 88% and 95.5% with 15.8% of false negative rate using blue dye technique. The use of carbon nanoparticles suspension showed significantly superior results in identification rate (p = 0.001) and reduced false-negative results compared with blue dye technique. Conclusion Our study demonstrated feasibility and accuracy of using carbon nanoparticles for SLNs mapping in breast cancer patients. Carbon nanoparticles are useful in SLNs detection in institutions without access to radioisotope. PMID:26296136

Technical management techniques for identification and control of industrial safety and pollution hazards

NASA Technical Reports Server (NTRS)

Campbell, R.; Dyer, M. K.; Hoard, E. G.; Little, D. G.; Taylor, A. C.

1972-01-01

Constructive recommendations are suggested for pollution problems from offshore energy resources industries on outer continental shelf. Technical management techniques for pollution identification and control offer possible applications to space engineering and management.

Optimal Design of Material and Process Parameters in Powder Injection Molding

NASA Astrophysics Data System (ADS)

Ayad, G.; Barriere, T.; Gelin, J. C.; Song, J.; Liu, B.

2007-04-01

The paper is concerned with optimization and parametric identification for the different stages in Powder Injection Molding process that consists first in injection of powder mixture with polymer binder and then to the sintering of the resulting powders part by solid state diffusion. In the first part, one describes an original methodology to optimize the process and geometry parameters in injection stage based on the combination of design of experiments and an adaptive Response Surface Modeling. Then the second part of the paper describes the identification strategy that one proposes for the sintering stage, using the identification of sintering parameters from dilatometeric curves followed by the optimization of the sintering process. The proposed approaches are applied to the optimization of material and process parameters for manufacturing a ceramic femoral implant. One demonstrates that the proposed approach give satisfactory results.

3D topography measurements on correlation cells—a new approach to forensic ballistics identifications

NASA Astrophysics Data System (ADS)

Song, John; Chu, Wei; Tong, Mingsi; Soons, Johannes

2014-06-01

Based on three-dimensional (3D) topography measurements on correlation cells, the National Institute of Standards and Technology (NIST) has developed the ‘NIST Ballistics Identification System (NBIS)’ aimed at accurate ballistics identifications and fast ballistics evidence searches. The 3D topographies are divided into arrays of correlation cells to identify ‘valid correlation areas’ and eliminate ‘invalid correlation areas’ from the matching and identification procedure. A ‘congruent matching cells’ (CMC)’ method using three types of identification parameters of the paired correlation cells (cross correlation function maximum CCFmax, spatial registration position in x-y and registration angle θ) is used for high accuracy ballistics identifications. ‘Synchronous processing’ is proposed for correlating multiple cell pairs at the same time to increase the correlation speed. The proposed NBIS can be used for correlations of both geometrical topographies and optical intensity images. All the correlation parameters and algorithms are in the public domain and subject to open tests. An error rate reporting procedure has been developed that can greatly add to the scientific support for the firearm and toolmark identification specialty, and give confidence to the trier of fact in court proceedings. The NBIS is engineered to employ transparent identification parameters and criteria, statistical models and correlation algorithms. In this way, interoperability between different ballistics identification systems can be more easily achieved. This interoperability will make the NBIS suitable for ballistics identifications and evidence searches with large national databases, such as the National Integrated Ballistic Information Network in the United States.

Target Capturing Control for Space Robots with Unknown Mass Properties: A Self-Tuning Method Based on Gyros and Cameras.

PubMed

Li, Zhenyu; Wang, Bin; Liu, Hong

2016-08-30

Satellite capturing with free-floating space robots is still a challenging task due to the non-fixed base and unknown mass property issues. In this paper gyro and eye-in-hand camera data are adopted as an alternative choice for solving this problem. For this improved system, a new modeling approach that reduces the complexity of system control and identification is proposed. With the newly developed model, the space robot is equivalent to a ground-fixed manipulator system. Accordingly, a self-tuning control scheme is applied to handle such a control problem including unknown parameters. To determine the controller parameters, an estimator is designed based on the least-squares technique for identifying the unknown mass properties in real time. The proposed method is tested with a credible 3-dimensional ground verification experimental system, and the experimental results confirm the effectiveness of the proposed control scheme.

Target Capturing Control for Space Robots with Unknown Mass Properties: A Self-Tuning Method Based on Gyros and Cameras

PubMed Central

Li, Zhenyu; Wang, Bin; Liu, Hong

2016-01-01

Satellite capturing with free-floating space robots is still a challenging task due to the non-fixed base and unknown mass property issues. In this paper gyro and eye-in-hand camera data are adopted as an alternative choice for solving this problem. For this improved system, a new modeling approach that reduces the complexity of system control and identification is proposed. With the newly developed model, the space robot is equivalent to a ground-fixed manipulator system. Accordingly, a self-tuning control scheme is applied to handle such a control problem including unknown parameters. To determine the controller parameters, an estimator is designed based on the least-squares technique for identifying the unknown mass properties in real time. The proposed method is tested with a credible 3-dimensional ground verification experimental system, and the experimental results confirm the effectiveness of the proposed control scheme. PMID:27589748

Vertical-Substrate MPCVD Epitaxial Nanodiamond Growth

DOE PAGES

Tzeng, Yan-Kai; Zhang, Jingyuan Linda; Lu, Haiyu; ...

2017-02-09

Color center-containing nanodiamonds have many applications in quantum technologies and biology. Diamondoids, molecular-sized diamonds have been used as seeds in chemical vapor deposition (CVD) growth. However, optimizing growth conditions to produce high crystal quality nanodiamonds with color centers requires varying growth conditions that often leads to ad-hoc and time-consuming, one-at-a-time testing of reaction conditions. In order to rapidly explore parameter space, we developed a microwave plasma CVD technique using a vertical, rather than horizontally oriented stage-substrate geometry. With this configuration, temperature, plasma density, and atomic hydrogen density vary continuously along the vertical axis of the substrate. Finally, this variation allowedmore » rapid identification of growth parameters that yield single crystal diamonds down to 10 nm in size and 75 nm diameter optically active center silicon-vacancy (Si-V) nanoparticles. Furthermore, this method may provide a means of incorporating a wide variety of dopants in nanodiamonds without ion irradiation damage.« less

The determination of third order linear models from a seventh order nonlinear jet engine model

NASA Technical Reports Server (NTRS)

Lalonde, Rick J.; Hartley, Tom T.; De Abreu-Garcia, J. Alex

1989-01-01

Results are presented that demonstrate how good reduced-order models can be obtained directly by recursive parameter identification using input/output (I/O) data of high-order nonlinear systems. Three different methods of obtaining a third-order linear model from a seventh-order nonlinear turbojet engine model are compared. The first method is to obtain a linear model from the original model and then reduce the linear model by standard reduction techniques such as residualization and balancing. The second method is to identify directly a third-order linear model by recursive least-squares parameter estimation using I/O data of the original model. The third method is to obtain a reduced-order model from the original model and then linearize the reduced model. Frequency responses are used as the performance measure to evaluate the reduced models. The reduced-order models along with their Bode plots are presented for comparison purposes.

An improved wavelet-Galerkin method for dynamic response reconstruction and parameter identification of shear-type frames

NASA Astrophysics Data System (ADS)

Bu, Haifeng; Wang, Dansheng; Zhou, Pin; Zhu, Hongping

2018-04-01

An improved wavelet-Galerkin (IWG) method based on the Daubechies wavelet is proposed for reconstructing the dynamic responses of shear structures. The proposed method flexibly manages wavelet resolution level according to excitation, thereby avoiding the weakness of the wavelet-Galerkin multiresolution analysis (WGMA) method in terms of resolution and the requirement of external excitation. IWG is implemented by this work in certain case studies, involving single- and n-degree-of-freedom frame structures subjected to a determined discrete excitation. Results demonstrate that IWG performs better than WGMA in terms of accuracy and computation efficiency. Furthermore, a new method for parameter identification based on IWG and an optimization algorithm are also developed for shear frame structures, and a simultaneous identification of structural parameters and excitation is implemented. Numerical results demonstrate that the proposed identification method is effective for shear frame structures.

An approximation theory for the identification of nonlinear distributed parameter systems

NASA Technical Reports Server (NTRS)

Banks, H. T.; Reich, Simeon; Rosen, I. G.

1988-01-01

An abstract approximation framework for the identification of nonlinear distributed parameter systems is developed. Inverse problems for nonlinear systems governed by strongly maximal monotone operators (satisfying a mild continuous dependence condition with respect to the unknown parameters to be identified) are treated. Convergence of Galerkin approximations and the corresponding solutions of finite dimensional approximating identification problems to a solution of the original finite dimensional identification problem is demonstrated using the theory of nonlinear evolution systems and a nonlinear analog of the Trotter-Kato approximation result for semigroups of bounded linear operators. The nonlinear theory developed here is shown to subsume an existing linear theory as a special case. It is also shown to be applicable to a broad class of nonlinear elliptic operators and the corresponding nonlinear parabolic partial differential equations to which they lead. An application of the theory to a quasilinear model for heat conduction or mass transfer is discussed.

A Novel Identification Methodology for the Coordinate Relationship between a 3D Vision System and a Legged Robot.

PubMed

Chai, Xun; Gao, Feng; Pan, Yang; Qi, Chenkun; Xu, Yilin

2015-04-22

Coordinate identification between vision systems and robots is quite a challenging issue in the field of intelligent robotic applications, involving steps such as perceiving the immediate environment, building the terrain map and planning the locomotion automatically. It is now well established that current identification methods have non-negligible limitations such as a difficult feature matching, the requirement of external tools and the intervention of multiple people. In this paper, we propose a novel methodology to identify the geometric parameters of 3D vision systems mounted on robots without involving other people or additional equipment. In particular, our method focuses on legged robots which have complex body structures and excellent locomotion ability compared to their wheeled/tracked counterparts. The parameters can be identified only by moving robots on a relatively flat ground. Concretely, an estimation approach is provided to calculate the ground plane. In addition, the relationship between the robot and the ground is modeled. The parameters are obtained by formulating the identification problem as an optimization problem. The methodology is integrated on a legged robot called "Octopus", which can traverse through rough terrains with high stability after obtaining the identification parameters of its mounted vision system using the proposed method. Diverse experiments in different environments demonstrate our novel method is accurate and robust.

Engineering studies related to geodetic and oceanographic remote sensing using short pulse techniques

NASA Technical Reports Server (NTRS)

Miller, L. S.; Brown, G. S.; Hayne, G. S.

1973-01-01

For the Skylab S-193 radar altimeter, data processing flow charts and identification of calibration requirements and problem areas for defined S-193 altimeter experiments are presented. An analysis and simulation of the relationship between one particular S-193 measurement and the parameter of interest for determining the sea surface scattering cross-section are considered. For the GEOS-C radar altimeter, results are presented for system analyses pertaining to signal-to-noise ratio, pulse compression threshold behavior, altimeter measurement variance characteristics, desirability of onboard averaging, tracker bandwidth considerations, and statistical character of the altimeter data in relation to harmonic analysis properties of the geodetic signal.

Dynamic modeling of GMA fillet welding using cross-correlation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hellinga, M.; Huissoon, J.; Kerr, H.

1996-12-31

The feasibility of employing the cross-correlation system identification technique as a dynamic modeling method for the GMAW process was examined. This approach has the advantages of modeling speed, the ability to operate in low signal to noise environments, the ease of digital implementation, and the lack of model order assumption, making it ideal in a welding application. The width of the weld pool was the parameter investigated as a function of torch travel speed. Both on-line and off-line width measurements were used to identify the impulse response. Experimental results are presented and comparisons made with both step and ramp response.

Smart Optical RAM for Fast Information Management and Analysis

NASA Technical Reports Server (NTRS)

Liu, Hua-Kuang

1998-01-01

Statement of Problem Instruments for high speed and high capacity in-situ data identification, classification and storage capabilities are needed by NASA for the information management and analysis of extremely large volume of data sets in future space exploration, space habitation and utilization, in addition to the various missions to planet-earth programs. Parameters such as communication delays, limited resources, and inaccessibility of human manipulation require more intelligent, compact, low power, and light weight information management and data storage techniques. New and innovative algorithms and architecture using photonics will enable us to meet these challenges. The technology has applications for other government and public agencies.

Identification of boiler inlet transfer functions and estimation of system parameters

NASA Technical Reports Server (NTRS)

Miles, J. H.

1972-01-01

An iterative computer method is described for identifying boiler transfer functions using frequency response data. An objective penalized performance measure and a nonlinear minimization technique are used to cause the locus of points generated by a transfer function to resemble the locus of points obtained from frequency response measurements. Different transfer functions can be tried until a satisfactory empirical transfer function of the system is found. To illustrate the method, some examples and some results from a study of a set of data consisting of measurements of the inlet impedance of a single tube forced flow boiler with inserts are given.

Fluorescence Spectroscopy for the Monitoring of Food Processes.

PubMed

Ahmad, Muhammad Haseeb; Sahar, Amna; Hitzmann, Bernd

Different analytical techniques have been used to examine the complexity of food samples. Among them, fluorescence spectroscopy cannot be ignored in developing rapid and non-invasive analytical methodologies. It is one of the most sensitive spectroscopic approaches employed in identification, classification, authentication, quantification, and optimization of different parameters during food handling, processing, and storage and uses different chemometric tools. Chemometrics helps to retrieve useful information from spectral data utilized in the characterization of food samples. This contribution discusses in detail the potential of fluorescence spectroscopy of different foods, such as dairy, meat, fish, eggs, edible oil, cereals, fruit, vegetables, etc., for qualitative and quantitative analysis with different chemometric approaches.

Numerical analysis of the effect of the kind of activating agent and the impregnation ratio on the parameters of the microporous structure of the active carbons

NASA Astrophysics Data System (ADS)

Kwiatkowski, Mirosław

2015-09-01

The paper presents the results of the research on the application of the LBET class adsorption models with the fast multivariant identification procedure as a tool for analysing the microporous structure of the active carbons obtained by chemical activation using potassium and sodium hydroxides as an activator. The proposed technique of the fast multivariant fitting of the LBET class models to the empirical adsorption data was employed particularly to evaluate the impact of the used activator and the impregnation ratio on the obtained microporous structure of the carbonaceous adsorbents.

Identification of mine rescue equipment reduction gears technical condition

NASA Astrophysics Data System (ADS)

Gerike, B. L.; Klishin, V. I.; Kuzin, E. G.

2017-09-01

The article presents the reasons for adopting intelligent service of mine belt conveyer drives concerning evaluation of their technical condition based on the diagnostic techniques instead of regular preventative maintenance. The article reveals the diagnostic results of belt conveyer drive reduction gears condition taking into account the parameters of lubricating oil, vibration and temperature. Usage of a complex approach to evaluate technical conditions allows reliability of the forecast to be improved, which makes it possible not only to prevent accidental breakdowns and eliminate unscheduled downtime, but also to bring sufficient economic benefits through reduction of the term and scope of work during overhauls.

System Identification for the Clipper Liberty C96 Wind Turbine

NASA Astrophysics Data System (ADS)

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.

TOXICITY IDENTIFICATION EVALUATION (TIE) RESULTS FOR METAL CONTAMINATED SEDIMENTS

EPA Science Inventory

Identification of contaminants in sediment is necessary for sound management decisions on sediment disposal, remediation, determination of ecological risk, and source identification. We have been developing sediment toxicity identification evaluation (TIE) techniques that allow ...

The dimensional reduction method for identification of parameters that trade-off due to similar model roles.

PubMed

Davidson, Shaun M; Docherty, Paul D; Murray, Rua

2017-03-01

Parameter identification is an important and widely used process across the field of biomedical engineering. However, it is susceptible to a number of potential difficulties, such as parameter trade-off, causing premature convergence at non-optimal parameter values. The proposed Dimensional Reduction Method (DRM) addresses this issue by iteratively reducing the dimension of hyperplanes where trade off occurs, and running subsequent identification processes within these hyperplanes. The DRM was validated using clinical data to optimize 4 parameters of the widely used Bergman Minimal Model of glucose and insulin kinetics, as well as in-silico data to optimize 5 parameters of the Pulmonary Recruitment (PR) Model. Results were compared with the popular Levenberg-Marquardt (LMQ) Algorithm using a Monte-Carlo methodology, with both methods afforded equivalent computational resources. The DRM converged to a lower or equal residual value in all tests run using the Bergman Minimal Model and actual patient data. For the PR model, the DRM attained significantly lower overall median parameter error values and lower residuals in the vast majority of tests. This shows the DRM has potential to provide better resolution of optimum parameter values for the variety of biomedical models in which significant levels of parameter trade-off occur. Copyright © 2017 Elsevier Inc. All rights reserved.

Motion capture based identification of the human body inertial parameters.

PubMed

Venture, Gentiane; Ayusawa, Ko; Nakamura, Yoshihiko

2008-01-01

Identification of body inertia, masses and center of mass is an important data to simulate, monitor and understand dynamics of motion, to personalize rehabilitation programs. This paper proposes an original method to identify the inertial parameters of the human body, making use of motion capture data and contact forces measurements. It allows in-vivo painless estimation and monitoring of the inertial parameters. The method is described and then obtained experimental results are presented and discussed.

Crop Identification Technolgy Assessment for Remote Sensing (CITARS). Volume 1: Task design plan

NASA Technical Reports Server (NTRS)

Hall, F. G.; Bizzell, R. M.

1975-01-01

A plan for quantifying the crop identification performances resulting from the remote identification of corn, soybeans, and wheat is described. Steps for the conversion of multispectral data tapes to classification results are specified. The crop identification performances resulting from the use of several basic types of automatic data processing techniques are compared and examined for significant differences. The techniques are evaluated also for changes in geographic location, time of the year, management practices, and other physical factors. The results of the Crop Identification Technology Assessment for Remote Sensing task will be applied extensively in the Large Area Crop Inventory Experiment.

Parameter estimation and actuator characteristics of hybrid magnetic bearings for axial flow blood pump applications.

PubMed

Lim, Tau Meng; Cheng, Shanbao; Chua, Leok Poh

2009-07-01

Axial flow blood pumps are generally smaller as compared to centrifugal pumps. This is very beneficial because they can provide better anatomical fit in the chest cavity, as well as lower the risk of infection. This article discusses the design, levitated responses, and parameter estimation of the dynamic characteristics of a compact hybrid magnetic bearing (HMB) system for axial flow blood pump applications. The rotor/impeller of the pump is driven by a three-phase permanent magnet brushless and sensorless motor. It is levitated by two HMBs at both ends in five degree of freedom with proportional-integral-derivative controllers, among which four radial directions are actively controlled and one axial direction is passively controlled. The frequency domain parameter estimation technique with statistical analysis is adopted to validate the stiffness and damping coefficients of the HMB system. A specially designed test rig facilitated the estimation of the bearing's coefficients in air-in both the radial and axial directions. Experimental estimation showed that the dynamic characteristics of the HMB system are dominated by the frequency-dependent stiffness coefficients. By injecting a multifrequency excitation force signal onto the rotor through the HMBs, it is noticed in the experimental results the maximum displacement linear operating range is 20% of the static eccentricity with respect to the rotor and stator gap clearance. The actuator gain was also successfully calibrated and may potentially extend the parameter estimation technique developed in the study of identification and monitoring of the pump's dynamic properties under normal operating conditions with fluid.

A hybrid system identification methodology for wireless structural health monitoring systems based on dynamic substructuring

NASA Astrophysics Data System (ADS)

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.

Advances in Raman spectroscopy for In Situ Identification of Minerals and Organics on Diverse Planetary Surfaces: from Mars to Titan

NASA Astrophysics Data System (ADS)

Blacksberg, J.; Alerstam, E.; Maruyama, Y.; Cochrane, C.; Rossman, G. R.

2015-12-01

We present recent developments in time-resolved Raman spectroscopy for in situ planetary surface exploration, aimed at identification of both minerals and organics. Raman is a non-destructive surface technique that requires no sample preparation. Raman spectra are highly material specific and can be used for identification of a wide range of unknown samples. In combination with micro-scale imaging and point mapping, Raman spectroscopy can be used to directly interrogate rocks and regolith materials, while placing compositional analyses within a microtextural context, essential for understanding surface evolutionary pathways. Due to these unique capabilities, Raman spectroscopy is of great interest for the exploration of all rocky and icy bodies, for example Mars, Venus, the Moon, Mars' moons, asteroids, comets, Europa, and Titan. In this work, we focus on overcoming one of the most difficult challenges faced in Raman spectroscopy: interference from background fluorescence of the very minerals and organics that we wish to characterize. To tackle this problem we use time-resolved Raman spectroscopy, which separates the Raman from background processes in the time domain. This same technique also enables operation in daylight without the need for light shielding. Two key components are essential for the success of this technique: a fast solid-state detector and a short-pulse laser. Our detector is a custom developed Single Photon Avalanche Diode (SPAD) array, capable of sub-ns time-gating. Our pulsed lasers are solid-state miniature pulsed microchip lasers. We discuss optimization of laser and detector parameters for our application. We then present Raman spectra of particularly challenging planetary analog samples to demonstrate the unique capabilities of this time-resolved Raman instrument, for example, Mars-analog clays and Titan-analog organics. The research described here was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration (NASA).

Image processing techniques for noise removal, enhancement and segmentation of cartilage OCT images

NASA Astrophysics Data System (ADS)

Rogowska, Jadwiga; Brezinski, Mark E.

2002-02-01

Osteoarthritis, whose hallmark is the progressive loss of joint cartilage, is a major cause of morbidity worldwide. Recently, optical coherence tomography (OCT) has demonstrated considerable promise for the assessment of articular cartilage. Among the most important parameters to be assessed is cartilage width. However, detection of the bone cartilage interface is critical for the assessment of cartilage width. At present, the quantitative evaluations of cartilage thickness are being done using manual tracing of cartilage-bone borders. Since data is being obtained near video rate with OCT, automated identification of the bone-cartilage interface is critical. In order to automate the process of boundary detection on OCT images, there is a need for developing new image processing techniques. In this paper we describe the image processing techniques for speckle removal, image enhancement and segmentation of cartilage OCT images. In particular, this paper focuses on rabbit cartilage since this is an important animal model for testing both chondroprotective agents and cartilage repair techniques. In this study, a variety of techniques were examined. Ultimately, by combining an adaptive filtering technique with edge detection (vertical gradient, Sobel edge detection), cartilage edges can be detected. The procedure requires several steps and can be automated. Once the cartilage edges are outlined, the cartilage thickness can be measured.

Chemical vapor deposition growth

NASA Technical Reports Server (NTRS)

Ruth, R. P.; Manasevit, H. M.; Campbell, A. G.; Johnson, R. E.; Kenty, J. L.; Moudy, L. A.; Shaw, G. L.; Simpson, W. I.; Yang, J. J.

1978-01-01

The objective was to investigate and develop chemical vapor deposition (CVD) techniques for the growth of large areas of Si sheet on inexpensive substrate materials, with resulting sheet properties suitable for fabricating solar cells that would meet the technical goals of the Low Cost Silicon Solar Array Project. The program involved six main technical tasks: (1) modification and test of an existing vertical-chamber CVD reactor system; (2) identification and/or development of suitable inexpensive substrate materials; (3) experimental investigation of CVD process parameters using various candidate substrate materials; (4) preparation of Si sheet samples for various special studies, including solar cell fabrication; (5) evaluation of the properties of the Si sheet material produced by the CVD process; and (6) fabrication and evaluation of experimental solar cell structures, using impurity diffusion and other standard and near-standard processing techniques supplemented late in the program by the in situ CVD growth of n(+)/p/p(+) sheet structures subsequently processed into experimental cells.

Modeling of endoluminal and interstitial ultrasound hyperthermia and thermal ablation: applications to device design, feedback control, and treatment planning

PubMed Central

Prakash, Punit; Salgaonkar, Vasant A.; Diederich, Chris J.

2014-01-01

Endoluminal and catheter-based ultrasound applicators are currently under development and are in clinical use for minimally invasive hyperthermia and thermal ablation of various tissue targets. Computational models play a critical role in in device design and optimization, assessment of therapeutic feasibility and safety, devising treatment monitoring and feedback control strategies, and performing patient-specific treatment planning with this technology. The critical aspects of theoretical modeling, applied specifically to endoluminal and interstitial ultrasound thermotherapy, are reviewed. Principles and practical techniques for modeling acoustic energy deposition, bioheat transfer, thermal tissue damage, and dynamic changes in the physical and physiological state of tissue are reviewed. The integration of these models and applications of simulation techniques in identification of device design parameters, development of real time feedback-control platforms, assessing the quality and safety of treatment delivery strategies, and optimization of inverse treatment plans are presented. PMID:23738697

Towards developing standard operating procedures for pre-clinical testing in the mdx mouse model of Duchenne muscular dystrophy

PubMed Central

Grounds, Miranda D.; Radley, Hannah G.; Lynch, Gordon S.; Nagaraju, Kanneboyina; De Luca, Annamaria

2008-01-01

This review discusses various issues to consider when developing standard operating procedures for pre-clinical studies in the mdx mouse model of Duchenne muscular dystrophy (DMD). The review describes and evaluates a wide range of techniques used to measure parameters of muscle pathology in mdx mice and identifies some basic techniques that might comprise standardised approaches for evaluation. While the central aim is to provide a basis for the development of standardised procedures to evaluate efficacy of a drug or a therapeutic strategy, a further aim is to gain insight into pathophysiological mechanisms in order to identify other therapeutic targets. The desired outcome is to enable easier and more rigorous comparison of pre-clinical data from different laboratories around the world, in order to accelerate identification of the best pre-clinical therapies in the mdx mouse that will fast-track translation into effective clinical treatments for DMD. PMID:18499465

Modal identification of structures from the responses and random decrement signatures

NASA Technical Reports Server (NTRS)

Brahim, S. R.; Goglia, G. L.

1977-01-01

The theory and application of a method which utilizes the free response of a structure to determine its vibration parameters is described. The time-domain free response is digitized and used in a digital computer program to determine the number of modes excited, the natural frequencies, the damping factors, and the modal vectors. The technique is applied to a complex generalized payload model previously tested using sine sweep method and analyzed by NASTRAN. Ten modes of the payload model are identified. In case free decay response is not readily available, an algorithm is developed to obtain the free responses of a structure from its random responses, due to some unknown or known random input or inputs, using the random decrement technique without changing time correlation between signals. The algorithm is tested using random responses from a generalized payload model and from the space shuttle model.

Monte Carlo-based Reconstruction in Water Cherenkov Detectors using Chroma

NASA Astrophysics Data System (ADS)

Seibert, Stanley; Latorre, Anthony

2012-03-01

We demonstrate the feasibility of event reconstruction---including position, direction, energy and particle identification---in water Cherenkov detectors with a purely Monte Carlo-based method. Using a fast optical Monte Carlo package we have written, called Chroma, in combination with several variance reduction techniques, we can estimate the value of a likelihood function for an arbitrary event hypothesis. The likelihood can then be maximized over the parameter space of interest using a form of gradient descent designed for stochastic functions. Although slower than more traditional reconstruction algorithms, this completely Monte Carlo-based technique is universal and can be applied to a detector of any size or shape, which is a major advantage during the design phase of an experiment. As a specific example, we focus on reconstruction results from a simulation of the 200 kiloton water Cherenkov far detector option for LBNE.

Microbial quantification in activated sludge: the hits and misses.

PubMed

Hall, S J; Keller, J; Blackall, L L

2003-01-01

Since the implementation of the activated sludge process for treating wastewater, there has been a reliance on chemical and physical parameters to monitor the system. However, in biological nutrient removal (BNR) processes, the microorganisms responsible for some of the transformations should be used to monitor the processes with the overall goal to achieve better treatment performance. The development of in situ identification and rapid quantification techniques for key microorganisms involved in BNR are required to achieve this goal. This study explored the quantification of Nitrospira, a key organism in the oxidation of nitrite to nitrate in BNR. Two molecular genetic microbial quantification techniques were evaluated: real-time polymerase chain reaction (PCR) and fluorescence in situ hybridisation (FISH) followed by digital image analysis. A correlation between the Nitrospira quantitative data and the nitrate production rate, determined in batch tests, was attempted. The disadvantages and advantages of both methods will be discussed.

The potential of statistical shape modelling for geometric morphometric analysis of human teeth in archaeological research

PubMed Central

Fernee, Christianne; Browne, Martin; Zakrzewski, Sonia

2017-01-01

This paper introduces statistical shape modelling (SSM) for use in osteoarchaeology research. SSM is a full field, multi-material analytical technique, and is presented as a supplementary geometric morphometric (GM) tool. Lower mandibular canines from two archaeological populations and one modern population were sampled, digitised using micro-CT, aligned, registered to a baseline and statistically modelled using principal component analysis (PCA). Sample material properties were incorporated as a binary enamel/dentin parameter. Results were assessed qualitatively and quantitatively using anatomical landmarks. Finally, the technique’s application was demonstrated for inter-sample comparison through analysis of the principal component (PC) weights. It was found that SSM could provide high detail qualitative and quantitative insight with respect to archaeological inter- and intra-sample variability. This technique has value for archaeological, biomechanical and forensic applications including identification, finite element analysis (FEA) and reconstruction from partial datasets. PMID:29216199

Stimulant Paste Preparation and Bark Streak Tapping Technique for Pine Oleoresin Extraction.

PubMed

Füller, Thanise Nogueira; de Lima, Júlio César; de Costa, Fernanda; Rodrigues-Corrêa, Kelly C S; Fett-Neto, Arthur G

2016-01-01

Tapping technique comprises the extraction of pine oleoresin, a non-wood forest product consisting of a complex mixture of mono, sesqui, and diterpenes biosynthesized and exuded as a defense response to wounding. Oleoresin is used to produce gum rosin, turpentine, and their multiple derivatives. Oleoresin yield and quality are objects of interest in pine tree biotechnology, both in terms of environmental and genetic control. Monitoring these parameters in individual trees grown in the field provides a means to examine the control of terpene production in resin canals, as well as the identification of genetic-based differences in resinosis. A typical method of tapping involves the removal of bark and application of a chemical stimulant on the wounded area. Here we describe the methods for preparing the resin-stimulant paste with different adjuvants, as well as the bark streaking process in adult pine trees.

A Gender Identification System for Customers in a Shop Using Infrared Area Scanners

NASA Astrophysics Data System (ADS)

Tajima, Takuya; Kimura, Haruhiko; Abe, Takehiko; Abe, Koji; Nakamoto, Yoshinori

Information about customers in shops plays an important role in marketing analysis. Currently, in convenience stores and supermarkets, the identification of customer's gender is examined by clerks. On the other hand, gender identification systems using camera images are investigated. However, these systems have a problem of invading human privacies in identifying attributes of customers. The proposed system identifies gender by using infrared area scanners and Bayesian network. In the proposed system, since infrared area scanners do not take customers' images directly, invasion of privacies are not occurred. The proposed method uses three parameters of height, walking speed and pace for humans. In general, it is shown that these parameters have factors of sexual distinction in humans, and Bayesian network is designed with these three parameters. The proposed method resolves the existent problems of restricting the locations where the systems are set and invading human privacies. Experimental results using data obtained from 450 people show that the identification rate for the proposed method was 91.3% on the average of both of male and female identifications.

Event Classification and Identification Based on the Characteristic Ellipsoid of Phasor Measurement

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ma, Jian; Diao, Ruisheng; Makarov, Yuri V.

2011-09-23

In this paper, a method to classify and identify power system events based on the characteristic ellipsoid of phasor measurement is presented. The decision tree technique is used to perform the event classification and identification. Event types, event locations and clearance times are identified by decision trees based on the indices of the characteristic ellipsoid. A sufficiently large number of transient events were simulated on the New England 10-machine 39-bus system based on different system configurations. Transient simulations taking into account different event types, clearance times and various locations are conducted to simulate phasor measurement. Bus voltage magnitudes and recordedmore » reactive and active power flows are used to build the characteristic ellipsoid. The volume, eccentricity, center and projection of the longest axis in the parameter space coordinates of the characteristic ellipsoids are used to classify and identify events. Results demonstrate that the characteristic ellipsoid and the decision tree are capable to detect the event type, location, and clearance time with very high accuracy.« less

Crystallographic Orientation Identification in Multicrystalline Silicon Wafers Using NIR Transmission Intensity

NASA Astrophysics Data System (ADS)

Skenes, Kevin; Kumar, Arkadeep; Prasath, R. G. R.; Danyluk, Steven

2018-02-01

Near-infrared (NIR) polariscopy is a technique used for the non-destructive evaluation of the in-plane stresses in photovoltaic silicon wafers. Accurate evaluation of these stresses requires correct identification of the stress-optic coefficient, a material property which relates photoelastic parameters to physical stresses. The material stress-optic coefficient of silicon varies with crystallographic orientation. This variation poses a unique problem when measuring stresses in multicrystalline silicon (mc-Si) wafers. This paper concludes that the crystallographic orientation of silicon can be estimated by measuring the transmission of NIR light through the material. The transmission of NIR light through monocrystalline wafers of known orientation were compared with the transmission of NIR light through various grains in mc-Si wafers. X-ray diffraction was then used to verify the relationship by obtaining the crystallographic orientations of these assorted mc-Si grains. Variation of transmission intensity for different crystallographic orientations is further explained by using planar atomic density. The relationship between transmission intensity and planar atomic density appears to be linear.

Arabic writer identification based on diacritic's features

NASA Astrophysics Data System (ADS)

Maliki, Makki; Al-Jawad, Naseer; Jassim, Sabah A.

2012-06-01

Natural languages like Arabic, Kurdish, Farsi (Persian), Urdu, and any other similar languages have many features, which make them different from other languages like Latin's script. One of these important features is diacritics. These diacritics are classified as: compulsory like dots which are used to identify/differentiate letters, and optional like short vowels which are used to emphasis consonants. Most indigenous and well trained writers often do not use all or some of these second class of diacritics, and expert readers can infer their presence within the context of the writer text. In this paper, we investigate the use of diacritics shapes and other characteristic as parameters of feature vectors for Arabic writer identification/verification. Segmentation techniques are used to extract the diacritics-based feature vectors from examples of Arabic handwritten text. The results of evaluation test will be presented, which has been carried out on an in-house database of 50 writers. Also the viability of using diacritics for writer recognition will be demonstrated.

Parameter identification of JONSWAP spectrum acquired by airborne LIDAR

NASA Astrophysics Data System (ADS)

Yu, Yang; Pei, Hailong; Xu, Chengzhong

2017-12-01

In this study, we developed the first linear Joint North Sea Wave Project (JONSWAP) spectrum (JS), which involves a transformation from the JS solution to the natural logarithmic scale. This transformation is convenient for defining the least squares function in terms of the scale and shape parameters. We identified these two wind-dependent parameters to better understand the wind effect on surface waves. Due to its efficiency and high-resolution, we employed the airborne Light Detection and Ranging (LIDAR) system for our measurements. Due to the lack of actual data, we simulated ocean waves in the MATLAB environment, which can be easily translated into industrial programming language. We utilized the Longuet-Higgin (LH) random-phase method to generate the time series of wave records and used the fast Fourier transform (FFT) technique to compute the power spectra density. After validating these procedures, we identified the JS parameters by minimizing the mean-square error of the target spectrum to that of the estimated spectrum obtained by FFT. We determined that the estimation error is relative to the amount of available wave record data. Finally, we found the inverse computation of wind factors (wind speed and wind fetch length) to be robust and sufficiently precise for wave forecasting.

High-Throughput Block Optical DNA Sequence Identification.

PubMed

Sagar, Dodderi Manjunatha; Korshoj, Lee Erik; Hanson, Katrina Bethany; Chowdhury, Partha Pratim; Otoupal, Peter Britton; Chatterjee, Anushree; Nagpal, Prashant

2018-01-01

Optical techniques for molecular diagnostics or DNA sequencing generally rely on small molecule fluorescent labels, which utilize light with a wavelength of several hundred nanometers for detection. Developing a label-free optical DNA sequencing technique will require nanoscale focusing of light, a high-throughput and multiplexed identification method, and a data compression technique to rapidly identify sequences and analyze genomic heterogeneity for big datasets. Such a method should identify characteristic molecular vibrations using optical spectroscopy, especially in the "fingerprinting region" from ≈400-1400 cm -1 . Here, surface-enhanced Raman spectroscopy is used to demonstrate label-free identification of DNA nucleobases with multiplexed 3D plasmonic nanofocusing. While nanometer-scale mode volumes prevent identification of single nucleobases within a DNA sequence, the block optical technique can identify A, T, G, and C content in DNA k-mers. The content of each nucleotide in a DNA block can be a unique and high-throughput method for identifying sequences, genes, and other biomarkers as an alternative to single-letter sequencing. Additionally, coupling two complementary vibrational spectroscopy techniques (infrared and Raman) can improve block characterization. These results pave the way for developing a novel, high-throughput block optical sequencing method with lossy genomic data compression using k-mer identification from multiplexed optical data acquisition. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Theoretic aspects of the identification of the parameters in the optimal control model

NASA Technical Reports Server (NTRS)

Vanwijk, R. A.; Kok, J. J.

1977-01-01

The identification of the parameters of the optimal control model from input-output data of the human operator is considered. Accepting the basic structure of the model as a cascade of a full-order observer and a feedback law, and suppressing the inherent optimality of the human controller, the parameters to be identified are the feedback matrix, the observer gain matrix, and the intensity matrices of the observation noise and the motor noise. The identification of the parameters is a statistical problem, because the system and output are corrupted by noise, and therefore the solution must be based on the statistics (probability density function) of the input and output data of the human operator. However, based on the statistics of the input-output data of the human operator, no distinction can be made between the observation and the motor noise, which shows that the model suffers from overparameterization.

Evolutionary optimization with data collocation for reverse engineering of biological networks.

PubMed

Tsai, Kuan-Yao; Wang, Feng-Sheng

2005-04-01

Modern experimental biology is moving away from analyses of single elements to whole-organism measurements. Such measured time-course data contain a wealth of information about the structure and dynamic of the pathway or network. The dynamic modeling of the whole systems is formulated as a reverse problem that requires a well-suited mathematical model and a very efficient computational method to identify the model structure and parameters. Numerical integration for differential equations and finding global parameter values are still two major challenges in this field of the parameter estimation of nonlinear dynamic biological systems. We compare three techniques of parameter estimation for nonlinear dynamic biological systems. In the proposed scheme, the modified collocation method is applied to convert the differential equations to the system of algebraic equations. The observed time-course data are then substituted into the algebraic system equations to decouple system interactions in order to obtain the approximate model profiles. Hybrid differential evolution (HDE) with population size of five is able to find a global solution. The method is not only suited for parameter estimation but also can be applied for structure identification. The solution obtained by HDE is then used as the starting point for a local search method to yield the refined estimates.

Torsional Vibration in the National Wind Technology Center’s 2.5-Megawatt Dynamometer

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sethuraman, Latha; Keller, Jonathan; Wallen, Robb

2016-08-31

This report documents the torsional drivetrain dynamics of the NWTC's 2.5-megawatt dynamometer as identified experimentally and as calculated using lumped parameter models using known inertia and stiffness parameters. The report is presented in two parts beginning with the identification of the primary torsional modes followed by the investigation of approaches to damp the torsional vibrations. The key mechanical parameters for the lumped parameter models and justification for the element grouping used in the derivation of the torsional modes are presented. The sensitivities of the torsional modes to different test article properties are discussed. The oscillations observed from the low-speed andmore » generator torque measurements were used to identify the extent of damping inherently achieved through active and passive compensation techniques. A simplified Simulink model of the dynamometer test article integrating the electro-mechanical power conversion and control features was established to emulate the torque behavior that was observed during testing. The torque response in the high-speed, low-speed, and generator shafts were tested and validated against experimental measurements involving step changes in load with the dynamometer operating under speed-regulation mode. The Simulink model serves as a ready reference to identify the torque sensitivities to various system parameters and to explore opportunities to improve torsional damping under different conditions.« less

On-board adaptive model for state of charge estimation of lithium-ion batteries based on Kalman filter with proportional integral-based error adjustment

NASA Astrophysics Data System (ADS)

Wei, Jingwen; Dong, Guangzhong; Chen, Zonghai

2017-10-01

With the rapid development of battery-powered electric vehicles, the lithium-ion battery plays a critical role in the reliability of vehicle system. In order to provide timely management and protection for battery systems, it is necessary to develop a reliable battery model and accurate battery parameters estimation to describe battery dynamic behaviors. Therefore, this paper focuses on an on-board adaptive model for state-of-charge (SOC) estimation of lithium-ion batteries. Firstly, a first-order equivalent circuit battery model is employed to describe battery dynamic characteristics. Then, the recursive least square algorithm and the off-line identification method are used to provide good initial values of model parameters to ensure filter stability and reduce the convergence time. Thirdly, an extended-Kalman-filter (EKF) is applied to on-line estimate battery SOC and model parameters. Considering that the EKF is essentially a first-order Taylor approximation of battery model, which contains inevitable model errors, thus, a proportional integral-based error adjustment technique is employed to improve the performance of EKF method and correct model parameters. Finally, the experimental results on lithium-ion batteries indicate that the proposed EKF with proportional integral-based error adjustment method can provide robust and accurate battery model and on-line parameter estimation.

Analysis of Relationships between the Level of Errors in Leg and Monofin Movement and Stroke Parameters in Monofin Swimming

PubMed Central

Rejman, Marek

2013-01-01

The aim of this study was to analyze the error structure in propulsive movements with regard to its influence on monofin swimming speed. The random cycles performed by six swimmers were filmed during a progressive test (900m). An objective method to estimate errors committed in the area of angular displacement of the feet and monofin segments was employed. The parameters were compared with a previously described model. Mutual dependences between the level of errors, stroke frequency, stroke length and amplitude in relation to swimming velocity were analyzed. The results showed that proper foot movements and the avoidance of errors, arising at the distal part of the fin, ensure the progression of swimming speed. The individual stroke parameters distribution which consists of optimally increasing stroke frequency to the maximal possible level that enables the stabilization of stroke length leads to the minimization of errors. Identification of key elements in the stroke structure based on the analysis of errors committed should aid in improving monofin swimming technique. Key points The monofin swimming technique was evaluated through the prism of objectively defined errors committed by the swimmers. The dependences between the level of errors, stroke rate, stroke length and amplitude in relation to swimming velocity were analyzed. Optimally increasing stroke rate to the maximal possible level that enables the stabilization of stroke length leads to the minimization of errors. Propriety foot movement and the avoidance of errors arising at the distal part of fin, provide for the progression of swimming speed. The key elements improving monofin swimming technique, based on the analysis of errors committed, were designated. PMID:24149742

The use of the decision tree technique and image cytometry to characterize aggressiveness in World Health Organization (WHO) grade II superficial transitional cell carcinomas of the bladder.

PubMed

Decaestecker, C; van Velthoven, R; Petein, M; Janssen, T; Salmon, I; Pasteels, J L; van Ham, P; Schulman, C; Kiss, R

1996-03-01

The aggressiveness of human bladder tumours can be assessed by means of various classification systems, including the one proposed by the World Health Organization (WHO). According to the WHO classification, three levels of malignancy are identified as grades I (low), II (intermediate), and III (high). This classification system operates satisfactorily for two of the three grades in forecasting clinical progression, most grade I tumours being associated with good prognoses and most grade III with bad. In contrast, the grade II group is very heterogeneous in terms of their clinical behaviour. The present study used two computer-assisted methods to investigate whether it is possible to sub-classify grade II tumours: computer-assisted microscope analysis (image cytometry) of Feulgen-stained nuclei and the Decision Tree Technique. This latter technique belongs to the Supervised Learning Algorithm and enables an objective assessment to be made of the diagnostic value associated with a given parameter. The combined use of these two methods in a series of 292 superficial transitional cell carcinomas shows that it is possible to identify one subgroup of grade II tumours which behave clinically like grade I tumours and a second subgroup which behaves clinically like grade III tumours. Of the nine ploidy-related parameters computed by means of image cytometry [the DNA index (DI), DNA histogram type (DHT), and the percentages of diploid, hyperdiploid, triploid, hypertriploid, tetraploid, hypertetraploid, and polyploid cell nuclei], it was the percentage of hyperdiploid and hypertetraploid cell nuclei which enabled identification, rather than conventional parameters such as the DI or the DHT.

DC servomechanism parameter identification: a Closed Loop Input Error approach.

PubMed

Garrido, Ruben; Miranda, Roger

2012-01-01

This paper presents a Closed Loop Input Error (CLIE) approach for on-line parametric estimation of a continuous-time model of a DC servomechanism functioning in closed loop. A standard Proportional Derivative (PD) position controller stabilizes the loop without requiring knowledge on the servomechanism parameters. The analysis of the identification algorithm takes into account the control law employed for closing the loop. The model contains four parameters that depend on the servo inertia, viscous, and Coulomb friction as well as on a constant disturbance. Lyapunov stability theory permits assessing boundedness of the signals associated to the identification algorithm. Experiments on a laboratory prototype allows evaluating the performance of the approach. Copyright © 2011 ISA. Published by Elsevier Ltd. All rights reserved.

Scanning ion conductance microscopy: a convergent high-resolution technology for multi-parametric analysis of living cardiovascular cells

PubMed Central

Miragoli, Michele; Moshkov, Alexey; Novak, Pavel; Shevchuk, Andrew; Nikolaev, Viacheslav O.; El-Hamamsy, Ismail; Potter, Claire M. F.; Wright, Peter; Kadir, S.H. Sheikh Abdul; Lyon, Alexander R.; Mitchell, Jane A.; Chester, Adrian H.; Klenerman, David; Lab, Max J.; Korchev, Yuri E.; Harding, Sian E.; Gorelik, Julia

2011-01-01

Cardiovascular diseases are complex pathologies that include alterations of various cell functions at the levels of intact tissue, single cells and subcellular signalling compartments. Conventional techniques to study these processes are extremely divergent and rely on a combination of individual methods, which usually provide spatially and temporally limited information on single parameters of interest. This review describes scanning ion conductance microscopy (SICM) as a novel versatile technique capable of simultaneously reporting various structural and functional parameters at nanometre resolution in living cardiovascular cells at the level of the whole tissue, single cells and at the subcellular level, to investigate the mechanisms of cardiovascular disease. SICM is a multimodal imaging technology that allows concurrent and dynamic analysis of membrane morphology and various functional parameters (cell volume, membrane potentials, cellular contraction, single ion-channel currents and some parameters of intracellular signalling) in intact living cardiovascular cells and tissues with nanometre resolution at different levels of organization (tissue, cellular and subcellular levels). Using this technique, we showed that at the tissue level, cell orientation in the inner and outer aortic arch distinguishes atheroprone and atheroprotected regions. At the cellular level, heart failure leads to a pronounced loss of T-tubules in cardiac myocytes accompanied by a reduction in Z-groove ratio. We also demonstrated the capability of SICM to measure the entire cell volume as an index of cellular hypertrophy. This method can be further combined with fluorescence to simultaneously measure cardiomyocyte contraction and intracellular calcium transients or to map subcellular localization of membrane receptors coupled to cyclic adenosine monophosphate production. The SICM pipette can be used for patch-clamp recordings of membrane potential and single channel currents. In conclusion, SICM provides a highly informative multimodal imaging platform for functional analysis of the mechanisms of cardiovascular diseases, which should facilitate identification of novel therapeutic strategies. PMID:21325316

Application of Two-Parameter Stabilizing Functions in Solving a Convolution-Type Integral Equation by Regularization Method

NASA Astrophysics Data System (ADS)

Maslakov, M. L.

2018-04-01

This paper examines the solution of convolution-type integral equations of the first kind by applying the Tikhonov regularization method with two-parameter stabilizing functions. The class of stabilizing functions is expanded in order to improve the accuracy of the resulting solution. The features of the problem formulation for identification and adaptive signal correction are described. A method for choosing regularization parameters in problems of identification and adaptive signal correction is suggested.

Identification of high versus lower risk clinical subgroups in a group of adult patients with supratentorial anaplastic astrocytomas.

PubMed

Decaestecker, C; Salmon, I; Camby, I; Dewitte, O; Pasteels, J L; Brotchi, J; Van Ham, P; Kiss, R

1995-05-01

The present work investigates whether computer-assisted techniques can contribute any significant information to the characterization of astrocytic tumor aggressiveness. Two complementary computer-assisted methods were used. The first method made use of the digital image analysis of Feulgen-stained nuclei, making it possible to compute 15 morphonuclear and 8 nuclear DNA content-related (ploidy level) parameters. The second method enabled the most discriminatory parameters to be determined. This second method is the Decision Tree technique, which forms part of the Supervised Learning Algorithms. These two techniques were applied to a series of 250 supratentorial astrocytic tumors of the adult. This series included 39 low-grade (astrocytomas, AST) and 211 high-grade (47 anaplastic astrocytomas, ANA, and 164 glioblastomas, GBM) astrocytic tumors. The results show that some AST, ANA and GBM did not fit within simple logical rules. These "complex" cases were labeled NC-AST, NC-ANA and NC-GBM because they were "non-classical" (NC) with respect to their cytological features. An analysis of survival data revealed that the patients with NC-GBM had the same survival period as patients with GBM. In sharp contrast, patients with ANA survived significantly longer than patients with NC-ANA. In fact, the patients with ANA had the same survival period as patients who died from AST, while the patients with NC-ANA had a survival period similar to those with GBM. All these data show that the computer-assisted techniques used in this study can actually provide the pathologist with significant information on the characterization of astrocytic tumor aggressiveness.

A Review of System Identification Methods Applied to Aircraft

NASA Technical Reports Server (NTRS)

Klein, V.

1983-01-01

Airplane identification, equation error method, maximum likelihood method, parameter estimation in frequency domain, extended Kalman filter, aircraft equations of motion, aerodynamic model equations, criteria for the selection of a parsimonious model, and online aircraft identification are addressed.

An overview of the essential differences and similarities of system identification techniques

NASA Technical Reports Server (NTRS)

Mehra, Raman K.

1991-01-01

Information is given in the form of outlines, graphs, tables and charts. Topics include system identification, Bayesian statistical decision theory, Maximum Likelihood Estimation, identification methods, structural mode identification using a stochastic realization algorithm, and identification results regarding membrane simulations and X-29 flutter flight test data.

Complementarity of dark matter searches in the phenomenological MSSM

DOE PAGES

Cahill-Rowley, Matthew; Cotta, Randy; Drlica-Wagner, Alex; ...

2015-03-11

As is well known, the search for and eventual identification of dark matter in supersymmetry requires a simultaneous, multipronged approach with important roles played by the LHC as well as both direct and indirect dark matter detection experiments. We examine the capabilities of these approaches in the 19-parameter phenomenological MSSM which provides a general framework for complementarity studies of neutralino dark matter. We summarize the sensitivity of dark matter searches at the 7 and 8 (and eventually 14) TeV LHC, combined with those by Fermi, CTA, IceCube/DeepCore, COUPP, LZ and XENON. The strengths and weaknesses of each of these techniques aremore » examined and contrasted and their interdependent roles in covering the model parameter space are discussed in detail. We find that these approaches explore orthogonal territory and that advances in each are necessary to cover the supersymmetric weakly interacting massive particle parameter space. We also find that different experiments have widely varying sensitivities to the various dark matter annihilation mechanisms, some of which would be completely excluded by null results from these experiments.« less

Differential surface models for tactile perception of shape and on-line tracking of features

NASA Technical Reports Server (NTRS)

Hemami, H.

1987-01-01

Tactile perception of shape involves an on-line controller and a shape perceptor. The purpose of the on-line controller is to maintain gliding or rolling contact with the surface, and collect information, or track specific features of the surface such as edges of a certain sharpness. The shape perceptor uses the information to perceive, estimate the parameters of, or recognize the shape. The differential surface model depends on the information collected and on the a priori information known about the robot and its physical parameters. These differential models are certain functionals that are projections of the dynamics of the robot onto the surface gradient or onto the tangent plane. A number of differential properties may be directly measured from present day tactile sensors. Others may have to be indirectly computed from measurements. Others may constitute design objectives for distributed tactile sensors of the future. A parameterization of the surface leads to linear and nonlinear sequential parameter estimation techniques for identification of the surface. Many interesting compromises between measurement and computation are possible.

On Using Intensity Interferometry for Feature Identification and Imaging of Remote Objects

NASA Technical Reports Server (NTRS)

Erkmen, Baris I.; Strekalov, Dmitry V.; Yu, Nan

2013-01-01

We derive an approximation to the intensity covariance function of two scanning pinhole detectors, facing a distant source (e.g., a star) being occluded partially by an absorptive object (e.g., a planet). We focus on using this technique to identify or image an object that is in the line-of-sight between a well-characterized source and the detectors. We derive the observed perturbation to the intensity covariance map due to the object, showing that under some reasonable approximations it is proportional to the real part of the Fourier transform of the source's photon-flux density times the Fourier transform of the object's intensity absorption. We highlight the key parameters impacting its visibility and discuss the requirements for estimating object-related parameters, e.g., its size, velocity or shape. We consider an application of this result to determining the orbit inclination of an exoplanet orbiting a distant star. Finally, motivated by the intrinsically weak nature of the signature, we study its signal-to-noise ratio and determine the impact of system parameters.

Local zone-wise elastic-plastic constitutive parameters of Laser-welded aluminium alloy 6061 using digital image correlation

NASA Astrophysics Data System (ADS)

Bai, Ruixiang; Wei, Yuepeng; Lei, Zhenkun; Jiang, Hao; Tao, Wang; Yan, Cheng; Li, Xiaolei

2018-02-01

The mechanical properties of aluminium alloys can be affected by the local high temperature in laser welding. In this paper, an inversion identification method of local zone-wise elastic-plastic constitutive parameters for laser welding of aluminium alloy 6061 was proposed based on full-field optical measurement data using digital image correlation (DIC). Three regions, i.e., the fusion zone, heat-affected zone, and base zone, of the laser-welded joint were distinguished by means of microstructure optical observation and micrometer hardness measurement. The stress data were obtained using a laser-welded specimen via a uniaxial tensile test. Meanwhile, the local strain data of the laser-welded specimen were obtained by the DIC technique. Thus, the stress-strain relationship for different local regions was established. Finally, the constitutive parameters of the Ramberg-Osgood model were identified by least-square fitting to the experimental stress-strain data. Experimental results revealed that the mechanical properties of the local zones of the welded joints clearly weakened, and these results are consistent with the results of the hardness measurement.

Implementation of a numerical holding furnace model in foundry and construction of a reduced model

NASA Astrophysics Data System (ADS)

Loussouarn, Thomas; Maillet, Denis; Remy, Benjamin; Dan, Diane

2016-09-01

Vacuum holding induction furnaces are used for the manufacturing of turbine blades by loss wax foundry process. The control of solidification parameters is a key factor for the manufacturing of these parts in according to geometrical and structural expectations. The definition of a reduced heat transfer model with experimental identification through an estimation of its parameters is required here. In a further stage this model will be used to characterize heat exchanges using internal sensors through inverse techniques to optimize the furnace command and the optimization of its design. Here, an axisymmetric furnace and its load have been numerically modelled using FlexPDE, a finite elements code. A detailed model allows the calculation of the internal induction heat source as well as transient radiative transfer inside the furnace. A reduced lumped body model has been defined to represent the numerical furnace. The model reduction and the estimation of the parameters of the lumped body have been made using a Levenberg-Marquardt least squares minimization algorithm with Matlab, using two synthetic temperature signals with a further validation test.