A Computer Program which Uses an Expert Systems Approach to Identifying Minerals.

ERIC Educational Resources Information Center

Hart, Allan Bruce; And Others

1988-01-01

Described is a mineral identification program which uses a shell system for creating expert systems of a classification nature. Discusses identification of minerals in hand specimens, thin sections, and polished sections of rocks. (Author/CW)

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.

Human factors engineering approaches to patient identification armband design.

PubMed

Probst, C Adam; Wolf, Laurie; Bollini, Mara; Xiao, Yan

2016-01-01

The task of patient identification is performed many times each day by nurses and other members of the care team. Armbands are used for both direct verification and barcode scanning during patient identification. Armbands and information layout are critical to reducing patient identification errors and dangerous workarounds. We report the effort at two large, integrated healthcare systems that employed human factors engineering approaches to the information layout design of new patient identification armbands. The different methods used illustrate potential pathways to obtain standardized armbands across healthcare systems that incorporate human factors principles. By extension, how the designs have been adopted provides examples of how to incorporate human factors engineering into key clinical processes. Copyright © 2015 Elsevier Ltd and The Ergonomics Society. All rights reserved.

Adaptive identification and control of structural dynamics systems using recursive lattice filters

NASA Technical Reports Server (NTRS)

Sundararajan, N.; Montgomery, R. C.; Williams, J. P.

1985-01-01

A new approach for adaptive identification and control of structural dynamic systems by using least squares lattice filters thar are widely used in the signal processing area is presented. Testing procedures for interfacing the lattice filter identification methods and modal control method for stable closed loop adaptive control are presented. The methods are illustrated for a free-free beam and for a complex flexible grid, with the basic control objective being vibration suppression. The approach is validated by using both simulations and experimental facilities available at the Langley Research Center.

Localization and identification of structural nonlinearities using cascaded optimization and neural networks

NASA Astrophysics Data System (ADS)

Koyuncu, A.; Cigeroglu, E.; Özgüven, H. N.

2017-10-01

In this study, a new approach is proposed for identification of structural nonlinearities by employing cascaded optimization and neural networks. Linear finite element model of the system and frequency response functions measured at arbitrary locations of the system are used in this approach. Using the finite element model, a training data set is created, which appropriately spans the possible nonlinear configurations space of the system. A classification neural network trained on these data sets then localizes and determines the types of all nonlinearities associated with the nonlinear degrees of freedom in the system. A new training data set spanning the parametric space associated with the determined nonlinearities is created to facilitate parametric identification. Utilizing this data set, initially, a feed forward regression neural network is trained, which parametrically identifies the classified nonlinearities. Then, the results obtained are further improved by carrying out an optimization which uses network identified values as starting points. Unlike identification methods available in literature, the proposed approach does not require data collection from the degrees of freedoms where nonlinear elements are attached, and furthermore, it is sufficiently accurate even in the presence of measurement noise. The application of the proposed approach is demonstrated on an example system with nonlinear elements and on a real life experimental setup with a local nonlinearity.

Combined non-parametric and parametric approach for identification of time-variant systems

NASA Astrophysics Data System (ADS)

Dziedziech, Kajetan; Czop, Piotr; Staszewski, Wieslaw J.; Uhl, Tadeusz

2018-03-01

Identification of systems, structures and machines with variable physical parameters is a challenging task especially when time-varying vibration modes are involved. The paper proposes a new combined, two-step - i.e. non-parametric and parametric - modelling approach in order to determine time-varying vibration modes based on input-output measurements. Single-degree-of-freedom (SDOF) vibration modes from multi-degree-of-freedom (MDOF) non-parametric system representation are extracted in the first step with the use of time-frequency wavelet-based filters. The second step involves time-varying parametric representation of extracted modes with the use of recursive linear autoregressive-moving-average with exogenous inputs (ARMAX) models. The combined approach is demonstrated using system identification analysis based on the experimental mass-varying MDOF frame-like structure subjected to random excitation. The results show that the proposed combined method correctly captures the dynamics of the analysed structure, using minimum a priori information on the model.

On using the Hilbert transform for blind identification of complex modes: A practical approach

NASA Astrophysics Data System (ADS)

Antunes, Jose; Debut, Vincent; Piteau, Pilippe; Delaune, Xavier; Borsoi, Laurent

2018-01-01

The modal identification of dynamical systems under operational conditions, when subjected to wide-band unmeasured excitations, is today a viable alternative to more traditional modal identification approaches based on processing sets of measured FRFs or impulse responses. Among current techniques for performing operational modal identification, the so-called blind identification methods are the subject of considerable investigation. In particular, the SOBI (Second-Order Blind Identification) method was found to be quite efficient. SOBI was originally developed for systems with normal modes. To address systems with complex modes, various extension approaches have been proposed, in particular: (a) Using a first-order state-space formulation for the system dynamics; (b) Building complex analytic signals from the measured responses using the Hilbert transform. In this paper we further explore the latter option, which is conceptually interesting while preserving the model order and size. Focus is on applicability of the SOBI technique for extracting the modal responses from analytic signals built from a set of vibratory responses. The novelty of this work is to propose a straightforward computational procedure for obtaining the complex cross-correlation response matrix to be used for the modal identification procedure. After clarifying subtle aspects of the general theoretical framework, we demonstrate that the correlation matrix of the analytic responses can be computed through a Hilbert transform of the real correlation matrix, so that the actual time-domain responses are no longer required for modal identification purposes. The numerical validation of the proposed technique is presented based on time-domain simulations of a conceptual physical multi-modal system, designed to display modes ranging from normal to highly complex, while keeping modal damping low and nearly independent of the modal complexity, and which can prove very interesting in test bench applications. Numerical results for complex modal identifications are presented, and the quality of the identified modal matrix and modal responses, extracted using the complex SOBI technique and implementing the proposed formulation, is assessed.

Methods and application of system identification in shock and vibration.

NASA Technical Reports Server (NTRS)

Collins, J. D.; Young, J. P.; Kiefling, L.

1972-01-01

A logical picture is presented of current useful system identification techniques in the shock and vibration field. A technology tree diagram is developed for the purpose of organizing and categorizing the widely varying approaches according to the fundamental nature of each. Specific examples of accomplished activity for each identification category are noted and discussed. To provide greater insight into the most current trends in the system identification field, a somewhat detailed description is presented of the essential features of a recently developed technique that is based on making the maximum use of all statistically known information about a system.

A grass molecular identification system for forensic botany: a critical evaluation of the strengths and limitations.

PubMed

Ward, Jodie; Gilmore, Simon R; Robertson, James; Peakall, Rod

2009-11-01

Plant material is frequently encountered in criminal investigations but often overlooked as potential evidence. We designed a DNA-based molecular identification system for 100 Australian grasses that consisted of a series of polymerase chain reaction assays that enabled the progressive identification of grasses to different taxonomic levels. The identification system was based on DNA sequence variation at four chloroplast and two mitochondrial loci. Seventeen informative indels and 68 single-nucleotide polymorphisms were utilized as molecular markers for subfamily to species-level identification. To identify an unknown sample to subfamily level required a minimum of four markers or nine markers for species identification. The accuracy of the system was confirmed by blind tests. We have demonstrated "proof of concept" of a molecular identification system for trace botanical samples. Our evaluation suggests that the adoption of a system that combines this approach with DNA sequencing could assist the morphological identification of grasses found as forensic evidence.

Network-Based Approaches in Drug Discovery and Early Development

PubMed Central

Harrold, JM; Ramanathan, M; Mager, DE

2015-01-01

Identification of novel targets is a critical first step in the drug discovery and development process. Most diseases such as cancer, metabolic disorders, and neurological disorders are complex, and their pathogenesis involves multiple genetic and environmental factors. Finding a viable drug target–drug combination with high potential for yielding clinical success within the efficacy–toxicity spectrum is extremely challenging. Many examples are now available in which network-based approaches show potential for the identification of novel targets and for the repositioning of established targets. The objective of this article is to highlight network approaches for identifying novel targets with greater chances of gaining approved drugs with maximal efficacy and minimal side effects. Further enhancement of these approaches may emerge from effectively integrating computational systems biology with pharmacodynamic systems analysis. Coupling genomics, proteomics, and metabolomics databases with systems pharmacology modeling may aid in the development of disease-specific networks that can be further used to build confidence in target identification. PMID:24025802

Hybrid neuro-fuzzy approach for automatic vehicle license plate recognition

NASA Astrophysics Data System (ADS)

Lee, Hsi-Chieh; Jong, Chung-Shi

1998-03-01

Most currently available vehicle identification systems use techniques such as R.F., microwave, or infrared to help identifying the vehicle. Transponders are usually installed in the vehicle in order to transmit the corresponding information to the sensory system. It is considered expensive to install a transponder in each vehicle and the malfunction of the transponder will result in the failure of the vehicle identification system. In this study, novel hybrid approach is proposed for automatic vehicle license plate recognition. A system prototype is built which can be used independently or cooperating with current vehicle identification system in identifying a vehicle. The prototype consists of four major modules including the module for license plate region identification, the module for character extraction from the license plate, the module for character recognition, and the module for the SimNet neuro-fuzzy system. To test the performance of the proposed system, three hundred and eighty vehicle image samples are taken by a digital camera. The license plate recognition success rate of the prototype is approximately 91% while the character recognition success rate of the prototype is approximately 97%.

Correlation techniques to determine model form in robust nonlinear system realization/identification

NASA Technical Reports Server (NTRS)

Stry, Greselda I.; Mook, D. Joseph

1991-01-01

The fundamental challenge in identification of nonlinear dynamic systems is determining the appropriate form of the model. A robust technique is presented which essentially eliminates this problem for many applications. The technique is based on the Minimum Model Error (MME) optimal estimation approach. A detailed literature review is included in which fundamental differences between the current approach and previous work is described. The most significant feature is the ability to identify nonlinear dynamic systems without prior assumption regarding the form of the nonlinearities, in contrast to existing nonlinear identification approaches which usually require detailed assumptions of the nonlinearities. Model form is determined via statistical correlation of the MME optimal state estimates with the MME optimal model error estimates. The example illustrations indicate that the method is robust with respect to prior ignorance of the model, and with respect to measurement noise, measurement frequency, and measurement record length.

Inverse problem studies of biochemical systems with structure identification of S-systems by embedding training functions in a genetic algorithm.

PubMed

Sarode, Ketan Dinkar; Kumar, V Ravi; Kulkarni, B D

2016-05-01

An efficient inverse problem approach for parameter estimation, state and structure identification from dynamic data by embedding training functions in a genetic algorithm methodology (ETFGA) is proposed for nonlinear dynamical biosystems using S-system canonical models. Use of multiple shooting and decomposition approach as training functions has been shown for handling of noisy datasets and computational efficiency in studying the inverse problem. The advantages of the methodology are brought out systematically by studying it for three biochemical model systems of interest. By studying a small-scale gene regulatory system described by a S-system model, the first example demonstrates the use of ETFGA for the multifold aims of the inverse problem. The estimation of a large number of parameters with simultaneous state and network identification is shown by training a generalized S-system canonical model with noisy datasets. The results of this study bring out the superior performance of ETFGA on comparison with other metaheuristic approaches. The second example studies the regulation of cAMP oscillations in Dictyostelium cells now assuming limited availability of noisy data. Here, flexibility of the approach to incorporate partial system information in the identification process is shown and its effect on accuracy and predictive ability of the estimated model are studied. The third example studies the phenomenological toy model of the regulation of circadian oscillations in Drosophila that follows rate laws different from S-system power-law. For the limited noisy data, using a priori information about properties of the system, we could estimate an alternate S-system model that showed robust oscillatory behavior with predictive abilities. Copyright © 2016 Elsevier Inc. All rights reserved.

An on-line equivalent system identification scheme for adaptive control. Ph.D. Thesis - Stanford Univ.

NASA Technical Reports Server (NTRS)

Sliwa, S. M.

1984-01-01

A prime obstacle to the widespread use of adaptive control is the degradation of performance and possible instability resulting from the presence of unmodeled dynamics. The approach taken is to explicitly include the unstructured model uncertainty in the output error identification algorithm. The order of the compensator is successively increased by including identified modes. During this model building stage, heuristic rules are used to test for convergence prior to designing compensators. Additionally, the recursive identification algorithm as extended to multi-input, multi-output systems. Enhancements were also made to reduce the computational burden of an algorithm for obtaining minimal state space realizations from the inexact, multivariate transfer functions which result from the identification process. A number of potential adaptive control applications for this approach are illustrated using computer simulations. Results indicated that when speed of adaptation and plant stability are not critical, the proposed schemes converge to enhance system performance.

Linear control of oscillator and amplifier flows*

NASA Astrophysics Data System (ADS)

Schmid, Peter J.; Sipp, Denis

2016-08-01

Linear control applied to fluid systems near an equilibrium point has important applications for many flows of industrial or fundamental interest. In this article we give an exposition of tools and approaches for the design of control strategies for globally stable or unstable flows. For unstable oscillator flows a feedback configuration and a model-based approach is proposed, while for stable noise-amplifier flows a feedforward setup and an approach based on system identification is advocated. Model reduction and robustness issues are addressed for the oscillator case; statistical learning techniques are emphasized for the amplifier case. Effective suppression of global and convective instabilities could be demonstrated for either case, even though the system-identification approach results in a superior robustness to off-design conditions.

Identification of secreted bacterial proteins by noncanonical amino acid tagging

PubMed Central

Mahdavi, Alborz; Szychowski, Janek; Ngo, John T.; Sweredoski, Michael J.; Graham, Robert L. J.; Hess, Sonja; Schneewind, Olaf; Mazmanian, Sarkis K.; Tirrell, David A.

2014-01-01

Pathogenic microbes have evolved complex secretion systems to deliver virulence factors into host cells. Identification of these factors is critical for understanding the infection process. We report a powerful and versatile approach to the selective labeling and identification of secreted pathogen proteins. Selective labeling of microbial proteins is accomplished via translational incorporation of azidonorleucine (Anl), a methionine surrogate that requires a mutant form of the methionyl-tRNA synthetase for activation. Secreted pathogen proteins containing Anl can be tagged by azide-alkyne cycloaddition and enriched by affinity purification. Application of the method to analysis of the type III secretion system of the human pathogen Yersinia enterocolitica enabled efficient identification of secreted proteins, identification of distinct secretion profiles for intracellular and extracellular bacteria, and determination of the order of substrate injection into host cells. This approach should be widely useful for the identification of virulence factors in microbial pathogens and the development of potential new targets for antimicrobial therapy. PMID:24347637

Controlling aliased dynamics in motion systems? An identification for sampled-data control approach

NASA Astrophysics Data System (ADS)

Oomen, Tom

2014-07-01

Sampled-data control systems occasionally exhibit aliased resonance phenomena within the control bandwidth. The aim of this paper is to investigate the aspect of these aliased dynamics with application to a high performance industrial nano-positioning machine. This necessitates a full sampled-data control design approach, since these aliased dynamics endanger both the at-sample performance and the intersample behaviour. The proposed framework comprises both system identification and sampled-data control. In particular, the sampled-data control objective necessitates models that encompass the intersample behaviour, i.e., ideally continuous time models. Application of the proposed approach on an industrial wafer stage system provides a thorough insight and new control design guidelines for controlling aliased dynamics.

Nonlinear dynamic macromodeling techniques for audio systems

NASA Astrophysics Data System (ADS)

Ogrodzki, Jan; Bieńkowski, Piotr

2015-09-01

This paper develops a modelling method and a models identification technique for the nonlinear dynamic audio systems. Identification is performed by means of a behavioral approach based on a polynomial approximation. This approach makes use of Discrete Fourier Transform and Harmonic Balance Method. A model of an audio system is first created and identified and then it is simulated in real time using an algorithm of low computational complexity. The algorithm consists in real time emulation of the system response rather than in simulation of the system itself. The proposed software is written in Python language using object oriented programming techniques. The code is optimized for a multithreads environment.

Intergration of system identification and robust controller designs for flexible structures in space

NASA Technical Reports Server (NTRS)

Juang, Jer-Nan; Lew, Jiann-Shiun

1990-01-01

An approach is developed using experimental data to identify a reduced-order model and its model error for a robust controller design. There are three steps involved in the approach. First, an approximately balanced model is identified using the Eigensystem Realization Algorithm, which is an identification algorithm. Second, the model error is calculated and described in frequency domain in terms of the H(infinity) norm. Third, a pole placement technique in combination with a H(infinity) control method is applied to design a controller for the considered system. A set experimental data from an existing setup, namely the Mini-Mast system, is used to illustrate and verify the approach.

Time domain nonlinear SMA damper force identification approach and its numerical validation

NASA Astrophysics Data System (ADS)

Xin, Lulu; Xu, Bin; He, Jia

2012-04-01

Most of the currently available vibration-based identification approaches for structural damage detection are based on eigenvalues and/or eigenvectors extracted from vibration measurements and, strictly speaking, are only suitable for linear system. However, the initiation and development of damage in engineering structures under severe dynamic loadings are typical nonlinear procedure. Studies on the identification of restoring force which is a direct indicator of the extent of the nonlinearity have received increasing attention in recent years. In this study, a date-based time domain identification approach for general nonlinear system was developed. The applied excitation and the corresponding response time series of the structure were used for identification by means of standard least-square techniques and a power series polynomial model (PSPM) which was utilized to model the nonlinear restoring force (NRF). The feasibility and robustness of the proposed approach was verified by a 2 degree-of-freedoms (DOFs) lumped mass numerical model equipped with a shape memory ally (SMA) damper mimicking nonlinear behavior. The results show that the proposed data-based time domain method is capable of identifying the NRF in engineering structures without any assumptions on the mass distribution and the topology of the structure, and provides a promising way for damage detection in the presence of structural nonlinearities.

Real-time diagnostics of the reusable rocket engine using on-line system identification

NASA Technical Reports Server (NTRS)

Guo, T.-H.; Merrill, W.; Duyar, A.

1990-01-01

A model-based failure diagnosis system has been proposed for real-time diagnosis of SSME failures. Actuation, sensor, and system degradation failure modes are all considered by the proposed system. In the case of SSME actuation failures, it was shown that real-time identification can effectively be used for failure diagnosis purposes. It is a direct approach since it reduces the detection, isolation, and the estimation of the extent of the failures to the comparison of parameter values before and after the failure. As with any model-based failure detection system, the proposed approach requires a fault model that embodies the essential characteristics of the failure process. The proposed diagnosis approach has the added advantage that it can be used as part of an intelligent control system for failure accommodation purposes.

Generalized approach for identification and evaluation of technology-insertion options for military avionics systems

NASA Astrophysics Data System (ADS)

Harkness, Linda L.; Sjoberg, Eric S.

1996-06-01

The Georgia Tech Research Institute, sponsored by the Warner Robins Air Logistics Center, has developed an approach for efficiently postulating and evaluating methods for extending the life of radars and other avionics systems. The technique identified specific assemblies for potential replacement and evaluates the system level impact, including performance, reliability and life-cycle cost of each action. The initial impetus for this research was the increasing obsolescence of integrated circuits contained in the AN/APG-63 system. The operational life of military electronics is typically in excess of twenty years, which encompasses several generations of IC technology. GTRI has developed a systems approach to inserting modern technology components into older systems based upon identification of those functions which limit the system's performance or reliability and which are cost drivers. The presentation will discuss the above methodology and a technique for evaluating and ranking the different potential system upgrade options.

Triple redundant computer system/display and keyboard subsystem interface

NASA Technical Reports Server (NTRS)

Gulde, F. J.

1973-01-01

Interfacing of the redundant display and keyboard subsystem with the triple redundant computer system is defined according to space shuttle design. The study is performed in three phases: (1) TRCS configuration and characteristics identification; (2) display and keyboard subsystem configuration and characteristics identification, and (3) interface approach definition.

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.

Structure Computation of Quiet Spike[Trademark] Flight-Test Data During Envelope Expansion

NASA Technical Reports Server (NTRS)

Kukreja, Sunil L.

2008-01-01

System identification or mathematical modeling is used in the aerospace community for development of simulation models for robust control law design. These models are often described as linear time-invariant processes. Nevertheless, it is well known that the underlying process is often nonlinear. The reason for using a linear approach has been due to the lack of a proper set of tools for the identification of nonlinear systems. Over the past several decades, the controls and biomedical communities have made great advances in developing tools for the identification of nonlinear systems. These approaches are robust and readily applicable to aerospace systems. In this paper, we show the application of one such nonlinear system identification technique, structure detection, for the analysis of F-15B Quiet Spike(TradeMark) aeroservoelastic flight-test data. Structure detection is concerned with the selection of a subset of candidate terms that best describe the observed output. This is a necessary procedure to compute an efficient system description that may afford greater insight into the functionality of the system or a simpler controller design. Structure computation as a tool for black-box modeling may be of critical importance for the development of robust parsimonious models for the flight-test community. Moreover, this approach may lead to efficient strategies for rapid envelope expansion, which may save significant development time and costs. The objectives of this study are to demonstrate via analysis of F-15B Quiet Spike aeroservoelastic flight-test data for several flight conditions that 1) linear models are inefficient for modeling aeroservoelastic data, 2) nonlinear identification provides a parsimonious model description while providing a high percent fit for cross-validated data, and 3) the model structure and parameters vary as the flight condition is altered.

Strategies for de-identification and anonymization of electronic health record data for use in multicenter research studies.

PubMed

Kushida, Clete A; Nichols, Deborah A; Jadrnicek, Rik; Miller, Ric; Walsh, James K; Griffin, Kara

2012-07-01

De-identification and anonymization are strategies that are used to remove patient identifiers in electronic health record data. The use of these strategies in multicenter research studies is paramount in importance, given the need to share electronic health record data across multiple environments and institutions while safeguarding patient privacy. Systematic literature search using keywords of de-identify, deidentify, de-identification, deidentification, anonymize, anonymization, data scrubbing, and text scrubbing. Search was conducted up to June 30, 2011 and involved 6 different common literature databases. A total of 1798 prospective citations were identified, and 94 full-text articles met the criteria for review and the corresponding articles were obtained. Search results were supplemented by review of 26 additional full-text articles; a total of 120 full-text articles were reviewed. A final sample of 45 articles met inclusion criteria for review and discussion. Articles were grouped into text, images, and biological sample categories. For text-based strategies, the approaches were segregated into heuristic, lexical, and pattern-based systems versus statistical learning-based systems. For images, approaches that de-identified photographic facial images and magnetic resonance image data were described. For biological samples, approaches that managed the identifiers linked with these samples were discussed, particularly with respect to meeting the anonymization requirements needed for Institutional Review Board exemption under the Common Rule. Current de-identification strategies have their limitations, and statistical learning-based systems have distinct advantages over other approaches for the de-identification of free text. True anonymization is challenging, and further work is needed in the areas of de-identification of datasets and protection of genetic information.

Classification of cancerous cells based on the one-class problem approach

NASA Astrophysics Data System (ADS)

Murshed, Nabeel A.; Bortolozzi, Flavio; Sabourin, Robert

1996-03-01

One of the most important factors in reducing the effect of cancerous diseases is the early diagnosis, which requires a good and a robust method. With the advancement of computer technologies and digital image processing, the development of a computer-based system has become feasible. In this paper, we introduce a new approach for the detection of cancerous cells. This approach is based on the one-class problem approach, through which the classification system need only be trained with patterns of cancerous cells. This reduces the burden of the training task by about 50%. Based on this approach, a computer-based classification system is developed, based on the Fuzzy ARTMAP neural networks. Experimental results were performed using a set of 542 patterns taken from a sample of breast cancer. Results of the experiment show 98% correct identification of cancerous cells and 95% correct identification of non-cancerous cells.

Aerodynamic parameter estimation via Fourier modulating function techniques

NASA Technical Reports Server (NTRS)

Pearson, A. E.

1995-01-01

Parameter estimation algorithms are developed in the frequency domain for systems modeled by input/output ordinary differential equations. The approach is based on Shinbrot's method of moment functionals utilizing Fourier based modulating functions. Assuming white measurement noises for linear multivariable system models, an adaptive weighted least squares algorithm is developed which approximates a maximum likelihood estimate and cannot be biased by unknown initial or boundary conditions in the data owing to a special property attending Shinbrot-type modulating functions. Application is made to perturbation equation modeling of the longitudinal and lateral dynamics of a high performance aircraft using flight-test data. Comparative studies are included which demonstrate potential advantages of the algorithm relative to some well established techniques for parameter identification. Deterministic least squares extensions of the approach are made to the frequency transfer function identification problem for linear systems and to the parameter identification problem for a class of nonlinear-time-varying differential system models.

Search-based model identification of smart-structure damage

NASA Technical Reports Server (NTRS)

Glass, B. J.; Macalou, A.

1991-01-01

This paper describes the use of a combined model and parameter identification approach, based on modal analysis and artificial intelligence (AI) techniques, for identifying damage or flaws in a rotating truss structure incorporating embedded piezoceramic sensors. This smart structure example is representative of a class of structures commonly found in aerospace systems and next generation space structures. Artificial intelligence techniques of classification, heuristic search, and an object-oriented knowledge base are used in an AI-based model identification approach. A finite model space is classified into a search tree, over which a variant of best-first search is used to identify the model whose stored response most closely matches that of the input. Newly-encountered models can be incorporated into the model space. This adaptativeness demonstrates the potential for learning control. Following this output-error model identification, numerical parameter identification is used to further refine the identified model. Given the rotating truss example in this paper, noisy data corresponding to various damage configurations are input to both this approach and a conventional parameter identification method. The combination of the AI-based model identification with parameter identification is shown to lead to smaller parameter corrections than required by the use of parameter identification alone.

A Systems Analysis Role Play Case: We Sell Stuff, Inc.

ERIC Educational Resources Information Center

Mitri, Michel; Cole, Carey

2007-01-01

Most systems development projects incorporate some sort of life cycle approach in their development. Whether the development methodology involves a traditional life cycle, prototyping, rapid application development, or some other approach, the first step usually involves a system investigation, which includes problem identification, feasibility…

Approaches for Language Identification in Mismatched Environments

DTIC Science & Technology

2016-09-08

different i-vector systems are considered, which differ in their feature extraction mechanism. The first, which we refer to as the standard i-vector, or...both conversational telephone speech and narrowband broadcast speech. Multiple experiments are conducted to assess the performance of the system in...bottleneck features using i-vectors. The proposed system results in a 30% improvement over the baseline result. Index Terms: language identification

Data Driven Model Development for the Supersonic Semispan Transport (S(sup 4)T)

NASA Technical Reports Server (NTRS)

Kukreja, Sunil L.

2011-01-01

We investigate two common approaches to model development for robust control synthesis in the aerospace community; namely, reduced order aeroservoelastic modelling based on structural finite-element and computational fluid dynamics based aerodynamic models and a data-driven system identification procedure. It is shown via analysis of experimental Super- Sonic SemiSpan Transport (S4T) wind-tunnel data using a system identification approach it is possible to estimate a model at a fixed Mach, which is parsimonious and robust across varying dynamic pressures.

Developing Human-Machine Interfaces to Support Appropriate Trust and Reliance on Automated Combat Identification Systems (Developpement d’Interfaces Homme-Machine Pour Appuyer la Confiance dans les Systemes Automatises d’Identification au Combat)

DTIC Science & Technology

2008-03-31

on automation; the ‘response bias’ approach. This new approach is based on Signal Detection Theory (SDT) (Macmillan & Creelman , 1991; Wickens...SDT), response bias will vary with the expectation of the target probability, whereas their sensitivity will stay constant (Macmillan & Creelman ...measures, C has the simplest statistical properties (Macmillan & Creelman , 1991, p273), and it was also the measure used in Dzindolet et al.’s study

Rapid identification of areas of interest in thin film materials libraries by combining electrical, optical, X-ray diffraction, and mechanical high-throughput measurements: a case study for the system Ni-Al.

PubMed

Thienhaus, S; Naujoks, D; Pfetzing-Micklich, J; König, D; Ludwig, A

2014-12-08

The efficient identification of compositional areas of interest in thin film materials systems fabricated by combinatorial deposition methods is essential in combinatorial materials science. We use a combination of compositional screening by EDX together with high-throughput measurements of electrical and optical properties of thin film libraries to determine efficiently the areas of interest in a materials system. Areas of interest are compositions which show distinctive properties. The crystallinity of the thus determined areas is identified by X-ray diffraction. Additionally, by using automated nanoindentation across the materials library, mechanical data of the thin films can be obtained which complements the identification of areas of interest. The feasibility of this approach is demonstrated by using a Ni-Al thin film library as a reference system. The obtained results promise that this approach can be used for the case of ternary and higher order systems.

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.

Identification of Boolean Network Models From Time Series Data Incorporating Prior Knowledge.

PubMed

Leifeld, Thomas; Zhang, Zhihua; Zhang, Ping

2018-01-01

Motivation: Mathematical models take an important place in science and engineering. A model can help scientists to explain dynamic behavior of a system and to understand the functionality of system components. Since length of a time series and number of replicates is limited by the cost of experiments, Boolean networks as a structurally simple and parameter-free logical model for gene regulatory networks have attracted interests of many scientists. In order to fit into the biological contexts and to lower the data requirements, biological prior knowledge is taken into consideration during the inference procedure. In the literature, the existing identification approaches can only deal with a subset of possible types of prior knowledge. Results: We propose a new approach to identify Boolean networks from time series data incorporating prior knowledge, such as partial network structure, canalizing property, positive and negative unateness. Using vector form of Boolean variables and applying a generalized matrix multiplication called the semi-tensor product (STP), each Boolean function can be equivalently converted into a matrix expression. Based on this, the identification problem is reformulated as an integer linear programming problem to reveal the system matrix of Boolean model in a computationally efficient way, whose dynamics are consistent with the important dynamics captured in the data. By using prior knowledge the number of candidate functions can be reduced during the inference. Hence, identification incorporating prior knowledge is especially suitable for the case of small size time series data and data without sufficient stimuli. The proposed approach is illustrated with the help of a biological model of the network of oxidative stress response. Conclusions: The combination of efficient reformulation of the identification problem with the possibility to incorporate various types of prior knowledge enables the application of computational model inference to systems with limited amount of time series data. The general applicability of this methodological approach makes it suitable for a variety of biological systems and of general interest for biological and medical research.

The use of roving discs and orthogonal natural frequencies for crack identification and location in rotors

NASA Astrophysics Data System (ADS)

Haji, Zyad N.; Olutunde Oyadiji, S.

2014-11-01

A variety of approaches that have been developed for the identification and localisation of cracks in a rotor system, which exploit natural frequencies, require a finite element model to obtain the natural frequencies of the intact rotor as baseline data. In fact, such approaches can give erroneous results about the location and depth of a crack if an inaccurate finite element model is used to represent an uncracked model. A new approach for the identification and localisation of cracks in rotor systems, which does not require the use of the natural frequencies of an intact rotor as a baseline data, is presented in this paper. The approach, named orthogonal natural frequencies (ONFs), is based only on the natural frequencies of the non-rotating cracked rotor in the two lateral bending vibration x-z and y-z planes. The approach uses the cracked natural frequencies in the horizontal x-z plane as the reference data instead of the intact natural frequencies. Also, a roving disc is traversed along the rotor in order to enhance the dynamics of the rotor at the cracked locations. At each spatial location of the roving disc, the two ONFs of the rotor-disc system are determined from which the corresponding ONF ratio is computed. The ONF ratios are normalised by the maximum ONF ratio to obtain normalised orthogonal natural frequency curves (NONFCs). The non-rotating cracked rotor is simulated by the finite element method using the Bernoulli-Euler beam theory. The unique characteristics of the proposed approach are the sharp, notched peaks at the crack locations but rounded peaks at non-cracked locations. These features facilitate the unambiguous identification and locations of cracks in rotors. The effects of crack depth, crack location, and mass of a roving disc are investigated. The results show that the proposed method has a great potential in the identification and localisation of cracks in a non-rotating cracked rotor.

DNA barcode-based molecular identification system for fish species.

PubMed

Kim, Sungmin; Eo, Hae-Seok; Koo, Hyeyoung; Choi, Jun-Kil; Kim, Won

2010-12-01

In this study, we applied DNA barcoding to identify species using short DNA sequence analysis. We examined the utility of DNA barcoding by identifying 53 Korean freshwater fish species, 233 other freshwater fish species, and 1339 saltwater fish species. We successfully developed a web-based molecular identification system for fish (MISF) using a profile hidden Markov model. MISF facilitates efficient and reliable species identification, overcoming the limitations of conventional taxonomic approaches. MISF is freely accessible at http://bioinfosys.snu.ac.kr:8080/MISF/misf.jsp .

HAZARDOUS WASTE IDENTIFICATION

EPA Science Inventory

This research is in direct support of the regulatory reform efforts under the Hazarous Waste Identification (HWIR) and is related to the development of national "exit levels" based on sound scientific data and models. Research focuses on developing a systems approach to modelin...

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.

Dynamic Stiffness Transfer Function of an Electromechanical Actuator Using System Identification

NASA Astrophysics Data System (ADS)

Kim, Sang Hwa; Tahk, Min-Jea

2018-04-01

In the aeroelastic analysis of flight vehicles with electromechanical actuators (EMAs), an accurate prediction of flutter requires dynamic stiffness characteristics of the EMA. The dynamic stiffness transfer function of the EMA with brushless direct current (BLDC) motor can be obtained by conducting complicated mathematical calculations of control algorithms and mechanical/electrical nonlinearities using linearization techniques. Thus, system identification approaches using experimental data, as an alternative, have considerable advantages. However, the test setup for system identification is expensive and complex, and experimental procedures for data collection are time-consuming tasks. To obtain the dynamic stiffness transfer function, this paper proposes a linear system identification method that uses information obtained from a reliable dynamic stiffness model with a control algorithm and nonlinearities. The results of this study show that the system identification procedure is compact, and the transfer function is able to describe the dynamic stiffness characteristics of the EMA. In addition, to verify the validity of the system identification method, the simulation results of the dynamic stiffness transfer function and the dynamic stiffness model were compared with the experimental data for various external loads.

Numerical studies of identification in nonlinear distributed parameter systems

NASA Technical Reports Server (NTRS)

Banks, H. T.; Lo, C. K.; Reich, Simeon; Rosen, I. G.

1989-01-01

An abstract approximation framework and convergence theory for the identification of first and second order nonlinear distributed parameter systems developed previously by the authors and reported on in detail elsewhere are summarized and discussed. The theory is based upon results for systems whose dynamics can be described by monotone operators in Hilbert space and an abstract approximation theorem for the resulting nonlinear evolution system. The application of the theory together with numerical evidence demonstrating the feasibility of the general approach are discussed in the context of the identification of a first order quasi-linear parabolic model for one dimensional heat conduction/mass transport and the identification of a nonlinear dissipation mechanism (i.e., damping) in a second order one dimensional wave equation. Computational and implementational considerations, in particular, with regard to supercomputing, are addressed.

A fast iterative recursive least squares algorithm for Wiener model identification of highly nonlinear systems.

PubMed

Kazemi, Mahdi; Arefi, Mohammad Mehdi

2017-03-01

In this paper, an online identification algorithm is presented for nonlinear systems in the presence of output colored noise. The proposed method is based on extended recursive least squares (ERLS) algorithm, where the identified system is in polynomial Wiener form. To this end, an unknown intermediate signal is estimated by using an inner iterative algorithm. The iterative recursive algorithm adaptively modifies the vector of parameters of the presented Wiener model when the system parameters vary. In addition, to increase the robustness of the proposed method against variations, a robust RLS algorithm is applied to the model. Simulation results are provided to show the effectiveness of the proposed approach. Results confirm that the proposed method has fast convergence rate with robust characteristics, which increases the efficiency of the proposed model and identification approach. For instance, the FIT criterion will be achieved 92% in CSTR process where about 400 data is used. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

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

NASA Technical Reports Server (NTRS)

Kukreja, Sunil L.

2007-01-01

System identification or mathematical modelling is utilised in the aerospace community for the 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. The reason for utilising a linear approach has been due to the lack of a proper set of tools for the identification of nonlinear systems. Over the past several decades the controls and biomedical communities have made great advances in developing tools for the identification of nonlinear systems. These approaches are robust and readily applicable to aerospace systems. In this paper, we show the application of one such nonlinear system identification technique, structure detection, for the analysis of F-15B QuietSpike(TradeMark) aeroservoelastic flight test data. Structure detection is concerned with the selection of a subset of candidate terms that best describe the observed output. This is a necessary procedure to compute an efficient system description which may afford greater insight into the functionality of the system or a simpler controller design. Structure computation as a tool for black-box modelling may be of critical importance for the development of robust, parsimonious models for the flight-test community. Moreover, this approach may lead to efficient strategies for rapid envelope expansion which may save significant development time and costs. The objectives of this study are to demonstrate via analysis of F-15B QuietSpike(TradeMark) aeroservoelastic flight test data for several flight conditions (Mach number) that (i) linear models are inefficient for modelling aeroservoelastic data, (ii) nonlinear identification provides a parsimonious model description whilst providing a high percent fit for cross-validated data and (iii) the model structure and parameters vary as the flight condition is altered.

Automatic identification of alpine mass movements based on seismic and infrasound signals

NASA Astrophysics Data System (ADS)

Schimmel, Andreas; Hübl, Johannes

2017-04-01

The automatic detection and identification of alpine mass movements like debris flows, debris floods or landslides gets increasing importance for mitigation measures in the densely populated and intensively used alpine regions. Since this mass movement processes emits characteristically seismic and acoustic waves in the low frequency range this events can be detected and identified based on this signals. So already several approaches for detection and warning systems based on seismic or infrasound signals has been developed. But a combination of both methods, which can increase detection probability and reduce false alarms is currently used very rarely and can serve as a promising method for developing an automatic detection and identification system. So this work presents an approach for a detection and identification system based on a combination of seismic and infrasound sensors, which can detect sediment related mass movements from a remote location unaffected by the process. The system is based on one infrasound sensor and one geophone which are placed co-located and a microcontroller where a specially designed detection algorithm is executed which can detect mass movements in real time directly at the sensor site. Further this work tries to get out more information from the seismic and infrasound spectrum produced by different sediment related mass movements to identify the process type and estimate the magnitude of the event. The system is currently installed and tested on five test sites in Austria, two in Italy and one in Switzerland as well as one in Germany. This high number of test sites is used to get a large database of very different events which will be the basis for a new identification method for alpine mass movements. These tests shows promising results and so this system provides an easy to install and inexpensive approach for a detection and warning system.

Decentralized and Tactical Air Traffic Flow Management

NASA Technical Reports Server (NTRS)

Odoni, Amedeo R.; Bertsimas, Dimitris

1997-01-01

This project dealt with the following topics: 1. Review and description of the existing air traffic flow management system (ATFM) and identification of aspects with potential for improvement. 2. Identification and review of existing models and simulations dealing with all system segments (enroute, terminal area, ground) 3. Formulation of concepts for overall decentralization of the ATFM system, ranging from moderate decentralization to full decentralization 4. Specification of the modifications to the ATFM system required to accommodate each of the alternative concepts. 5. Identification of issues that need to be addressed with regard to: determination of the way the ATFM system would be operating; types of flow management strategies that would be used; and estimation of the effectiveness of ATFM with regard to reducing delay and re-routing costs. 6. Concept evaluation through identification of criteria and methodologies for accommodating the interests of stakeholders and of approaches to optimization of operational procedures for all segments of the ATFM system.

Data Driven Model Development for the SuperSonic SemiSpan Transport (S(sup 4)T)

NASA Technical Reports Server (NTRS)

Kukreja, Sunil L.

2011-01-01

In this report, we will investigate two common approaches to model development for robust control synthesis in the aerospace community; namely, reduced order aeroservoelastic modelling based on structural finite-element and computational fluid dynamics based aerodynamic models, and a data-driven system identification procedure. It is shown via analysis of experimental SuperSonic SemiSpan Transport (S4T) wind-tunnel data that by using a system identification approach it is possible to estimate a model at a fixed Mach, which is parsimonious and robust across varying dynamic pressures.

Identification of the structure parameters using short-time non-stationary stochastic excitation

NASA Astrophysics Data System (ADS)

Jarczewska, Kamila; Koszela, Piotr; Śniady, PaweŁ; Korzec, Aleksandra

2011-07-01

In this paper, we propose an approach to the flexural stiffness or eigenvalue frequency identification of a linear structure using a non-stationary stochastic excitation process. The idea of the proposed approach lies within time domain input-output methods. The proposed method is based on transforming the dynamical problem into a static one by integrating the input and the output signals. The output signal is the structure reaction, i.e. structure displacements due to the short-time, irregular load of random type. The systems with single and multiple degrees of freedom, as well as continuous systems are considered.

IDENTIFICATION OF REGIME SHIFTS IN TIME SERIES USING NEIGHBORHOOD STATISTICS

EPA Science Inventory

The identification of alternative dynamic regimes in ecological systems requires several lines of evidence. Previous work on time series analysis of dynamic regimes includes mainly model-fitting methods. We introduce two methods that do not use models. These approaches use state-...

Decentralized system identification using stochastic subspace identification on wireless smart sensor networks

NASA Astrophysics Data System (ADS)

Sim, Sung-Han; Spencer, Billie F., Jr.; Park, Jongwoong; Jung, Hyungjo

2012-04-01

Wireless Smart Sensor Networks (WSSNs) facilitates a new paradigm to structural identification and monitoring for civil infrastructure. Conventional monitoring systems based on wired sensors and centralized data acquisition and processing have been considered to be challenging and costly due to cabling and expensive equipment and maintenance costs. WSSNs have emerged as a technology that can overcome such difficulties, making deployment of a dense array of sensors on large civil structures both feasible and economical. However, as opposed to wired sensor networks in which centralized data acquisition and processing is common practice, WSSNs require decentralized computing algorithms to reduce data transmission due to the limitation associated with wireless communication. Thus, several system identification methods have been implemented to process sensor data and extract essential information, including Natural Excitation Technique with Eigensystem Realization Algorithm, Frequency Domain Decomposition (FDD), and Random Decrement Technique (RDT); however, Stochastic Subspace Identification (SSI) has not been fully utilized in WSSNs, while SSI has the strong potential to enhance the system identification. This study presents a decentralized system identification using SSI in WSSNs. The approach is implemented on MEMSIC's Imote2 sensor platform and experimentally verified using a 5-story shear building model.

An efficient identification approach for stable and unstable nonlinear systems using Colliding Bodies Optimization algorithm.

PubMed

Pal, Partha S; Kar, R; Mandal, D; Ghoshal, S P

2015-11-01

This paper presents an efficient approach to identify different stable and practically useful Hammerstein models as well as unstable nonlinear process along with its stable closed loop counterpart with the help of an evolutionary algorithm as Colliding Bodies Optimization (CBO) optimization algorithm. The performance measures of the CBO based optimization approach such as precision, accuracy are justified with the minimum output mean square value (MSE) which signifies that the amount of bias and variance in the output domain are also the least. It is also observed that the optimization of output MSE in the presence of outliers has resulted in a very close estimation of the output parameters consistently, which also justifies the effective general applicability of the CBO algorithm towards the system identification problem and also establishes the practical usefulness of the applied approach. Optimum values of the MSEs, computational times and statistical information of the MSEs are all found to be the superior as compared with those of the other existing similar types of stochastic algorithms based approaches reported in different recent literature, which establish the robustness and efficiency of the applied CBO based identification scheme. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

System identification using Nuclear Norm & Tabu Search optimization

NASA Astrophysics Data System (ADS)

Ahmed, Asif A.; Schoen, Marco P.; Bosworth, Ken W.

2018-01-01

In recent years, subspace System Identification (SI) algorithms have seen increased research, stemming from advanced minimization methods being applied to the Nuclear Norm (NN) approach in system identification. These minimization algorithms are based on hard computing methodologies. To the authors’ knowledge, as of now, there has been no work reported that utilizes soft computing algorithms to address the minimization problem within the nuclear norm SI framework. A linear, time-invariant, discrete time system is used in this work as the basic model for characterizing a dynamical system to be identified. The main objective is to extract a mathematical model from collected experimental input-output data. Hankel matrices are constructed from experimental data, and the extended observability matrix is employed to define an estimated output of the system. This estimated output and the actual - measured - output are utilized to construct a minimization problem. An embedded rank measure assures minimum state realization outcomes. Current NN-SI algorithms employ hard computing algorithms for minimization. In this work, we propose a simple Tabu Search (TS) algorithm for minimization. TS algorithm based SI is compared with the iterative Alternating Direction Method of Multipliers (ADMM) line search optimization based NN-SI. For comparison, several different benchmark system identification problems are solved by both approaches. Results show improved performance of the proposed SI-TS algorithm compared to the NN-SI ADMM algorithm.

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

The identification of living persons on images: A literature review.

PubMed

Gibelli, D; Obertová, Z; Ritz-Timme, S; Gabriel, P; Arent, T; Ratnayake, M; De Angelis, D; Cattaneo, C

2016-03-01

Personal identification in the forensic context commonly concerns unknown decedents. However, recently there has been an increase in cases which require identification of living persons, especially from surveillance systems. These cases bring about a relatively new challenge for forensic anthropologists and pathologists concerning the selection of the most suitable methodological approaches with regard to the limitations of the photographic representation of a given person for individualization and identity. Facial features are instinctively the primary focus for identification approaches. However, other body parts (e.g. hands), and body height and gait (on videos) have been considered in cases of personal identification. This review aims at summarizing the state-of-the-art concerning the identification of the living on images and videos, including a critical evaluation of the advantages and limitations of different methods. Recommendations are given in order to aid forensic practitioners who face cases of identification of living persons. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

High-speed holographic correlation system for video identification on the internet

NASA Astrophysics Data System (ADS)

Watanabe, Eriko; Ikeda, Kanami; Kodate, Kashiko

2013-12-01

Automatic video identification is important for indexing, search purposes, and removing illegal material on the Internet. By combining a high-speed correlation engine and web-scanning technology, we developed the Fast Recognition Correlation system (FReCs), a video identification system for the Internet. FReCs is an application thatsearches through a number of websites with user-generated content (UGC) and detects video content that violates copyright law. In this paper, we describe the FReCs configuration and an approach to investigating UGC websites using FReCs. The paper also illustrates the combination of FReCs with an optical correlation system, which is capable of easily replacing a digital authorization sever in FReCs with optical correlation.

Assessment of Multi Fragment Melting Analysis System (MFMAS) for the Identification of Food-Borne Yeasts.

PubMed

Kesmen, Zülal; Büyükkiraz, Mine E; Özbekar, Esra; Çelik, Mete; Özkök, F Özge; Kılıç, Özge; Çetin, Bülent; Yetim, Hasan

2018-06-01

Multi Fragment Melting Analysis System (MFMAS) is a novel approach that was developed for the species-level identification of microorganisms. It is a software-assisted system that performs concurrent melting analysis of 8 different DNA fragments to obtain a fingerprint of each strain analyzed. The identification is performed according to the comparison of these fingerprints with the fingerprints of known yeast species recorded in a database to obtain the best possible match. In this study, applicability of the yeast version of the MFMAS (MFMAS-yeast) was evaluated for the identification of food-associated yeast species. For this purpose, in this study, a total of 145 yeast strains originated from foods and beverages and 19 standard yeast strains were tested. The DNAs isolated from these yeast strains were analyzed by the MFMAS, and their species were successfully identified with a similarity rate of 95% or higher. It was shown that the strains belonged to 43 different yeast species that are widely found in the foods. A clear discrimination was also observed in the phylogenetically related species. In conclusion, it might be suggested that the MFMAS-yeast seems to be a highly promising approach for a rapid, accurate, and one-step identification of the yeasts isolated from food products and/or their processing environments.

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...

Identification and interpretation of patterns in rocket engine data: Artificial intelligence and neural network approaches

NASA Technical Reports Server (NTRS)

Ali, Moonis; Whitehead, Bruce; Gupta, Uday K.; Ferber, Harry

1995-01-01

This paper describes an expert system which is designed to perform automatic data analysis, identify anomalous events and determine the characteristic features of these events. We have employed both artificial intelligence and neural net approaches in the design of this expert system.

A biometric identification system based on eigenpalm and eigenfinger features.

PubMed

Ribaric, Slobodan; Fratric, Ivan

2005-11-01

This paper presents a multimodal biometric identification system based on the features of the human hand. We describe a new biometric approach to personal identification using eigenfinger and eigenpalm features, with fusion applied at the matching-score level. The identification process can be divided into the following phases: capturing the image; preprocessing; extracting and normalizing the palm and strip-like finger subimages; extracting the eigenpalm and eigenfinger features based on the K-L transform; matching and fusion; and, finally, a decision based on the (k, l)-NN classifier and thresholding. The system was tested on a database of 237 people (1,820 hand images). The experimental results showed the effectiveness of the system in terms of the recognition rate (100 percent), the equal error rate (EER = 0.58 percent), and the total error rate (TER = 0.72 percent).

Comparison of two matrix-assisted laser desorption ionization-time of flight mass spectrometry systems for the identification of clinical filamentous fungi.

PubMed

Huang, Yanfei; Zhang, Mingxin; Zhu, Min; Wang, Mei; Sun, Yufeng; Gu, Haitong; Cao, Jingjing; Li, Xue; Zhang, Shaoya; Wang, Jinglin; Lu, Xinxin

2017-07-01

Infections caused by filamentous fungi have become a health concern, and require rapid and accurate identification in order for effective treatment of the pathogens. To compare the performance of two MALDI-TOF MS systems (Bruker Microflex LT and Xiamen Microtyper) in the identification of filamentous fungal species. A total of 374 clinical filamentous fungal isolates sequentially collected in the Clinical Laboratory at the Beijing Tongren Hospital between January 2014 and December 2015 were identified by traditional phenotypic methods, Bruker Microflex LT and Xiamen Microtyper MALDI-TOF MS, respectively. The discrepancy between these methods was resolved by sequencing for definitive identification. Bruker Microflex LT and Xiamen Microtyper had similar correct species ID (98.9 vs. 99.2%), genus ID (99.7 vs. 100%), mis-ID (0.3 vs. 0%) and no ID (0 vs. 0). The rate of correct species identification by both MALDI-TOF MS (98.9 and 99.2%, respectively) was much higher compared with phenotypic approach (91.9%). Both MALDI-TOF MS systems provide accurate identification of clinical filamentous fungi compared with conventional phenotypic method, and have the potential to replace identification for routine identification of these fungi in clinical mycology laboratories. Both systems have similar performance in the identification of clinical filamentous fungi.

Iterative LQG Controller Design Through Closed-Loop Identification

NASA Technical Reports Server (NTRS)

Hsiao, Min-Hung; Huang, Jen-Kuang; Cox, David E.

1996-01-01

This paper presents an iterative Linear Quadratic Gaussian (LQG) controller design approach for a linear stochastic system with an uncertain open-loop model and unknown noise statistics. This approach consists of closed-loop identification and controller redesign cycles. In each cycle, the closed-loop identification method is used to identify an open-loop model and a steady-state Kalman filter gain from closed-loop input/output test data obtained by using a feedback LQG controller designed from the previous cycle. Then the identified open-loop model is used to redesign the state feedback. The state feedback and the identified Kalman filter gain are used to form an updated LQC controller for the next cycle. This iterative process continues until the updated controller converges. The proposed controller design is demonstrated by numerical simulations and experiments on a highly unstable large-gap magnetic suspension system.

Classification of Suncus murinus species complex (Soricidae: Crocidurinae) in Peninsular Malaysia using image analysis and machine learning approaches.

PubMed

Abu, Arpah; Leow, Lee Kien; Ramli, Rosli; Omar, Hasmahzaiti

2016-12-22

Taxonomists frequently identify specimen from various populations based on the morphological characteristics and molecular data. This study looks into another invasive process in identification of house shrew (Suncus murinus) using image analysis and machine learning approaches. Thus, an automated identification system is developed to assist and simplify this task. In this study, seven descriptors namely area, convex area, major axis length, minor axis length, perimeter, equivalent diameter and extent which are based on the shape are used as features to represent digital image of skull that consists of dorsal, lateral and jaw views for each specimen. An Artificial Neural Network (ANN) is used as classifier to classify the skulls of S. murinus based on region (northern and southern populations of Peninsular Malaysia) and sex (adult male and female). Thus, specimen classification using Training data set and identification using Testing data set were performed through two stages of ANNs. At present, the classifier used has achieved an accuracy of 100% based on skulls' views. Classification and identification to regions and sexes have also attained 72.5%, 87.5% and 80.0% of accuracy for dorsal, lateral, and jaw views, respectively. This results show that the shape characteristic features used are substantial because they can differentiate the specimens based on regions and sexes up to the accuracy of 80% and above. Finally, an application was developed and can be used for the scientific community. This automated system demonstrates the practicability of using computer-assisted systems in providing interesting alternative approach for quick and easy identification of unknown species.

Differential identification of Candida species and other yeasts by analysis of (/sup 35/S)methionine-labeled polypeptide profiles

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

Shen, H.D.; Choo, K.B.; Tsai, W.C.

1988-12-01

This paper describes a scheme for differential identification of Candida species and other yeasts based on autoradiographic analysis of protein profiles of (/sup 35/S)methionine-labeled cellular proteins separated by sodium dodecyl sulfate-polyacrylamide gel electrophoresis. Using ATCC strains as references, protein profile analysis showed that different Candida and other yeast species produced distinctively different patterns. Good agreement in results obtained with this approach and with other conventional systems was observed. Being accurate and reproducible, this approach provides a basis for the development of an alternative method for the identification of yeasts isolated from clinical specimens.

An animal tracking system for behavior analysis using radio frequency identification.

PubMed

Catarinucci, Luca; Colella, Riccardo; Mainetti, Luca; Patrono, Luigi; Pieretti, Stefano; Secco, Andrea; Sergi, Ilaria

2014-09-01

Evaluating the behavior of mice and rats has substantially contributed to the progress of research in many scientific fields. Researchers commonly observe recorded video of animal behavior and manually record their observations for later analysis, but this approach has several limitations. The authors developed an automated system for tracking and analyzing the behavior of rodents that is based on radio frequency identification (RFID) in an ultra-high-frequency bandwidth. They provide an overview of the system's hardware and software components as well as describe their technique for surgically implanting passive RFID tags in mice. Finally, the authors present the findings of two validation studies to compare the accuracy of the RFID system versus commonly used approaches for evaluating the locomotor activity and object exploration of mice.

Talent identification in youth soccer.

PubMed

Unnithan, Viswanath; White, Jordan; Georgiou, Andreas; Iga, John; Drust, Barry

2012-01-01

The purpose of this review article was firstly to evaluate the traditional approach to talent identification in youth soccer and secondly present pilot data on a more holistic method for talent identification. Research evidence exists to suggest that talent identification mechanisms that are predicated upon the physical (anthropometric) attributes of the early maturing individual only serve to identify current performance levels. Greater body mass and stature have both been related to faster ball shooting speed and vertical jump capacity respectively in elite youth soccer players. This approach, however, may prematurely exclude those late maturing individuals. Multiple physiological measures have also been used in an effort to determine key predictors of performance; with agility and sprint times, being identified as variables that could discriminate between elite and sub-elite groups of adolescent soccer players. Successful soccer performance is the product of multiple systems interacting with one another. Consequently, a more holistic approach to talent identification should be considered. Recent work, with elite youth soccer players, has considered whether multiple small-sided games could act as a talent identification tool in this population. The results demonstrated that there was a moderate agreement between the more technically gifted soccer player and success during multiple small-sided games.

Spatial-Temporal Synchrophasor Data Characterization and Analytics in Smart Grid Fault Detection, Identification, and Impact Causal Analysis

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

Jiang, Huaiguang; Dai, Xiaoxiao; Gao, David Wenzhong

An approach of big data characterization for smart grids (SGs) and its applications in fault detection, identification, and causal impact analysis is proposed in this paper, which aims to provide substantial data volume reduction while keeping comprehensive information from synchrophasor measurements in spatial and temporal domains. Especially, based on secondary voltage control (SVC) and local SG observation algorithm, a two-layer dynamic optimal synchrophasor measurement devices selection algorithm (OSMDSA) is proposed to determine SVC zones, their corresponding pilot buses, and the optimal synchrophasor measurement devices. Combining the two-layer dynamic OSMDSA and matching pursuit decomposition, the synchrophasor data is completely characterized inmore » the spatial-temporal domain. To demonstrate the effectiveness of the proposed characterization approach, SG situational awareness is investigated based on hidden Markov model based fault detection and identification using the spatial-temporal characteristics generated from the reduced data. To identify the major impact buses, the weighted Granger causality for SGs is proposed to investigate the causal relationship of buses during system disturbance. The IEEE 39-bus system and IEEE 118-bus system are employed to validate and evaluate the proposed approach.« less

Modeling and Model Identification of Autonomous Underwater Vehicles

DTIC Science & Technology

2015-06-01

setup, based on a quadrifilar pendulum , is developed to measure the moments of inertia of the vehicle. System identification techniques, based on...parametric models of the platforms: an individual channel excitation approach and a free decay pendulum test. The former is applied to THAUS, which can...excite the system in individual channels in four degrees of freedom. These results are verified in the free decay pendulum setup, which has the

Acquisition Management for Systems-of-Systems: Analysis of Alternatives via Computational Exploratory Model

DTIC Science & Technology

2012-02-03

node to the analysis of eigenmodes (connected trees /networks) of disruption sequences. The identification of disruption eigenmodes is particularly...investment portfolio approach enables the identification of optimal SoS network topologies and provides a tool for acquisition professionals to...a program based on its ability to provide a new capability for a given cost, and not on its ability to meet specific performance requirements ( Spacy

An experimental approach to identify dynamical models of transcriptional regulation in living cells

NASA Astrophysics Data System (ADS)

Fiore, G.; Menolascina, F.; di Bernardo, M.; di Bernardo, D.

2013-06-01

We describe an innovative experimental approach, and a proof of principle investigation, for the application of System Identification techniques to derive quantitative dynamical models of transcriptional regulation in living cells. Specifically, we constructed an experimental platform for System Identification based on a microfluidic device, a time-lapse microscope, and a set of automated syringes all controlled by a computer. The platform allows delivering a time-varying concentration of any molecule of interest to the cells trapped in the microfluidics device (input) and real-time monitoring of a fluorescent reporter protein (output) at a high sampling rate. We tested this platform on the GAL1 promoter in the yeast Saccharomyces cerevisiae driving expression of a green fluorescent protein (Gfp) fused to the GAL1 gene. We demonstrated that the System Identification platform enables accurate measurements of the input (sugars concentrations in the medium) and output (Gfp fluorescence intensity) signals, thus making it possible to apply System Identification techniques to obtain a quantitative dynamical model of the promoter. We explored and compared linear and nonlinear model structures in order to select the most appropriate to derive a quantitative model of the promoter dynamics. Our platform can be used to quickly obtain quantitative models of eukaryotic promoters, currently a complex and time-consuming process.

BoB, a best-of-breed automated text de-identification system for VHA clinical documents.

PubMed

Ferrández, Oscar; South, Brett R; Shen, Shuying; Friedlin, F Jeffrey; Samore, Matthew H; Meystre, Stéphane M

2013-01-01

De-identification allows faster and more collaborative clinical research while protecting patient confidentiality. Clinical narrative de-identification is a tedious process that can be alleviated by automated natural language processing methods. The goal of this research is the development of an automated text de-identification system for Veterans Health Administration (VHA) clinical documents. We devised a novel stepwise hybrid approach designed to improve the current strategies used for text de-identification. The proposed system is based on a previous study on the best de-identification methods for VHA documents. This best-of-breed automated clinical text de-identification system (aka BoB) tackles the problem as two separate tasks: (1) maximize patient confidentiality by redacting as much protected health information (PHI) as possible; and (2) leave de-identified documents in a usable state preserving as much clinical information as possible. We evaluated BoB with a manually annotated corpus of a variety of VHA clinical notes, as well as with the 2006 i2b2 de-identification challenge corpus. We present evaluations at the instance- and token-level, with detailed results for BoB's main components. Moreover, an existing text de-identification system was also included in our evaluation. BoB's design efficiently takes advantage of the methods implemented in its pipeline, resulting in high sensitivity values (especially for sensitive PHI categories) and a limited number of false positives. Our system successfully addressed VHA clinical document de-identification, and its hybrid stepwise design demonstrates robustness and efficiency, prioritizing patient confidentiality while leaving most clinical information intact.

Approach to the problem of the parameters optimization of the shooting system

NASA Astrophysics Data System (ADS)

Demidova, L. A.; Sablina, V. A.; Sokolova, Y. S.

2018-02-01

The problem of the objects identification on the base of their hyperspectral features has been considered. It is offered to use the SVM classifiers’ ensembles, adapted to specifics of the problem of the objects identification on the base of their hyperspectral features. The results of the objects identification on the base of their hyperspectral features with using of the SVM classifiers have been presented.

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.

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.

Leadership in the Organization Context: A Conceptual Approach and Its Applications

DTIC Science & Technology

1986-02-01

Tornow & Pinto, 1976). Leadership Identification One potential application would lie in the identification of indi- viduals who are most likely to...tions, 4, 3-38. Tornow , W. W., & Pinto, P. R. (1976). The development of a managerial job taxonomy: A system for describing, classifying and

Model of Emotional Expressions in Movements

ERIC Educational Resources Information Center

Rozaliev, Vladimir L.; Orlova, Yulia A.

2013-01-01

This paper presents a new approach to automated identification of human emotions based on analysis of body movements, a recognition of gestures and poses. Methodology, models and automated system for emotion identification are considered. To characterize the person emotions in the model, body movements are described with linguistic variables and a…

System-morphological approach: Another look at morphology research and geomorphological mapping

NASA Astrophysics Data System (ADS)

Lastochkin, Alexander N.; Zhirov, Andrey I.; Boltramovich, Sergei F.

2018-02-01

A large number of studies require a clear and unambiguous morphological basis. For over thirty years, Russian scientists have been applying a system-morphological approach for the Arctic and Antarctic research, ocean floor investigation, for various infrastructure construction projects (oil and gas, sports, etc.), in landscape and environmental studies. This article is a review aimed to introduce this methodological approach to the international scientific community. The details of the methods and techniques can be found in a series of earlier papers published in the Russian language in 1987-2016. The proposed system-morphological approach includes: 1) partitioning of the Earth surface, i.e. precise identification of linear, point, and areal elements of topography considered as a two-dimensional surface without any geological substance; 2) further identification of larger formations: geomorphological systems and regions; 3) analysis of structural relations and symmetry of topography; and 4) various dynamic (litho- and glaciodynamic, tectonic, etc.) interpretations of the observed morphology. This method can be used to study the morphology of the surface topography as well as less accessible interfaces such as submarine and subglacial ones.

A new modelling and identification scheme for time-delay systems with experimental investigation: a relay feedback approach

NASA Astrophysics Data System (ADS)

Pandey, Saurabh; Majhi, Somanath; Ghorai, Prasenjit

2017-07-01

In this paper, the conventional relay feedback test has been modified for modelling and identification of a class of real-time dynamical systems in terms of linear transfer function models with time-delay. An ideal relay and unknown systems are connected through a negative feedback loop to bring the sustained oscillatory output around the non-zero setpoint. Thereafter, the obtained limit cycle information is substituted in the derived mathematical equations for accurate identification of unknown plants in terms of overdamped, underdamped, critically damped second-order plus dead time and stable first-order plus dead time transfer function models. Typical examples from the literature are included for the validation of the proposed identification scheme through computer simulations. Subsequently, the comparisons between estimated model and true system are drawn through integral absolute error criterion and frequency response plots. Finally, the obtained output responses through simulations are verified experimentally on real-time liquid level control system using Yokogawa Distributed Control System CENTUM CS3000 set up.

A knowledge-based, concept-oriented view generation system for clinical data.

PubMed

Zeng, Q; Cimino, J J

2001-04-01

Information overload is a well-known problem for clinicians who must review large amounts of data in patient records. Concept-oriented views, which organize patient data around clinical concepts such as diagnostic strategies and therapeutic goals, may offer a solution to the problem of information overload. However, although concept-oriented views are desirable, they are difficult to create and maintain. We have developed a general-purpose, knowledge-based approach to the generation of concept-oriented views and have developed a system to test our approach. The system creates concept-oriented views through automated identification of relevant patient data. The knowledge in the system is represented by both a semantic network and rules. The key relevant data identification function is accomplished by a rule-based traversal of the semantic network. This paper focuses on the design and implementation of the system; an evaluation of the system is reported separately.

Preliminary design activities for solar heating and cooling systems

NASA Technical Reports Server (NTRS)

1978-01-01

Information on the development of solar heating and cooling systems is presented. The major emphasis is placed on program organization, system size definition, site identification, system approaches, heat pump and equipment design, collector procurement, and other preliminary design activities.

CASTE (Course Assembly System and Tutorial Environment) and CVI: (Combat Vehicle Identification) A First Application of an Intelligent Tutorial System to Combat Vehicle Identification.

DTIC Science & Technology

1984-09-01

based training systems and hence to realize an embedded trainer that is both intelligent and effective . The o(Continued) DO,; FOAM AM 71 1ឹ...Performance Effectiveness and Simulation Approved for public releate; dlitribution unlimited iii &a3laAfc*ia £&&etaL* ■’—’,£-«.■£./■.,’-f...oriented approaches to computer-based training systems and hence realise an embedded trainer that is both intelli- gent and effective . To this end

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.

Eccentricity error identification and compensation for high-accuracy 3D optical measurement

PubMed Central

He, Dong; Liu, Xiaoli; Peng, Xiang; Ding, Yabin; Gao, Bruce Z

2016-01-01

The circular target has been widely used in various three-dimensional optical measurements, such as camera calibration, photogrammetry and structured light projection measurement system. The identification and compensation of the circular target systematic eccentricity error caused by perspective projection is an important issue for ensuring accurate measurement. This paper introduces a novel approach for identifying and correcting the eccentricity error with the help of a concentric circles target. Compared with previous eccentricity error correction methods, our approach does not require taking care of the geometric parameters of the measurement system regarding target and camera. Therefore, the proposed approach is very flexible in practical applications, and in particular, it is also applicable in the case of only one image with a single target available. The experimental results are presented to prove the efficiency and stability of the proposed approach for eccentricity error compensation. PMID:26900265

Eccentricity error identification and compensation for high-accuracy 3D optical measurement.

PubMed

He, Dong; Liu, Xiaoli; Peng, Xiang; Ding, Yabin; Gao, Bruce Z

2013-07-01

The circular target has been widely used in various three-dimensional optical measurements, such as camera calibration, photogrammetry and structured light projection measurement system. The identification and compensation of the circular target systematic eccentricity error caused by perspective projection is an important issue for ensuring accurate measurement. This paper introduces a novel approach for identifying and correcting the eccentricity error with the help of a concentric circles target. Compared with previous eccentricity error correction methods, our approach does not require taking care of the geometric parameters of the measurement system regarding target and camera. Therefore, the proposed approach is very flexible in practical applications, and in particular, it is also applicable in the case of only one image with a single target available. The experimental results are presented to prove the efficiency and stability of the proposed approach for eccentricity error compensation.

Semantic policy and adversarial modeling for cyber threat identification and avoidance

NASA Astrophysics Data System (ADS)

DeFrancesco, Anton; McQueary, Bruce

2009-05-01

Today's enterprise networks undergo a relentless barrage of attacks from foreign and domestic adversaries. These attacks may be perpetrated with little to no funding, but may wreck incalculable damage upon the enterprises security, network infrastructure, and services. As more services come online, systems that were once in isolation now provide information that may be combined dynamically with information from other systems to create new meaning on the fly. Security issues are compounded by the potential to aggregate individual pieces of information and infer knowledge at a higher classification than any of its constituent parts. To help alleviate these challenges, in this paper we introduce the notion of semantic policy and discuss how it's use is evolving from a robust approach to access control to preempting and combating attacks in the cyber domain, The introduction of semantic policy and adversarial modeling to network security aims to ask 'where is the network most vulnerable', 'how is the network being attacked', and 'why is the network being attacked'. The first aspect of our approach is integration of semantic policy into enterprise security to augment traditional network security with an overall awareness of policy access and violations. This awareness allows the semantic policy to look at the big picture - analyzing trends and identifying critical relations in system wide data access. The second aspect of our approach is to couple adversarial modeling with semantic policy to move beyond reactive security measures and into a proactive identification of system weaknesses and areas of vulnerability. By utilizing Bayesian-based methodologies, the enterprise wide meaning of data and semantic policy is applied to probability and high-level risk identification. This risk identification will help mitigate potential harm to enterprise networks by enabling resources to proactively isolate, lock-down, and secure systems that are most vulnerable.

Continuous-Time Bilinear System Identification

NASA Technical Reports Server (NTRS)

Juang, Jer-Nan

2003-01-01

The objective of this paper is to describe a new method for identification of a continuous-time multi-input and multi-output bilinear system. The approach is to make judicious use of the linear-model properties of the bilinear system when subjected to a constant input. Two steps are required in the identification process. The first step is to use a set of pulse responses resulting from a constant input of one sample period to identify the state matrix, the output matrix, and the direct transmission matrix. The second step is to use another set of pulse responses with the same constant input over multiple sample periods to identify the input matrix and the coefficient matrices associated with the coupling terms between the state and the inputs. Numerical examples are given to illustrate the concept and the computational algorithm for the identification method.

Flight test planning and parameter extraction for rotorcraft system identification

NASA Technical Reports Server (NTRS)

Wang, J. C.; Demiroz, M. Y.; Talbot, P. D.

1986-01-01

The present study is concerned with the mathematical modelling of aircraft dynamics on the basis of an investigation conducted with the aid of the Rotor System Research Aircraft (RSRA). The particular characteristics of RSRA make it possible to investigate aircraft properties which cannot be readily studied elsewhere, for example in the wind tunnel. The considered experiment had mainly the objective to develop an improved understanding of the physics of rotor flapping dynamics and rotor loads in maneuvers. The employed approach is based on a utilization of parameter identification methodology (PID) with application to helicopters. A better understanding of the contribution of the main rotor to the overall aircraft forces and moments is also to be obtained. Attention is given to the mathematical model of a rotorcraft system, an integrated identification method, flight data processing, and the identification of RSRA mathematical models.

Dynamic Stability Analysis of Linear Time-varying Systems via an Extended Modal Identification Approach

NASA Astrophysics Data System (ADS)

Ma, Zhisai; Liu, Li; Zhou, Sida; Naets, Frank; Heylen, Ward; Desmet, Wim

2017-03-01

The problem of linear time-varying(LTV) system modal analysis is considered based on time-dependent state space representations, as classical modal analysis of linear time-invariant systems and current LTV system modal analysis under the "frozen-time" assumption are not able to determine the dynamic stability of LTV systems. Time-dependent state space representations of LTV systems are first introduced, and the corresponding modal analysis theories are subsequently presented via a stability-preserving state transformation. The time-varying modes of LTV systems are extended in terms of uniqueness, and are further interpreted to determine the system's stability. An extended modal identification is proposed to estimate the time-varying modes, consisting of the estimation of the state transition matrix via a subspace-based method and the extraction of the time-varying modes by the QR decomposition. The proposed approach is numerically validated by three numerical cases, and is experimentally validated by a coupled moving-mass simply supported beam experimental case. The proposed approach is capable of accurately estimating the time-varying modes, and provides a new way to determine the dynamic stability of LTV systems by using the estimated time-varying modes.

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.

Systems Toxicology: From Basic Research to Risk Assessment

PubMed Central

2014-01-01

Systems Toxicology is the integration of classical toxicology with quantitative analysis of large networks of molecular and functional changes occurring across multiple levels of biological organization. Society demands increasingly close scrutiny of the potential health risks associated with exposure to chemicals present in our everyday life, leading to an increasing need for more predictive and accurate risk-assessment approaches. Developing such approaches requires a detailed mechanistic understanding of the ways in which xenobiotic substances perturb biological systems and lead to adverse outcomes. Thus, Systems Toxicology approaches offer modern strategies for gaining such mechanistic knowledge by combining advanced analytical and computational tools. Furthermore, Systems Toxicology is a means for the identification and application of biomarkers for improved safety assessments. In Systems Toxicology, quantitative systems-wide molecular changes in the context of an exposure are measured, and a causal chain of molecular events linking exposures with adverse outcomes (i.e., functional and apical end points) is deciphered. Mathematical models are then built to describe these processes in a quantitative manner. The integrated data analysis leads to the identification of how biological networks are perturbed by the exposure and enables the development of predictive mathematical models of toxicological processes. This perspective integrates current knowledge regarding bioanalytical approaches, computational analysis, and the potential for improved risk assessment. PMID:24446777

Systems toxicology: from basic research to risk assessment.

PubMed

Sturla, Shana J; Boobis, Alan R; FitzGerald, Rex E; Hoeng, Julia; Kavlock, Robert J; Schirmer, Kristin; Whelan, Maurice; Wilks, Martin F; Peitsch, Manuel C

2014-03-17

Systems Toxicology is the integration of classical toxicology with quantitative analysis of large networks of molecular and functional changes occurring across multiple levels of biological organization. Society demands increasingly close scrutiny of the potential health risks associated with exposure to chemicals present in our everyday life, leading to an increasing need for more predictive and accurate risk-assessment approaches. Developing such approaches requires a detailed mechanistic understanding of the ways in which xenobiotic substances perturb biological systems and lead to adverse outcomes. Thus, Systems Toxicology approaches offer modern strategies for gaining such mechanistic knowledge by combining advanced analytical and computational tools. Furthermore, Systems Toxicology is a means for the identification and application of biomarkers for improved safety assessments. In Systems Toxicology, quantitative systems-wide molecular changes in the context of an exposure are measured, and a causal chain of molecular events linking exposures with adverse outcomes (i.e., functional and apical end points) is deciphered. Mathematical models are then built to describe these processes in a quantitative manner. The integrated data analysis leads to the identification of how biological networks are perturbed by the exposure and enables the development of predictive mathematical models of toxicological processes. This perspective integrates current knowledge regarding bioanalytical approaches, computational analysis, and the potential for improved risk assessment.

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.

Comparison of Five System Identification Algorithms for Rotorcraft Higher Harmonic Control

NASA Technical Reports Server (NTRS)

Jacklin, Stephen A.

1998-01-01

This report presents an analysis and performance comparison of five system identification algorithms. The methods are presented in the context of identifying a frequency-domain transfer matrix for the higher harmonic control (HHC) of helicopter vibration. The five system identification algorithms include three previously proposed methods: (1) the weighted-least- squares-error approach (in moving-block format), (2) the Kalman filter method, and (3) the least-mean-squares (LMS) filter method. In addition there are two new ones: (4) a generalized Kalman filter method and (5) a generalized LMS filter method. The generalized Kalman filter method and the generalized LMS filter method were derived as extensions of the classic methods to permit identification by using more than one measurement per identification cycle. Simulation results are presented for conditions ranging from the ideal case of a stationary transfer matrix and no measurement noise to the more complex cases involving both measurement noise and transfer-matrix variation. Both open-loop identification and closed- loop identification were simulated. Closed-loop mode identification was more challenging than open-loop identification because of the decreasing signal-to-noise ratio as the vibration became reduced. The closed-loop simulation considered both local-model identification, with measured vibration feedback and global-model identification with feedback of the identified uncontrolled vibration. The algorithms were evaluated in terms of their accuracy, stability, convergence properties, computation speeds, and relative ease of implementation.

Identification of Dynamic Simulation Models for Variable Speed Pumped Storage Power Plants

NASA Astrophysics Data System (ADS)

Moreira, C.; Fulgêncio, N.; Silva, B.; Nicolet, C.; Béguin, A.

2017-04-01

This paper addresses the identification of reduced order models for variable speed pump-turbine plants, including the representation of the dynamic behaviour of the main components: hydraulic system, turbine governors, electromechanical equipment and power converters. A methodology for the identification of appropriated reduced order models both for turbine and pump operating modes is presented and discussed. The methodological approach consists of three main steps: 1) detailed pumped-storage power plant modelling in SIMSEN; 2) reduced order models identification and 3) specification of test conditions for performance evaluation.

An approach to communications security for a communications data delivery system for V2V/V2I safety : technical description and identification of policy and institutional issues.

DOT National Transportation Integrated Search

2011-11-01

This report identifies the security approach associated with a communications data delivery system that supports vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications. The report describes the risks associated with communication...

A modular approach to detection and identification of defects in rough lumber

Treesearch

Sang Mook Lee; A. Lynn Abbott; Daniel L. Schmoldt

2001-01-01

This paper describes a prototype scanning system that can automatically identify several important defects on rough hardwood lumber. The scanning system utilizes 3 laser sources and an embedded-processor camera to capture and analyze profile and gray-scale images. The modular approach combines the detection of wane (the curved sides of a board, possibly containing...

Performance metrics for the evaluation of hyperspectral chemical identification systems

NASA Astrophysics Data System (ADS)

Truslow, Eric; Golowich, Steven; Manolakis, Dimitris; Ingle, Vinay

2016-02-01

Remote sensing of chemical vapor plumes is a difficult but important task for many military and civilian applications. Hyperspectral sensors operating in the long-wave infrared regime have well-demonstrated detection capabilities. However, the identification of a plume's chemical constituents, based on a chemical library, is a multiple hypothesis testing problem which standard detection metrics do not fully describe. We propose using an additional performance metric for identification based on the so-called Dice index. Our approach partitions and weights a confusion matrix to develop both the standard detection metrics and identification metric. Using the proposed metrics, we demonstrate that the intuitive system design of a detector bank followed by an identifier is indeed justified when incorporating performance information beyond the standard detection metrics.

Comparison of System Identification Techniques for the Hydraulic Manipulator Test Bed (HMTB)

NASA Technical Reports Server (NTRS)

Morris, A. Terry

1996-01-01

In this thesis linear, dynamic, multivariable state-space models for three joints of the ground-based Hydraulic Manipulator Test Bed (HMTB) are identified. HMTB, housed at the NASA Langley Research Center, is a ground-based version of the Dexterous Orbital Servicing System (DOSS), a representative space station manipulator. The dynamic models of the HMTB manipulator will first be estimated by applying nonparametric identification methods to determine each joint's response characteristics using various input excitations. These excitations include sum of sinusoids, pseudorandom binary sequences (PRBS), bipolar ramping pulses, and chirp input signals. Next, two different parametric system identification techniques will be applied to identify the best dynamical description of the joints. The manipulator is localized about a representative space station orbital replacement unit (ORU) task allowing the use of linear system identification methods. Comparisons, observations, and results of both parametric system identification techniques are discussed. The thesis concludes by proposing a model reference control system to aid in astronaut ground tests. This approach would allow the identified models to mimic on-orbit dynamic characteristics of the actual flight manipulator thus providing astronauts with realistic on-orbit responses to perform space station tasks in a ground-based environment.

Adaptive convex combination approach for the identification of improper quaternion processes.

PubMed

Ujang, Bukhari Che; Jahanchahi, Cyrus; Took, Clive Cheong; Mandic, Danilo P

2014-01-01

Data-adaptive optimal modeling and identification of real-world vector sensor data is provided by combining the fractional tap-length (FT) approach with model order selection in the quaternion domain. To account rigorously for the generality of such processes, both second-order circular (proper) and noncircular (improper), the proposed approach in this paper combines the FT length optimization with both the strictly linear quaternion least mean square (QLMS) and widely linear QLMS (WL-QLMS). A collaborative approach based on QLMS and WL-QLMS is shown to both identify the type of processes (proper or improper) and to track their optimal parameters in real time. Analysis shows that monitoring the evolution of the convex mixing parameter within the collaborative approach allows us to track the improperness in real time. Further insight into the properties of those algorithms is provided by establishing a relationship between the steady-state error and optimal model order. The approach is supported by simulations on model order selection and identification of both strictly linear and widely linear quaternion-valued systems, such as those routinely used in renewable energy (wind) and human-centered computing (biomechanics).

Use of the MicroSeq 500 16S rRNA Gene-Based Sequencing for Identification of Bacterial Isolates That Commercial Automated Systems Failed To Identify Correctly

PubMed Central

Fontana, Carla; Favaro, Marco; Pelliccioni, Marco; Pistoia, Enrico Salvatore; Favalli, Cartesio

2005-01-01

Reliable automated identification and susceptibility testing of clinically relevant bacteria is an essential routine for microbiology laboratories, thus improving patient care. Examples of automated identification systems include the Phoenix (Becton Dickinson) and the VITEK 2 (bioMérieux). However, more and more frequently, microbiologists must isolate “difficult” strains that automated systems often fail to identify. An alternative approach could be the genetic identification of isolates; this is based on 16S rRNA gene sequencing and analysis. The aim of the present study was to evaluate the possible use of MicroSeq 500 (Applera) for sequencing the 16S rRNA gene to identify isolates whose identification is unobtainable by conventional systems. We analyzed 83 “difficult” clinical isolates: 25 gram-positive and 58 gram-negative strains that were contemporaneously identified by both systems—VITEK 2 and Phoenix—while genetic identification was performed by using the MicroSeq 500 system. The results showed that phenotypic identifications by VITEK 2 and Phoenix were remarkably similar: 74% for gram-negative strains (43 of 58) and 80% for gram-positive strains were concordant by both systems and also concordant with genetic characterization. The exceptions were the 15 gram-negative and 9 gram-positive isolates whose phenotypic identifications were contrasting or inconclusive. For these, the use of MicroSeq 500 was fundamental to achieving species identification. In clinical microbiology the use of MicroSeq 500, particularly for strains with ambiguous biochemical profiles (including slow-growing strains), identifies strains more easily than do conventional systems. Moreover, MicroSeq 500 is easy to use and cost-effective, making it applicable also in the clinical laboratory. PMID:15695654

System driven technology selection for future European launch systems

NASA Astrophysics Data System (ADS)

Baiocco, P.; Ramusat, G.; Sirbi, A.; Bouilly, Th.; Lavelle, F.; Cardone, T.; Fischer, H.; Appel, S.

2015-02-01

In the framework of the next generation launcher activity at ESA, a top-down approach and a bottom-up approach have been performed for the identification of promising technologies and alternative conception of future European launch vehicles. The top-down approach consists in looking for system-driven design solutions and the bottom-up approach features design solutions leading to substantial advantages for the system. The main investigations have been focused on the future launch vehicle technologies. Preliminary specifications have been used in order to permit sub-system design to find the major benefit for the overall launch system. The development cost, non-recurring and recurring cost, industrialization and operational aspects have been considered as competitiveness factors for the identification and down-selection of the most interesting technologies. The recurring cost per unit payload mass has been evaluated. The TRL/IRL has been assessed and a preliminary development plan has been traced for the most promising technologies. The potentially applicable launch systems are Ariane and VEGA evolution. The main FLPP technologies aim at reducing overall structural mass, increasing structural margins for robustness, metallic and composite containment of cryogenic hydrogen and oxygen propellants, propellant management subsystems, elements significantly reducing fabrication and operational costs, avionics, pyrotechnics, etc. to derive performing upper and booster stages. Application of the system driven approach allows creating performing technology demonstrators in terms of need, demonstration objective, size and cost. This paper outlines the process of technology down selection using a system driven approach, the accomplishments already achieved in the various technology fields up to now, as well as the potential associated benefit in terms of competitiveness factors.

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.

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.

Exceptional responders in conservation.

PubMed

Post, Gerald; Geldmann, Jonas

2017-08-30

Conservation operates within complex systems with incomplete knowledge of the system and the interventions utilized. This frequently results in the inability to find generally applicable methods to alleviate threats to Earth's vanishing wildlife. One approach used in medicine and the social sciences has been to develop a deeper understanding of positive outliers. Where such outliers share similar characteristics, they may be considered exceptional responders. We devised a 4-step framework for identifying exceptional responders in conservation: identification of the study system, identification of the response structure, identification of the threshold for exceptionalism, and identification of commonalities among outliers. Evaluation of exceptional responders provides additional information that is often ignored in randomized controlled trials and before-after control-intervention experiments. Interrogating the contextual factors that contribute to an exceptional outcome allow exceptional responders to become valuable pieces of information leading to unexpected discoveries and novel hypotheses. © 2017 Society for Conservation Biology.

Satellite provided customer premises services: A forecast of potential domestic demand through the year 2000. Volume 1: Executive summary

NASA Technical Reports Server (NTRS)

Kratochvil, D.; Bowyer, J.; Bhushan, C.; Steinnagel, K.; Kaushal, D.; Al-Kinani, G.

1983-01-01

Development of a forecast of the total domestic telecommunications demand, identification of that portion of the telecommunications demand suitable for transmission by satellite systems, identification of that portion of the satellite market addressable by CPS systems, identification of that portion of the satellite market addressable by Ka-band CPS system, and postulation of a Ka-band CPS network on a nationwide and local level were achieved. The approach employed included the use of a variety of forecasting models, a parametric cost model, a market distribution model and a network optimization model. Forecasts were developed for: 1980, 1990, 2000; voice, data and video services; terrestrial and satellite delivery modes; and C, Ku and Ka-bands.

Satellite provided customer premises services: A forecast of potential domestic demand through the year 2000. Volume: Executive summary

NASA Astrophysics Data System (ADS)

Kratochvil, D.; Bowyer, J.; Bhushan, C.; Steinnagel, K.; Kaushal, D.; Al-Kinani, G.

1983-08-01

Development of a forecast of the total domestic telecommunications demand, identification of that portion of the telecommunications demand suitable for transmission by satellite systems, identification of that portion of the satellite market addressable by CPS systems, identification of that portion of the satellite market addressable by Ka-band CPS system, and postulation of a Ka-band CPS network on a nationwide and local level were achieved. The approach employed included the use of a variety of forecasting models, a parametric cost model, a market distribution model and a network optimization model. Forecasts were developed for: 1980, 1990, 2000; voice, data and video services; terrestrial and satellite delivery modes; and C, Ku and Ka-bands.

FBI fingerprint identification automation study: AIDS 3 evaluation report. Volume 8: Measures of effectiveness

NASA Technical Reports Server (NTRS)

Mulhall, B. D. L.

1980-01-01

The development of both quantitative criteria that were used to evaluate conceptional systems for automating the functions for the FBI Identification Division is described. Specific alternative systems for automation were compared by using these developed criteria, defined as Measures of Effectiveness (MOE), to gauge system's performance in attempting to achieve certain goals. The MOE, essentially measurement tools that were developed through the combination of suitable parameters, pertain to each conceivable area of system operation. The methods and approaches used, both in selecting the parameters and in using the resulting MOE, are described.

The Impact of Hierarchy and Group Structure on Information Processing in Decision Making: Application of a Networks/Systems Approach.

ERIC Educational Resources Information Center

Ford, David L., Jr.

When one engages in organizational diagnosis, it has been suggested that greater understanding of the organization can come through: (1) an identification of all the channels conveying material and information, and (2) a description of the means by which this communication influences the behavior of the organization. A networks/system approach is…

The Confidence-Accuracy Relationship in Eyewitness Identification: Effects of Lineup Instructions, Foil Similarity, and Target-Absent Base Rates

ERIC Educational Resources Information Center

Brewer, Neil; Wells, Gary L.

2006-01-01

Discriminating accurate from mistaken eyewitness identifications is a major issue facing criminal justice systems. This study examined whether eyewitness confidence assists such decisions under a variety of conditions using a confidence-accuracy (CA) calibration approach. Participants (N = 1,200) viewed a simulated crime and attempted 2 separate…

Analysis of Additive Manufacturing for Sustainment of Naval Aviation Systems

DTIC Science & Technology

2017-09-01

selection methodology to query the aviation spare-parts inventory for identification of additive manufacturing candidates. The methodology organizes...a component selection methodology to query the aviation spare-parts inventory for identification of additive manufacturing candidates. The... methodology organizes the resultant data using a top-down approach that aligns technical feasibility with programmatic objectives. Finally, a discrete event

Rocket Engine Health Management: Early Definition of Critical Flight Measurements

NASA Technical Reports Server (NTRS)

Christenson, Rick L.; Nelson, Michael A.; Butas, John P.

2003-01-01

The NASA led Space Launch Initiative (SLI) program has established key requirements related to safety, reliability, launch availability and operations cost to be met by the next generation of reusable launch vehicles. Key to meeting these requirements will be an integrated vehicle health management ( M) system that includes sensors, harnesses, software, memory, and processors. Such a system must be integrated across all the vehicle subsystems and meet component, subsystem, and system requirements relative to fault detection, fault isolation, and false alarm rate. The purpose of this activity is to evolve techniques for defining critical flight engine system measurements-early within the definition of an engine health management system (EHMS). Two approaches, performance-based and failure mode-based, are integrated to provide a proposed set of measurements to be collected. This integrated approach is applied to MSFC s MC-1 engine. Early identification of measurements supports early identification of candidate sensor systems whose design and impacts to the engine components must be considered in engine design.

Direct maldi-tof mass spectrometry assay of blood culture broths for rapid identification of Candida species causing bloodstream infections: an observational study in two large microbiology laboratories.

PubMed

Spanu, Teresa; Posteraro, Brunella; Fiori, Barbara; D'Inzeo, Tiziana; Campoli, Serena; Ruggeri, Alberto; Tumbarello, Mario; Canu, Giulia; Trecarichi, Enrico Maria; Parisi, Gabriella; Tronci, Mirella; Sanguinetti, Maurizio; Fadda, Giovanni

2012-01-01

We evaluated the reliability of the Bruker Daltonik's MALDI Biotyper system in species-level identification of yeasts directly from blood culture bottles. Identification results were concordant with those of the conventional culture-based method for 95.9% of Candida albicans (187/195) and 86.5% of non-albicans Candida species (128/148). Results were available in 30 min (median), suggesting that this approach is a reliable, time-saving tool for routine identification of Candida species causing bloodstream infection.

Parameter identification for nonlinear aerodynamic systems

NASA Technical Reports Server (NTRS)

Pearson, Allan E.

1993-01-01

This final technical report covers a three and one-half year period preceding February 28, 1993 during which support was provided under NASA Grant NAG-1-1065. Following a general description of the system identification problem and a brief survey of methods to attack it, the basic ideas behind the approach taken in this research effort are presented. The results obtained are described with reference to the published work, including the five semiannual progress reports previously submitted and two interim technical reports.

Dynamic modeling and parameter estimation of a radial and loop type distribution system network

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

Jun Qui; Heng Chen; Girgis, A.A.

1993-05-01

This paper presents a new identification approach to three-phase power system modeling and model reduction taking power system network as multi-input, multi-output (MIMO) processes. The model estimate can be obtained in discrete-time input-output form, discrete- or continuous-time state-space variable form, or frequency-domain impedance transfer function matrix form. An algorithm for determining the model structure of this MIMO process is described. The effect of measurement noise on the approach is also discussed. This approach has been applied on a sample system and simulation results are also presented in this paper.

White blood cells identification system based on convolutional deep neural learning networks.

PubMed

Shahin, A I; Guo, Yanhui; Amin, K M; Sharawi, Amr A

2017-11-16

White blood cells (WBCs) differential counting yields valued information about human health and disease. The current developed automated cell morphology equipments perform differential count which is based on blood smear image analysis. Previous identification systems for WBCs consist of successive dependent stages; pre-processing, segmentation, feature extraction, feature selection, and classification. There is a real need to employ deep learning methodologies so that the performance of previous WBCs identification systems can be increased. Classifying small limited datasets through deep learning systems is a major challenge and should be investigated. In this paper, we propose a novel identification system for WBCs based on deep convolutional neural networks. Two methodologies based on transfer learning are followed: transfer learning based on deep activation features and fine-tuning of existed deep networks. Deep acrivation featues are extracted from several pre-trained networks and employed in a traditional identification system. Moreover, a novel end-to-end convolutional deep architecture called "WBCsNet" is proposed and built from scratch. Finally, a limited balanced WBCs dataset classification is performed through the WBCsNet as a pre-trained network. During our experiments, three different public WBCs datasets (2551 images) have been used which contain 5 healthy WBCs types. The overall system accuracy achieved by the proposed WBCsNet is (96.1%) which is more than different transfer learning approaches or even the previous traditional identification system. We also present features visualization for the WBCsNet activation which reflects higher response than the pre-trained activated one. a novel WBCs identification system based on deep learning theory is proposed and a high performance WBCsNet can be employed as a pre-trained network. Copyright © 2017. Published by Elsevier B.V.

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.

Fusion of imaging and nonimaging data for surveillance aircraft

NASA Astrophysics Data System (ADS)

Shahbazian, Elisa; Gagnon, Langis; Duquet, Jean Remi; Macieszczak, Maciej; Valin, Pierre

1997-06-01

This paper describes a phased incremental integration approach for application of image analysis and data fusion technologies to provide automated intelligent target tracking and identification for airborne surveillance on board an Aurora Maritime Patrol Aircraft. The sensor suite of the Aurora consists of a radar, an identification friend or foe (IFF) system, an electronic support measures (ESM) system, a spotlight synthetic aperture radar (SSAR), a forward looking infra-red (FLIR) sensor and a link-11 tactical datalink system. Lockheed Martin Canada (LMCan) is developing a testbed, which will be used to analyze and evaluate approaches for combining the data provided by the existing sensors, which were initially not designed to feed a fusion system. Three concurrent research proof-of-concept activities provide techniques, algorithms and methodology into three sequential phases of integration of this testbed. These activities are: (1) analysis of the fusion architecture (track/contact/hybrid) most appropriate for the type of data available, (2) extraction and fusion of simple features from the imaging data into the fusion system performing automatic target identification, and (3) development of a unique software architecture which will permit integration and independent evolution, enhancement and optimization of various decision aid capabilities, such as multi-sensor data fusion (MSDF), situation and threat assessment (STA) and resource management (RM).

Robust uncertainty evaluation for system identification on distributed wireless platforms

NASA Astrophysics Data System (ADS)

Crinière, Antoine; Döhler, Michael; Le Cam, Vincent; Mevel, Laurent

2016-04-01

Health monitoring of civil structures by system identification procedures from automatic control is now accepted as a valid approach. These methods provide frequencies and modeshapes from the structure over time. For a continuous monitoring the excitation of a structure is usually ambient, thus unknown and assumed to be noise. Hence, all estimates from the vibration measurements are realizations of random variables with inherent uncertainty due to (unknown) process and measurement noise and finite data length. The underlying algorithms are usually running under Matlab under the assumption of large memory pool and considerable computational power. Even under these premises, computational and memory usage are heavy and not realistic for being embedded in on-site sensor platforms such as the PEGASE platform. Moreover, the current push for distributed wireless systems calls for algorithmic adaptation for lowering data exchanges and maximizing local processing. Finally, the recent breakthrough in system identification allows us to process both frequency information and its related uncertainty together from one and only one data sequence, at the expense of computational and memory explosion that require even more careful attention than before. The current approach will focus on presenting a system identification procedure called multi-setup subspace identification that allows to process both frequencies and their related variances from a set of interconnected wireless systems with all computation running locally within the limited memory pool of each system before being merged on a host supervisor. Careful attention will be given to data exchanges and I/O satisfying OGC standards, as well as minimizing memory footprints and maximizing computational efficiency. Those systems are built in a way of autonomous operations on field and could be later included in a wide distributed architecture such as the Cloud2SM project. The usefulness of these strategies is illustrated on data from a progressive damage action on a prestressed concrete bridge. References [1] E. Carden and P. Fanning. Vibration based condition monitoring: a review. Structural Health Monitoring, 3(4):355-377, 2004. [2] M. Döhler and L. Mevel. Efficient multi-order uncertainty computation for stochastic subspace identification. Mechanical Systems and Signal Processing, 38(2):346-366, 2013. [3] M.Döhler, L. Mevel. Modular subspace-based system identification from multi-setup measurements. IEEE Transactions on Automatic Control, 57(11):2951-2956, 2012. [4] M. Döhler, X.-B. Lam, and L. Mevel. Uncertainty quantification for modal parameters from stochastic subspace identification on multi-setup measurements. MechanicalSystems and Signal Processing, 36(2):562-581, 2013. [5] A Crinière, J Dumoulin, L Mevel, G Andrade-Barosso, M Simonin. The Cloud2SM Project.European Geosciences Union General Assembly (EGU2015), Apr 2015, Vienne, Austria. 2015.

System Identification and POD Method Applied to Unsteady Aerodynamics

NASA Technical Reports Server (NTRS)

Tang, Deman; Kholodar, Denis; Juang, Jer-Nan; Dowell, Earl H.

2001-01-01

The representation of unsteady aerodynamic flow fields in terms of global aerodynamic modes has proven to be a useful method for reducing the size of the aerodynamic model over those representations that use local variables at discrete grid points in the flow field. Eigenmodes and Proper Orthogonal Decomposition (POD) modes have been used for this purpose with good effect. This suggests that system identification models may also be used to represent the aerodynamic flow field. Implicit in the use of a systems identification technique is the notion that a relative small state space model can be useful in describing a dynamical system. The POD model is first used to show that indeed a reduced order model can be obtained from a much larger numerical aerodynamical model (the vortex lattice method is used for illustrative purposes) and the results from the POD and the system identification methods are then compared. For the example considered, the two methods are shown to give comparable results in terms of accuracy and reduced model size. The advantages and limitations of each approach are briefly discussed. Both appear promising and complementary in their characteristics.

Effects-Based Operations: Useful or Useless

DTIC Science & Technology

2010-05-03

the effects -based approach largely irrelevant. 30 The idea of Consequence Identification, however, is not to identify all outcomes , but rather to... effects -based thinking could provide operational planners and commanders with a valuable consequence identification tool. It further argues that System...to achieve specific effects that contribute directly to desired military and political outcomes .” 14 Air Force Brig Gen David Deptula further writes

An offline approach for output-only Bayesian identification of stochastic nonlinear systems using unscented Kalman filtering

NASA Astrophysics Data System (ADS)

Erazo, Kalil; Nagarajaiah, Satish

2017-06-01

In this paper an offline approach for output-only Bayesian identification of stochastic nonlinear systems is presented. The approach is based on a re-parameterization of the joint posterior distribution of the parameters that define a postulated state-space stochastic model class. In the re-parameterization the state predictive distribution is included, marginalized, and estimated recursively in a state estimation step using an unscented Kalman filter, bypassing state augmentation as required by existing online methods. In applications expectations of functions of the parameters are of interest, which requires the evaluation of potentially high-dimensional integrals; Markov chain Monte Carlo is adopted to sample the posterior distribution and estimate the expectations. The proposed approach is suitable for nonlinear systems subjected to non-stationary inputs whose realization is unknown, and that are modeled as stochastic processes. Numerical verification and experimental validation examples illustrate the effectiveness and advantages of the approach, including: (i) an increased numerical stability with respect to augmented-state unscented Kalman filtering, avoiding divergence of the estimates when the forcing input is unmeasured; (ii) the ability to handle arbitrary prior and posterior distributions. The experimental validation of the approach is conducted using data from a large-scale structure tested on a shake table. It is shown that the approach is robust to inherent modeling errors in the description of the system and forcing input, providing accurate prediction of the dynamic response when the excitation history is unknown.

Continuous-time system identification of a smoking cessation intervention

NASA Astrophysics Data System (ADS)

Timms, Kevin P.; Rivera, Daniel E.; Collins, Linda M.; Piper, Megan E.

2014-07-01

Cigarette smoking is a major global public health issue and the leading cause of preventable death in the United States. Toward a goal of designing better smoking cessation treatments, system identification techniques are applied to intervention data to describe smoking cessation as a process of behaviour change. System identification problems that draw from two modelling paradigms in quantitative psychology (statistical mediation and self-regulation) are considered, consisting of a series of continuous-time estimation problems. A continuous-time dynamic modelling approach is employed to describe the response of craving and smoking rates during a quit attempt, as captured in data from a smoking cessation clinical trial. The use of continuous-time models provide benefits of parsimony, ease of interpretation, and the opportunity to work with uneven or missing data.

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.

Validation of a Self-Administered Computerized System to Detect Cognitive Impairment in Older Adults

PubMed Central

Brinkman, Samuel D.; Reese, Robert J.; Norsworthy, Larry A.; Dellaria, Donna K.; Kinkade, Jacob W.; Benge, Jared; Brown, Kimberly; Ratka, Anna; Simpkins, James W.

2015-01-01

There is increasing interest in the development of economical and accurate approaches to identifying persons in the community who have mild, undetected cognitive impairments. Computerized assessment systems have been suggested as a viable approach to identifying these persons. The validity of a computerized assessment system for identification of memory and executive deficits in older individuals was evaluated in the current study. Volunteers (N = 235) completed a 3-hr battery of neuropsychological tests and a computerized cognitive assessment system. Participants were classified as impaired (n = 78) or unimpaired (n = 157) on the basis of the Mini Mental State Exam, Wechsler Memory Scale-III and the Trail Making Test (TMT), Part B. All six variables (three memory variables and three executive variables) derived from the computerized assessment differed significantly between groups in the expected direction. There was also evidence of temporal stability and concurrent validity. Application of computerized assessment systems for clinical practice and for identification of research participants is discussed in this article. PMID:25332303

Five-way smoking status classification using text hot-spot identification and error-correcting output codes.

PubMed

Cohen, Aaron M

2008-01-01

We participated in the i2b2 smoking status classification challenge task. The purpose of this task was to evaluate the ability of systems to automatically identify patient smoking status from discharge summaries. Our submission included several techniques that we compared and studied, including hot-spot identification, zero-vector filtering, inverse class frequency weighting, error-correcting output codes, and post-processing rules. We evaluated our approaches using the same methods as the i2b2 task organizers, using micro- and macro-averaged F1 as the primary performance metric. Our best performing system achieved a micro-F1 of 0.9000 on the test collection, equivalent to the best performing system submitted to the i2b2 challenge. Hot-spot identification, zero-vector filtering, classifier weighting, and error correcting output coding contributed additively to increased performance, with hot-spot identification having by far the largest positive effect. High performance on automatic identification of patient smoking status from discharge summaries is achievable with the efficient and straightforward machine learning techniques studied here.

Teaching Case: A Systems Analysis Role-Play Exercise and Assignment

ERIC Educational Resources Information Center

Mitri, Michel; Cole, Carey; Atkins, Laura

2017-01-01

This paper presents a role-play exercise and assignment that provides an active learning experience related to the system investigation phase of an SDLC. Whether using waterfall or agile approaches, the first SDLC step usually involves system investigation activities, including problem identification, feasibility study, cost-benefit analysis, and…

An Oracle-based co-training framework for writer identification in offline handwriting

NASA Astrophysics Data System (ADS)

Porwal, Utkarsh; Rajan, Sreeranga; Govindaraju, Venu

2012-01-01

State-of-the-art techniques for writer identification have been centered primarily on enhancing the performance of the system for writer identification. Machine learning algorithms have been used extensively to improve the accuracy of such system assuming sufficient amount of data is available for training. Little attention has been paid to the prospect of harnessing the information tapped in a large amount of un-annotated data. This paper focuses on co-training based framework that can be used for iterative labeling of the unlabeled data set exploiting the independence between the multiple views (features) of the data. This paradigm relaxes the assumption of sufficiency of the data available and tries to generate labeled data from unlabeled data set along with improving the accuracy of the system. However, performance of co-training based framework is dependent on the effectiveness of the algorithm used for the selection of data points to be added in the labeled set. We propose an Oracle based approach for data selection that learns the patterns in the score distribution of classes for labeled data points and then predicts the labels (writers) of the unlabeled data point. This method for selection statistically learns the class distribution and predicts the most probable class unlike traditional selection algorithms which were based on heuristic approaches. We conducted experiments on publicly available IAM dataset and illustrate the efficacy of the proposed approach.

System Identification for Nonlinear Control Using Neural Networks

NASA Technical Reports Server (NTRS)

Stengel, Robert F.; Linse, Dennis J.

1990-01-01

An approach to incorporating artificial neural networks in nonlinear, adaptive control systems is described. The controller contains three principal elements: a nonlinear inverse dynamic control law whose coefficients depend on a comprehensive model of the plant, a neural network that models system dynamics, and a state estimator whose outputs drive the control law and train the neural network. Attention is focused on the system identification task, which combines an extended Kalman filter with generalized spline function approximation. Continual learning is possible during normal operation, without taking the system off line for specialized training. Nonlinear inverse dynamic control requires smooth derivatives as well as function estimates, imposing stringent goals on the approximating technique.

Early identification of microorganisms in blood culture prior to the detection of a positive signal in the BACTEC FX system using matrix-assisted laser desorption/ionization-time of flight mass spectrometry.

PubMed

Wang, Ming-Cheng; Lin, Wei-Hung; Yan, Jing-Jou; Fang, Hsin-Yi; Kuo, Te-Hui; Tseng, Chin-Chung; Wu, Jiunn-Jong

2015-08-01

Matrix-assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-TOF MS) is a valuable method for rapid identification of blood stream infection (BSI) pathogens. Integration of MALDI-TOF MS and blood culture system can speed the identification of causative BSI microorganisms. We investigated the minimal microorganism concentrations of common BSI pathogens required for positive blood culture using BACTEC FX and for positive identification using MALDI-TOF MS. The time to detection with positive BACTEC FX and minimal incubation time with positive MALDI-TOF MS identification were determined for earlier identification of common BSI pathogens. The minimal microorganism concentrations required for positive blood culture using BACTEC FX were >10(7)-10(8) colony forming units/mL for most of the BSI pathogens. The minimal microorganism concentrations required for identification using MALDI-TOF MS were > 10(7) colony forming units/mL. Using simulated BSI models, one can obtain enough bacterial concentration from blood culture bottles for successful identification of five common Gram-positive and Gram-negative bacteria using MALDI-TOF MS 1.7-2.3 hours earlier than the usual time to detection in blood culture systems. This study provides an approach to earlier identification of BSI pathogens prior to the detection of a positive signal in the blood culture system using MALDI-TOF MS, compared to current methods. It can speed the time for identification of BSI pathogens and may have benefits of earlier therapy choice and on patient outcome. Copyright © 2013. Published by Elsevier B.V.

Identification of glia phenotype modulators based on select glial function regulatory signaling pathways.

PubMed

Lee, Sun-Hwa; Suk, Kyoungho

2018-04-20

Despite the considerable social and economic burden on the healthcare system worldwide due to neurodegenerative diseases, there are currently few disease-altering treatment options for many of these conditions. Therefore, new approaches for both prevention and intervention for neurodegenerative diseases are urgently required. Microglia-mediated neurotoxicity is one of the pathologic hallmarks common to Alzheimer's disease, Parkinson's disease, and amyotrophic lateral sclerosis. Current therapeutic approaches to target microglia-mediated neurotoxicity are focused on the identification of glia phenotype modulators (GPMs), which can inhibit the 'classical' pro-inflammatory and neurotoxic phenotypes of microglia. Areas covered: This article reviews selected microglial molecular targets and pathways involved in either neurotoxicity or neuroprotection and how their identification. Expert opinion: Microglial activation and their signaling pathways have important implications in the neurotoxicity and brain disorders. Pharmacological modulation of microglial activation may serve as a potential therapeutic approach for targeting microglia-mediated neurotoxicity. However, given that microglia change their activation states depending on the timing, stage, and severity of disease, and even aging, the appropriate window should be considered for this approach to be clinically effective. In the future, the identification of unknown extracellular signals and intracellular molecular switches that control phenotypic shifts may facilitate the development of novel therapeutics targeting microglia-mediated neurotoxicity.

The confidence-accuracy relationship in eyewitness identification: effects of lineup instructions, foil similarity, and target-absent base rates.

PubMed

Brewer, Neil; Wells, Gary L

2006-03-01

Discriminating accurate from mistaken eyewitness identifications is a major issue facing criminal justice systems. This study examined whether eyewitness confidence assists such decisions under a variety of conditions using a confidence-accuracy (CA) calibration approach. Participants (N = 1,200) viewed a simulated crime and attempted 2 separate identifications from 8-person target-present or target-absent lineups. Confidence and accuracy were calibrated for choosers (but not nonchoosers) for both targets under all conditions. Lower overconfidence was associated with higher diagnosticity, lower target-absent base rates, and shorter identification latencies. Although researchers agree that courtroom expressions of confidence are uninformative, our findings indicate that confidence assessments obtained immediately after a positive identification can provide a useful guide for investigators about the likely accuracy of an identification.

Text de-identification for privacy protection: a study of its impact on clinical text information content.

PubMed

Meystre, Stéphane M; Ferrández, Óscar; Friedlin, F Jeffrey; South, Brett R; Shen, Shuying; Samore, Matthew H

2014-08-01

As more and more electronic clinical information is becoming easier to access for secondary uses such as clinical research, approaches that enable faster and more collaborative research while protecting patient privacy and confidentiality are becoming more important. Clinical text de-identification offers such advantages but is typically a tedious manual process. Automated Natural Language Processing (NLP) methods can alleviate this process, but their impact on subsequent uses of the automatically de-identified clinical narratives has only barely been investigated. In the context of a larger project to develop and investigate automated text de-identification for Veterans Health Administration (VHA) clinical notes, we studied the impact of automated text de-identification on clinical information in a stepwise manner. Our approach started with a high-level assessment of clinical notes informativeness and formatting, and ended with a detailed study of the overlap of select clinical information types and Protected Health Information (PHI). To investigate the informativeness (i.e., document type information, select clinical data types, and interpretation or conclusion) of VHA clinical notes, we used five different existing text de-identification systems. The informativeness was only minimally altered by these systems while formatting was only modified by one system. To examine the impact of de-identification on clinical information extraction, we compared counts of SNOMED-CT concepts found by an open source information extraction application in the original (i.e., not de-identified) version of a corpus of VHA clinical notes, and in the same corpus after de-identification. Only about 1.2-3% less SNOMED-CT concepts were found in de-identified versions of our corpus, and many of these concepts were PHI that was erroneously identified as clinical information. To study this impact in more details and assess how generalizable our findings were, we examined the overlap between select clinical information annotated in the 2010 i2b2 NLP challenge corpus and automatic PHI annotations from our best-of-breed VHA clinical text de-identification system (nicknamed 'BoB'). Overall, only 0.81% of the clinical information exactly overlapped with PHI, and 1.78% partly overlapped. We conclude that automated text de-identification's impact on clinical information is small, but not negligible, and that improved clinical acronyms and eponyms disambiguation could significantly reduce this impact. Copyright © 2014 Elsevier Inc. All rights reserved.

Experimental estimation of transmissibility matrices for industrial multi-axis vibration isolation systems

NASA Astrophysics Data System (ADS)

Beijen, Michiel A.; Voorhoeve, Robbert; Heertjes, Marcel F.; Oomen, Tom

2018-07-01

Vibration isolation is essential for industrial high-precision systems to suppress external disturbances. The aim of this paper is to develop a general identification approach to estimate the frequency response function (FRF) of the transmissibility matrix, which is a key performance indicator for vibration isolation systems. The major challenge lies in obtaining a good signal-to-noise ratio in view of a large system weight. A non-parametric system identification method is proposed that combines floor and shaker excitations. Furthermore, a method is presented to analyze the input power spectrum of the floor excitations, both in terms of magnitude and direction. In turn, the input design of the shaker excitation signals is investigated to obtain sufficient excitation power in all directions with minimum experiment cost. The proposed methods are shown to provide an accurate FRF of the transmissibility matrix in three relevant directions on an industrial active vibration isolation system over a large frequency range. This demonstrates that, despite their heavy weight, industrial vibration isolation systems can be accurately identified using this approach.

Data-driven simultaneous fault diagnosis for solid oxide fuel cell system using multi-label pattern identification

NASA Astrophysics Data System (ADS)

Li, Shuanghong; Cao, Hongliang; Yang, Yupu

2018-02-01

Fault diagnosis is a key process for the reliability and safety of solid oxide fuel cell (SOFC) systems. However, it is difficult to rapidly and accurately identify faults for complicated SOFC systems, especially when simultaneous faults appear. In this research, a data-driven Multi-Label (ML) pattern identification approach is proposed to address the simultaneous fault diagnosis of SOFC systems. The framework of the simultaneous-fault diagnosis primarily includes two components: feature extraction and ML-SVM classifier. The simultaneous-fault diagnosis approach can be trained to diagnose simultaneous SOFC faults, such as fuel leakage, air leakage in different positions in the SOFC system, by just using simple training data sets consisting only single fault and not demanding simultaneous faults data. The experimental result shows the proposed framework can diagnose the simultaneous SOFC system faults with high accuracy requiring small number training data and low computational burden. In addition, Fault Inference Tree Analysis (FITA) is employed to identify the correlations among possible faults and their corresponding symptoms at the system component level.

Sounding the warning bells: the need for a systems approach to understanding behaviour at rail level crossings.

PubMed

Read, Gemma J M; Salmon, Paul M; Lenné, Michael G

2013-09-01

Collisions at rail level crossings are an international safety concern and have been the subject of considerable research effort. Modern human factors practice advocates a systems approach to investigating safety issues in complex systems. This paper describes the results of a structured review of the level crossing literature to determine the extent to which a systems approach has been applied. The measures used to determine if previous research was underpinned by a systems approach were: the type of analysis method utilised, the number of component relationships considered, the number of user groups considered, the number of system levels considered and the type of model described in the research. None of research reviewed was found to be consistent with a systems approach. It is recommended that further research utilise a systems approach to the study of the level crossing system to enable the identification of effective design improvements. Copyright © 2013 Elsevier Ltd and The Ergonomics Society. All rights reserved.

On the identifiability of inertia parameters of planar Multi-Body Space Systems

NASA Astrophysics Data System (ADS)

Nabavi-Chashmi, Seyed Yaser; Malaek, Seyed Mohammad-Bagher

2018-04-01

This work describes a new formulation to study the identifiability characteristics of Serially Linked Multi-body Space Systems (SLMBSS). The process exploits the so called "Lagrange Formulation" to develop a linear form of Equations of Motion w.r.t the system Inertia Parameters (IPs). Having developed a specific form of regressor matrix, we aim to expedite the identification process. The new approach allows analytical as well as numerical identification and identifiability analysis for different SLMBSSs' configurations. Moreover, the explicit forms of SLMBSSs identifiable parameters are derived by analyzing the identifiability characteristics of the robot. We further show that any SLMBSS designed with Variable Configurations Joint allows all IPs to be identifiable through comparing two successive identification outcomes. This feature paves the way to design new class of SLMBSS for which accurate identification of all IPs is at hand. Different case studies reveal that proposed formulation provides fast and accurate results, as required by the space applications. Further studies might be necessary for cases where planar-body assumption becomes inaccurate.

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.

In situ damage monitoring in vibration mechanics: diagnostics and predictive maintenance

NASA Astrophysics Data System (ADS)

Basseville, M.; Benveniste, A.; Gach-Devauchelle, B.; Goursat, M.; Bonnecase, D.; Dorey, P.; Prevosto, M.; Olagnon, M.

1993-09-01

A system identification approach is presented for damage monitoring in vibration mechanics. Identification, detection, and diagnostics are performed using accelerometer measurements from the system at work so that the excitation is not controlled, usually not observed and may involve turbulent phenomena. Targeted applications include power engineering (rotating machines, core and pipes of nuclear power plants), civil engineering (large buildings subject to hurricanes or earthquakes, bridges, dams, offshore structures), aeronautics (wings and other structures subject to strength), automobile, rail transportation etc. The method is illustrated by a laboratory example, and the results of 3 years industrial usage. This paper is a progress report on a 10 year project involving three people almost permanently. We describe here the whole approach but omit the technical details which are available in previous papers.

Parameter identification of pedestrian's spring-mass-damper model by ground reaction force records through a particle filter approach

NASA Astrophysics Data System (ADS)

Wang, Haoqi; Chen, Jun; Brownjohn, James M. W.

2017-12-01

The spring-mass-damper (SMD) model with a pair of internal biomechanical forces is the simplest model for a walking pedestrian to represent his/her mechanical properties, and thus can be used in human-structure-interaction analysis in the vertical direction. However, the values of SMD stiffness and damping, though very important, are typically taken as those measured from stationary people due to lack of a parameter identification methods for a walking pedestrian. This study adopts a step-by-step system identification approach known as particle filter to simultaneously identify the stiffness, damping coefficient, and coefficients of the SMD model's biomechanical forces by ground reaction force (GRF) records. After a brief introduction of the SMD model, the proposed identification approach is explained in detail, with a focus on the theory of particle filter and its integration with the SMD model. A numerical example is first provided to verify the feasibility of the proposed approach which is then applied to several experimental GRF records. Identification results demonstrate that natural frequency and the damping ratio of a walking pedestrian are not constant but have a dependence of mean value and distribution on pacing frequency. The mean value first-order coefficient of the biomechanical force, which is expressed by the Fourier series function, also has a linear relationship with pacing frequency. Higher order coefficients do not show a clear relationship with pacing frequency but follow a logarithmic normal distribution.

Cross-Correlation-Based Structural System Identification Using Unmanned Aerial Vehicles

PubMed Central

Yoon, Hyungchul; Hoskere, Vedhus; Park, Jong-Woong; Spencer, Billie F.

2017-01-01

Computer vision techniques have been employed to characterize dynamic properties of structures, as well as to capture structural motion for system identification purposes. All of these methods leverage image-processing techniques using a stationary camera. This requirement makes finding an effective location for camera installation difficult, because civil infrastructure (i.e., bridges, buildings, etc.) are often difficult to access, being constructed over rivers, roads, or other obstacles. This paper seeks to use video from Unmanned Aerial Vehicles (UAVs) to address this problem. As opposed to the traditional way of using stationary cameras, the use of UAVs brings the issue of the camera itself moving; thus, the displacements of the structure obtained by processing UAV video are relative to the UAV camera. Some efforts have been reported to compensate for the camera motion, but they require certain assumptions that may be difficult to satisfy. This paper proposes a new method for structural system identification using the UAV video directly. Several challenges are addressed, including: (1) estimation of an appropriate scale factor; and (2) compensation for the rolling shutter effect. Experimental validation is carried out to validate the proposed approach. The experimental results demonstrate the efficacy and significant potential of the proposed approach. PMID:28891985

Non-contact wearable single forearm cardiac biopotential acquisition device

NASA Astrophysics Data System (ADS)

Gonçalves, Sérgio; Carneiro Martins, Raul

2013-09-01

In this work the authors propose a novel approach to obtain the electrocardiogram in the forearm using non-contact sensing. This new solution should be at same time portable, ergonomic and robust, enabling its use in different set of applications. A system of four electrodes was used in an adjustable sleeve to be wrapped in the forearm. No additional electrode references were used in other body parts. In order to increase the sensitivity of the system, an harmonium like approach was used in the design of the electrodes. The prototype was then compared with a similar system with a flat conformation. The developed prototype enabled the acquisition of an ECG signal in the forearm and the inclusion of the harmonium like electrode conformation resulted in a considerable increase of the sensitivity of the system. The acquired signal did not enable the identification of all characteristic cardiac waves. However, it was possible to identify clearly a signal pattern, characteristic of the QRS complex. The properties of the acquired signal restrict their use in rigorous electrocardiographic studies, allowing, however, its application in heart rate variability monitoring and biometric identification without the disadvantages usually associated with conventional electrodes. This makes it specially useful for man-machine interfaces and automated identification.

A High-Throughput Approach for Identification of Nontuberculous Mycobacteria in Drinking Water Reveals Relationship between Water Age and Mycobacterium avium

PubMed Central

Haig, Sarah-Jane; Kotlarz, Nadine; LiPuma, John J.

2018-01-01

ABSTRACT Nontuberculous mycobacteria (NTM) frequently detected in drinking water (DW) include species associated with human infections, as well as species rarely linked to disease. Methods for improved the recovery of NTM DNA and high-throughput identification of NTM are needed for risk assessment of NTM infection through DW exposure. In this study, different methods of recovering bacterial DNA from DW were compared, revealing that a phenol-chloroform DNA extraction method yielded two to four times as much total DNA and eight times as much NTM DNA as two commercial DNA extraction kits. This method, combined with high-throughput, single-molecule real-time sequencing of NTM rpoB genes, allowed the identification of NTM to the species, subspecies, and (in some cases) strain levels. This approach was applied to DW samples collected from 15 households serviced by a chloraminated distribution system, with homes located in areas representing short (<24 h) and long (>24 h) distribution system residence times. Multivariate statistical analysis revealed that greater water age (i.e., combined distribution system residence time and home plumbing stagnation time) was associated with a greater relative abundance of Mycobacterium avium subsp. avium, one of the most prevalent NTM causing infections in humans. DW from homes closer to the treatment plant (with a shorter water age) contained more diverse NTM species, including Mycobacterium abscessus and Mycobacterium chelonae. Overall, our approach allows NTM identification to the species and subspecies levels and can be used in future studies to assess the risk of waterborne infection by providing insight into the similarity between environmental and infection-associated NTM. PMID:29440575

Self-Learning Embedded System for Object Identification in Intelligent Infrastructure Sensors.

PubMed

Villaverde, Monica; Perez, David; Moreno, Felix

2015-11-17

The emergence of new horizons in the field of travel assistant management leads to the development of cutting-edge systems focused on improving the existing ones. Moreover, new opportunities are being also presented since systems trend to be more reliable and autonomous. In this paper, a self-learning embedded system for object identification based on adaptive-cooperative dynamic approaches is presented for intelligent sensor's infrastructures. The proposed system is able to detect and identify moving objects using a dynamic decision tree. Consequently, it combines machine learning algorithms and cooperative strategies in order to make the system more adaptive to changing environments. Therefore, the proposed system may be very useful for many applications like shadow tolls since several types of vehicles may be distinguished, parking optimization systems, improved traffic conditions systems, etc.

Development of an advanced system identification technique for comparing ADAMS analytical results with modal test data for a MICON 65/13 wind turbine

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

Bialasiewicz, J.T.

1995-07-01

This work uses the theory developed in NREL/TP--442-7110 to analyze simulated data from an ADAMS (Automated Dynamic Analysis of Mechanical Systems) model of the MICON 65/13 wind turbine. The Observer/Kalman Filter identification approach is expanded to use input-output time histories from ADAMS simulations or structural test data. A step by step outline is offered on how the tools developed in this research, can be used for validation of the ADAMS model.

Face recognition for criminal identification: An implementation of principal component analysis for face recognition

NASA Astrophysics Data System (ADS)

Abdullah, Nurul Azma; Saidi, Md. Jamri; Rahman, Nurul Hidayah Ab; Wen, Chuah Chai; Hamid, Isredza Rahmi A.

2017-10-01

In practice, identification of criminal in Malaysia is done through thumbprint identification. However, this type of identification is constrained as most of criminal nowadays getting cleverer not to leave their thumbprint on the scene. With the advent of security technology, cameras especially CCTV have been installed in many public and private areas to provide surveillance activities. The footage of the CCTV can be used to identify suspects on scene. However, because of limited software developed to automatically detect the similarity between photo in the footage and recorded photo of criminals, the law enforce thumbprint identification. In this paper, an automated facial recognition system for criminal database was proposed using known Principal Component Analysis approach. This system will be able to detect face and recognize face automatically. This will help the law enforcements to detect or recognize suspect of the case if no thumbprint present on the scene. The results show that about 80% of input photo can be matched with the template data.

Automated colour identification in melanocytic lesions.

PubMed

Sabbaghi, S; Aldeen, M; Garnavi, R; Varigos, G; Doliantis, C; Nicolopoulos, J

2015-08-01

Colour information plays an important role in classifying skin lesion. However, colour identification by dermatologists can be very subjective, leading to cases of misdiagnosis. Therefore, a computer-assisted system for quantitative colour identification is highly desirable for dermatologists to use. Although numerous colour detection systems have been developed, few studies have focused on imitating the human visual perception of colours in melanoma application. In this paper we propose a new methodology based on QuadTree decomposition technique for automatic colour identification in dermoscopy images. Our approach mimics the human perception of lesion colours. The proposed method is trained on a set of 47 images from NIH dataset and applied to a test set of 190 skin lesions obtained from PH2 dataset. The results of our proposed method are compared with a recently reported colour identification method using the same dataset. The effectiveness of our method in detecting colours in dermoscopy images is vindicated by obtaining approximately 93% accuracy when the CIELab1 colour space is used.

MIMO model of an interacting series process for Robust MPC via System Identification.

PubMed

Wibowo, Tri Chandra S; Saad, Nordin

2010-07-01

This paper discusses the empirical modeling using system identification technique with a focus on an interacting series process. The study is carried out experimentally using a gaseous pilot plant as the process, in which the dynamic of such a plant exhibits the typical dynamic of an interacting series process. Three practical approaches are investigated and their performances are evaluated. The models developed are also examined in real-time implementation of a linear model predictive control. The selected model is able to reproduce the main dynamic characteristics of the plant in open-loop and produces zero steady-state errors in closed-loop control system. Several issues concerning the identification process and the construction of a MIMO state space model for a series interacting process are deliberated. 2010 ISA. Published by Elsevier Ltd. All rights reserved.

Nonlinear system identification technique validation

NASA Astrophysics Data System (ADS)

Rudko, M.; Bussgang, J. J.

1982-01-01

This final technical report describes the results obtained by SIGNATRON, Inc. of Lexington MA on Air Force Contract F30602-80-C-0104 for Rome Air Development Center. The objective of this effort is to develop a technique for identifying system response of nonlinear circuits by measurements of output response to known inputs. The report describes results of a study into the system identification technique based on the pencil-of-function method previously explored by Jain (1974) and Ewen (1979). The procedure identified roles of the linear response and is intended as a first step in nonlinear response and is intended as a first step in nonlinear circuit identification. There are serious implementation problems associated with the original approach such as loss of accuracy due to repeated integrations, lack of good measures of accuracy and computational iteration to identify the number of poles.

An intercomparison of artificial intelligence approaches for polar scene identification

NASA Technical Reports Server (NTRS)

Tovinkere, V. R.; Penaloza, M.; Logar, A.; Lee, J.; Weger, R. C.; Berendes, T. A.; Welch, R. M.

1993-01-01

The following six different artificial-intelligence (AI) approaches to polar scene identification are examined: (1) a feed forward back propagation neural network, (2) a probabilistic neural network, (3) a hybrid neural network, (4) a 'don't care' feed forward perception model, (5) a 'don't care' feed forward back propagation neural network, and (6) a fuzzy logic based expert system. The ten classes into which six AVHRR local-coverage arctic scenes were classified were: water, solid sea ice, broken sea ice, snow-covered mountains, land, stratus over ice, stratus over water, cirrus over water, cumulus over water, and multilayer cloudiness. It was found that 'don't care' back propagation neural network produced the highest accuracies. This approach has also low CPU requirement.

Identification of dynamic systems, theory and formulation

NASA Technical Reports Server (NTRS)

Maine, R. E.; Iliff, K. W.

1985-01-01

The problem of estimating parameters of dynamic systems is addressed in order to present the theoretical basis of system identification and parameter estimation in a manner that is complete and rigorous, yet understandable with minimal prerequisites. Maximum likelihood and related estimators are highlighted. The approach used requires familiarity with calculus, linear algebra, and probability, but does not require knowledge of stochastic processes or functional analysis. The treatment emphasizes unification of the various areas in estimation in dynamic systems is treated as a direct outgrowth of the static system theory. Topics covered include basic concepts and definitions; numerical optimization methods; probability; statistical estimators; estimation in static systems; stochastic processes; state estimation in dynamic systems; output error, filter error, and equation error methods of parameter estimation in dynamic systems, and the accuracy of the estimates.

System identification and sensorimotor determinants of flight maneuvers in an insect

NASA Astrophysics Data System (ADS)

Sponberg, Simon; Hall, Robert; Roth, Eatai

Locomotor maneuvers are inherently closed-loop processes. They are generally characterized by the integration of multiple sensory inputs and adaptation or learning over time. To probe sensorimotor processing we take a system identification approach treating the underlying physiological systems as dynamic processes and altering the feedback topology in experiment and analysis. As a model system, we use agile hawk moths (Manduca sexta), which feed from real and robotic flowers while hovering in mid air. Moths rely on vision and mechanosensation to track floral targets and can do so at exceptionally low luminance levels despite hovering being a mechanically unstable behavior that requires neural feedback to stabilize. By altering the sensory environment and placing mechanical and visual signals in conflict we show a surprisingly simple linear summation of visual and mechanosensation produces a generative prediction of behavior to novel stimuli. Tracking performance is also limited more by the mechanics of flight than the magnitude of the sensory cue. A feedback systems approach to locomotor control results in new insights into how behavior emerges from the interaction of nonlinear physiological systems.

A neural network-based approach to noise identification of interferometric GW antennas: the case of the 40 m Caltech laser interferometer

NASA Astrophysics Data System (ADS)

Acernese, F.; Barone, F.; de Rosa, M.; De Rosa, R.; Eleuteri, A.; Milano, L.; Tagliaferri, R.

2002-06-01

In this paper, a neural network-based approach is presented for the real time noise identification of a GW laser interferometric antenna. The 40 m Caltech laser interferometer output data provide a realistic test bed for noise identification algorithms because of the presence of many relevant effects: violin resonances in the suspensions, main power harmonics, ring-down noise from servo control systems, electronic noises, glitches and so on. These effects can be assumed to be present in all the first interferometric long baseline GW antennas such as VIRGO, LIGO, GEO and TAMA. For noise identification, we used the Caltech-40 m laser interferometer data. The results we obtained are pretty good notwithstanding the high initial computational cost. The algorithm we propose is general and robust, taking into account that it does not require a priori information on the data, nor a precise model, and it constitutes a powerful tool for time series data analysis.

Distributed parameter system coupled ARMA expansion identification and adaptive parallel IIR filtering - A unified problem statement. [Auto Regressive Moving-Average

NASA Technical Reports Server (NTRS)

Johnson, C. R., Jr.; Balas, M. J.

1980-01-01

A novel interconnection of distributed parameter system (DPS) identification and adaptive filtering is presented, which culminates in a common statement of coupled autoregressive, moving-average expansion or parallel infinite impulse response configuration adaptive parameterization. The common restricted complexity filter objectives are seen as similar to the reduced-order requirements of the DPS expansion description. The interconnection presents the possibility of an exchange of problem formulations and solution approaches not yet easily addressed in the common finite dimensional lumped-parameter system context. It is concluded that the shared problems raised are nevertheless many and difficult.

How Does Social Inequality Continue to Influence Young People's Trajectories through the Apprenticeship Pathway System in South Africa? An Analytical Approach

ERIC Educational Resources Information Center

Kruss, Glenda; Wildschut, Angelique

2016-01-01

The paper contributes by proposing an analytical approach that allows for the identification of patterns of participation in education and training and the labour market, through empirical measurement of the number of transitions and distinct trajectories traversed by groups of individuals. To illustrate the value of the approach, we focus on an…

Continuity and Change in the Measurement of Infant Attachment: Comment on Fraley and Spieker (2003).

ERIC Educational Resources Information Center

Cassidy, Jude

2003-01-01

Highlights usefulness of the categorical approach to measuring infant attachment by reviewing some major advances in the field that have been fostered by that approach. Advances include identification of the disorganized attachment group, development of the concept of conditional behavior strategies, creation of systems for coding attachment…

A least-squares parameter estimation algorithm for switched hammerstein systems with applications to the VOR

NASA Technical Reports Server (NTRS)

Kukreja, Sunil L.; Kearney, Robert E.; Galiana, Henrietta L.

2005-01-01

A "Multimode" or "switched" system is one that switches between various modes of operation. When a switch occurs from one mode to another, a discontinuity may result followed by a smooth evolution under the new regime. Characterizing the switching behavior of these systems is not well understood and, therefore, identification of multimode systems typically requires a preprocessing step to classify the observed data according to a mode of operation. A further consequence of the switched nature of these systems is that data available for parameter estimation of any subsystem may be inadequate. As such, identification and parameter estimation of multimode systems remains an unresolved problem. In this paper, we 1) show that the NARMAX model structure can be used to describe the impulsive-smooth behavior of switched systems, 2) propose a modified extended least squares (MELS) algorithm to estimate the coefficients of such models, and 3) demonstrate its applicability to simulated and real data from the Vestibulo-Ocular Reflex (VOR). The approach will also allow the identification of other nonlinear bio-systems, suspected of containing "hard" nonlinearities.

A fault-tolerant control architecture for unmanned aerial vehicles

NASA Astrophysics Data System (ADS)

Drozeski, Graham R.

Research has presented several approaches to achieve varying degrees of fault-tolerance in unmanned aircraft. Approaches in reconfigurable flight control are generally divided into two categories: those which incorporate multiple non-adaptive controllers and switch between them based on the output of a fault detection and identification element, and those that employ a single adaptive controller capable of compensating for a variety of fault modes. Regardless of the approach for reconfigurable flight control, certain fault modes dictate system restructuring in order to prevent a catastrophic failure. System restructuring enables active control of actuation not employed by the nominal system to recover controllability of the aircraft. After system restructuring, continued operation requires the generation of flight paths that adhere to an altered flight envelope. The control architecture developed in this research employs a multi-tiered hierarchy to allow unmanned aircraft to generate and track safe flight paths despite the occurrence of potentially catastrophic faults. The hierarchical architecture increases the level of autonomy of the system by integrating five functionalities with the baseline system: fault detection and identification, active system restructuring, reconfigurable flight control; reconfigurable path planning, and mission adaptation. Fault detection and identification algorithms continually monitor aircraft performance and issue fault declarations. When the severity of a fault exceeds the capability of the baseline flight controller, active system restructuring expands the controllability of the aircraft using unconventional control strategies not exploited by the baseline controller. Each of the reconfigurable flight controllers and the baseline controller employ a proven adaptive neural network control strategy. A reconfigurable path planner employs an adaptive model of the vehicle to re-shape the desired flight path. Generation of the revised flight path is posed as a linear program constrained by the response of the degraded system. Finally, a mission adaptation component estimates limitations on the closed-loop performance of the aircraft and adjusts the aircraft mission accordingly. A combination of simulation and flight test results using two unmanned helicopters validates the utility of the hierarchical architecture.

Waste in health information systems: a systematic review.

PubMed

Awang Kalong, Nadia; Yusof, Maryati

2017-05-08

Purpose The purpose of this paper is to discuss a systematic review on waste identification related to health information systems (HIS) in Lean transformation. Design/methodology/approach A systematic review was conducted on 19 studies to evaluate Lean transformation and tools used to remove waste related to HIS in clinical settings. Findings Ten waste categories were identified, along with their relationships and applications of Lean tool types related to HIS. Different Lean tools were used at the early and final stages of Lean transformation; the tool selection depended on the waste characteristic. Nine studies reported a positive impact from Lean transformation in improving daily work processes. The selection of Lean tools should be made based on the timing, purpose and characteristics of waste to be removed. Research limitations/implications Overview of waste and its category within HIS and its analysis from socio-technical perspectives enabled the identification of its root cause in a holistic and rigorous manner. Practical implications Understanding waste types, their root cause and review of Lean tools could subsequently lead to the identification of mitigation approach to prevent future error occurrence. Originality/value Specific waste models for HIS settings are yet to be developed. Hence, the identification of the waste categories could guide future implementation of Lean transformations in HIS settings.

Performance enhancement for audio-visual speaker identification using dynamic facial muscle model.

PubMed

Asadpour, Vahid; Towhidkhah, Farzad; Homayounpour, Mohammad Mehdi

2006-10-01

Science of human identification using physiological characteristics or biometry has been of great concern in security systems. However, robust multimodal identification systems based on audio-visual information has not been thoroughly investigated yet. Therefore, the aim of this work to propose a model-based feature extraction method which employs physiological characteristics of facial muscles producing lip movements. This approach adopts the intrinsic properties of muscles such as viscosity, elasticity, and mass which are extracted from the dynamic lip model. These parameters are exclusively dependent on the neuro-muscular properties of speaker; consequently, imitation of valid speakers could be reduced to a large extent. These parameters are applied to a hidden Markov model (HMM) audio-visual identification system. In this work, a combination of audio and video features has been employed by adopting a multistream pseudo-synchronized HMM training method. Noise robust audio features such as Mel-frequency cepstral coefficients (MFCC), spectral subtraction (SS), and relative spectra perceptual linear prediction (J-RASTA-PLP) have been used to evaluate the performance of the multimodal system once efficient audio feature extraction methods have been utilized. The superior performance of the proposed system is demonstrated on a large multispeaker database of continuously spoken digits, along with a sentence that is phonetically rich. To evaluate the robustness of algorithms, some experiments were performed on genetically identical twins. Furthermore, changes in speaker voice were simulated with drug inhalation tests. In 3 dB signal to noise ratio (SNR), the dynamic muscle model improved the identification rate of the audio-visual system from 91 to 98%. Results on identical twins revealed that there was an apparent improvement on the performance for the dynamic muscle model-based system, in which the identification rate of the audio-visual system was enhanced from 87 to 96%.

Characterizing the Physics of Plant Root Gravitropism: A Systems Modeling Approach

DTIC Science & Technology

1999-01-01

with its root directly downward, the root and stem undergo a gravitropic response. Statoliths (gravity-sensing organelles) within the root cap respond...this study is to model the plant root gravitropic response using classical controls and system identification principles. Specific objectives of this

Identification of Putative Cardiovascular System Developmental Toxicants using a Classification Model based on Signaling Pathway-Adverse Outcome Pathways

EPA Science Inventory

An important challenge for an integrative approach to developmental systems toxicology is associating putative molecular initiating events (MIEs), cell signaling pathways, cell function and modeled fetal exposure kinetics. We have developed a chemical classification model based o...

Hiding in plain sight: use of realistic surrogates to reduce exposure of protected health information in clinical text.

PubMed

Carrell, David; Malin, Bradley; Aberdeen, John; Bayer, Samuel; Clark, Cheryl; Wellner, Ben; Hirschman, Lynette

2013-01-01

Secondary use of clinical text is impeded by a lack of highly effective, low-cost de-identification methods. Both, manual and automated methods for removing protected health information, are known to leave behind residual identifiers. The authors propose a novel approach for addressing the residual identifier problem based on the theory of Hiding In Plain Sight (HIPS). HIPS relies on obfuscation to conceal residual identifiers. According to this theory, replacing the detected identifiers with realistic but synthetic surrogates should collectively render the few 'leaked' identifiers difficult to distinguish from the synthetic surrogates. The authors conducted a pilot study to test this theory on clinical narrative, de-identified by an automated system. Test corpora included 31 oncology and 50 family practice progress notes read by two trained chart abstractors and an informaticist. Experimental results suggest approximately 90% of residual identifiers can be effectively concealed by the HIPS approach in text containing average and high densities of personal identifying information. This pilot test suggests HIPS is feasible, but requires further evaluation. The results need to be replicated on larger corpora of diverse origin under a range of detection scenarios. Error analyses also suggest areas where surrogate generation techniques can be refined to improve efficacy. If these results generalize to existing high-performing de-identification systems with recall rates of 94-98%, HIPS could increase the effective de-identification rates of these systems to levels above 99% without further advancements in system recall. Additional and more rigorous assessment of the HIPS approach is warranted.

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.

Systems genetics approaches to understand complex traits

PubMed Central

Civelek, Mete; Lusis, Aldons J.

2014-01-01

Systems genetics is an approach to understand the flow of biological information that underlies complex traits. It uses a range of experimental and statistical methods to quantitate and integrate intermediate phenotypes, such as transcript, protein or metabolite levels, in populations that vary for traits of interest. Systems genetics studies have provided the first global view of the molecular architecture of complex traits and are useful for the identification of genes, pathways and networks that underlie common human diseases. Given the urgent need to understand how the thousands of loci that have been identified in genome-wide association studies contribute to disease susceptibility, systems genetics is likely to become an increasingly important approach to understanding both biology and disease. PMID:24296534

Recursive inversion of externally defined linear systems

NASA Technical Reports Server (NTRS)

Bach, Ralph E., Jr.; Baram, Yoram

1988-01-01

The approximate inversion of an internally unknown linear system, given by its impulse response sequence, by an inverse system having a finite impulse response, is considered. The recursive least squares procedure is shown to have an exact initialization, based on the triangular Toeplitz structure of the matrix involved. The proposed approach also suggests solutions to the problems of system identification and compensation.

A new analytical platform based on field-flow fractionation and olfactory sensor to improve the detection of viable and non-viable bacteria in food.

PubMed

Roda, Barbara; Mirasoli, Mara; Zattoni, Andrea; Casale, Monica; Oliveri, Paolo; Bigi, Alessandro; Reschiglian, Pierluigi; Simoni, Patrizia; Roda, Aldo

2016-10-01

An integrated sensing system is presented for the first time, where a metal oxide semiconductor sensor-based electronic olfactory system (MOS array), employed for pathogen bacteria identification based on their volatile organic compound (VOC) characterisation, is assisted by a preliminary separative technique based on gravitational field-flow fractionation (GrFFF). In the integrated system, a preliminary step using GrFFF fractionation of a complex sample provided bacteria-enriched fractions readily available for subsequent MOS array analysis. The MOS array signals were then analysed employing a chemometric approach using principal components analysis (PCA) for a first-data exploration, followed by linear discriminant analysis (LDA) as a classification tool, using the PCA scores as input variables. The ability of the GrFFF-MOS system to distinguish between viable and non-viable cells of the same strain was demonstrated for the first time, yielding 100 % ability of correct prediction. The integrated system was also applied as a proof of concept for multianalyte purposes, for the detection of two bacterial strains (Escherichia coli O157:H7 and Yersinia enterocolitica) simultaneously present in artificially contaminated milk samples, obtaining a 100 % ability of correct prediction. Acquired results show that GrFFF band slicing before MOS array analysis can significantly increase reliability and reproducibility of pathogen bacteria identification based on their VOC production, simplifying the analytical procedure and largely eliminating sample matrix effects. The developed GrFFF-MOS integrated system can be considered a simple straightforward approach for pathogen bacteria identification directly from their food matrix. Graphical abstract An integrated sensing system is presented for pathogen bacteria identification in food, in which field-flow fractionation is exploited to prepare enriched cell fractions prior to their analysis by electronic olfactory system analysis.

Scalable persistent identifier systems for dynamic datasets

NASA Astrophysics Data System (ADS)

Golodoniuc, P.; Cox, S. J. D.; Klump, J. F.

2016-12-01

Reliable and persistent identification of objects, whether tangible or not, is essential in information management. Many Internet-based systems have been developed to identify digital data objects, e.g., PURL, LSID, Handle, ARK. These were largely designed for identification of static digital objects. The amount of data made available online has grown exponentially over the last two decades and fine-grained identification of dynamically generated data objects within large datasets using conventional systems (e.g., PURL) has become impractical. We have compared capabilities of various technological solutions to enable resolvability of data objects in dynamic datasets, and developed a dataset-centric approach to resolution of identifiers. This is particularly important in Semantic Linked Data environments where dynamic frequently changing data is delivered live via web services, so registration of individual data objects to obtain identifiers is impractical. We use identifier patterns and pattern hierarchies for identification of data objects, which allows relationships between identifiers to be expressed, and also provides means for resolving a single identifier into multiple forms (i.e. views or representations of an object). The latter can be implemented through (a) HTTP content negotiation, or (b) use of URI querystring parameters. The pattern and hierarchy approach has been implemented in the Linked Data API supporting the United Nations Spatial Data Infrastructure (UNSDI) initiative and later in the implementation of geoscientific data delivery for the Capricorn Distal Footprints project using International Geo Sample Numbers (IGSN). This enables flexible resolution of multi-view persistent identifiers and provides a scalable solution for large heterogeneous datasets.

Sustainable urban systems: Co-design and framing for transformation.

PubMed

Webb, Robert; Bai, Xuemei; Smith, Mark Stafford; Costanza, Robert; Griggs, David; Moglia, Magnus; Neuman, Michael; Newman, Peter; Newton, Peter; Norman, Barbara; Ryan, Chris; Schandl, Heinz; Steffen, Will; Tapper, Nigel; Thomson, Giles

2018-02-01

Rapid urbanisation generates risks and opportunities for sustainable development. Urban policy and decision makers are challenged by the complexity of cities as social-ecological-technical systems. Consequently there is an increasing need for collaborative knowledge development that supports a whole-of-system view, and transformational change at multiple scales. Such holistic urban approaches are rare in practice. A co-design process involving researchers, practitioners and other stakeholders, has progressed such an approach in the Australian context, aiming to also contribute to international knowledge development and sharing. This process has generated three outputs: (1) a shared framework to support more systematic knowledge development and use, (2) identification of barriers that create a gap between stated urban goals and actual practice, and (3) identification of strategic focal areas to address this gap. Developing integrated strategies at broader urban scales is seen as the most pressing need. The knowledge framework adopts a systems perspective that incorporates the many urban trade-offs and synergies revealed by a systems view. Broader implications are drawn for policy and decision makers, for researchers and for a shared forward agenda.

Unnatural selection: talent identification and development in sport.

PubMed

Abbott, Angela; Button, Chris; Pepping, Gert-Jan; Collins, Dave

2005-01-01

The early identification of talented individuals has become increasingly important across many performance domains. Current talent identification (TI) schemes in sport typically select on the basis of discrete, unidimensional measures at unstable periods in the athlete's development. In this article, the concept of talent is revised as a complex, dynamical system in which future behaviors emerge from an interaction of key performance determinants such as psychological behaviors, motor abilities, and physical characteristics. Key nonlinear dynamics concepts are related to TI approaches such as sensitivity to initial conditions, transitions, and exponential behavioral distributions. It is concluded that many TI models place an overemphasis on early identification rather than the development of potentially talented performers. A generic model of talent identification and development is proposed that addresses these issues and provides direction for future research.

DVI missions in the Carribean-the practical aspects of disaster victim identification.

PubMed

Winskog, Calle

2012-06-01

Human trafficking of young men from Africa to Europe is a crime with often devastating consequences. The African continent loses members of the younger generation and many die during the attempt to reach their destinations. The identification of these victims is often difficult, however the structured and by now well-established procedures utilizing standard disaster victim identification protocols provide a reliable and functional approach. The logistics involved are straightforward, and one of the many functions of the team leader is to monitor and control the flow of cases through the system. The importance of ante mortem data for the purposes of identification is clear-no ante mortem data means no identification. Two different missions conducted in the Caribbean are described to illustrate particular difficulties that may occur.

Level-set techniques for facies identification in reservoir modeling

NASA Astrophysics Data System (ADS)

Iglesias, Marco A.; McLaughlin, Dennis

2011-03-01

In this paper we investigate the application of level-set techniques for facies identification in reservoir models. The identification of facies is a geometrical inverse ill-posed problem that we formulate in terms of shape optimization. The goal is to find a region (a geologic facies) that minimizes the misfit between predicted and measured data from an oil-water reservoir. In order to address the shape optimization problem, we present a novel application of the level-set iterative framework developed by Burger in (2002 Interfaces Free Bound. 5 301-29 2004 Inverse Problems 20 259-82) for inverse obstacle problems. The optimization is constrained by (the reservoir model) a nonlinear large-scale system of PDEs that describes the reservoir dynamics. We reformulate this reservoir model in a weak (integral) form whose shape derivative can be formally computed from standard results of shape calculus. At each iteration of the scheme, the current estimate of the shape derivative is utilized to define a velocity in the level-set equation. The proper selection of this velocity ensures that the new shape decreases the cost functional. We present results of facies identification where the velocity is computed with the gradient-based (GB) approach of Burger (2002) and the Levenberg-Marquardt (LM) technique of Burger (2004). While an adjoint formulation allows the straightforward application of the GB approach, the LM technique requires the computation of the large-scale Karush-Kuhn-Tucker system that arises at each iteration of the scheme. We efficiently solve this system by means of the representer method. We present some synthetic experiments to show and compare the capabilities and limitations of the proposed implementations of level-set techniques for the identification of geologic facies.

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.

Everything Is Connected: Giftedness within a Broad Framework for Cognition

ERIC Educational Resources Information Center

Woolcott, Geoff

2012-01-01

Ziegler and Phillipson (Z&P) have provided a valuable discussion of the identification and development of giftedness under a systemic umbrella, including issues of differing theoretical approaches and efficacy. This commentary considers their discussion within a novel systemic framework that links also individuals and environment, but in terms of…

Neural network modeling of nonlinear systems based on Volterra series extension of a linear model

NASA Technical Reports Server (NTRS)

Soloway, Donald I.; Bialasiewicz, Jan T.

1992-01-01

A Volterra series approach was applied to the identification of nonlinear systems which are described by a neural network model. A procedure is outlined by which a mathematical model can be developed from experimental data obtained from the network structure. Applications of the results to the control of robotic systems are discussed.

Identification, Description, and Perceived Viability of K-12 Consolidated Catholic School Systems

ERIC Educational Resources Information Center

Britt, Kenith C.

2013-01-01

Catholic education has been in a state of substantial decline since 1965. In order to help sustain the ministry of Catholic schools, one approach that several dozen dioceses have embraced is the K-12 consolidated Catholic school system. This study investigated the organizational structures within consolidated school systems, factors that led to…

Identification of the Radiative and Nonradiative Parts of a Wave Field

NASA Astrophysics Data System (ADS)

Hoenders, B. J.; Ferwerda, H. A.

2001-08-01

We present a method for decomposing a wave field, described by a second-order ordinary differential equation, into a radiative component and a nonradiative one, using a biorthonormal system related to the problem under consideration. We show that it is possible to select a special system such that the wave field is purely radiating. We discuss the differences and analogies with approaches which, unlike our approach, start from the corresponding sources of the field.

Novel therapeutic strategy in the management of COPD: a systems medicine approach.

PubMed

Lococo, Filippo; Cesario, Alfredo; Del Bufalo, Alessandra; Ciarrocchi, Alessia; Prinzi, Giulia; Mina, Marco; Bonassi, Stefano; Russo, Patrizia

2015-01-01

Respiratory diseases including chronic-obstructive-pulmonary-disease (COPD) are globally increasing, with COPD predicted to become the third leading cause of global mortality by 2020. COPD is a heterogeneous disease with COPD-patients displaying different phenotypes as a result of a complex interaction between various genetic, environmental and life-style factors. In recent years, several investigations have been performed to better define such interactions, but the identification of the resulting phenotypes is still somewhat difficult, and may lead to inadequate assessment and management of COPD (usually based solely on the severity of airflow limitation parameter FEV1). In this new scenario, the management of COPD has been driven towards an integrative and holistic approach. The degree of complexity requires analyses based on large datasets (also including advanced functional genomic assays) and novel computational biology approaches (essential to extract information relevant for the clinical decision process and for the development of new drugs). Therefore, according to the emerging "systems/network medicine", COPD should be re.-evaluated considering multiple network(s) perturbations such as genetic and environmental changes. Systems Medicine (SM) platforms, in which patients are extensively characterized, offer a basis for a more targeted clinical approach, which is predictive, preventive, personalized and participatory ("P4-medicine"). It clearly emerges that in the next future, new opportunities will become available for clinical research on rare COPD patterns and for the identification of new biomarkers of comorbidity, severity, and progression. Herein, we overview the literature discussing the opportunity coming from the adoption of SMapproaches in COPD management, focusing on proteomics and metabolomics, and emphasizing the identification of disease sub-clusters, to improve the development of more effective therapies.

DNA-mounted self-assembly: new approaches for genomic analysis and SNP detection.

PubMed

Bichenkova, Elena V; Lang, Zhaolei; Yu, Xuan; Rogert, Candelaria; Douglas, Kenneth T

2011-01-01

This article presents an overview of new emerging approaches for nucleic acid detection via hybridization techniques that can potentially be applied to genomic analysis and SNP identification in clinical diagnostics. Despite the availability of a diverse variety of SNP genotyping technologies on the diagnostic market, none has truly succeeded in dominating its competitors thus far. Having been designed for specific diagnostic purposes or clinical applications, each of the existing bio-assay systems (briefly outlined here) is usually limited to a relatively narrow aspect or format of nucleic acid detection, and thus cannot entirely satisfy all the varieties of commercial requirements and clinical demands. This drives the diagnostic sector to pursue novel, cost-effective approaches to ensure rapid and reliable identification of pathogenic or hereditary human diseases. Hence, the purpose of this review is to highlight some new strategic directions in DNA detection technologies in order to inspire development of novel molecular diagnostic tools and bio-assay systems with superior reliability, reproducibility, robustness, accuracy and sensitivity at lower assay cost. One approach to improving the sensitivity of an assay to confidently discriminate between single point mutations is based on the use of target assembled, split-probe systems, which constitutes the main focus of this review. Copyright © 2010 Elsevier B.V. All rights reserved.

Acoustic surface perception from naturally occurring step sounds of a dexterous hexapod robot

NASA Astrophysics Data System (ADS)

Cuneyitoglu Ozkul, Mine; Saranli, Afsar; Yazicioglu, Yigit

2013-10-01

Legged robots that exhibit dynamic dexterity naturally interact with the surface to generate complex acoustic signals carrying rich information on the surface as well as the robot platform itself. However, the nature of a legged robot, which is a complex, hybrid dynamic system, renders the more common approach of model-based system identification impractical. The present paper focuses on acoustic surface identification and proposes a non-model-based analysis and classification approach adopted from the speech processing literature. A novel feature set composed of spectral band energies augmented by their vector time derivatives and time-domain averaged zero crossing rate is proposed. Using a multi-dimensional vector classifier, these features carry enough information to accurately classify a range of commonly occurring indoor and outdoor surfaces without using of any mechanical system model. A comparative experimental study is carried out and classification performance and computational complexity are characterized. Different feature combinations, classifiers and changes in critical design parameters are investigated. A realistic and representative acoustic data set is collected with the robot moving at different speeds on a number of surfaces. The study demonstrates promising performance of this non-model-based approach, even in an acoustically uncontrolled environment. The approach also has good chance of performing in real-time.

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.

Approach to identifying pollutant source and matching flow field

NASA Astrophysics Data System (ADS)

Liping, Pang; Yu, Zhang; Hongquan, Qu; Tao, Hu; Wei, Wang

2013-07-01

Accidental pollution events often threaten people's health and lives, and it is necessary to identify a pollutant source rapidly so that prompt actions can be taken to prevent the spread of pollution. But this identification process is one of the difficulties in the inverse problem areas. This paper carries out some studies on this issue. An approach using single sensor information with noise was developed to identify a sudden continuous emission trace pollutant source in a steady velocity field. This approach first compares the characteristic distance of the measured concentration sequence to the multiple hypothetical measured concentration sequences at the sensor position, which are obtained based on a source-three-parameter multiple hypotheses. Then we realize the source identification by globally searching the optimal values with the objective function of the maximum location probability. Considering the large amount of computation load resulting from this global searching, a local fine-mesh source search method based on priori coarse-mesh location probabilities is further used to improve the efficiency of identification. Studies have shown that the flow field has a very important influence on the source identification. Therefore, we also discuss the impact of non-matching flow fields with estimation deviation on identification. Based on this analysis, a method for matching accurate flow field is presented to improve the accuracy of identification. In order to verify the practical application of the above method, an experimental system simulating a sudden pollution process in a steady flow field was set up and some experiments were conducted when the diffusion coefficient was known. The studies showed that the three parameters (position, emission strength and initial emission time) of the pollutant source in the experiment can be estimated by using the method for matching flow field and source identification.

Neurological Disease in Lupus: Toward a Personalized Medicine Approach.

PubMed

McGlasson, Sarah; Wiseman, Stewart; Wardlaw, Joanna; Dhaun, Neeraj; Hunt, David P J

2018-01-01

The brain and nervous system are important targets for immune-mediated damage in systemic lupus erythematosus (SLE), resulting in a complex spectrum of neurological syndromes. Defining nervous system disease in lupus poses significant challenges. Among the difficulties to be addressed are a diversity of clinical manifestations and a lack of understanding of their mechanistic basis. However, despite these challenges, progress has been made in the identification of pathways which contribute to neurological disease in SLE. Understanding the molecular pathogenesis of neurological disease in lupus will inform both classification and approaches to clinical trials.

Identification of sea ice types in spaceborne synthetic aperture radar data

NASA Technical Reports Server (NTRS)

Kwok, Ronald; Rignot, Eric; Holt, Benjamin; Onstott, R.

1992-01-01

This study presents an approach for identification of sea ice types in spaceborne SAR image data. The unsupervised classification approach involves cluster analysis for segmentation of the image data followed by cluster labeling based on previously defined look-up tables containing the expected backscatter signatures of different ice types measured by a land-based scatterometer. Extensive scatterometer observations and experience accumulated in field campaigns during the last 10 yr were used to construct these look-up tables. The classification approach, its expected performance, the dependence of this performance on radar system performance, and expected ice scattering characteristics are discussed. Results using both aircraft and simulated ERS-1 SAR data are presented and compared to limited field ice property measurements and coincident passive microwave imagery. The importance of an integrated postlaunch program for the validation and improvement of this approach is discussed.

Neutron Detection With Ultra-Fast Digitizer and Pulse Identification Techniques on DIII-D

NASA Astrophysics Data System (ADS)

Zhu, Y. B.; Heidbrink, W. W.; Piglowski, D. A.

2013-10-01

A prototype system for neutron detection with an ultra-fast digitizer and pulse identification techniques has been implemented on the DIII-D tokamak. The system consists of a cylindrical neutron fission chamber, a charge sensitive amplifier, and a GaGe Octopus 12-bit CompuScope digitizer card installed in a Linux computer. Digital pulse identification techniques have been successfully performed at maximum data acquisition rate of 50 MSPS with on-board memory of 2 GS. Compared to the traditional approach with fast nuclear electronics for pulse counting, this straightforward digital solution has many advantages, including reduced expense, improved accuracy, higher counting rate, and easier maintenance. The system also provides the capability of neutron-gamma pulse shape discrimination and pulse height analysis. Plans for the upgrade of the old DIII-D neutron counting system with these techniques will be presented. Work supported by the US Department of Energy under SC-G903402, and DE-FC02-04ER54698.

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.

Automatic Identification of Alpine Mass Movements by a Combination of Seismic and Infrasound Sensors

PubMed Central

Hübl, Johannes; McArdell, Brian W.; Walter, Fabian

2018-01-01

The automatic detection and identification of alpine mass movements such as debris flows, debris floods, or landslides have been of increasing importance for devising mitigation measures in densely populated and intensively used alpine regions. Since these mass movements emit characteristic seismic and acoustic waves in the low-frequency range (<30 Hz), several approaches have already been developed for detection and warning systems based on these signals. However, a combination of the two methods, for improving detection probability and reducing false alarms, is still applied rarely. This paper presents an update and extension of a previously published approach for a detection and identification system based on a combination of seismic and infrasound sensors. Furthermore, this work evaluates the possible early warning times at several test sites and aims to analyze the seismic and infrasound spectral signature produced by different sediment-related mass movements to identify the process type and estimate the magnitude of the event. Thus, this study presents an initial method for estimating the peak discharge and total volume of debris flows based on infrasound data. Tests on several catchments show that this system can detect and identify mass movements in real time directly at the sensor site with high accuracy and a low false alarm ratio. PMID:29789449

Damage location and quantification of a pretensioned concrete beam using stochastic subspace identification

NASA Astrophysics Data System (ADS)

Cancelli, Alessandro; Micheli, Laura; Laflamme, Simon; Alipour, Alice; Sritharan, Sri; Ubertini, Filippo

2017-04-01

Stochastic subspace identification (SSID) is a first-order linear system identification technique enabling modal analysis through the time domain. Research in the field of structural health monitoring has demonstrated that SSID can be used to successfully retrieve modal properties, including modal damping ratios, using output-only measurements. In this paper, the utilization of SSID for indirectly retrieving structures' stiffness matrix was investigated, through the study of a simply supported reinforced concrete beam subjected to dynamic loads. Hence, by introducing a physical model of the structure, a second-order identification method is achieved. The reconstruction is based on system condensation methods, which enables calculation of reduced order stiffness, damping, and mass matrices for the structural system. The methods compute the reduced order matrices directly from the modal properties, obtained through the use of SSID. Lastly, the reduced properties of the system are used to reconstruct the stiffness matrix of the beam. The proposed approach is first verified through numerical simulations and then validated using experimental data obtained from a full-scale reinforced concrete beam that experienced progressive damage. Results show that the SSID technique can be used to diagnose, locate, and quantify damage through the reconstruction of the stiffness matrix.

Automated Steering Control Design by Visual Feedback Approach —System Identification and Control Experiments with a Radio-Controlled Car—

NASA Astrophysics Data System (ADS)

Fujiwara, Yukihiro; Yoshii, Masakazu; Arai, Yasuhito; Adachi, Shuichi

Advanced safety vehicle(ASV)assists drivers’ manipulation to avoid trafic accidents. A variety of researches on automatic driving systems are necessary as an element of ASV. Among them, we focus on visual feedback approach in which the automatic driving system is realized by recognizing road trajectory using image information. The purpose of this paper is to examine the validity of this approach by experiments using a radio-controlled car. First, a practical image processing algorithm to recognize white lines on the road is proposed. Second, a model of the radio-controlled car is built by system identication experiments. Third, an automatic steering control system is designed based on H∞ control theory. Finally, the effectiveness of the designed control system is examined via traveling experiments.

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.

Results of a Demonstration Assessment of Passive System Reliability Utilizing the Reliability Method for Passive Systems (RMPS)

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

Bucknor, Matthew; Grabaskas, David; Brunett, Acacia

2015-04-26

Advanced small modular reactor designs include many advantageous design features such as passively driven safety systems that are arguably more reliable and cost effective relative to conventional active systems. Despite their attractiveness, a reliability assessment of passive systems can be difficult using conventional reliability methods due to the nature of passive systems. Simple deviations in boundary conditions can induce functional failures in a passive system, and intermediate or unexpected operating modes can also occur. As part of an ongoing project, Argonne National Laboratory is investigating various methodologies to address passive system reliability. The Reliability Method for Passive Systems (RMPS), amore » systematic approach for examining reliability, is one technique chosen for this analysis. This methodology is combined with the Risk-Informed Safety Margin Characterization (RISMC) approach to assess the reliability of a passive system and the impact of its associated uncertainties. For this demonstration problem, an integrated plant model of an advanced small modular pool-type sodium fast reactor with a passive reactor cavity cooling system is subjected to a station blackout using RELAP5-3D. This paper discusses important aspects of the reliability assessment, including deployment of the methodology, the uncertainty identification and quantification process, and identification of key risk metrics.« less

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.

Systems identification technology development for large space systems

NASA Technical Reports Server (NTRS)

Armstrong, E. S.

1982-01-01

A methodology for synthesizinng systems identification, both parameter and state, estimation and related control schemes for flexible aerospace structures is developed with emphasis on the Maypole hoop column antenna as a real world application. Modeling studies of the Maypole cable hoop membrane type antenna are conducted using a transfer matrix numerical analysis approach. This methodology was chosen as particularly well suited for handling a large number of antenna configurations of a generic type. A dedicated transfer matrix analysis, both by virtue of its specialization and the inherently easy compartmentalization of the formulation and numerical procedures, is significantly more efficient not only in computer time required but, more importantly, in the time needed to review and interpret the results.

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.

The 32nd CDC: System identification using interval dynamic models

NASA Technical Reports Server (NTRS)

Keel, L. H.; Lew, J. S.; Bhattacharyya, S. P.

1992-01-01

Motivated by the recent explosive development of results in the area of parametric robust control, a new technique to identify a family of uncertain systems is identified. The new technique takes the frequency domain input and output data obtained from experimental test signals and produces an 'interval transfer function' that contains the complete frequency domain behavior with respect to the test signals. This interval transfer function is one of the key concepts in the parametric robust control approach and identification with such an interval model allows one to predict the worst case performance and stability margins using recent results on interval systems. The algorithm is illustrated by applying it to an 18 bay Mini-Mast truss structure.

Application of multiplex PCR approaches for shark molecular identification: feasibility and applications for fisheries management and conservation in the Eastern Tropical Pacific.

PubMed

Caballero, S; Cardeñosa, D; Soler, G; Hyde, J

2012-03-01

Here we describe the application of new and existing multiplex PCR methodologies for shark species molecular identification. Four multiplex systems (group ID, thresher sharks, hammerhead sharks and miscellaneous shark) were employed with primers previously described and some designed in this study, which allow for species identification after running PCR products through an agarose gel. This system was implemented for samples (bodies and fins) collected from unidentified sharks landed in the port of Buenaventura and from confiscated tissues obtained from illegal fishing around the Malpelo Island Marine Protected Area, Pacific Coast of Colombia. This method has allowed reliable identification, to date, of 407 samples to the genus and/or species levels, most of them (380) identified as the pelagic thresher shark (Alopias pelagicus). Another seven samples were identified as scalloped hammerhead sharks (Sphyrna lewini). This is an easy-to-implement and reliable identification method that could even be used locally to monitor shark captures in the main fishing ports of developed and developing countries. © 2011 Blackwell Publishing Ltd.

Background Perchlorate Source Identification Technical Guidance

DTIC Science & Technology

2013-12-01

Sciences Branch (Code 71752) of the Advanced Systems and Applied Sciences Division (Code 71700), Space and Naval Warfare Systems Center (SSC Pacific), San...Head Advanced Systems & Applied Sciences Division iii EXECUTIVE SUMMARY The objective of this document is to outline the approach, tools, and...Helium HMX Octahydro-1,3,5,7-Tetranitro-1,3,5,7- Tetrazocine IR Installation Restoration IRIS Integrated Risk Information System IR-MS Isotope-Ratio

Recursive inversion of externally defined linear systems by FIR filters

NASA Technical Reports Server (NTRS)

Bach, Ralph E., Jr.; Baram, Yoram

1989-01-01

The approximate inversion of an internally unknown linear system, given by its impulse response sequence, by an inverse system having a finite impulse response, is considered. The recursive least-squares procedure is shown to have an exact initialization, based on the triangular Toeplitz structure of the matrix involved. The proposed approach also suggests solutions to the problem of system identification and compensation.

Knowledge management in secondary pharmaceutical manufacturing by mining of data historians-A proof-of-concept study.

PubMed

Meneghetti, Natascia; Facco, Pierantonio; Bezzo, Fabrizio; Himawan, Chrismono; Zomer, Simeone; Barolo, Massimiliano

2016-05-30

In this proof-of-concept study, a methodology is proposed to systematically analyze large data historians of secondary pharmaceutical manufacturing systems using data mining techniques. The objective is to develop an approach enabling to automatically retrieve operation-relevant information that can assist the management in the periodic review of a manufactory system. The proposed methodology allows one to automatically perform three tasks: the identification of single batches within the entire data-sequence of the historical dataset, the identification of distinct operating phases within each batch, and the characterization of a batch with respect to an assigned multivariate set of operating characteristics. The approach is tested on a six-month dataset of a commercial-scale granulation/drying system, where several millions of data entries are recorded. The quality of results and the generality of the approach indicate that there is a strong potential for extending the method to even larger historical datasets and to different operations, thus making it an advanced PAT tool that can assist the implementation of continual improvement paradigms within a quality-by-design framework. Copyright © 2016 Elsevier B.V. All rights reserved.

Bifocal Stereo for Multipath Person Re-Identification

NASA Astrophysics Data System (ADS)

Blott, G.; Heipke, C.

2017-11-01

This work presents an approach for the task of person re-identification by exploiting bifocal stereo cameras. Present monocular person re-identification approaches show a decreasing working distance, when increasing the image resolution to obtain a higher reidentification performance. We propose a novel 3D multipath bifocal approach, containing a rectilinear lens with larger focal length for long range distances and a fish eye lens of a smaller focal length for the near range. The person re-identification performance is at least on par with 2D re-identification approaches but the working distance of the approach is increased and on average 10% more re-identification performance can be achieved in the overlapping field of view compared to a single camera. In addition, the 3D information is exploited from the overlapping field of view to solve potential 2D ambiguities.

Genome-scale identification of Legionella pneumophila effectors using a machine learning approach.

PubMed

Burstein, David; Zusman, Tal; Degtyar, Elena; Viner, Ram; Segal, Gil; Pupko, Tal

2009-07-01

A large number of highly pathogenic bacteria utilize secretion systems to translocate effector proteins into host cells. Using these effectors, the bacteria subvert host cell processes during infection. Legionella pneumophila translocates effectors via the Icm/Dot type-IV secretion system and to date, approximately 100 effectors have been identified by various experimental and computational techniques. Effector identification is a critical first step towards the understanding of the pathogenesis system in L. pneumophila as well as in other bacterial pathogens. Here, we formulate the task of effector identification as a classification problem: each L. pneumophila open reading frame (ORF) was classified as either effector or not. We computationally defined a set of features that best distinguish effectors from non-effectors. These features cover a wide range of characteristics including taxonomical dispersion, regulatory data, genomic organization, similarity to eukaryotic proteomes and more. Machine learning algorithms utilizing these features were then applied to classify all the ORFs within the L. pneumophila genome. Using this approach we were able to predict and experimentally validate 40 new effectors, reaching a success rate of above 90%. Increasing the number of validated effectors to around 140, we were able to gain novel insights into their characteristics. Effectors were found to have low G+C content, supporting the hypothesis that a large number of effectors originate via horizontal gene transfer, probably from their protozoan host. In addition, effectors were found to cluster in specific genomic regions. Finally, we were able to provide a novel description of the C-terminal translocation signal required for effector translocation by the Icm/Dot secretion system. To conclude, we have discovered 40 novel L. pneumophila effectors, predicted over a hundred additional highly probable effectors, and shown the applicability of machine learning algorithms for the identification and characterization of bacterial pathogenesis determinants.

Model-based safety analysis of human-robot interactions: the MIRAS walking assistance robot.

PubMed

Guiochet, Jérémie; Hoang, Quynh Anh Do; Kaaniche, Mohamed; Powell, David

2013-06-01

Robotic systems have to cope with various execution environments while guaranteeing safety, and in particular when they interact with humans during rehabilitation tasks. These systems are often critical since their failure can lead to human injury or even death. However, such systems are difficult to validate due to their high complexity and the fact that they operate within complex, variable and uncertain environments (including users), in which it is difficult to foresee all possible system behaviors. Because of the complexity of human-robot interactions, rigorous and systematic approaches are needed to assist the developers in the identification of significant threats and the implementation of efficient protection mechanisms, and in the elaboration of a sound argumentation to justify the level of safety that can be achieved by the system. For threat identification, we propose a method called HAZOP-UML based on a risk analysis technique adapted to system description models, focusing on human-robot interaction models. The output of this step is then injected in a structured safety argumentation using the GSN graphical notation. Those approaches have been successfully applied to the development of a walking assistant robot which is now in clinical validation.

Automatic detection of protected health information from clinic narratives.

PubMed

Yang, Hui; Garibaldi, Jonathan M

2015-12-01

This paper presents a natural language processing (NLP) system that was designed to participate in the 2014 i2b2 de-identification challenge. The challenge task aims to identify and classify seven main Protected Health Information (PHI) categories and 25 associated sub-categories. A hybrid model was proposed which combines machine learning techniques with keyword-based and rule-based approaches to deal with the complexity inherent in PHI categories. Our proposed approaches exploit a rich set of linguistic features, both syntactic and word surface-oriented, which are further enriched by task-specific features and regular expression template patterns to characterize the semantics of various PHI categories. Our system achieved promising accuracy on the challenge test data with an overall micro-averaged F-measure of 93.6%, which was the winner of this de-identification challenge. Copyright © 2015 Elsevier Inc. All rights reserved.

A conceptual modeling framework for discrete event simulation using hierarchical control structures.

PubMed

Furian, N; O'Sullivan, M; Walker, C; Vössner, S; Neubacher, D

2015-08-01

Conceptual Modeling (CM) is a fundamental step in a simulation project. Nevertheless, it is only recently that structured approaches towards the definition and formulation of conceptual models have gained importance in the Discrete Event Simulation (DES) community. As a consequence, frameworks and guidelines for applying CM to DES have emerged and discussion of CM for DES is increasing. However, both the organization of model-components and the identification of behavior and system control from standard CM approaches have shortcomings that limit CM's applicability to DES. Therefore, we discuss the different aspects of previous CM frameworks and identify their limitations. Further, we present the Hierarchical Control Conceptual Modeling framework that pays more attention to the identification of a models' system behavior, control policies and dispatching routines and their structured representation within a conceptual model. The framework guides the user step-by-step through the modeling process and is illustrated by a worked example.

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.

A conceptual modeling framework for discrete event simulation using hierarchical control structures

PubMed Central

Furian, N.; O’Sullivan, M.; Walker, C.; Vössner, S.; Neubacher, D.

2015-01-01

Conceptual Modeling (CM) is a fundamental step in a simulation project. Nevertheless, it is only recently that structured approaches towards the definition and formulation of conceptual models have gained importance in the Discrete Event Simulation (DES) community. As a consequence, frameworks and guidelines for applying CM to DES have emerged and discussion of CM for DES is increasing. However, both the organization of model-components and the identification of behavior and system control from standard CM approaches have shortcomings that limit CM’s applicability to DES. Therefore, we discuss the different aspects of previous CM frameworks and identify their limitations. Further, we present the Hierarchical Control Conceptual Modeling framework that pays more attention to the identification of a models’ system behavior, control policies and dispatching routines and their structured representation within a conceptual model. The framework guides the user step-by-step through the modeling process and is illustrated by a worked example. PMID:26778940

New Methodology for Known Metabolite Identification in Metabonomics/Metabolomics: Topological Metabolite Identification Carbon Efficiency (tMICE).

PubMed

Sanchon-Lopez, Beatriz; Everett, Jeremy R

2016-09-02

A new, simple-to-implement and quantitative approach to assessing the confidence in NMR-based identification of known metabolites is introduced. The approach is based on a topological analysis of metabolite identification information available from NMR spectroscopy studies and is a development of the metabolite identification carbon efficiency (MICE) method. New topological metabolite identification indices are introduced, analyzed, and proposed for general use, including topological metabolite identification carbon efficiency (tMICE). Because known metabolite identification is one of the key bottlenecks in either NMR-spectroscopy- or mass spectrometry-based metabonomics/metabolomics studies, and given the fact that there is no current consensus on how to assess metabolite identification confidence, it is hoped that these new approaches and the topological indices will find utility.

A Workforce Development Systems Model for Unemployed Job Seekers

ERIC Educational Resources Information Center

Holland, Brian

2015-01-01

Workforce development is a set of processes that govern the identification, recruitment, assessment and training of job seekers into employment as well as the maintenance and advancement of these persons in their careers. Given the complexity of what workforce development entails, a systems approach is illustrated to ensure that the broader goal…

StakeMeter: value-based stakeholder identification and quantification framework for value-based software systems.

PubMed

Babar, Muhammad Imran; Ghazali, Masitah; Jawawi, Dayang N A; Bin Zaheer, Kashif

2015-01-01

Value-based requirements engineering plays a vital role in the development of value-based software (VBS). Stakeholders are the key players in the requirements engineering process, and the selection of critical stakeholders for the VBS systems is highly desirable. Based on the stakeholder requirements, the innovative or value-based idea is realized. The quality of the VBS system is associated with the concrete set of valuable requirements, and the valuable requirements can only be obtained if all the relevant valuable stakeholders participate in the requirements elicitation phase. The existing value-based approaches focus on the design of the VBS systems. However, the focus on the valuable stakeholders and requirements is inadequate. The current stakeholder identification and quantification (SIQ) approaches are neither state-of-the-art nor systematic for the VBS systems. The existing approaches are time-consuming, complex and inconsistent which makes the initiation process difficult. Moreover, the main motivation of this research is that the existing SIQ approaches do not provide the low level implementation details for SIQ initiation and stakeholder metrics for quantification. Hence, keeping in view the existing SIQ problems, this research contributes in the form of a new SIQ framework called 'StakeMeter'. The StakeMeter framework is verified and validated through case studies. The proposed framework provides low-level implementation guidelines, attributes, metrics, quantification criteria and application procedure as compared to the other methods. The proposed framework solves the issues of stakeholder quantification or prioritization, higher time consumption, complexity, and process initiation. The framework helps in the selection of highly critical stakeholders for the VBS systems with less judgmental error.

Analysing hierarchy in the organization of biological and physical systems.

PubMed

Jagers op Akkerhuis, Gerard A J M

2008-02-01

A structured approach is discussed for analysing hierarchy in the organization of biological and physical systems. The need for a structured approach follows from the observation that many hierarchies in the literature apply conflicting hierarchy rules and include ill-defined systems. As an alternative, we suggest a framework that is based on the following analytical steps: determination of the succession stage of the universe, identification of a specific system as part of the universe, specification of external influences on a system's creation and analysis of a system's internal organization. At the end, the paper discusses practical implications of the proposed method for the analysis of system organization and hierarchy in biology, ecology and physics.

A Novel Identification Methodology for the Coordinate Relationship between a 3D Vision System and a Legged Robot

PubMed Central

Chai, Xun; Gao, Feng; Pan, Yang; Qi, Chenkun; Xu, Yilin

2015-01-01

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. PMID:25912350

Soft Biometrics; Human Identification Using Comparative Descriptions.

PubMed

Reid, Daniel A; Nixon, Mark S; Stevenage, Sarah V

2014-06-01

Soft biometrics are a new form of biometric identification which use physical or behavioral traits that can be naturally described by humans. Unlike other biometric approaches, this allows identification based solely on verbal descriptions, bridging the semantic gap between biometrics and human description. To permit soft biometric identification the description must be accurate, yet conventional human descriptions comprising of absolute labels and estimations are often unreliable. A novel method of obtaining human descriptions will be introduced which utilizes comparative categorical labels to describe differences between subjects. This innovative approach has been shown to address many problems associated with absolute categorical labels-most critically, the descriptions contain more objective information and have increased discriminatory capabilities. Relative measurements of the subjects' traits can be inferred from comparative human descriptions using the Elo rating system. The resulting soft biometric signatures have been demonstrated to be robust and allow accurate recognition of subjects. Relative measurements can also be obtained from other forms of human representation. This is demonstrated using a support vector machine to determine relative measurements from gait biometric signatures-allowing retrieval of subjects from video footage by using human comparisons, bridging the semantic gap.

Simple and fast multiplex PCR method for detection of species origin in meat products.

PubMed

Izadpanah, Mehrnaz; Mohebali, Nazanin; Elyasi Gorji, Zahra; Farzaneh, Parvaneh; Vakhshiteh, Faezeh; Shahzadeh Fazeli, Seyed Abolhassan

2018-02-01

Identification of animal species is one of the major concerns in food regulatory control and quality assurance system. Different approaches have been used for species identification in animal origin of feedstuff. This study aimed to develop a multiplex PCR approach to detect the origin of meat and meat products. Specific primers were designed based on the conserved region of mitochondrial Cytochrome C Oxidase subunit I ( COX1 ) gene. This method could successfully distinguish the origin of the pig, camel, sheep, donkey, goat, cow, and chicken in one single reaction. Since PCR products derived from each species represent unique molecular weight, the amplified products could be identified by electrophoresis and analyzed based on their size. Due to the synchronized amplification of segments within a single PCR reaction, multiplex PCR is considered to be a simple, fast, and inexpensive technique that can be applied for identification of meat products in food industries. Nowadays, this technique has been considered as a practical method to identify the species origin, which could further applied for animal feedstuffs identification.

A Cost-Efficient Approach to Inventory Management: Using IUID to Meet an Organization’s Specific Requirements

DTIC Science & Technology

2013-02-01

technology relies on radio waves to transfer information from the RFID “tagged” item, to an electronic reader . EODTECHDIV employed “passive” RFID ...frequency identification ( RFID ). EODTECHDIV, as one of DoD’s premier organizations respon- sible for Explosive Ordnance Disposal (EOD) mission-related...Identification ( RFID ) and a Real Time Locating System (RTLS) EODTECHDIV turned to technology in an attempt to solve some of its inventory management

A De-Identification Pipeline for Ultrasound Medical Images in DICOM Format.

PubMed

Monteiro, Eriksson; Costa, Carlos; Oliveira, José Luís

2017-05-01

Clinical data sharing between healthcare institutions, and between practitioners is often hindered by privacy protection requirements. This problem is critical in collaborative scenarios where data sharing is fundamental for establishing a workflow among parties. The anonymization of patient information burned in DICOM images requires elaborate processes somewhat more complex than simple de-identification of textual information. Usually, before sharing, there is a need for manual removal of specific areas containing sensitive information in the images. In this paper, we present a pipeline for ultrasound medical image de-identification, provided as a free anonymization REST service for medical image applications, and a Software-as-a-Service to streamline automatic de-identification of medical images, which is freely available for end-users. The proposed approach applies image processing functions and machine-learning models to bring about an automatic system to anonymize medical images. To perform character recognition, we evaluated several machine-learning models, being Convolutional Neural Networks (CNN) selected as the best approach. For accessing the system quality, 500 processed images were manually inspected showing an anonymization rate of 89.2%. The tool can be accessed at https://bioinformatics.ua.pt/dicom/anonymizer and it is available with the most recent version of Google Chrome, Mozilla Firefox and Safari. A Docker image containing the proposed service is also publicly available for the community.

A geometric approach to failure detection and identification in linear systems

NASA Technical Reports Server (NTRS)

Massoumnia, M. A.

1986-01-01

Using concepts of (C,A)-invariant and unobservability (complementary observability) subspaces, a geometric formulation of the failure detection and identification filter problem is stated. Using these geometric concepts, it is shown that it is possible to design a causal linear time-invariant processor that can be used to detect and uniquely identify a component failure in a linear time-invariant system, assuming: (1) The components can fail simultaneously, and (2) The components can fail only one at a time. In addition, a geometric formulation of Beard's failure detection filter problem is stated. This new formulation completely clarifies of output separability and mutual detectability introduced by Beard and also exploits the dual relationship between a restricted version of the failure detection and identification problem and the control decoupling problem. Moreover, the frequency domain interpretation of the results is used to relate the concepts of failure sensitive observers with the generalized parity relations introduced by Chow. This interpretation unifies the various failure detection and identification concepts and design procedures.

Identification of technology options for reducing nitrogen pollution in cropping systems of Pujiang*

PubMed Central

Fang, Bin; Wang, Guang-huo; Van den berg, Marrit; Roetter, Reimund

2005-01-01

This work analyses the potential role of nitrogen pollution technology of crop systems of Pujiang, County in Eastern China’s Zhejiang Province, rice and vegetables are important cropping systems. We used a case study approach involving comparison of farmer practices and improved technologies. This approach allows assessing the impact of technology on pollution, is forward looking, and can yield information on the potential of on-the-shelf technology and provide opportunities for technology development. The approach particularly suits newly developed rice technologies with large potential of reducing nitrogen pollution and for future rice and vegetables technologies. The results showed that substantial reductions in nitrogen pollution are feasible for both types of crops. PMID:16187411

Identification of technology options for reducing nitrogen pollution in cropping systems of Pujiang.

PubMed

Fang, Bin; Wang, Guang-Huo; Van, Den Berg Marrit; Roetter, Reimund

2005-10-01

This work analyses the potential role of nitrogen pollution technology of crop systems of Pujiang, County in Eastern China's Zhejiang Province, rice and vegetables are important cropping systems. We used a case study approach involving comparison of farmer practices and improved technologies. This approach allows assessing the impact of technology on pollution, is forward looking, and can yield information on the potential of on-the-shelf technology and provide opportunities for technology development. The approach particularly suits newly developed rice technologies with large potential of reducing nitrogen pollution and for future rice and vegetables technologies. The results showed that substantial reductions in nitrogen pollution are feasible for both types of crops.

Identifying Emotional and Behavioral Risk among Gifted and Nongifted Children: A Multi-Gate, Multi-Informant Approach

ERIC Educational Resources Information Center

Eklund, Katie; Tanner, Nick; Stoll, Katie; Anway, Leslie

2015-01-01

The purpose of the current investigation was to compare 1,206 gifted and nongifted elementary students on the identification of emotional and behavioral risk (EBR) as rated by teachers and parents using a multigate, multi-informant approach to assessment. The Parent and Teacher Behavioral Assessment System for Children, Second Edition (BASC-2) and…

Security and matching of partial fingerprint recognition systems

NASA Astrophysics Data System (ADS)

Jea, Tsai-Yang; Chavan, Viraj S.; Govindaraju, Venu; Schneider, John K.

2004-08-01

Despite advances in fingerprint identification techniques, matching incomplete or partial fingerprints still poses a difficult challenge. While the introduction of compact silicon chip-based sensors that capture only a part of the fingerprint area have made this problem important from a commercial perspective, there is also considerable interest on the topic for processing partial and latent fingerprints obtained at crime scenes. Attempts to match partial fingerprints using singular ridge structures-based alignment techniques fail when the partial print does not include such structures (e.g., core or delta). We present a multi-path fingerprint matching approach that utilizes localized secondary features derived using only the relative information of minutiae. Since the minutia-based fingerprint representation, is an ANSI-NIST standard, our approach has the advantage of being directly applicable to already existing databases. We also analyze the vulnerability of partial fingerprint identification systems to brute force attacks. The described matching approach has been tested on one of FVC2002"s DB1 database11. The experimental results show that our approach achieves an equal error rate of 1.25% and a total error rate of 1.8% (with FAR at 0.2% and FRR at 1.6%).

Tracking by Identification Using Computer Vision and Radio

PubMed Central

Mandeljc, Rok; Kovačič, Stanislav; Kristan, Matej; Perš, Janez

2013-01-01

We present a novel system for detection, localization and tracking of multiple people, which fuses a multi-view computer vision approach with a radio-based localization system. The proposed fusion combines the best of both worlds, excellent computer-vision-based localization, and strong identity information provided by the radio system, and is therefore able to perform tracking by identification, which makes it impervious to propagated identity switches. We present comprehensive methodology for evaluation of systems that perform person localization in world coordinate system and use it to evaluate the proposed system as well as its components. Experimental results on a challenging indoor dataset, which involves multiple people walking around a realistically cluttered room, confirm that proposed fusion of both systems significantly outperforms its individual components. Compared to the radio-based system, it achieves better localization results, while at the same time it successfully prevents propagation of identity switches that occur in pure computer-vision-based tracking. PMID:23262485

Similarity Metrics for Closed Loop Dynamic Systems

NASA Technical Reports Server (NTRS)

Whorton, Mark S.; Yang, Lee C.; Bedrossian, Naz; Hall, Robert A.

2008-01-01

To what extent and in what ways can two closed-loop dynamic systems be said to be "similar?" This question arises in a wide range of dynamic systems modeling and control system design applications. For example, bounds on error models are fundamental to the controller optimization with modern control design methods. Metrics such as the structured singular value are direct measures of the degree to which properties such as stability or performance are maintained in the presence of specified uncertainties or variations in the plant model. Similarly, controls-related areas such as system identification, model reduction, and experimental model validation employ measures of similarity between multiple realizations of a dynamic system. Each area has its tools and approaches, with each tool more or less suited for one application or the other. Similarity in the context of closed-loop model validation via flight test is subtly different from error measures in the typical controls oriented application. Whereas similarity in a robust control context relates to plant variation and the attendant affect on stability and performance, in this context similarity metrics are sought that assess the relevance of a dynamic system test for the purpose of validating the stability and performance of a "similar" dynamic system. Similarity in the context of system identification is much more relevant than are robust control analogies in that errors between one dynamic system (the test article) and another (the nominal "design" model) are sought for the purpose of bounding the validity of a model for control design and analysis. Yet system identification typically involves open-loop plant models which are independent of the control system (with the exception of limited developments in closed-loop system identification which is nonetheless focused on obtaining open-loop plant models from closed-loop data). Moreover the objectives of system identification are not the same as a flight test and hence system identification error metrics are not directly relevant. In applications such as launch vehicles where the open loop plant is unstable it is similarity of the closed-loop system dynamics of a flight test that are relevant.

Molecular prey identification in Central European piscivores.

PubMed

Thalinger, Bettina; Oehm, Johannes; Mayr, Hannes; Obwexer, Armin; Zeisler, Christiane; Traugott, Michael

2016-01-01

Diet analysis is an important aspect when investigating the ecology of fish-eating animals and essential for assessing their functional role in food webs across aquatic and terrestrial ecosystems. The identification of fish remains in dietary samples, however, can be time-consuming and unsatisfying using conventional morphological analysis of prey remains. Here, we present a two-step multiplex PCR system, comprised of six assays, allowing for rapid, sensitive and specific detection of fish DNA in dietary samples. This approach encompasses 78 fish and lamprey species native to Central European freshwaters and enables the identification of 31 species, six genera, two families, two orders and two fish family clusters. All targeted taxa were successfully amplified from 25 template molecules, and each assay was specific when tested against a wide range of invertebrates and vertebrates inhabiting aquatic environments. The applicability of the multiplex PCR system was evaluated in a feeding trial, wherein it outperformed morphological prey analysis regarding species-specific prey identification in faeces of Eurasian otters. Additionally, a wide spectrum of fish species was detected in field-collected faecal samples and regurgitated pellets of Common Kingfishers and Great Cormorants, demonstrating the broad applicability of the approach. In conclusion, this multiplex PCR system provides an efficient, easy to use and cost-effective tool for assessing the trophic ecology of piscivores in Central Europe. Furthermore, the multiplex PCRs and the primers described therein will be applicable wherever DNA of the targeted fish species needs to be detected at high sensitivity and specificity. © 2015 The Authors. Molecular Ecology Resources Published by John Wiley & Sons Ltd.

Identification and robust control of an experimental servo motor.

PubMed

Adam, E J; Guestrin, E D

2002-04-01

In this work, the design of a robust controller for an experimental laboratory-scale position control system based on a dc motor drive as well as the corresponding identification and robust stability analysis are presented. In order to carry out the robust design procedure, first, a classic closed-loop identification technique is applied and then, the parametrization by internal model control is used. The model uncertainty is evaluated under both parametric and global representation. For the latter case, an interesting discussion about the conservativeness of this description is presented by means of a comparison between the uncertainty disk and the critical perturbation radius approaches. Finally, conclusions about the performance of the experimental system with the robust controller are discussed using comparative graphics of the controlled variable and the Nyquist stability margin as a robustness measurement.

Methods for optimizing solutions when considering group arguments by team of experts

NASA Astrophysics Data System (ADS)

Chernyi, Sergei; Budnik, Vlad

2017-11-01

The article is devoted to methods of expert evaluation. The technology of expert evaluation is presented from the standpoint of precedent structures. In this paper, an aspect of the mathematical basis for constructing a component of decision analysis is considered. In fact, this approach leaves out any identification of their knowledge and skills of simulating organizational and manufacturing situations and taking efficient managerial decisions; it doesn't enable any identification and assessment of their knowledge on the basis of multi-informational and least loss-making methods and information technologies. Hence the problem is to research and develop a methodology for systemic identification of professional problem-focused knowledge acquired by employees operating adaptive automated systems of training management (AASTM operators), which shall also further the theory and practice of the intelligence-related aspects thereof.

Consistency of the Performance and Nonperformance Methods in Gifted Identification

ERIC Educational Resources Information Center

Acar, Selcuk; Sen, Sedat; Cayirdag, Nur

2016-01-01

Current approaches to gifted identification suggest collecting multiple sources of evidence. Some gifted identification guidelines allow for the interchangeable use of "performance" and "nonperformance" identification methods. This multiple criteria approach lacks a strong overlap between the assessment tools; however,…

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.

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.

Nonlinear system identification based on Takagi-Sugeno fuzzy modeling and unscented Kalman filter.

PubMed

Vafamand, Navid; Arefi, Mohammad Mehdi; Khayatian, Alireza

2018-03-01

This paper proposes two novel Kalman-based learning algorithms for an online Takagi-Sugeno (TS) fuzzy model identification. The proposed approaches are designed based on the unscented Kalman filter (UKF) and the concept of dual estimation. Contrary to the extended Kalman filter (EKF) which utilizes derivatives of nonlinear functions, the UKF employs the unscented transformation. Consequently, non-differentiable membership functions can be considered in the structure of the TS models. This makes the proposed algorithms to be applicable for the online parameter calculation of wider classes of TS models compared to the recently published papers concerning the same issue. Furthermore, because of the great capability of the UKF in handling severe nonlinear dynamics, the proposed approaches can effectively approximate the nonlinear systems. Finally, numerical and practical examples are provided to show the advantages of the proposed approaches. Simulation results reveal the effectiveness of the proposed methods and performance improvement based on the root mean square (RMS) of the estimation error compared to the existing results. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

A novel thermal face recognition approach using face pattern words

NASA Astrophysics Data System (ADS)

Zheng, Yufeng

2010-04-01

A reliable thermal face recognition system can enhance the national security applications such as prevention against terrorism, surveillance, monitoring and tracking, especially at nighttime. The system can be applied at airports, customs or high-alert facilities (e.g., nuclear power plant) for 24 hours a day. In this paper, we propose a novel face recognition approach utilizing thermal (long wave infrared) face images that can automatically identify a subject at both daytime and nighttime. With a properly acquired thermal image (as a query image) in monitoring zone, the following processes will be employed: normalization and denoising, face detection, face alignment, face masking, Gabor wavelet transform, face pattern words (FPWs) creation, face identification by similarity measure (Hamming distance). If eyeglasses are present on a subject's face, an eyeglasses mask will be automatically extracted from the querying face image, and then masked with all comparing FPWs (no more transforms). A high identification rate (97.44% with Top-1 match) has been achieved upon our preliminary face dataset (of 39 subjects) from the proposed approach regardless operating time and glasses-wearing condition.e

Device-Free Passive Identity Identification via WiFi Signals.

PubMed

Lv, Jiguang; Yang, Wu; Man, Dapeng

2017-11-02

Device-free passive identity identification attracts much attention in recent years, and it is a representative application in sensorless sensing. It can be used in many applications such as intrusion detection and smart building. Previous studies show the sensing potential of WiFi signals in a device-free passive manner. It is confirmed that human's gait is unique from each other similar to fingerprint and iris. However, the identification accuracy of existing approaches is not satisfactory in practice. In this paper, we present Wii, a device-free WiFi-based Identity Identification approach utilizing human's gait based on Channel State Information (CSI) of WiFi signals. Principle Component Analysis (PCA) and low pass filter are applied to remove the noises in the signals. We then extract several entities' gait features from both time and frequency domain, and select the most effective features according to information gain. Based on these features, Wii realizes stranger recognition through Gaussian Mixture Model (GMM) and identity identification through a Support Vector Machine (SVM) with Radial Basis Function (RBF) kernel. It is implemented using commercial WiFi devices and evaluated on a dataset with more than 1500 gait instances collected from eight subjects walking in a room. The results indicate that Wii can effectively recognize strangers and can achieves high identification accuracy with low computational cost. As a result, Wii has the potential to work in typical home security systems.

Device-Free Passive Identity Identification via WiFi Signals

PubMed Central

Yang, Wu; Man, Dapeng

2017-01-01

Device-free passive identity identification attracts much attention in recent years, and it is a representative application in sensorless sensing. It can be used in many applications such as intrusion detection and smart building. Previous studies show the sensing potential of WiFi signals in a device-free passive manner. It is confirmed that human’s gait is unique from each other similar to fingerprint and iris. However, the identification accuracy of existing approaches is not satisfactory in practice. In this paper, we present Wii, a device-free WiFi-based Identity Identification approach utilizing human’s gait based on Channel State Information (CSI) of WiFi signals. Principle Component Analysis (PCA) and low pass filter are applied to remove the noises in the signals. We then extract several entities’ gait features from both time and frequency domain, and select the most effective features according to information gain. Based on these features, Wii realizes stranger recognition through Gaussian Mixture Model (GMM) and identity identification through a Support Vector Machine (SVM) with Radial Basis Function (RBF) kernel. It is implemented using commercial WiFi devices and evaluated on a dataset with more than 1500 gait instances collected from eight subjects walking in a room. The results indicate that Wii can effectively recognize strangers and can achieves high identification accuracy with low computational cost. As a result, Wii has the potential to work in typical home security systems. PMID:29099091

Neurological Disease in Lupus: Toward a Personalized Medicine Approach

PubMed Central

McGlasson, Sarah; Wiseman, Stewart; Wardlaw, Joanna; Dhaun, Neeraj; Hunt, David P. J.

2018-01-01

The brain and nervous system are important targets for immune-mediated damage in systemic lupus erythematosus (SLE), resulting in a complex spectrum of neurological syndromes. Defining nervous system disease in lupus poses significant challenges. Among the difficulties to be addressed are a diversity of clinical manifestations and a lack of understanding of their mechanistic basis. However, despite these challenges, progress has been made in the identification of pathways which contribute to neurological disease in SLE. Understanding the molecular pathogenesis of neurological disease in lupus will inform both classification and approaches to clinical trials. PMID:29928273

A Landscape Indicator Approach to the Identification and Articulation of the Ecological Consequences of Land Cover Change in the Chesapeake Bay Watershed, 1970-2000

USGS Publications Warehouse

Slonecker, Terrence

2008-01-01

The advancement of geographic science in the area of land surface status and trends and land cover change is at the core of the current geographic scientific research of the U.S. Geological Survey (USGS) (McMahon and others, 2005). Perhaps the least developed or articulated aspects of USGS land change science have been the identification and analysis of the ecological consequences of land cover change. Changes in land use and land cover significantly affect the ability of ecosystems to provide essential ecological goods and services, which, in turn, affect the economic, public health, and social benefits that these ecosystems provide. One of the great scientific challenges for geographic science is to understand and calibrate the effects of land use and land cover change and the complex interaction between human and biotic systems at a variety of natural, geographic, and political scales. Understanding the dynamics of land surface change requires an increased understanding of the complex nature of human-environmental systems and will require a suite of scientific tools that include traditional geographic data and analysis methods, such as remote sensing and geographic information systems (GIS), as well as innovative approaches to understanding the dynamics of complex systems. One such approach that has gained much recent scientific attention is the landscape indicator, or landscape assessment, approach, which has been developed with the emergence of the science of landscape ecology.

A Simple and Practical Dictionary-based Approach for Identification of Proteins in Medline Abstracts

PubMed Central

Egorov, Sergei; Yuryev, Anton; Daraselia, Nikolai

2004-01-01

Objective: The aim of this study was to develop a practical and efficient protein identification system for biomedical corpora. Design: The developed system, called ProtScan, utilizes a carefully constructed dictionary of mammalian proteins in conjunction with a specialized tokenization algorithm to identify and tag protein name occurrences in biomedical texts and also takes advantage of Medline “Name-of-Substance” (NOS) annotation. The dictionaries for ProtScan were constructed in a semi-automatic way from various public-domain sequence databases followed by an intensive expert curation step. Measurements: The recall and precision of the system have been determined using 1,000 randomly selected and hand-tagged Medline abstracts. Results: The developed system is capable of identifying protein occurrences in Medline abstracts with a 98% precision and 88% recall. It was also found to be capable of processing approximately 300 abstracts per second. Without utilization of NOS annotation, precision and recall were found to be 98.5% and 84%, respectively. Conclusion: The developed system appears to be well suited for protein-based Medline indexing and can help to improve biomedical information retrieval. Further approaches to ProtScan's recall improvement also are discussed. PMID:14764613

A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms

PubMed Central

Meiring, Gys Albertus Marthinus; Myburgh, Hermanus Carel

2015-01-01

In this paper the various driving style analysis solutions are investigated. An in-depth investigation is performed to identify the relevant machine learning and artificial intelligence algorithms utilised in current driver behaviour and driving style analysis systems. This review therefore serves as a trove of information, and will inform the specialist and the student regarding the current state of the art in driver style analysis systems, the application of these systems and the underlying artificial intelligence algorithms applied to these applications. The aim of the investigation is to evaluate the possibilities for unique driver identification utilizing the approaches identified in other driver behaviour studies. It was found that Fuzzy Logic inference systems, Hidden Markov Models and Support Vector Machines consist of promising capabilities to address unique driver identification algorithms if model complexity can be reduced. PMID:26690164

A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms.

PubMed

Meiring, Gys Albertus Marthinus; Myburgh, Hermanus Carel

2015-12-04

In this paper the various driving style analysis solutions are investigated. An in-depth investigation is performed to identify the relevant machine learning and artificial intelligence algorithms utilised in current driver behaviour and driving style analysis systems. This review therefore serves as a trove of information, and will inform the specialist and the student regarding the current state of the art in driver style analysis systems, the application of these systems and the underlying artificial intelligence algorithms applied to these applications. The aim of the investigation is to evaluate the possibilities for unique driver identification utilizing the approaches identified in other driver behaviour studies. It was found that Fuzzy Logic inference systems, Hidden Markov Models and Support Vector Machines consist of promising capabilities to address unique driver identification algorithms if model complexity can be reduced.

A comprehensive inversion approach for feedforward compensation of piezoactuator system at high frequency

NASA Astrophysics Data System (ADS)

Tian, Lizhi; Xiong, Zhenhua; Wu, Jianhua; Ding, Han

2016-09-01

Motion control of the piezoactuator system over broadband frequencies is limited due to its inherent hysteresis and system dynamics. One of the suggested ways is to use feedforward controller to linearize the input-output relationship of the piezoactuator system. Although there have been many feedforward approaches, it is still a challenge to develop feedforward controller for the piezoactuator system at high frequency. Hence, this paper presents a comprehensive inversion approach in consideration of the coupling of hysteresis and dynamics. In this work, the influence of dynamics compensation on the input-output relationship of the piezoactuator system is investigated first. With system dynamics compensation, the input-output relationship of the piezoactuator system will be further represented as rate-dependent nonlinearity due to the inevitable dynamics compensation error, especially at high frequency. Base on this result, the feedforward controller composed by a cascade of linear dynamics inversion and rate-dependent nonlinearity inversion is developed. Then, the system identification of the comprehensive inversion approach is proposed. Finally, experimental results show that the proposed approach can improve the performance on tracking of both periodic and non-periodic trajectories at medium and high frequency compared with the conventional feedforward approaches.

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.

Exploring MALDI-TOF MS approach for a rapid identification of Mycobacterium avium ssp. paratuberculosis field isolates.

PubMed

Ricchi, M; Mazzarelli, A; Piscini, A; Di Caro, A; Cannas, A; Leo, S; Russo, S; Arrigoni, N

2017-03-01

The aim of the study was to explore the suitability of matrix-assisted laser desorption/ionisation time-of-flight mass spectrometry (MALDI-TOF MS) for a rapid and correct identification of Mycobacterium avium ssp. paratuberculosis (MAP) field isolates. MALDI-TOF MS approach is becoming one of the most popular tests for the identification of intact bacterial cells which has been shown to be fast and reliable. For this purpose, 36 MAP field isolates were analysed through MALDI-TOF MS and the spectra compared with two different databases: one provided by the vendor of the system employed (Biotyper ver. 3·0; Bruker Daltonics) and a homemade database containing spectra from both tuberculous and nontuberculous Mycobacteria. Moreover, principal component analysis procedure was employed to confirm the ability of MALDI-TOF MS to discriminate between very closely related subspecies. Our results suggest MAP can be differentiated from other Mycobacterium species, both when the species are very close (M. intracellulare) and when belonging to different subspecies (M. avium ssp. avium and M. avium ssp. silvaticum). The procedure applied is fast, easy to perform, and achieves an earlier accurate species identification of MAP and nontuberculous Mycobacteria in comparison to other procedures. The gold standard test for the diagnosis of paratuberculosis is still isolation of MAP by cultural methods, but additional assays, such as qPCR and subculturing for determination of mycobactin dependency are required to confirm its identification. We have provided here evidence pertaining to the usefulness of MALDI-TOF MS approach for a rapid identification of this mycobacterium among other members of M. avium complex. © 2016 The Society for Applied Microbiology.

Wiener-Hammerstein system identification - an evolutionary approach

NASA Astrophysics Data System (ADS)

Naitali, Abdessamad; Giri, Fouad

2016-01-01

The problem of identifying parametric Wiener-Hammerstein (WH) systems is addressed within the evolutionary optimisation context. Specifically, a hybrid culture identification method is developed that involves model structure adaptation using genetic recombination and model parameter learning using particle swarm optimisation. The method enjoys three interesting features: (1) the risk of premature convergence of model parameter estimates to local optima is significantly reduced, due to the constantly maintained diversity of model candidates; (2) no prior knowledge is needed except for upper bounds on the system structure indices; (3) the method is fully autonomous as no interaction is needed with the user during the optimum search process. The performances of the proposed method will be illustrated and compared to alternative methods using a well-established WH benchmark.

Predictor-based multivariable closed-loop system identification of the EXTRAP T2R reversed field pinch external plasma response

NASA Astrophysics Data System (ADS)

Olofsson, K. Erik J.; Brunsell, Per R.; Rojas, Cristian R.; Drake, James R.; Hjalmarsson, Håkan

2011-08-01

The usage of computationally feasible overparametrized and nonregularized system identification signal processing methods is assessed for automated determination of the full reversed-field pinch external plasma response spectrum for the experiment EXTRAP T2R. No assumptions on the geometry of eigenmodes are imposed. The attempted approach consists of high-order autoregressive exogenous estimation followed by Markov block coefficient construction and Hankel matrix singular value decomposition. It is seen that the obtained 'black-box' state-space models indeed can be compared with the commonplace ideal magnetohydrodynamics (MHD) resistive thin-shell model in cylindrical geometry. It is possible to directly map the most unstable autodetected empirical system pole to the corresponding theoretical resistive shell MHD eigenmode.

Motion estimation of subcellular structures from fluorescence microscopy images.

PubMed

Vallmitjana, A; Civera-Tregon, A; Hoenicka, J; Palau, F; Benitez, R

2017-07-01

We present an automatic image processing framework to study moving intracellular structures from live cell fluorescence microscopy. The system includes the identification of static and dynamic structures from time-lapse images using data clustering as well as the identification of the trajectory of moving objects with a probabilistic tracking algorithm. The method has been successfully applied to study mitochondrial movement in neurons. The approach provides excellent performance under different experimental conditions and is robust to common sources of noise including experimental, molecular and biological fluctuations.

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.

An iterated cubature unscented Kalman filter for large-DoF systems identification with noisy data

NASA Astrophysics Data System (ADS)

Ghorbani, Esmaeil; Cha, Young-Jin

2018-04-01

Structural and mechanical system identification under dynamic loading has been an important research topic over the last three or four decades. Many Kalman-filtering-based approaches have been developed for linear and nonlinear systems. For example, to predict nonlinear systems, an unscented Kalman filter was applied. However, from extensive literature reviews, the unscented Kalman filter still showed weak performance on systems with large degrees of freedom. In this research, a modified unscented Kalman filter is proposed by integration of a cubature Kalman filter to improve the system identification performance of systems with large degrees of freedom. The novelty of this work lies on conjugating the unscented transform with the cubature integration concept to find a more accurate output from the transformation of the state vector and its related covariance matrix. To evaluate the proposed method, three different numerical models (i.e., the single degree-of-freedom Bouc-Wen model, the linear 3-degrees-of-freedom system, and the 10-degrees-of-freedom system) are investigated. To evaluate the robustness of the proposed method, high levels of noise in the measured response data are considered. The results show that the proposed method is significantly superior to the traditional UKF for noisy measured data in systems with large degrees of freedom.

Teaching Case: IS Security Requirements Identification from Conceptual Models in Systems Analysis and Design: The Fun & Fitness, Inc. Case

ERIC Educational Resources Information Center

Spears, Janine L.; Parrish, James L., Jr.

2013-01-01

This teaching case introduces students to a relatively simple approach to identifying and documenting security requirements within conceptual models that are commonly taught in systems analysis and design courses. An introduction to information security is provided, followed by a classroom example of a fictitious company, "Fun &…

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.

Early Engagement of Safety and Mission Assurance Expertise Using Systems Engineering Tools: A Risk-Based Approach to Early Identification of Safety and Assurance Requirements

NASA Technical Reports Server (NTRS)

Darpel, Scott; Beckman, Sean

2016-01-01

Decades of systems engineering practice have demonstrated that the earlier the identification of requirements occurs, the lower the chance that costly redesigns will needed later in the project life cycle. A better understanding of all requirements can also improve the likelihood of a design's success. Significant effort has been put into developing tools and practices that facilitate requirements determination, including those that are part of the model-based systems engineering (MBSE) paradigm. These efforts have yielded improvements in requirements definition, but have thus far focused on a design's performance needs. The identification of safety & mission assurance (S&MA) related requirements, in comparison, can occur after preliminary designs are already established, yielding forced redesigns. Engaging S&MA expertise at an earlier stage, facilitated by the use of MBSE tools, and focused on actual project risk, can yield the same type of design life cycle improvements that have been realized in technical and performance requirements.

Satellite provided customer premise services: A forecast of potential domestic demand through the year 2000. Volume 2: Technical report

NASA Technical Reports Server (NTRS)

Kratochvil, D.; Bowyer, J.; Bhushan, C.; Steinnagel, K.; Al-Kinani, G.

1983-01-01

The potential United States domestic telecommunications demand for satellite provided customer premises voice, data and video services through the year 2000 were forecast, so that this information on service demand would be available to aid in NASA program planning. To accomplish this overall purpose the following objectives were achieved: development of a forecast of the total domestic telecommunications demand, identification of that portion of the telecommunications demand suitable for transmission by satellite systems, identification of that portion of the satellite market addressable by Computer premises services systems, identification of that portion of the satellite market addressabble by Ka-band CPS system, and postulation of a Ka-band CPS network on a nationwide and local level. The approach employed included the use of a variety of forecasting models, a market distribution model and a network optimization model. Forecasts were developed for; 1980, 1990, and 2000; voice, data and video services; terrestrial and satellite delivery modes; and C, Ku and Ka-bands.

Satellite provided customer premise services: A forecast of potential domestic demand through the year 2000. Volume 2: Technical report

NASA Astrophysics Data System (ADS)

Kratochvil, D.; Bowyer, J.; Bhushan, C.; Steinnagel, K.; Al-Kinani, G.

1983-08-01

The potential United States domestic telecommunications demand for satellite provided customer premises voice, data and video services through the year 2000 were forecast, so that this information on service demand would be available to aid in NASA program planning. To accomplish this overall purpose the following objectives were achieved: development of a forecast of the total domestic telecommunications demand, identification of that portion of the telecommunications demand suitable for transmission by satellite systems, identification of that portion of the satellite market addressable by Computer premises services systems, identification of that portion of the satellite market addressabble by Ka-band CPS system, and postulation of a Ka-band CPS network on a nationwide and local level. The approach employed included the use of a variety of forecasting models, a market distribution model and a network optimization model. Forecasts were developed for; 1980, 1990, and 2000; voice, data and video services; terrestrial and satellite delivery modes; and C, Ku and Ka-bands.

Tracking blood products in blood centres using radio frequency identification: a comprehensive assessment.

PubMed

Davis, Rodeina; Geiger, Bradley; Gutierrez, Alfonso; Heaser, Julie; Veeramani, Dharmaraj

2009-07-01

Radio frequency identification (RFID) can be a key enabler for enhancing productivity and safety of the blood product supply chain. This article describes a systematic approach developed by the RFID Blood Consortium for a comprehensive feasibility and impact assessment of RFID application in blood centre operations. Our comprehensive assessment approach incorporates process-orientated and technological perspectives as well as impact analysis. Assessment of RFID-enabled process redesign is based on generic core processes derived from the three participating blood centres. The technological assessment includes RFID tag readability and performance evaluation, testing of temperature and biological effects of RF energy on blood products, and RFID system architecture design and standards. The scope of this article is limited to blood centre processes (from donation to manufacturing/distribution) for selected mainstream blood products (red blood cells and platelets). Radio frequency identification can help overcome a number of common challenges and process inefficiencies associated with identification and tracking of blood products. High frequency-based RFID technology performs adequately and safely for red blood cell and platelet products. Productivity and quality improvements in RFID-enabled blood centre processes can recoup investment cost in a 4-year payback period. Radio frequency identification application has significant process-orientated and technological implications. It is feasible and economically justifiable to incorporate RFID into blood centre processes.

Risk assessment, identification, and notification (RAIN) system : a novel approach for traffic management.

DOT National Transportation Integrated Search

2009-08-31

Primary research focused on the design and development of an energy-efficient Risk Notification Message Dissemination Protocol (RNMDP) for vehicular ad hoc networks (VANETs). RNMDP propagates Risk Notification Messages (RNMs) from a location of origi...

IMMUNE SYSTEM ONTOGENY AND DEVELOPMENTAL IMMUNOTOXICOLOGY

EPA Science Inventory

Animal testing for the identification and characterization of hazard(s), associated with exposure to toxic chemicals, is an accepted approach for identifying the potential risk to humans. The rodent, in particular the rat, has been the most commonly used species for routine toxi...

Lithofacies identification using multiple adaptive resonance theory neural networks and group decision expert system

USGS Publications Warehouse

Chang, H.-C.; Kopaska-Merkel, D. C.; Chen, H.-C.; Rocky, Durrans S.

2000-01-01

Lithofacies identification supplies qualitative information about rocks. Lithofacies represent rock textures and are important components of hydrocarbon reservoir description. Traditional techniques of lithofacies identification from core data are costly and different geologists may provide different interpretations. In this paper, we present a low-cost intelligent system consisting of three adaptive resonance theory neural networks and a rule-based expert system to consistently and objectively identify lithofacies from well-log data. The input data are altered into different forms representing different perspectives of observation of lithofacies. Each form of input is processed by a different adaptive resonance theory neural network. Among these three adaptive resonance theory neural networks, one neural network processes the raw continuous data, another processes categorial data, and the third processes fuzzy-set data. Outputs from these three networks are then combined by the expert system using fuzzy inference to determine to which facies the input data should be assigned. Rules are prioritized to emphasize the importance of firing order. This new approach combines the learning ability of neural networks, the adaptability of fuzzy logic, and the expertise of geologists to infer facies of the rocks. This approach is applied to the Appleton Field, an oil field located in Escambia County, Alabama. The hybrid intelligence system predicts lithofacies identity from log data with 87.6% accuracy. This prediction is more accurate than those of single adaptive resonance theory networks, 79.3%, 68.0% and 66.0%, using raw, fuzzy-set, and categorical data, respectively, and by an error-backpropagation neural network, 57.3%. (C) 2000 Published by Elsevier Science Ltd. All rights reserved.

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.

StakeMeter: Value-Based Stakeholder Identification and Quantification Framework for Value-Based Software Systems

PubMed Central

Babar, Muhammad Imran; Ghazali, Masitah; Jawawi, Dayang N. A.; Zaheer, Kashif Bin

2015-01-01

Value-based requirements engineering plays a vital role in the development of value-based software (VBS). Stakeholders are the key players in the requirements engineering process, and the selection of critical stakeholders for the VBS systems is highly desirable. Based on the stakeholder requirements, the innovative or value-based idea is realized. The quality of the VBS system is associated with the concrete set of valuable requirements, and the valuable requirements can only be obtained if all the relevant valuable stakeholders participate in the requirements elicitation phase. The existing value-based approaches focus on the design of the VBS systems. However, the focus on the valuable stakeholders and requirements is inadequate. The current stakeholder identification and quantification (SIQ) approaches are neither state-of-the-art nor systematic for the VBS systems. The existing approaches are time-consuming, complex and inconsistent which makes the initiation process difficult. Moreover, the main motivation of this research is that the existing SIQ approaches do not provide the low level implementation details for SIQ initiation and stakeholder metrics for quantification. Hence, keeping in view the existing SIQ problems, this research contributes in the form of a new SIQ framework called ‘StakeMeter’. The StakeMeter framework is verified and validated through case studies. The proposed framework provides low-level implementation guidelines, attributes, metrics, quantification criteria and application procedure as compared to the other methods. The proposed framework solves the issues of stakeholder quantification or prioritization, higher time consumption, complexity, and process initiation. The framework helps in the selection of highly critical stakeholders for the VBS systems with less judgmental error. PMID:25799490

Mouse forward genetics in the study of the peripheral nervous system and human peripheral neuropathy

PubMed Central

Douglas, Darlene S.; Popko, Brian

2009-01-01

Forward genetics, the phenotype-driven approach to investigating gene identity and function, has a long history in mouse genetics. Random mutations in the mouse transcend bias about gene function and provide avenues towards unique discoveries. The study of the peripheral nervous system is no exception; from historical strains such as the trembler mouse, which led to the identification of PMP22 as a human disease gene causing multiple forms of peripheral neuropathy, to the more recent identification of the claw paw and sprawling mutations, forward genetics has long been a tool for probing the physiology, pathogenesis, and genetics of the PNS. Even as spontaneous and mutagenized mice continue to enable the identification of novel genes, provide allelic series for detailed functional studies, and generate models useful for clinical research, new methods, such as the piggyBac transposon, are being developed to further harness the power of forward genetics. PMID:18481175

An approximation theory for the identification of linear thermoelastic systems

NASA Technical Reports Server (NTRS)

Rosen, I. G.; Su, Chien-Hua Frank

1990-01-01

An abstract approximation framework and convergence theory for the identification of thermoelastic systems is developed. Starting from an abstract operator formulation consisting of a coupled second order hyperbolic equation of elasticity and first order parabolic equation for heat conduction, well-posedness is established using linear semigroup theory in Hilbert space, and a class of parameter estimation problems is then defined involving mild solutions. The approximation framework is based upon generic Galerkin approximation of the mild solutions, and convergence of solutions of the resulting sequence of approximating finite dimensional parameter identification problems to a solution of the original infinite dimensional inverse problem is established using approximation results for operator semigroups. An example involving the basic equations of one dimensional linear thermoelasticity and a linear spline based scheme are discussed. Numerical results indicate how the approach might be used in a study of damping mechanisms in flexible structures.

Expert system for identification of simultaneous and sequential reactor fuel failures with gas tagging

DOEpatents

Gross, Kenny C.

1994-01-01

Failure of a fuel element in a nuclear reactor core is determined by a gas tagging failure detection system and method. Failures are catalogued and characterized after the event so that samples of the reactor's cover gas are taken at regular intervals and analyzed by mass spectroscopy. Employing a first set of systematic heuristic rules which are applied in a transformed node space allows the number of node combinations which must be processed within a barycentric algorithm to be substantially reduced. A second set of heuristic rules treats the tag nodes of the most recent one or two leakers as "background" gases, further reducing the number of trial node combinations. Lastly, a "fuzzy" set theory formalism minimizes experimental uncertainties in the identification of the most likely volumes of tag gases. This approach allows for the identification of virtually any number of sequential leaks and up to five simultaneous gas leaks from fuel elements.

MULTI-DIMENSIONAL MASS SPECTROMETRY-BASED SHOTGUN LIPIDOMICS AND NOVEL STRATEGIES FOR LIPIDOMIC ANALYSES

PubMed Central

Han, Xianlin; Yang, Kui; Gross, Richard W.

2011-01-01

Since our last comprehensive review on multi-dimensional mass spectrometry-based shotgun lipidomics (Mass Spectrom. Rev. 24 (2005), 367), many new developments in the field of lipidomics have occurred. These developments include new strategies and refinements for shotgun lipidomic approaches that use direct infusion, including novel fragmentation strategies, identification of multiple new informative dimensions for mass spectrometric interrogation, and the development of new bioinformatic approaches for enhanced identification and quantitation of the individual molecular constituents that comprise each cell’s lipidome. Concurrently, advances in liquid chromatography-based platforms and novel strategies for quantitative matrix-assisted laser desorption/ionization mass spectrometry for lipidomic analyses have been developed. Through the synergistic use of this repertoire of new mass spectrometric approaches, the power and scope of lipidomics has been greatly expanded to accelerate progress toward the comprehensive understanding of the pleiotropic roles of lipids in biological systems. PMID:21755525

Microfluidic microscopy-assisted label-free approach for cancer screening: automated microfluidic cytology for cancer screening.

PubMed

Jagannadh, Veerendra Kalyan; Gopakumar, G; Subrahmanyam, Gorthi R K Sai; Gorthi, Sai Siva

2017-05-01

Each year, about 7-8 million deaths occur due to cancer around the world. More than half of the cancer-related deaths occur in the less-developed parts of the world. Cancer mortality rate can be reduced with early detection and subsequent treatment of the disease. In this paper, we introduce a microfluidic microscopy-based cost-effective and label-free approach for identification of cancerous cells. We outline a diagnostic framework for the same and detail an instrumentation layout. We have employed classical computer vision techniques such as 2D principal component analysis-based cell type representation followed by support vector machine-based classification. Analogous to criminal face recognition systems implemented with help of surveillance cameras, a signature-based approach for cancerous cell identification using microfluidic microscopy surveillance is demonstrated. Such a platform would facilitate affordable mass screening camps in the developing countries and therefore help decrease cancer mortality rate.

Automated identification of Monogeneans using digital image processing and K-nearest neighbour approaches.

PubMed

Yousef Kalafi, Elham; Tan, Wooi Boon; Town, Christopher; Dhillon, Sarinder Kaur

2016-12-22

Monogeneans are flatworms (Platyhelminthes) that are primarily found on gills and skin of fishes. Monogenean parasites have attachment appendages at their haptoral regions that help them to move about the body surface and feed on skin and gill debris. Haptoral attachment organs consist of sclerotized hard parts such as hooks, anchors and marginal hooks. Monogenean species are differentiated based on their haptoral bars, anchors, marginal hooks, reproductive parts' (male and female copulatory organs) morphological characters and soft anatomical parts. The complex structure of these diagnostic organs and also their overlapping in microscopic digital images are impediments for developing fully automated identification system for monogeneans (LNCS 7666:256-263, 2012), (ISDA; 457-462, 2011), (J Zoolog Syst Evol Res 52(2): 95-99. 2013;). In this study images of hard parts of the haptoral organs such as bars and anchors are used to develop a fully automated identification technique for monogenean species identification by implementing image processing techniques and machine learning methods. Images of four monogenean species namely Sinodiplectanotrema malayanus, Trianchoratus pahangensis, Metahaliotrema mizellei and Metahaliotrema sp. (undescribed) were used to develop an automated technique for identification. K-nearest neighbour (KNN) was applied to classify the monogenean specimens based on the extracted features. 50% of the dataset was used for training and the other 50% was used as testing for system evaluation. Our approach demonstrated overall classification accuracy of 90%. In this study Leave One Out (LOO) cross validation is used for validation of our system and the accuracy is 91.25%. The methods presented in this study facilitate fast and accurate fully automated classification of monogeneans at the species level. In future studies more classes will be included in the model, the time to capture the monogenean images will be reduced and improvements in extraction and selection of features will be implemented.

A unified framework for evaluating the risk of re-identification of text de-identification tools.

PubMed

Scaiano, Martin; Middleton, Grant; Arbuckle, Luk; Kolhatkar, Varada; Peyton, Liam; Dowling, Moira; Gipson, Debbie S; El Emam, Khaled

2016-10-01

It has become regular practice to de-identify unstructured medical text for use in research using automatic methods, the goal of which is to remove patient identifying information to minimize re-identification risk. The metrics commonly used to determine if these systems are performing well do not accurately reflect the risk of a patient being re-identified. We therefore developed a framework for measuring the risk of re-identification associated with textual data releases. We apply the proposed evaluation framework to a data set from the University of Michigan Medical School. Our risk assessment results are then compared with those that would be obtained using a typical contemporary micro-average evaluation of recall in order to illustrate the difference between the proposed evaluation framework and the current baseline method. We demonstrate how this framework compares against common measures of the re-identification risk associated with an automated text de-identification process. For the probability of re-identification using our evaluation framework we obtained a mean value for direct identifiers of 0.0074 and a mean value for quasi-identifiers of 0.0022. The 95% confidence interval for these estimates were below the relevant thresholds. The threshold for direct identifier risk was based on previously used approaches in the literature. The threshold for quasi-identifiers was determined based on the context of the data release following commonly used de-identification criteria for structured data. Our framework attempts to correct for poorly distributed evaluation corpora, accounts for the data release context, and avoids the often optimistic assumptions that are made using the more traditional evaluation approach. It therefore provides a more realistic estimate of the true probability of re-identification. This framework should be used as a basis for computing re-identification risk in order to more realistically evaluate future text de-identification tools. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

CATSI EDM: recent advances in the development and validation of a ruggedized passive standoff CWA sensor

NASA Astrophysics Data System (ADS)

Lavoie, Hugo; Thériault, Jean-Marc; Bouffard, François; Puckrin, Eldon; Turcotte, Caroline S.; Lacasse, Paul

2008-04-01

Defence Research and Development Canada (DRDC) - Valcartier is currently developing a ruggedized passive standoff sensor for the detection of chemical warfare agents (CWAs) based on differential Fourier-transform infrared (FTIR) radiometry. This system is referred to as the Compact ATmospheric Sounding Interferometer (CATSI) Engineering Development Model (EDM). The CATSI EDM sensor is based on the use of a double-beam FTIR spectrometer that is optimized for optical subtraction. A description of the customized sensor is given along with a discussion on the detection and identification approaches that have been developed. Preliminary results of validation from a number of laboratory measurements and open-air trials are analyzed to establish the capability of detection and identification of various toxic and non-toxic chemical vapor plumes. These results clearly demonstrate the capability of the passive differential radiometric approach for the standoff detection and identification of chemical vapors at distances up to a few kilometers from the sensor.

Estimating False Positive Contamination in Crater Annotations from Citizen Science Data

NASA Astrophysics Data System (ADS)

Tar, P. D.; Bugiolacchi, R.; Thacker, N. A.; Gilmour, J. D.

2017-01-01

Web-based citizen science often involves the classification of image features by large numbers of minimally trained volunteers, such as the identification of lunar impact craters under the Moon Zoo project. Whilst such approaches facilitate the analysis of large image data sets, the inexperience of users and ambiguity in image content can lead to contamination from false positive identifications. We give an approach, using Linear Poisson Models and image template matching, that can quantify levels of false positive contamination in citizen science Moon Zoo crater annotations. Linear Poisson Models are a form of machine learning which supports predictive error modelling and goodness-of-fits, unlike most alternative machine learning methods. The proposed supervised learning system can reduce the variability in crater counts whilst providing predictive error assessments of estimated quantities of remaining true verses false annotations. In an area of research influenced by human subjectivity, the proposed method provides a level of objectivity through the utilisation of image evidence, guided by candidate crater identifications.

HAZARD ASSESSMENT OF METALS AND METAL COMPOUNDS IN TERRESTRIAL SYSTEMS

EPA Science Inventory

Metal accumulation in soil can result in adverse effects on soil biota, and may concentrate metals in food chains to levels detrimental to humans and wildlife. A SETAC Pellston Workshop entitled " Hazard Identification Approach For Metals And Inorganic Metal Substances" examined...

Persons with Diet-Related Diseases.

ERIC Educational Resources Information Center

McNutt, Kristen W.; Steinberg, Louis H.

1980-01-01

This article focuses on the educational approach to dealing with people with diet related diseases, their prevention, detection, and treatment. Issues include content and goals of education, identification of factors affecting food choices, professional education improvement, coordination of nutrition education systems, and nutrition concerns. (SA)

Acoustic-tactile rendering of visual information

NASA Astrophysics Data System (ADS)

Silva, Pubudu Madhawa; Pappas, Thrasyvoulos N.; Atkins, Joshua; West, James E.; Hartmann, William M.

2012-03-01

In previous work, we have proposed a dynamic, interactive system for conveying visual information via hearing and touch. The system is implemented with a touch screen that allows the user to interrogate a two-dimensional (2-D) object layout by active finger scanning while listening to spatialized auditory feedback. Sound is used as the primary source of information for object localization and identification, while touch is used both for pointing and for kinesthetic feedback. Our previous work considered shape and size perception of simple objects via hearing and touch. The focus of this paper is on the perception of a 2-D layout of simple objects with identical size and shape. We consider the selection and rendition of sounds for object identification and localization. We rely on the head-related transfer function for rendering sound directionality, and consider variations of sound intensity and tempo as two alternative approaches for rendering proximity. Subjective experiments with visually-blocked subjects are used to evaluate the effectiveness of the proposed approaches. Our results indicate that intensity outperforms tempo as a proximity cue, and that the overall system for conveying a 2-D layout is quite promising.

Space shuttle flying qualities and criteria assessment

NASA Technical Reports Server (NTRS)

Myers, T. T.; Johnston, D. E.; Mcruer, Duane T.

1987-01-01

Work accomplished under a series of study tasks for the Flying Qualities and Flight Control Systems Design Criteria Experiment (OFQ) of the Shuttle Orbiter Experiments Program (OEX) is summarized. The tasks involved review of applicability of existing flying quality and flight control system specification and criteria for the Shuttle; identification of potentially crucial flying quality deficiencies; dynamic modeling of the Shuttle Orbiter pilot/vehicle system in the terminal flight phases; devising a nonintrusive experimental program for extraction and identification of vehicle dynamics, pilot control strategy, and approach and landing performance metrics, and preparation of an OEX approach to produce a data archive and optimize use of the data to develop flying qualities for future space shuttle craft in general. Analytic modeling of the Orbiter's unconventional closed-loop dynamics in landing, modeling pilot control strategies, verification of vehicle dynamics and pilot control strategy from flight data, review of various existent or proposed aircraft flying quality parameters and criteria in comparison with the unique dynamic characteristics and control aspects of the Shuttle in landing; and finally a summary of conclusions and recommendations for developing flying quality criteria and design guides for future Shuttle craft.

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.

Forensic botany: species identification of botanical trace evidence using a multigene barcoding approach.

PubMed

Ferri, Gianmarco; Alù, Milena; Corradini, Beatrice; Beduschi, Giovanni

2009-09-01

Forensic botany can provide significant supporting evidence during criminal investigations. However, it is still an underutilized field of investigation with its most common application limited to identifying specific as well as suspected illegal plants. The ubiquitous presence of plant species can be useful in forensics, but the absence of an accurate identification system remains the major obstacle to the present inability to routinely and correctly identify trace botanical evidence. Many plant materials cannot be identified and differentiated to the species level by traditional morphological characteristics when botanical specimens are degraded and lack physical features. By taking advantage of a universal barcode system, DNA sequencing, and other biomolecular techniques used routinely in forensic investigations, two chloroplast DNA regions were evaluated for their use as "barcoding" markers for plant identification in the field of forensics. We therefore investigated the forensic use of two non-coding plastid regions, psbA-trnH and trnL-trnF, to create a multimarker system for species identification that could be useful throughout the plant kingdom. The sequences from 63 plants belonging to our local flora were submitted and registered on the GenBank database. Sequence comparison to set up the level of identification (species, genus, or family) through Blast algorithms allowed us to assess the suitability of this method. The results confirmed the effectiveness of our botanic universal multimarker assay in forensic investigations.

Gaussian mixture model based identification of arterial wall movement for computation of distension waveform.

PubMed

Patil, Ravindra B; Krishnamoorthy, P; Sethuraman, Shriram

2015-01-01

This work proposes a novel Gaussian Mixture Model (GMM) based approach for accurate tracking of the arterial wall and subsequent computation of the distension waveform using Radio Frequency (RF) ultrasound signal. The approach was evaluated on ultrasound RF data acquired using a prototype ultrasound system from an artery mimicking flow phantom. The effectiveness of the proposed algorithm is demonstrated by comparing with existing wall tracking algorithms. The experimental results show that the proposed method provides 20% reduction in the error margin compared to the existing approaches in tracking the arterial wall movement. This approach coupled with ultrasound system can be used to estimate the arterial compliance parameters required for screening of cardiovascular related disorders.

Yeast Two-Hybrid: State of the Art

PubMed Central

Beyaert, Rudi

1999-01-01

Genome projects are approaching completion and are saturating sequence databases. This paper discusses the role of the two-hybrid system as a generator of hypotheses. Apart from this rather exhaustive, financially and labour intensive procedure, more refined functional studies can be undertaken. Indeed, by making hybrids of two-hybrid systems, customised approaches can be developed in order to attack specific function-related problems. For example, one could set-up a "differential" screen by combining a forward and a reverse approach in a three-hybrid set-up. Another very interesting project is the use of peptide libraries in two-hybrid approaches. This could enable the identification of peptides with very high specificity comparable to "real" antibodies. With the technology available, the only limitation is imagination. PMID:12734586

Systems analysis of urban wastewater systems--two systematic approaches to analyse a complex system.

PubMed

Benedetti, L; Blumensaat, F; Bönisch, G; Dirckx, G; Jardin, N; Krebs, P; Vanrolleghem, P A

2005-01-01

This work was aimed at performing an analysis of the integrated urban wastewater system (catchment area, sewer, WWTP, receiving water). It focused on analysing the substance fluxes going through the system to identify critical pathways of pollution, as well as assessing the effectiveness of energy consumption and operational/capital costs. Two different approaches were adopted in the study to analyse urban wastewater systems of diverse characteristics. In the first approach a wide ranged analysis of a system at river basin scale is applied. The Nete river basin in Belgium, a tributary of the Schelde, was analysed through the 29 sewer catchments constituting the basin. In the second approach a more detailed methodology was developed to separately analyse two urban wastewater systems situated within the Ruhr basin (Germany) on a river stretch scale. The paper mainly focuses on the description of the method applied. Only the most important results are presented. The main outcomes of these studies are: the identification of stressors on the receiving water bodies, an extensive benchmarking of wastewater systems, and the evidence of the scale dependency of results in such studies.

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.

An odor identification approach based on event-related pupil dilation and gaze focus.

PubMed

Aguillon-Hernandez, Nadia; Naudin, Marine; Roché, Laëtitia; Bonnet-Brilhault, Frédérique; Belzung, Catherine; Martineau, Joëlle; Atanasova, Boriana

2015-06-01

Olfactory disorders constitute a potential marker of many diseases and are considered valuable clues to the diagnosis and evaluation of progression for many disorders. The most commonly used test for the evaluation of impairments of olfactory identification requires the active participation of the subject, who must select the correct name of the perceived odor from a list. An alternative method is required because speech may be impaired or not yet learned in many patients. As odor identification is known to be facilitated by searching for visual clues, we aimed to develop an objective, vision-based approach for the evaluation of odor identification. We used an eye tracking method to quantify pupillary and ocular responses during the simultaneous presentation of olfactory and visual stimuli, in 39 healthy participants aged from 19 to 77years. Odor presentation triggered an increase in pupil dilation and gaze focus on the picture corresponding to the odor presented. These results suggest that odorant stimuli increase recruitment of the sympathetic system (as demonstrated by the reactivity of the pupil) and draw attention to the visual clue. These results validate the objectivity of this method. Copyright © 2015 Elsevier B.V. All rights reserved.

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.

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.

Semi-automatic 10/20 Identification Method for MRI-Free Probe Placement in Transcranial Brain Mapping Techniques.

PubMed

Xiao, Xiang; Zhu, Hao; Liu, Wei-Jie; Yu, Xiao-Ting; Duan, Lian; Li, Zheng; Zhu, Chao-Zhe

2017-01-01

The International 10/20 system is an important head-surface-based positioning system for transcranial brain mapping techniques, e.g., fNIRS and TMS. As guidance for probe placement, the 10/20 system permits both proper ROI coverage and spatial consistency among multiple subjects and experiments in a MRI-free context. However, the traditional manual approach to the identification of 10/20 landmarks faces problems in reliability and time cost. In this study, we propose a semi-automatic method to address these problems. First, a novel head surface reconstruction algorithm reconstructs head geometry from a set of points uniformly and sparsely sampled on the subject's head. Second, virtual 10/20 landmarks are determined on the reconstructed head surface in computational space. Finally, a visually-guided real-time navigation system guides the experimenter to each of the identified 10/20 landmarks on the physical head of the subject. Compared with the traditional manual approach, our proposed method provides a significant improvement both in reliability and time cost and thus could contribute to improving both the effectiveness and efficiency of 10/20-guided MRI-free probe placement.

The UAB Informatics Institute and 2016 CEGS N-GRID de-identification shared task challenge.

PubMed

Bui, Duy Duc An; Wyatt, Mathew; Cimino, James J

2017-11-01

Clinical narratives (the text notes found in patients' medical records) are important information sources for secondary use in research. However, in order to protect patient privacy, they must be de-identified prior to use. Manual de-identification is considered to be the gold standard approach but is tedious, expensive, slow, and impractical for use with large-scale clinical data. Automated or semi-automated de-identification using computer algorithms is a potentially promising alternative. The Informatics Institute of the University of Alabama at Birmingham is applying de-identification to clinical data drawn from the UAB hospital's electronic medical records system before releasing them for research. We participated in a shared task challenge by the Centers of Excellence in Genomic Science (CEGS) Neuropsychiatric Genome-Scale and RDoC Individualized Domains (N-GRID) at the de-identification regular track to gain experience developing our own automatic de-identification tool. We focused on the popular and successful methods from previous challenges: rule-based, dictionary-matching, and machine-learning approaches. We also explored new techniques such as disambiguation rules, term ambiguity measurement, and used multi-pass sieve framework at a micro level. For the challenge's primary measure (strict entity), our submissions achieved competitive results (f-measures: 87.3%, 87.1%, and 86.7%). For our preferred measure (binary token HIPAA), our submissions achieved superior results (f-measures: 93.7%, 93.6%, and 93%). With those encouraging results, we gain the confidence to improve and use the tool for the real de-identification task at the UAB Informatics Institute. Copyright © 2017 Elsevier Inc. All rights reserved.

Ultrabroadband phased-array radio frequency (RF) receivers based on optical techniques

NASA Astrophysics Data System (ADS)

Overmiller, Brock M.; Schuetz, Christopher A.; Schneider, Garrett; Murakowski, Janusz; Prather, Dennis W.

2014-03-01

Military operations require the ability to locate and identify electronic emissions in the battlefield environment. However, recent developments in radio detection and ranging (RADAR) and communications technology are making it harder to effectively identify such emissions. Phased array systems aid in discriminating emitters in the scene by virtue of their relatively high-gain beam steering and nulling capabilities. For the purpose of locating emitters, we present an approach realize a broadband receiver based on optical processing techniques applied to the response of detectors in conformal antenna arrays. This approach utilizes photonic techniques that enable us to capture, route, and process the incoming signals. Optical modulators convert the incoming signals up to and exceeding 110 GHz with appreciable conversion efficiency and route these signals via fiber optics to a central processing location. This central processor consists of a closed loop phase control system which compensates for phase fluctuations induced on the fibers due to thermal or acoustic vibrations as well as an optical heterodyne approach for signal conversion down to baseband. Our optical heterodyne approach uses injection-locked paired optical sources to perform heterodyne downconversion/frequency identification of the detected emission. Preliminary geolocation and frequency identification testing of electronic emissions has been performed demonstrating the capabilities of our RF receiver.

Robust Fault Detection and Isolation for Stochastic Systems

NASA Technical Reports Server (NTRS)

George, Jemin; Gregory, Irene M.

2010-01-01

This paper outlines the formulation of a robust fault detection and isolation scheme that can precisely detect and isolate simultaneous actuator and sensor faults for uncertain linear stochastic systems. The given robust fault detection scheme based on the discontinuous robust observer approach would be able to distinguish between model uncertainties and actuator failures and therefore eliminate the problem of false alarms. Since the proposed approach involves precise reconstruction of sensor faults, it can also be used for sensor fault identification and the reconstruction of true outputs from faulty sensor outputs. Simulation results presented here validate the effectiveness of the robust fault detection and isolation system.

Prospective Evaluation of a Matrix-Assisted Laser Desorption Ionization–Time of Flight Mass Spectrometry System in a Hospital Clinical Microbiology Laboratory for Identification of Bacteria and Yeasts: a Bench-by-Bench Study for Assessing the Impact on Time to Identification and Cost-Effectiveness

PubMed Central

Tan, K. E.; Ellis, B. C.; Lee, R.; Stamper, P. D.; Zhang, S. X.

2012-01-01

Matrix-assisted laser desorption ionization–time of flight mass spectrometry (MALDI-TOF MS) has been found to be an accurate, rapid, and inexpensive method for the identification of bacteria and yeasts. Previous evaluations have compared the accuracy, time to identification, and costs of the MALDI-TOF MS method against standard identification systems or commercial panels. In this prospective study, we compared a protocol incorporating MALDI-TOF MS (MALDI protocol) with the current standard identification protocols (standard protocol) to determine the performance in actual practice using a specimen-based, bench-by-bench approach. The potential impact on time to identification (TTI) and costs had MALDI-TOF MS been the first-line identification method was quantitated. The MALDI protocol includes supplementary tests, notably for Streptococcus pneumoniae and Shigella, and indications for repeat MALDI-TOF MS attempts, often not measured in previous studies. A total of 952 isolates (824 bacterial isolates and 128 yeast isolates) recovered from 2,214 specimens were assessed using the MALDI protocol. Compared with standard protocols, the MALDI protocol provided identifications 1.45 days earlier on average (P < 0.001). In our laboratory, we anticipate that the incorporation of the MALDI protocol can reduce reagent and labor costs of identification by $102,424 or 56.9% within 12 months. The model included the fixed annual costs of the MALDI-TOF MS, such as the cost of protein standards and instrument maintenance, and the annual prevalence of organisms encountered in our laboratory. This comprehensive cost analysis model can be generalized to other moderate- to high-volume laboratories. PMID:22855510

Prospective evaluation of a matrix-assisted laser desorption ionization-time of flight mass spectrometry system in a hospital clinical microbiology laboratory for identification of bacteria and yeasts: a bench-by-bench study for assessing the impact on time to identification and cost-effectiveness.

PubMed

Tan, K E; Ellis, B C; Lee, R; Stamper, P D; Zhang, S X; Carroll, K C

2012-10-01

Matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) has been found to be an accurate, rapid, and inexpensive method for the identification of bacteria and yeasts. Previous evaluations have compared the accuracy, time to identification, and costs of the MALDI-TOF MS method against standard identification systems or commercial panels. In this prospective study, we compared a protocol incorporating MALDI-TOF MS (MALDI protocol) with the current standard identification protocols (standard protocol) to determine the performance in actual practice using a specimen-based, bench-by-bench approach. The potential impact on time to identification (TTI) and costs had MALDI-TOF MS been the first-line identification method was quantitated. The MALDI protocol includes supplementary tests, notably for Streptococcus pneumoniae and Shigella, and indications for repeat MALDI-TOF MS attempts, often not measured in previous studies. A total of 952 isolates (824 bacterial isolates and 128 yeast isolates) recovered from 2,214 specimens were assessed using the MALDI protocol. Compared with standard protocols, the MALDI protocol provided identifications 1.45 days earlier on average (P < 0.001). In our laboratory, we anticipate that the incorporation of the MALDI protocol can reduce reagent and labor costs of identification by $102,424 or 56.9% within 12 months. The model included the fixed annual costs of the MALDI-TOF MS, such as the cost of protein standards and instrument maintenance, and the annual prevalence of organisms encountered in our laboratory. This comprehensive cost analysis model can be generalized to other moderate- to high-volume laboratories.

Laboratory services series: a programmed maintenance system

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

Tuxbury, D.C.; Srite, B.E.

1980-01-01

The diverse facilities, operations and equipment at a major national research and development laboratory require a systematic, analytical approach to operating equipment maintenance. A computer-scheduled preventive maintenance program is described including program development, equipment identification, maintenance and inspection instructions, scheduling, personnel, and equipment history.

EXPERIMENTAL APPROACHES TO EVALUATING THE ROLE OF ENVIRONMENTAL FACTORS IN THE DEVELOPMENT OF CARDIOVASCULAR DISEASE

EPA Science Inventory

Epidemiologic studies have suggested factors in drinking water influence on the human cardiovascular system. A clear identification of the factors involved requires more invasive techniques and more strict experimental controls than can usually be applied in epidemiologic studies...

Methods for assessment of stream-related hazards to highways and bridges.

DOT National Transportation Integrated Search

1981-03-01

particular river reach, but also on the behavior of the entire fluvial system of which it is a part. Rivers are complex landforms. A simple and straight forward approach to the identification of river hazards is not always possible. A complete evalua...

Mechanism-based testing strategy using in vitro approaches for identification of thyroid hormone disrupting chemicals

EPA Science Inventory

The thyroid hormone (TH) system is involved in several important physiological processes, including regulation of energy metabolism, growth and differentiation, development and maintenance of brain function, thermo-regulation, osmo-regulation, and axis of regulation of other endo...

Redesign of the Stabilized Pitch Control System of a Semi-Active Terminal Homing Missile System.

DTIC Science & Technology

1979-04-20

34 AIEE Trans. Application and Industry , pp. 65-77, May 1961. [3] L. S. Shieh, "An Algebraic Approach to System Identification and Compensator Design...34A Quick Method for Estimating Closed-Loop Poles of Control Systems," Trans. AIEE, Applications and Industry , Vol. 76, pp. 80-87, May 1957. [101 C...Mathe- matical and Statistical Library). [16] C. J. Huang and L. S. Shieh, "Modeling Large Dynamical Systems with industrial Specifications," Int. J

Integration between terrestrial-based and satellite-based land mobile communications systems

NASA Technical Reports Server (NTRS)

Arcidiancono, Antonio

1990-01-01

A survey is given of several approaches to improving the performance and marketability of mobile satellite systems (MSS). The provision of voice/data services in the future regional European Land Mobile Satellite System (LMSS), network integration between the Digital Cellular Mobile System (GSM) and LMSS, the identification of critical areas for the implementation of integrated GSM/LMSS areas, space segment scenarios, LMSS for digital trunked private mobile radio (PMR) services, and code division multiple access (CDMA) techniques for a terrestrial/satellite system are covered.

Identifiability and Performance Analysis of Output Over-sampling Approach to Direct Closed-loop Identification

NASA Astrophysics Data System (ADS)

Sun, Lianming; Sano, Akira

Output over-sampling based closed-loop identification algorithm is investigated in this paper. Some instinct properties of the continuous stochastic noise and the plant input, output in the over-sampling approach are analyzed, and they are used to demonstrate the identifiability in the over-sampling approach and to evaluate its identification performance. Furthermore, the selection of plant model order, the asymptotic variance of estimated parameters and the asymptotic variance of frequency response of the estimated model are also explored. It shows that the over-sampling approach can guarantee the identifiability and improve the performance of closed-loop identification greatly.

An analytic modeling and system identification study of rotor/fuselage dynamics at hover

NASA Technical Reports Server (NTRS)

Hong, Steven W.; Curtiss, H. C., Jr.

1993-01-01

A combination of analytic modeling and system identification methods have been used to develop an improved dynamic model describing the response of articulated rotor helicopters to control inputs. A high-order linearized model of coupled rotor/body dynamics including flap and lag degrees of freedom and inflow dynamics with literal coefficients is compared to flight test data from single rotor helicopters in the near hover trim condition. The identification problem was formulated using the maximum likelihood function in the time domain. The dynamic model with literal coefficients was used to generate the model states, and the model was parametrized in terms of physical constants of the aircraft rather than the stability derivatives resulting in a significant reduction in the number of quantities to be identified. The likelihood function was optimized using the genetic algorithm approach. This method proved highly effective in producing an estimated model from flight test data which included coupled fuselage/rotor dynamics. Using this approach it has been shown that blade flexibility is a significant contributing factor to the discrepancies between theory and experiment shown in previous studies. Addition of flexible modes, properly incorporating the constraint due to the lag dampers, results in excellent agreement between flight test and theory, especially in the high frequency range.

The RiverFish Approach to Business Process Modeling: Linking Business Steps to Control-Flow Patterns

NASA Astrophysics Data System (ADS)

Zuliane, Devanir; Oikawa, Marcio K.; Malkowski, Simon; Alcazar, José Perez; Ferreira, João Eduardo

Despite the recent advances in the area of Business Process Management (BPM), today’s business processes have largely been implemented without clearly defined conceptual modeling. This results in growing difficulties for identification, maintenance, and reuse of rules, processes, and control-flow patterns. To mitigate these problems in future implementations, we propose a new approach to business process modeling using conceptual schemas, which represent hierarchies of concepts for rules and processes shared among collaborating information systems. This methodology bridges the gap between conceptual model description and identification of actual control-flow patterns for workflow implementation. We identify modeling guidelines that are characterized by clear phase separation, step-by-step execution, and process building through diagrams and tables. The separation of business process modeling in seven mutually exclusive phases clearly delimits information technology from business expertise. The sequential execution of these phases leads to the step-by-step creation of complex control-flow graphs. The process model is refined through intuitive table and diagram generation in each phase. Not only does the rigorous application of our modeling framework minimize the impact of rule and process changes, but it also facilitates the identification and maintenance of control-flow patterns in BPM-based information system architectures.

An analytic modeling and system identification study of rotor/fuselage dynamics at hover

NASA Technical Reports Server (NTRS)

Hong, Steven W.; Curtiss, H. C., Jr.

1993-01-01

A combination of analytic modeling and system identification methods have been used to develop an improved dynamic model describing the response of articulated rotor helicopters to control inputs. A high-order linearized model of coupled rotor/body dynamics including flap and lag degrees of freedom and inflow dynamics with literal coefficients is compared to flight test data from single rotor helicopters in the near hover trim condition. The identification problem was formulated using the maximum likelihood function in the time domain. The dynamic model with literal coefficients was used to generate the model states, and the model was parametrized in terms of physical constants of the aircraft rather than the stability derivatives, resulting in a significant reduction in the number of quantities to be identified. The likelihood function was optimized using the genetic algorithm approach. This method proved highly effective in producing an estimated model from flight test data which included coupled fuselage/rotor dynamics. Using this approach it has been shown that blade flexibility is a significant contributing factor to the discrepancies between theory and experiment shown in previous studies. Addition of flexible modes, properly incorporating the constraint due to the lag dampers, results in excellent agreement between flight test and theory, especially in the high frequency range.

A Pareto-optimal moving average multigene genetic programming model for daily streamflow prediction

NASA Astrophysics Data System (ADS)

Danandeh Mehr, Ali; Kahya, Ercan

2017-06-01

Genetic programming (GP) is able to systematically explore alternative model structures of different accuracy and complexity from observed input and output data. The effectiveness of GP in hydrological system identification has been recognized in recent studies. However, selecting a parsimonious (accurate and simple) model from such alternatives still remains a question. This paper proposes a Pareto-optimal moving average multigene genetic programming (MA-MGGP) approach to develop a parsimonious model for single-station streamflow prediction. The three main components of the approach that take us from observed data to a validated model are: (1) data pre-processing, (2) system identification and (3) system simplification. The data pre-processing ingredient uses a simple moving average filter to diminish the lagged prediction effect of stand-alone data-driven models. The multigene ingredient of the model tends to identify the underlying nonlinear system with expressions simpler than classical monolithic GP and, eventually simplification component exploits Pareto front plot to select a parsimonious model through an interactive complexity-efficiency trade-off. The approach was tested using the daily streamflow records from a station on Senoz Stream, Turkey. Comparing to the efficiency results of stand-alone GP, MGGP, and conventional multi linear regression prediction models as benchmarks, the proposed Pareto-optimal MA-MGGP model put forward a parsimonious solution, which has a noteworthy importance of being applied in practice. In addition, the approach allows the user to enter human insight into the problem to examine evolved models and pick the best performing programs out for further analysis.

DyHAP: Dynamic Hybrid ANFIS-PSO Approach for Predicting Mobile Malware.

PubMed

Afifi, Firdaus; Anuar, Nor Badrul; Shamshirband, Shahaboddin; Choo, Kim-Kwang Raymond

2016-01-01

To deal with the large number of malicious mobile applications (e.g. mobile malware), a number of malware detection systems have been proposed in the literature. In this paper, we propose a hybrid method to find the optimum parameters that can be used to facilitate mobile malware identification. We also present a multi agent system architecture comprising three system agents (i.e. sniffer, extraction and selection agent) to capture and manage the pcap file for data preparation phase. In our hybrid approach, we combine an adaptive neuro fuzzy inference system (ANFIS) and particle swarm optimization (PSO). Evaluations using data captured on a real-world Android device and the MalGenome dataset demonstrate the effectiveness of our approach, in comparison to two hybrid optimization methods which are differential evolution (ANFIS-DE) and ant colony optimization (ANFIS-ACO).

DyHAP: Dynamic Hybrid ANFIS-PSO Approach for Predicting Mobile Malware

PubMed Central

Afifi, Firdaus; Anuar, Nor Badrul; Shamshirband, Shahaboddin

2016-01-01

To deal with the large number of malicious mobile applications (e.g. mobile malware), a number of malware detection systems have been proposed in the literature. In this paper, we propose a hybrid method to find the optimum parameters that can be used to facilitate mobile malware identification. We also present a multi agent system architecture comprising three system agents (i.e. sniffer, extraction and selection agent) to capture and manage the pcap file for data preparation phase. In our hybrid approach, we combine an adaptive neuro fuzzy inference system (ANFIS) and particle swarm optimization (PSO). Evaluations using data captured on a real-world Android device and the MalGenome dataset demonstrate the effectiveness of our approach, in comparison to two hybrid optimization methods which are differential evolution (ANFIS-DE) and ant colony optimization (ANFIS-ACO). PMID:27611312

Decision support systems in water and wastewater treatment process selection and design: a review.

PubMed

Hamouda, M A; Anderson, W B; Huck, P M

2009-01-01

The continuously changing drivers of the water treatment industry, embodied by rigorous environmental and health regulations and the challenge of emerging contaminants, necessitates the development of decision support systems for the selection of appropriate treatment trains. This paper explores a systematic approach to developing decision support systems, which includes the analysis of the treatment problem(s), knowledge acquisition and representation, and the identification and evaluation of criteria controlling the selection of optimal treatment systems. The objective of this article is to review approaches and methods used in decision support systems developed to aid in the selection, sequencing of unit processes and design of drinking water, domestic wastewater, and industrial wastewater treatment systems. Not surprisingly, technical considerations were found to dominate the logic of the developed systems. Most of the existing decision-support tools employ heuristic knowledge. It has been determined that there is a need to develop integrated decision support systems that are generic, usable and consider a system analysis approach.

Improving the recognition of fingerprint biometric system using enhanced image fusion

NASA Astrophysics Data System (ADS)

Alsharif, Salim; El-Saba, Aed; Stripathi, Reshma

2010-04-01

Fingerprints recognition systems have been widely used by financial institutions, law enforcement, border control, visa issuing, just to mention few. Biometric identifiers can be counterfeited, but considered more reliable and secure compared to traditional ID cards or personal passwords methods. Fingerprint pattern fusion improves the performance of a fingerprint recognition system in terms of accuracy and security. This paper presents digital enhancement and fusion approaches that improve the biometric of the fingerprint recognition system. It is a two-step approach. In the first step raw fingerprint images are enhanced using high-frequency-emphasis filtering (HFEF). The second step is a simple linear fusion process between the raw images and the HFEF ones. It is shown that the proposed approach increases the verification and identification of the fingerprint biometric recognition system, where any improvement is justified using the correlation performance metrics of the matching algorithm.

Designing Business System Model using System Modeling Approach to the Small and Medium Enterprises (SME) of Furniture in Indonesia

NASA Astrophysics Data System (ADS)

Sukendar, Irwan; Fatmawati, Wiwiek; Much Ibnu Subroto, Imam; Arigama, Rizki

2017-04-01

This paper studies the design of business system model with System Modeling Approach on small and medium enterprises (SMEs) of furniture. Methods used consists of five phases: phase of identification of business processes actual on SMEs of Furniture, phase of identification of deficiencies and improvement of business processes, phase of design algorithm and flowchart business processes, phase of analysis of the elements of the system, and phase of the design of data flow diagram (DFD), The results of the analysis of the elements of the system are: Products and quantities ordered product consumers and DP paid by consumers identified as elements of system inputs 1,2 and 3. The result of the calculation, payment slips and mail order (SO) are identified as elements of system output 1, 2 and 3. Acceptance of orders, stocks checking of raw materials at the warehouse, calculating raw material requirements, adequacy of raw materials, the price of the contract, and the due date, as well as the submission of the results of calculations to consumers were identified as elements of system components 1, 2, 3, and 4. Admin taking orders, Admin check stocks of raw materials at the warehouse, Admin making calculation, and Admin convey the results of calculations to consumers were identified as an element of interaction system 1, 2, 3, and 4. Consumers were identified as element of environmental systems. Moreover, the boundary between SMEs and consumers were identified as elements of the system boundary.

Interior Noise Reduction by Adaptive Feedback Vibration Control

NASA Technical Reports Server (NTRS)

Lim, Tae W.

1998-01-01

The objective of this project is to investigate the possible use of adaptive digital filtering techniques in simultaneous, multiple-mode identification of the modal parameters of a vibrating structure in real-time. It is intended that the results obtained from this project will be used for state estimation needed in adaptive structural acoustics control. The work done in this project is basically an extension of the work on real-time single mode identification, which was performed successfully using a digital signal processor (DSP) at NASA, Langley. Initially, in this investigation the single mode identification work was duplicated on a different processor, namely the Texas Instruments TMS32OC40 DSP. The system identification results for the single mode case were very good. Then an algorithm for simultaneous two mode identification was developed and tested using analytical simulation. When it successfully performed the expected tasks, it was implemented in real-time on the DSP system to identify the first two modes of vibration of a cantilever aluminum beam. The results of the simultaneous two mode case were good but some problems were identified related to frequency warping and spurious mode identification. The frequency warping problem was found to be due to the bilinear transformation used in the algorithm to convert the system transfer function from the continuous-time domain to the discrete-time domain. An alternative approach was developed to rectify the problem. The spurious mode identification problem was found to be associated with high sampling rates. Noise in the signal is suspected to be the cause of this problem but further investigation will be needed to clarify the cause. For simultaneous identification of more than two modes, it was found that theoretically an adaptive digital filter can be designed to identify the required number of modes, but the algebra became very complex which made it impossible to implement in the DSP system used in this study. The on-line identification algorithm developed in this research will be useful in constructing a state estimator for feedback vibration control.

Adaptive dynamic programming approach to experience-based systems identification and control.

PubMed

Lendaris, George G

2009-01-01

Humans have the ability to make use of experience while selecting their control actions for distinct and changing situations, and their process speeds up and have enhanced effectiveness as more experience is gained. In contrast, current technological implementations slow down as more knowledge is stored. A novel way of employing Approximate (or Adaptive) Dynamic Programming (ADP) is described that shifts the underlying Adaptive Critic type of Reinforcement Learning method "up a level", away from designing individual (optimal) controllers to that of developing on-line algorithms that efficiently and effectively select designs from a repository of existing controller solutions (perhaps previously developed via application of ADP methods). The resulting approach is called Higher-Level Learning Algorithm. The approach and its rationale are described and some examples of its application are given. The notions of context and context discernment are important to understanding the human abilities noted above. These are first defined, in a manner appropriate to controls and system-identification, and as a foundation relating to the application arena, a historical view of the various phases during development of the controls field is given, organized by how the notion 'context' was, or was not, involved in each phase.

Evaluating current automatic de-identification methods with Veteran's health administration clinical documents.

PubMed

Ferrández, Oscar; South, Brett R; Shen, Shuying; Friedlin, F Jeffrey; Samore, Matthew H; Meystre, Stéphane M

2012-07-27

The increased use and adoption of Electronic Health Records (EHR) causes a tremendous growth in digital information useful for clinicians, researchers and many other operational purposes. However, this information is rich in Protected Health Information (PHI), which severely restricts its access and possible uses. A number of investigators have developed methods for automatically de-identifying EHR documents by removing PHI, as specified in the Health Insurance Portability and Accountability Act "Safe Harbor" method.This study focuses on the evaluation of existing automated text de-identification methods and tools, as applied to Veterans Health Administration (VHA) clinical documents, to assess which methods perform better with each category of PHI found in our clinical notes; and when new methods are needed to improve performance. We installed and evaluated five text de-identification systems "out-of-the-box" using a corpus of VHA clinical documents. The systems based on machine learning methods were trained with the 2006 i2b2 de-identification corpora and evaluated with our VHA corpus, and also evaluated with a ten-fold cross-validation experiment using our VHA corpus. We counted exact, partial, and fully contained matches with reference annotations, considering each PHI type separately, or only one unique 'PHI' category. Performance of the systems was assessed using recall (equivalent to sensitivity) and precision (equivalent to positive predictive value) metrics, as well as the F(2)-measure. Overall, systems based on rules and pattern matching achieved better recall, and precision was always better with systems based on machine learning approaches. The highest "out-of-the-box" F(2)-measure was 67% for partial matches; the best precision and recall were 95% and 78%, respectively. Finally, the ten-fold cross validation experiment allowed for an increase of the F(2)-measure to 79% with partial matches. The "out-of-the-box" evaluation of text de-identification systems provided us with compelling insight about the best methods for de-identification of VHA clinical documents. The errors analysis demonstrated an important need for customization to PHI formats specific to VHA documents. This study informed the planning and development of a "best-of-breed" automatic de-identification application for VHA clinical text.

MODAL TRACKING of A Structural Device: A Subspace Identification Approach

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

Candy, J. V.; Franco, S. N.; Ruggiero, E. L.

Mechanical devices operating in an environment contaminated by noise, uncertainties, and extraneous disturbances lead to low signal-to-noise-ratios creating an extremely challenging processing problem. To detect/classify a device subsystem from noisy data, it is necessary to identify unique signatures or particular features. An obvious feature would be resonant (modal) frequencies emitted during its normal operation. In this report, we discuss a model-based approach to incorporate these physical features into a dynamic structure that can be used for such an identification. The approach we take after pre-processing the raw vibration data and removing any extraneous disturbances is to obtain a representation ofmore » the structurally unknown device along with its subsystems that capture these salient features. One approach is to recognize that unique modal frequencies (sinusoidal lines) appear in the estimated power spectrum that are solely characteristic of the device under investigation. Therefore, the objective of this effort is based on constructing a black box model of the device that captures these physical features that can be exploited to “diagnose” whether or not the particular device subsystem (track/detect/classify) is operating normally from noisy vibrational data. Here we discuss the application of a modern system identification approach based on stochastic subspace realization techniques capable of both (1) identifying the underlying black-box structure thereby enabling the extraction of structural modes that can be used for analysis and modal tracking as well as (2) indicators of condition and possible changes from normal operation.« less

Methods for parameter identification in oscillatory networks and application to cortical and thalamic 600 Hz activity.

PubMed

Leistritz, L; Suesse, T; Haueisen, J; Hilgenfeld, B; Witte, H

2006-01-01

Directed information transfer in the human brain occurs presumably by oscillations. As of yet, most approaches for the analysis of these oscillations are based on time-frequency or coherence analysis. The present work concerns the modeling of cortical 600 Hz oscillations, localized within the Brodmann Areas 3b and 1 after stimulation of the nervus medianus, by means of coupled differential equations. This approach leads to the so-called parameter identification problem, where based on a given data set, a set of unknown parameters of a system of ordinary differential equations is determined by special optimization procedures. Some suitable algorithms for this task are presented in this paper. Finally an oscillatory network model is optimally fitted to the data taken from ten volunteers.

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.

High Quality Teaching in a University: Identification and Description.

ERIC Educational Resources Information Center

Moses, Ingrid

1985-01-01

The teaching approaches and attitudes toward teaching of University of Queensland faculty rated superior under one rating system are examined, including professional and personal skills and attitudes such as subject area competence, ability to communicate knowledge in various classroom contexts, and commitment to facilitating learning in…

EUROSCAN INTERNATIONAL NETWORK MEMBER AGENCIES: THEIR STRUCTURE, PROCESSES, AND OUTPUTS.

PubMed

Packer, Claire; Simpson, Sue; de Almeida, Rosimary Terezinha

2015-01-01

The EuroScan International Network is a global network of publicly funded early awareness and alert (EAA) systems for health technologies. We describe the EuroScan member agency systems and methods, and highlight the potential for increased collaboration. EuroScan members completed postal questionnaires supplemented with telephone interviews in 2012 to elicit additional information and check equivalence of responses. Information was updated between March and May 2013. Fifteen of the seventeen member agencies responded. The principal purpose of agencies is to inform decisions on coverage or reimbursement of health services and decisions on undertaking secondary research. The main users of information are national governments; health professionals; health services purchasers, commissioners, and decision makers; and healthcare providers. Most EuroScan agencies are small with almost half having fewer than two whole time equivalent staff. Ten agencies use both active and passive identification approaches, four use only active approaches. Most start identification in the experimental or investigational stages of the technology life cycle. All agencies assessed technologies when they are between the investigational and established, but under diffusion stages. Barriers to collaboration revolve around different system aims, purposes, and requirements; a lack of staff, finance, or opportunity; language differences; and restrictions on dissemination. Although many barriers to collaboration were identified, the majority of agencies were supportive of increased collaboration either involving the whole EuroScan Network or between individual agencies. Despite differences in the detailed identification processes, members thought that this was the most feasible phase to develop additional collaboration.

Demographic and health surveillance of mobile pastoralists in Chad: integration of biometric fingerprint identification into a geographical information system.

PubMed

Weibel, Daniel; Schelling, Esther; Bonfoh, Bassirou; Utzinger, Jürg; Hattendorf, Jan; Abdoulaye, Mahamat; Madjiade, Toguina; Zinsstag, Jakob

2008-11-01

There is a pressing need for baseline demographic and health-related data to plan, implement and evaluate health interventions in developing countries, and to monitor progress towards international development goals. However, mobile pastoralists, i.e. people who depend on a livestock production system and follow their herds as they move, remain marginalized from rural development plans and interventions. The fact that mobile people are hard to reach and stay in contact with is a plausible reason why they are underrepresented in national censuses and/or alternative sequential sample survey systems. We present a proof-of-concept of monitoring highly mobile, pastoral people by recording demographic and health-related data from 933 women and 2020 children and establishing a biometric identification system (BIS) based on the registration and identification of digital fingerprints. Although only 22 women, representing 2.4% of the total registered women, were encountered twice in the four survey rounds, the approach implemented is shown to be feasible. The BIS described here is linked to a geographical information system to facilitate the creation of the first health and demographic surveillance system in a mobile, pastoralist setting. Our ultimate goal is to implement and monitor interventions with the "one health" concept, thus integrating and improving human, animal and ecosystem health.

A distributed computational search strategy for the identification of diagnostics targets: application to finding aptamer targets for methicillin-resistant staphylococci.

PubMed

Flanagan, Keith; Cockell, Simon; Harwood, Colin; Hallinan, Jennifer; Nakjang, Sirintra; Lawry, Beth; Wipat, Anil

2014-06-30

The rapid and cost-effective identification of bacterial species is crucial, especially for clinical diagnosis and treatment. Peptide aptamers have been shown to be valuable for use as a component of novel, direct detection methods. These small peptides have a number of advantages over antibodies, including greater specificity and longer shelf life. These properties facilitate their use as the detector components of biosensor devices. However, the identification of suitable aptamer targets for particular groups of organisms is challenging. We present a semi-automated processing pipeline for the identification of candidate aptamer targets from whole bacterial genome sequences. The pipeline can be configured to search for protein sequence fragments that uniquely identify a set of strains of interest. The system is also capable of identifying additional organisms that may be of interest due to their possession of protein fragments in common with the initial set. Through the use of Cloud computing technology and distributed databases, our system is capable of scaling with the rapidly growing genome repositories, and consequently of keeping the resulting data sets up-to-date. The system described is also more generically applicable to the discovery of specific targets for other diagnostic approaches such as DNA probes, PCR primers and antibodies.

Neural Networks and other Techniques for Fault Identification and Isolation of Aircraft Systems

NASA Technical Reports Server (NTRS)

Innocenti, M.; Napolitano, M.

2003-01-01

Fault identification, isolation, and accomodation have become critical issues in the overall performance of advanced aircraft systems. Neural Networks have shown to be a very attractive alternative to classic adaptation methods for identification and control of non-linear dynamic systems. The purpose of this paper is to show the improvements in neural network applications achievable through the use of learning algorithms more efficient than the classic Back-Propagation, and through the implementation of the neural schemes in parallel hardware. The results of the analysis of a scheme for Sensor Failure, Detection, Identification and Accommodation (SFDIA) using experimental flight data of a research aircraft model are presented. Conventional approaches to the problem are based on observers and Kalman Filters while more recent methods are based on neural approximators. The work described in this paper is based on the use of neural networks (NNs) as on-line learning non-linear approximators. The performances of two different neural architectures were compared. The first architecture is based on a Multi Layer Perceptron (MLP) NN trained with the Extended Back Propagation algorithm (EBPA). The second architecture is based on a Radial Basis Function (RBF) NN trained with the Extended-MRAN (EMRAN) algorithms. In addition, alternative methods for communications links fault detection and accomodation are presented, relative to multiple unmanned aircraft applications.

A distributed computational search strategy for the identification of diagnostics targets: Application to finding aptamer targets for methicillin-resistant staphylococci.

PubMed

Flanagan, Keith; Cockell, Simon; Harwood, Colin; Hallinan, Jennifer; Nakjang, Sirintra; Lawry, Beth; Wipat, Anil

2014-06-01

The rapid and cost-effective identification of bacterial species is crucial, especially for clinical diagnosis and treatment. Peptide aptamers have been shown to be valuable for use as a component of novel, direct detection methods. These small peptides have a number of advantages over antibodies, including greater specificity and longer shelf life. These properties facilitate their use as the detector components of biosensor devices. However, the identification of suitable aptamer targets for particular groups of organisms is challenging. We present a semi-automated processing pipeline for the identification of candidate aptamer targets from whole bacterial genome sequences. The pipeline can be configured to search for protein sequence fragments that uniquely identify a set of strains of interest. The system is also capable of identifying additional organisms that may be of interest due to their possession of protein fragments in common with the initial set. Through the use of Cloud computing technology and distributed databases, our system is capable of scaling with the rapidly growing genome repositories, and consequently of keeping the resulting data sets up-to-date. The system described is also more generically applicable to the discovery of specific targets for other diagnostic approaches such as DNA probes, PCR primers and antibodies.

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.

A survey on the utility of the USEPA CADDIS stressor identification procedure.

PubMed

Harwood, John J; Stroud, Robert Adam

2012-06-01

The Environmental Protection Agency (EPA) has made available on the worldwide web a systematic stream stressor identification procedure, the "Causal Analysis/Diagnosis Decision Information System" or CADDIS. We report here the results of a survey of regulators and scientists in 11 states who use CADDIS or another stressor identification procedure in their work. The 13 survey questions address guidelines as to what impairment scenarios to approach with stressor identification, what information is needed to perform stressor identification, and what the stakeholder role is in performing stressor identification. At the time of this survey (the summer of 2009), the EPA CADDIS website was less commonly used among the state regulators surveyed than the published EPA stressor identification document on which it is based. The respondents generally find the EPA stressor identification procedure useful and capable of being adapted to their individual needs. Survey respondents all use stressor identification in their Total Maximum Daily Load work, but also in a wide variety of other applications. All the "types of evidence" included in the CADDIS stressor identification procedure are used by the practitioners surveyed with the exception of the results of ecological simulation models. While the CADDIS documentation encourages the involvement of stakeholders in stressor identification, most respondents do not assemble stakeholder teams of local officials and citizens to participate in stressor analyses.

Developing and Evaluating an Automated All-Cause Harm Trigger System.

PubMed

Sammer, Christine; Miller, Susanne; Jones, Cason; Nelson, Antoinette; Garrett, Paul; Classen, David; Stockwell, David

2017-04-01

From 2009 through 2012, the Adventist Health System Patient Safety Organization (AHS PSO) used the Global Trigger Tool method for harm identification and demonstrated harm reduction. Although the awareness of harm demonstrated opportunities for improvement across the system, leaders determined that the human and fiscal resources required to continue with a retrospective manual harm identification process were unsustainable. In addition, there was growing concern that the identification of harm after the patient's discharge did not allow for intervention during the hospital stay. Therefore, the AHS PSO decided to seek an alternative method for patient harm identification. The AHS PSO and another PSO jointly developed a novel automated all-cause harm trigger identification system that allowed for real-time bedside intervention, real-time trend analysis affecting patient safety, and continued learning about harm measurement. A sociotechnical approach of people, process, and technology was used at two pilot hospitals sharing the same electronic health record platform. Automated positive harm triggers and work-flow models were developed and evaluated. Combined data from the two hospitals in a period of 11 consecutive months indicated (1) a total of 2,696 harms (combined hospital-acquired and outside-acquired); (2) that hypoglycemia (blood glucose ≤ 40 mg/dL) was the most frequently identified harm; (3) 256 harms related to the Patient Safety Indicator 90 (PSI 90) Composite descriptions versus 77 harms reported to regulatory harm reduction programs; and (4) that almost one third (32%) of total harms were classified as outside-acquired. The automated harm trigger system revealed not only more harm but a broader scope of harm and led to a deeper understanding of patient safety vulnerabilities. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Arenani: pointing and information query system for object beyond your reach

NASA Astrophysics Data System (ADS)

Adachi, Mariko; Sakamoto, Kunio

2008-03-01

The authors developed a prototype information query system. It is easy to get the information about an object with in your reach. But it is troublesome to do the same in case that the object is far away. If someone is around you, you can ask an easy question with a finger pointing; "What is that?" Our developed system also realizes this approach using information technologies. The system consists of a laser pointer, transmitter and receiver units for an optical communication. The laser pointer is used for pointing an object. Moreover this laser light is modulated for sending information about user's identification (ID) codes to identify who asks a question. Each object has a receiver for laser light communication and sends user's identification to a main computer. After pointing an object, a questioner receives an answer through a wireless information network like an email on the cellular phone.

Road map to adaptive optimal control. [jet engine control

NASA Technical Reports Server (NTRS)

Boyer, R.

1980-01-01

A building block control structure leading toward adaptive, optimal control for jet engines is developed. This approach simplifies the addition of new features and allows for easier checkout of the control by providing a baseline system for comparison. Also, it is possible to eliminate certain features that do not have payoff by being selective in the addition of new building blocks to be added to the baseline system. The minimum risk approach specifically addresses the need for active identification of the plant to be controlled in real time and real time optimization of the control for the identified plant.

[A new methodological approach for leptospira persistence studies in case of mixed leptospirosis].

PubMed

Samsonova, A P; Petrov, E M; Vyshivkina, N V; Anan'ina, Iu V

2003-01-01

A new methodical approach for Leptospira persistence studies in case of mixed leptospirosis, based on the use of PCR test systems with different taxonomic specificity for the indication and identification of leptospires, was developed. Two PCR test systems (G and B) were used in experiments on BALB/c white mice to study patterns of the development of mixed infection caused by leptospires of serovar poi (genomospecies L. borgpeterseni) and grippotyphosa (genomospecies L. kirschneri). The conclusion was made of good prospects of this method application in studies on symbiotic relationships of leptospires both in vivo and in vitro.

Inverse problems in 1D hemodynamics on systemic networks: a sequential approach.

PubMed

Lombardi, D

2014-02-01

In this work, a sequential approach based on the unscented Kalman filter is applied to solve inverse problems in 1D hemodynamics, on a systemic network. For instance, the arterial stiffness is estimated by exploiting cross-sectional area and mean speed observations in several locations of the arteries. The results are compared with those ones obtained by estimating the pulse wave velocity and the Moens-Korteweg formula. In the last section, a perspective concerning the identification of the terminal models parameters and peripheral circulation (modeled by a Windkessel circuit) is presented. Copyright © 2013 John Wiley & Sons, Ltd.

Identification of small ORFs in vertebrates using ribosome footprinting and evolutionary conservation

PubMed Central

Bazzini, Ariel A; Johnstone, Timothy G; Christiano, Romain; Mackowiak, Sebastian D; Obermayer, Benedikt; Fleming, Elizabeth S; Vejnar, Charles E; Lee, Miler T; Rajewsky, Nikolaus; Walther, Tobias C; Giraldez, Antonio J

2014-01-01

Identification of the coding elements in the genome is a fundamental step to understanding the building blocks of living systems. Short peptides (< 100 aa) have emerged as important regulators of development and physiology, but their identification has been limited by their size. We have leveraged the periodicity of ribosome movement on the mRNA to define actively translated ORFs by ribosome footprinting. This approach identifies several hundred translated small ORFs in zebrafish and human. Computational prediction of small ORFs from codon conservation patterns corroborates and extends these findings and identifies conserved sequences in zebrafish and human, suggesting functional peptide products (micropeptides). These results identify micropeptide-encoding genes in vertebrates, providing an entry point to define their function in vivo. PMID:24705786

Gesellschaft fuer angewandte Mathematik und Mechanik, Scientific Annual Meeting, Universitaet Hannover, Hanover, Federal Republic of Germany, Apr. 8-12, 1990, Reports

NASA Astrophysics Data System (ADS)

Various papers on applied mathematics and mechanics are presented. Among the individual topics addressed are: dynamical systems with time-varying or unsteady structure, micromechanical modeling of creep rupture, forced vibrations of elastic sandwich plates with thick surface layers, postbuckling of a complete spherical shell under a line load, differential-geometric approach to the multibody system dynamics, stability of an oscillator with stochastic parametric excitation, identification strategies for crack-formation in rotors, identification of physical parameters of FEMs, impact model for elastic and partly plastic impacts on objects, varying delay and stability in dynamical systems. Also discussed are: parameter identification of a hybrid model for vibration analysis using the FEM, vibration behavior of a labyrinth seal with through-flow, similarities in the boundary layer of fiber composite materials, distortion parameter in shell theories, elastoplastic crack problem at finite strain, algorithm for computing effective stiffnesses of plates with periodic structure, plasticity of metal-matrix composites in a mixed stress-strain space formation, constitutive equations in directly formulated plate theories, microbuckling and homogenization for long fiber composites.

Satellite provided customer promises services, a forecast of potential domestic demand through the year 2000. Volume 4: Sensitivity analysis

NASA Technical Reports Server (NTRS)

Kratochvil, D.; Bowyer, J.; Bhushan, C.; Steinnagel, K.; Kaushal, D.; Al-Kinani, G.

1984-01-01

The overall purpose was to forecast the potential United States domestic telecommunications demand for satellite provided customer promises voice, data and video services through the year 2000, so that this information on service demand would be available to aid in NASA program planning. To accomplish this overall purpose the following objectives were achieved: (1) development of a forecast of the total domestic telecommunications demand; (2) identification of that portion of the telecommunications demand suitable for transmission by satellite systems; (3) identification of that portion of the satellite market addressable by consumer promises service (CPS) systems; (4) identification of that portion of the satellite market addressable by Ka-band CPS system; and (5) postulation of a Ka-band CPS network on a nationwide and local level. The approach employed included the use of a variety of forecasting models, a parametric cost model, a market distribution model and a network optimization model. Forecasts were developed for: 1980, 1990, and 2000; voice, data and video services; terrestrial and satellite delivery modes; and C, Ku and Ka-bands.

Interests, relationships, identities: three central issues for individuals and groups in negotiating their social environment.

PubMed

Kelman, Herbert C

2006-01-01

This chapter begins with a summary of a model, developed half a century ago, that distinguishes three qualitatively different processes of social influence: compliance, identification, and internalization. The model, originally geared to and experimentally tested in the context of persuasive communication, was subsequently applied to influence in the context of long-term relationships, including psychotherapy, international exchanges, and the socialization of national/ethnic identity. It has been extended to analysis of the relationship of individuals to social systems. Individuals' rule, role, and value orientations to a system--conceptually linked to compliance, identification, and internalization--predict different reactions to their own violations of societal standards, different patterns of personal involvement in the political system, and differences in attitude toward authorities and readiness to obey. In a further extension of the model, three approaches to peacemaking in international or intergroup conflicts are identified--conflict settlement, conflict resolution, and reconciliation--which, respectively, focus on the accommodation of interests, relationships, and identities, and are conducive to changes at the level of compliance, identification, and internalization.

Identification of multivariable nonlinear systems in the presence of colored noises using iterative hierarchical least squares algorithm.

PubMed

Jafari, Masoumeh; Salimifard, Maryam; Dehghani, Maryam

2014-07-01

This paper presents an efficient method for identification of nonlinear Multi-Input Multi-Output (MIMO) systems in the presence of colored noises. The method studies the multivariable nonlinear Hammerstein and Wiener models, in which, the nonlinear memory-less block is approximated based on arbitrary vector-based basis functions. The linear time-invariant (LTI) block is modeled by an autoregressive moving average with exogenous (ARMAX) model which can effectively describe the moving average noises as well as the autoregressive and the exogenous dynamics. According to the multivariable nature of the system, a pseudo-linear-in-the-parameter model is obtained which includes two different kinds of unknown parameters, a vector and a matrix. Therefore, the standard least squares algorithm cannot be applied directly. To overcome this problem, a Hierarchical Least Squares Iterative (HLSI) algorithm is used to simultaneously estimate the vector and the matrix of unknown parameters as well as the noises. The efficiency of the proposed identification approaches are investigated through three nonlinear MIMO case studies. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

Identification and compensation of friction for a novel two-axis differential micro-feed system

NASA Astrophysics Data System (ADS)

Du, Fuxin; Zhang, Mingyang; Wang, Zhaoguo; Yu, Chen; Feng, Xianying; Li, Peigang

2018-06-01

Non-linear friction in a conventional drive feed system (CDFS) feeding at low speed is one of the main factors that lead to the complexity of the feed drive. The CDFS will inevitably enter or approach a non-linear creeping work area at extremely low speed. A novel two-axis differential micro-feed system (TDMS) is developed in this paper to overcome the accuracy limitation of CDFS. A dynamic model of TDMS is first established. Then, a novel all-component friction parameter identification method (ACFPIM) using a genetic algorithm (GA) to identify the friction parameters of a TDMS is introduced. The friction parameters of the ball screw and linear motion guides are identified independently using the method, assuring the accurate modelling of friction force at all components. A proportional-derivate feed drive position controller with an observer-based friction compensator is implemented to achieve an accurate trajectory tracking performance. Finally, comparative experiments demonstrate the effectiveness of the TDMS in inhibiting the disadvantageous influence of non-linear friction and the validity of the proposed identification method for TDMS.

Satellite provided customer promises services, a forecast of potential domestic demand through the year 2000. Volume 4: Sensitivity analysis

NASA Astrophysics Data System (ADS)

Kratochvil, D.; Bowyer, J.; Bhushan, C.; Steinnagel, K.; Kaushal, D.; Al-Kinani, G.

1984-03-01

The overall purpose was to forecast the potential United States domestic telecommunications demand for satellite provided customer promises voice, data and video services through the year 2000, so that this information on service demand would be available to aid in NASA program planning. To accomplish this overall purpose the following objectives were achieved: (1) development of a forecast of the total domestic telecommunications demand; (2) identification of that portion of the telecommunications demand suitable for transmission by satellite systems; (3) identification of that portion of the satellite market addressable by consumer promises service (CPS) systems; (4) identification of that portion of the satellite market addressable by Ka-band CPS system; and (5) postulation of a Ka-band CPS network on a nationwide and local level. The approach employed included the use of a variety of forecasting models, a parametric cost model, a market distribution model and a network optimization model. Forecasts were developed for: 1980, 1990, and 2000; voice, data and video services; terrestrial and satellite delivery modes; and C, Ku and Ka-bands.

Identification of species based on DNA barcode using k-mer feature vector and Random forest classifier.

PubMed

Meher, Prabina Kumar; Sahu, Tanmaya Kumar; Rao, A R

2016-11-05

DNA barcoding is a molecular diagnostic method that allows automated and accurate identification of species based on a short and standardized fragment of DNA. To this end, an attempt has been made in this study to develop a computational approach for identifying the species by comparing its barcode with the barcode sequence of known species present in the reference library. Each barcode sequence was first mapped onto a numeric feature vector based on k-mer frequencies and then Random forest methodology was employed on the transformed dataset for species identification. The proposed approach outperformed similarity-based, tree-based, diagnostic-based approaches and found comparable with existing supervised learning based approaches in terms of species identification success rate, while compared using real and simulated datasets. Based on the proposed approach, an online web interface SPIDBAR has also been developed and made freely available at http://cabgrid.res.in:8080/spidbar/ for species identification by the taxonomists. Copyright © 2016 Elsevier B.V. All rights reserved.

Developing optimal input design strategies in cancer systems biology with applications to microfluidic device engineering.

PubMed

Menolascina, Filippo; Bellomo, Domenico; Maiwald, Thomas; Bevilacqua, Vitoantonio; Ciminelli, Caterina; Paradiso, Angelo; Tommasi, Stefania

2009-10-15

Mechanistic models are becoming more and more popular in Systems Biology; identification and control of models underlying biochemical pathways of interest in oncology is a primary goal in this field. Unfortunately the scarce availability of data still limits our understanding of the intrinsic characteristics of complex pathologies like cancer: acquiring information for a system understanding of complex reaction networks is time consuming and expensive. Stimulus response experiments (SRE) have been used to gain a deeper insight into the details of biochemical mechanisms underlying cell life and functioning. Optimisation of the input time-profile, however, still remains a major area of research due to the complexity of the problem and its relevance for the task of information retrieval in systems biology-related experiments. We have addressed the problem of quantifying the information associated to an experiment using the Fisher Information Matrix and we have proposed an optimal experimental design strategy based on evolutionary algorithm to cope with the problem of information gathering in Systems Biology. On the basis of the theoretical results obtained in the field of control systems theory, we have studied the dynamical properties of the signals to be used in cell stimulation. The results of this study have been used to develop a microfluidic device for the automation of the process of cell stimulation for system identification. We have applied the proposed approach to the Epidermal Growth Factor Receptor pathway and we observed that it minimises the amount of parametric uncertainty associated to the identified model. A statistical framework based on Monte-Carlo estimations of the uncertainty ellipsoid confirmed the superiority of optimally designed experiments over canonical inputs. The proposed approach can be easily extended to multiobjective formulations that can also take advantage of identifiability analysis. Moreover, the availability of fully automated microfluidic platforms explicitly developed for the task of biochemical model identification will hopefully reduce the effects of the 'data rich--data poor' paradox in Systems Biology.

Identification of legionella effectors using bioinformatic approaches.

PubMed

Segal, Gil

2013-01-01

Legionella pneumophila the causative agent of Legionnaires' disease, actively manipulates host cell processes to establish a replication niche inside host cells. The establishment of its replication niche requires a functional Icm/Dot type IV secretion system which translocates about 300 effector proteins into host cells during infection. Many of these effectors were first identified as effector candidates by several bioinformatic approaches, and these predicted effectors were later examined experimentally for translocation and a large number of which were validated as effector proteins. Here, I summarized the bioinformatic approaches that were used to identify these effectors.

Aircraft Abnormal Conditions Detection, Identification, and Evaluation Using Innate and Adaptive Immune Systems Interaction

NASA Astrophysics Data System (ADS)

Al Azzawi, Dia

Abnormal flight conditions play a major role in aircraft accidents frequently causing loss of control. To ensure aircraft operation safety in all situations, intelligent system monitoring and adaptation must rely on accurately detecting the presence of abnormal conditions as soon as they take place, identifying their root cause(s), estimating their nature and severity, and predicting their impact on the flight envelope. Due to the complexity and multidimensionality of the aircraft system under abnormal conditions, these requirements are extremely difficult to satisfy using existing analytical and/or statistical approaches. Moreover, current methodologies have addressed only isolated classes of abnormal conditions and a reduced number of aircraft dynamic parameters within a limited region of the flight envelope. This research effort aims at developing an integrated and comprehensive framework for the aircraft abnormal conditions detection, identification, and evaluation based on the artificial immune systems paradigm, which has the capability to address the complexity and multidimensionality issues related to aircraft systems. Within the proposed framework, a novel algorithm was developed for the abnormal conditions detection problem and extended to the abnormal conditions identification and evaluation. The algorithm and its extensions were inspired from the functionality of the biological dendritic cells (an important part of the innate immune system) and their interaction with the different components of the adaptive immune system. Immunity-based methodologies for re-assessing the flight envelope at post-failure and predicting the impact of the abnormal conditions on the performance and handling qualities are also proposed and investigated in this study. The generality of the approach makes it applicable to any system. Data for artificial immune system development were collected from flight tests of a supersonic research aircraft within a motion-based flight simulator. The abnormal conditions considered in this work include locked actuators (stabilator, aileron, rudder, and throttle), structural damage of the wing, horizontal tail, and vertical tail, malfunctioning sensors, and reduced engine effectiveness. The results of applying the proposed approach to this wide range of abnormal conditions show its high capability in detecting the abnormal conditions with zero false alarms and very high detection rates, correctly identifying the failed subsystem and evaluating the type and severity of the failure. The results also reveal that the post-failure flight envelope can be reasonably predicted within this framework.

Creep-fatigue life prediction for engine hot section materials (isotropic)

NASA Technical Reports Server (NTRS)

Moreno, V.

1982-01-01

The objectives of this program are the investigation of fundamental approaches to high temperature crack initiation life prediction, identification of specific modeling strategies and the development of specific models for component relevant loading conditions. A survey of the hot section material/coating systems used throughout the gas turbine industry is included. Two material/coating systems will be identified for the program. The material/coating system designated as the base system shall be used throughout Tasks 1-12. The alternate material/coating system will be used only in Task 12 for further evaluation of the models developed on the base material. In Task II, candidate life prediction approaches will be screened based on a set of criteria that includes experience of the approaches within the literature, correlation with isothermal data generated on the base material, and judgements relative to the applicability of the approach for the complex cycles to be considered in the option program. The two most promising approaches will be identified. Task 3 further evaluates the best approach using additional base material fatigue testing including verification tests. Task 4 consists of technical, schedular, financial and all other reporting requirements in accordance with the Reports of Work clause.

Model Transformation for a System of Systems Dependability Safety Case

NASA Technical Reports Server (NTRS)

Murphy, Judy; Driskell, Stephen B.

2010-01-01

Software plays an increasingly larger role in all aspects of NASA's science missions. This has been extended to the identification, management and control of faults which affect safety-critical functions and by default, the overall success of the mission. Traditionally, the analysis of fault identification, management and control are hardware based. Due to the increasing complexity of system, there has been a corresponding increase in the complexity in fault management software. The NASA Independent Validation & Verification (IV&V) program is creating processes and procedures to identify, and incorporate safety-critical software requirements along with corresponding software faults so that potential hazards may be mitigated. This Specific to Generic ... A Case for Reuse paper describes the phases of a dependability and safety study which identifies a new, process to create a foundation for reusable assets. These assets support the identification and management of specific software faults and, their transformation from specific to generic software faults. This approach also has applications to other systems outside of the NASA environment. This paper addresses how a mission specific dependability and safety case is being transformed to a generic dependability and safety case which can be reused for any type of space mission with an emphasis on software fault conditions.

Spectral identification of a 90Sr source in the presence of masking nuclides using Maximum-Likelihood deconvolution

NASA Astrophysics Data System (ADS)

Neuer, Marcus J.

2013-11-01

A technique for the spectral identification of strontium-90 is shown, utilising a Maximum-Likelihood deconvolution. Different deconvolution approaches are discussed and summarised. Based on the intensity distribution of the beta emission and Geant4 simulations, a combined response matrix is derived, tailored to the β- detection process in sodium iodide detectors. It includes scattering effects and attenuation by applying a base material decomposition extracted from Geant4 simulations with a CAD model for a realistic detector system. Inversion results of measurements show the agreement between deconvolution and reconstruction. A detailed investigation with additional masking sources like 40K, 226Ra and 131I shows that a contamination of strontium can be found in the presence of these nuisance sources. Identification algorithms for strontium are presented based on the derived technique. For the implementation of blind identification, an exemplary masking ratio is calculated.

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.

A Distributed, Collaborative Intelligent Agent System Approach for Proactive Postmarketing Drug Safety Surveillance

PubMed Central

Ji, Yanqing; Ying, Hao; Farber, Margo S.; Yen, John; Dews, Peter; Miller, Richard E.; Massanari, R. Michael

2014-01-01

Discovering unknown adverse drug reactions (ADRs) in postmarketing surveillance as early as possible is of great importance. The current approach to postmarketing surveillance primarily relies on spontaneous reporting. It is a passive surveillance system and limited by gross underreporting (<10% reporting rate), latency, and inconsistent reporting. We propose a novel team-based intelligent agent software system approach for proactively monitoring and detecting potential ADRs of interest using electronic patient records. We designed such a system and named it ADRMonitor. The intelligent agents, operating on computers located in different places, are capable of continuously and autonomously collaborating with each other and assisting the human users (e.g., the food and drug administration (FDA), drug safety professionals, and physicians). The agents should enhance current systems and accelerate early ADR identification. To evaluate the performance of the ADRMonitor with respect to the current spontaneous reporting approach, we conducted simulation experiments on identification of ADR signal pairs (i.e., potential links between drugs and apparent adverse reactions) under various conditions. The experiments involved over 275 000 simulated patients created on the basis of more than 1000 real patients treated by the drug cisapride that was on the market for seven years until its withdrawal by the FDA in 2000 due to serious ADRs. Healthcare professionals utilizing the spontaneous reporting approach and the ADRMonitor were separately simulated by decision-making models derived from a general cognitive decision model called fuzzy recognition-primed decision (RPD) model that we recently developed. The quantitative simulation results show that 1) the number of true ADR signal pairs detected by the ADRMonitor is 6.6 times higher than that by the spontaneous reporting strategy; 2) the ADR detection rate of the ADRMonitor agents with even moderate decision-making skills is five times higher than that of spontaneous reporting; and 3) as the number of patient cases increases, ADRs could be detected significantly earlier by the ADRMonitor. PMID:20007038

A system identification approach for developing model predictive controllers of antibody quality attributes in cell culture processes

PubMed Central

Schmitt, John; Beller, Justin; Russell, Brian; Quach, Anthony; Hermann, Elizabeth; Lyon, David; Breit, Jeffrey

2017-01-01

As the biopharmaceutical industry evolves to include more diverse protein formats and processes, more robust control of Critical Quality Attributes (CQAs) is needed to maintain processing flexibility without compromising quality. Active control of CQAs has been demonstrated using model predictive control techniques, which allow development of processes which are robust against disturbances associated with raw material variability and other potentially flexible operating conditions. Wide adoption of model predictive control in biopharmaceutical cell culture processes has been hampered, however, in part due to the large amount of data and expertise required to make a predictive model of controlled CQAs, a requirement for model predictive control. Here we developed a highly automated, perfusion apparatus to systematically and efficiently generate predictive models using application of system identification approaches. We successfully created a predictive model of %galactosylation using data obtained by manipulating galactose concentration in the perfusion apparatus in serialized step change experiments. We then demonstrated the use of the model in a model predictive controller in a simulated control scenario to successfully achieve a %galactosylation set point in a simulated fed‐batch culture. The automated model identification approach demonstrated here can potentially be generalized to many CQAs, and could be a more efficient, faster, and highly automated alternative to batch experiments for developing predictive models in cell culture processes, and allow the wider adoption of model predictive control in biopharmaceutical processes. © 2017 The Authors Biotechnology Progress published by Wiley Periodicals, Inc. on behalf of American Institute of Chemical Engineers Biotechnol. Prog., 33:1647–1661, 2017 PMID:28786215

Functional and Noncategorical Identification and Intervention in Special Education.

ERIC Educational Resources Information Center

Reschly, Daniel J., Ed.; Tilly, W. David, III, Ed.; Grimes, Jeffrey P., Ed.

This volume presents 12 papers that address the increasing shift toward functional and noncategorical approaches to special education. The papers are: (1) "Origins of Categorical Special Education Services in Schools and a Rationale for Changing Them" (Jim Ysseldyke and Doug Marston); (2) "Reform Trends and System Design Alternatives" (Dan Reschly…

A Case-Based Approach Improves Science Students' Experimental Variable Identification Skills

ERIC Educational Resources Information Center

Grunwald, Sandra; Hartman, Andrew

2010-01-01

Incorporation of experimental case studies into the laboratory curriculum increases students' abilities to identify experimental variables that affect the outcome of an experiment. Here the authors describe how such case studies were incorporated using an online course management system into a biochemistry laboratory curriculum and the assessment…

Attitude determination and calibration using a recursive maximum likelihood-based adaptive Kalman filter

NASA Technical Reports Server (NTRS)

Kelly, D. A.; Fermelia, A.; Lee, G. K. F.

1990-01-01

An adaptive Kalman filter design that utilizes recursive maximum likelihood parameter identification is discussed. At the center of this design is the Kalman filter itself, which has the responsibility for attitude determination. At the same time, the identification algorithm is continually identifying the system parameters. The approach is applicable to nonlinear, as well as linear systems. This adaptive Kalman filter design has much potential for real time implementation, especially considering the fast clock speeds, cache memory and internal RAM available today. The recursive maximum likelihood algorithm is discussed in detail, with special attention directed towards its unique matrix formulation. The procedure for using the algorithm is described along with comments on how this algorithm interacts with the Kalman filter.

DNA barcoding: an efficient tool to overcome authentication challenges in the herbal market.

PubMed

Mishra, Priyanka; Kumar, Amit; Nagireddy, Akshitha; Mani, Daya N; Shukla, Ashutosh K; Tiwari, Rakesh; Sundaresan, Velusamy

2016-01-01

The past couple of decades have witnessed global resurgence of herbal-based health care. As a result, the trade of raw drugs has surged globally. Accurate and fast scientific identification of the plant(s) is the key to success for the herbal drug industry. The conventional approach is to engage an expert taxonomist, who uses a mix of traditional and modern techniques for precise plant identification. However, for bulk identification at industrial scale, the process is protracted and time-consuming. DNA barcoding, on the other hand, offers an alternative and feasible taxonomic tool box for rapid and robust species identification. For the success of DNA barcode, the barcode loci must have sufficient information to differentiate unambiguously between closely related plant species and discover new cryptic species. For herbal plant identification, matK, rbcL, trnH-psbA, ITS, trnL-F, 5S-rRNA and 18S-rRNA have been used as successful DNA barcodes. Emerging advances in DNA barcoding coupled with next-generation sequencing and high-resolution melting curve analysis have paved the way for successful species-level resolution recovered from finished herbal products. Further, development of multilocus strategy and its application has provided new vistas to the DNA barcode-based plant identification for herbal drug industry. For successful and acceptable identification of herbal ingredients and a holistic quality control of the drug, DNA barcoding needs to work harmoniously with other components of the systems biology approach. We suggest that for effectively resolving authentication challenges associated with the herbal market, DNA barcoding must be used in conjunction with metabolomics along with need-based transcriptomics and proteomics. © 2015 Society for Experimental Biology, Association of Applied Biologists and John Wiley & Sons Ltd.

A Multiple Identity Approach to Gender: Identification with Women, Identification with Feminists, and Their Interaction

PubMed Central

van Breen, Jolien A.; Spears, Russell; Kuppens, Toon; de Lemus, Soledad

2017-01-01

Across four studies, we examine multiple identities in the context of gender and propose that women's attitudes toward gender group membership are governed by two largely orthogonal dimensions of gender identity: identification with women and identification with feminists. We argue that identification with women reflects attitudes toward the content society gives to group membership: what does it mean to be a woman in terms of group characteristics, interests and values? Identification with feminists, on the other hand, is a politicized identity dimension reflecting attitudes toward the social position of the group: what does it mean to be a woman in terms of disadvantage, inequality, and relative status? We examine the utility of this multiple identity approach in four studies. Study 1 showed that identification with women reflects attitudes toward group characteristics, such as femininity and self-stereotyping, while identification with feminists reflects attitudes toward the group's social position, such as perceived sexism. The two dimensions are shown to be largely independent, and as such provide support for the multiple identity approach. In Studies 2–4, we examine the utility of this multiple identity approach in predicting qualitative differences in gender attitudes. Results show that specific combinations of identification with women and feminists predicted attitudes toward collective action and gender stereotypes. Higher identification with feminists led to endorsement of radical collective action (Study 2) and critical attitudes toward gender stereotypes (Studies 3–4), especially at lower levels of identification with women. The different combinations of high vs. low identification with women and feminists can be thought of as reflecting four theoretical identity “types.” A woman can be (1) strongly identified with neither women nor feminists (“low identifier”), (2) strongly identified with women but less so with feminists (“traditional identifier”), (3) strongly identified with both women and feminists (“dual identifier”), or (4) strongly identified with feminists but less so with women (“distinctive feminist”). In sum, by considering identification with women and identification with feminists as multiple identities we aim to show how the multiple identity approach predicts distinct attitudes to gender issues and offer a new perspective on gender identity. PMID:28713297

A Multiple Identity Approach to Gender: Identification with Women, Identification with Feminists, and Their Interaction.

PubMed

van Breen, Jolien A; Spears, Russell; Kuppens, Toon; de Lemus, Soledad

2017-01-01

Across four studies, we examine multiple identities in the context of gender and propose that women's attitudes toward gender group membership are governed by two largely orthogonal dimensions of gender identity: identification with women and identification with feminists. We argue that identification with women reflects attitudes toward the content society gives to group membership: what does it mean to be a woman in terms of group characteristics, interests and values? Identification with feminists, on the other hand, is a politicized identity dimension reflecting attitudes toward the social position of the group: what does it mean to be a woman in terms of disadvantage, inequality, and relative status? We examine the utility of this multiple identity approach in four studies. Study 1 showed that identification with women reflects attitudes toward group characteristics, such as femininity and self-stereotyping, while identification with feminists reflects attitudes toward the group's social position, such as perceived sexism. The two dimensions are shown to be largely independent, and as such provide support for the multiple identity approach. In Studies 2-4, we examine the utility of this multiple identity approach in predicting qualitative differences in gender attitudes. Results show that specific combinations of identification with women and feminists predicted attitudes toward collective action and gender stereotypes. Higher identification with feminists led to endorsement of radical collective action (Study 2) and critical attitudes toward gender stereotypes (Studies 3-4), especially at lower levels of identification with women. The different combinations of high vs. low identification with women and feminists can be thought of as reflecting four theoretical identity "types." A woman can be (1) strongly identified with neither women nor feminists ("low identifier"), (2) strongly identified with women but less so with feminists ("traditional identifier"), (3) strongly identified with both women and feminists ("dual identifier"), or (4) strongly identified with feminists but less so with women ("distinctive feminist"). In sum, by considering identification with women and identification with feminists as multiple identities we aim to show how the multiple identity approach predicts distinct attitudes to gender issues and offer a new perspective on gender identity.

An Integrated Framework for Model-Based Distributed Diagnosis and Prognosis

NASA Technical Reports Server (NTRS)

Bregon, Anibal; Daigle, Matthew J.; Roychoudhury, Indranil

2012-01-01

Diagnosis and prognosis are necessary tasks for system reconfiguration and fault-adaptive control in complex systems. Diagnosis consists of detection, isolation and identification of faults, while prognosis consists of prediction of the remaining useful life of systems. This paper presents a novel integrated framework for model-based distributed diagnosis and prognosis, where system decomposition is used to enable the diagnosis and prognosis tasks to be performed in a distributed way. We show how different submodels can be automatically constructed to solve the local diagnosis and prognosis problems. We illustrate our approach using a simulated four-wheeled rover for different fault scenarios. Our experiments show that our approach correctly performs distributed fault diagnosis and prognosis in an efficient and robust manner.

Clone tag detection in distributed RFID systems.

PubMed

Kamaludin, Hazalila; Mahdin, Hairulnizam; Abawajy, Jemal H

2018-01-01

Although Radio Frequency Identification (RFID) is poised to displace barcodes, security vulnerabilities pose serious challenges for global adoption of the RFID technology. Specifically, RFID tags are prone to basic cloning and counterfeiting security attacks. A successful cloning of the RFID tags in many commercial applications can lead to many serious problems such as financial losses, brand damage, safety and health of the public. With many industries such as pharmaceutical and businesses deploying RFID technology with a variety of products, it is important to tackle RFID tag cloning problem and improve the resistance of the RFID systems. To this end, we propose an approach for detecting cloned RFID tags in RFID systems with high detection accuracy and minimal overhead thus overcoming practical challenges in existing approaches. The proposed approach is based on consistency of dual hash collisions and modified count-min sketch vector. We evaluated the proposed approach through extensive experiments and compared it with existing baseline approaches in terms of execution time and detection accuracy under varying RFID tag cloning ratio. The results of the experiments show that the proposed approach outperforms the baseline approaches in cloned RFID tag detection accuracy.

A Cooperative Approach To Teaching Mineral Identification.

ERIC Educational Resources Information Center

Constantopoulos, Terri Lynn

1994-01-01

Describes Jigsaw Teaching, a cooperative learning approach, in relation to mineral identification. This technique may also be applied to rock identification. Students work in groups of four and learn to identify 20 minerals, becoming an "expert" on five of them. Helping to teach other students reinforces what each student has learned.…

SSME fault monitoring and diagnosis expert system

NASA Technical Reports Server (NTRS)

Ali, Moonis; Norman, Arnold M.; Gupta, U. K.

1989-01-01

An expert system, called LEADER, has been designed and implemented for automatic learning, detection, identification, verification, and correction of anomalous propulsion system operations in real time. LEADER employs a set of sensors to monitor engine component performance and to detect, identify, and validate abnormalities with respect to varying engine dynamics and behavior. Two diagnostic approaches are adopted in the architecture of LEADER. In the first approach fault diagnosis is performed through learning and identifying engine behavior patterns. LEADER, utilizing this approach, generates few hypotheses about the possible abnormalities. These hypotheses are then validated based on the SSME design and functional knowledge. The second approach directs the processing of engine sensory data and performs reasoning based on the SSME design, functional knowledge, and the deep-level knowledge, i.e., the first principles (physics and mechanics) of SSME subsystems and components. This paper describes LEADER's architecture which integrates a design based reasoning approach with neural network-based fault pattern matching techniques. The fault diagnosis results obtained through the analyses of SSME ground test data are presented and discussed.

Treat the brain and treat the periphery: toward a holistic approach to major depressive disorder.

PubMed

Zheng, Xiao; Zhang, Xueli; Wang, Guangji; Hao, Haiping

2015-05-01

The limited medication for major depressive disorder (MDD) against an ever-rising disease burden presents an urgent need for therapeutic innovations. During recent years, studies looking at the systems regulation of mental health and disease have shown a remarkably powerful control of MDD by systemic signals. Meanwhile, the identification of a host of targets outside the brain opens the way to treat MDD by targeting systemic signals. We examine these emerging findings and consider the implications for current thinking regarding MDD pathogenesis and treatment. We highlight the opportunities and challenges of a periphery-targeting strategy and propose its incorporation into a holistic approach. Copyright © 2015 Elsevier Ltd. All rights reserved.

Frequency domain surface EMG sensor fusion for estimating finger forces.

PubMed

Potluri, Chandrasekhar; Kumar, Parmod; Anugolu, Madhavi; Urfer, Alex; Chiu, Steve; Naidu, D; Schoen, Marco P

2010-01-01

Extracting or estimating skeletal hand/finger forces using surface electro myographic (sEMG) signals poses many challenges due to cross-talk, noise, and a temporal and spatially modulated signal characteristics. Normal sEMG measurements are based on single sensor data. In this paper, array sensors are used along with a proposed sensor fusion scheme that result in a simple Multi-Input-Single-Output (MISO) transfer function. Experimental data is used along with system identification to find this MISO system. A Genetic Algorithm (GA) approach is employed to optimize the characteristics of the MISO system. The proposed fusion-based approach is tested experimentally and indicates improvement in finger/hand force estimation.

Linear system identification via backward-time observer models

NASA Technical Reports Server (NTRS)

Juang, Jer-Nan; Phan, Minh

1993-01-01

This paper presents an algorithm to identify a state-space model of a linear system using a backward-time approach. The procedure consists of three basic steps. First, the Markov parameters of a backward-time observer are computed from experimental input-output data. Second, the backward-time observer Markov parameters are decomposed to obtain the backward-time system Markov parameters (backward-time pulse response samples) from which a backward-time state-space model is realized using the Eigensystem Realization Algorithm. Third, the obtained backward-time state space model is converted to the usual forward-time representation. Stochastic properties of this approach will be discussed. Experimental results are given to illustrate when and to what extent this concept works.

A systems-level approach for metabolic engineering of yeast cell factories.

PubMed

Kim, Il-Kwon; Roldão, António; Siewers, Verena; Nielsen, Jens

2012-03-01

The generation of novel yeast cell factories for production of high-value industrial biotechnological products relies on three metabolic engineering principles: design, construction, and analysis. In the last two decades, strong efforts have been put on developing faster and more efficient strategies and/or technologies for each one of these principles. For design and construction, three major strategies are described in this review: (1) rational metabolic engineering; (2) inverse metabolic engineering; and (3) evolutionary strategies. Independent of the selected strategy, the process of designing yeast strains involves five decision points: (1) choice of product, (2) choice of chassis, (3) identification of target genes, (4) regulating the expression level of target genes, and (5) network balancing of the target genes. At the construction level, several molecular biology tools have been developed through the concept of synthetic biology and applied for the generation of novel, engineered yeast strains. For comprehensive and quantitative analysis of constructed strains, systems biology tools are commonly used and using a multi-omics approach. Key information about the biological system can be revealed, for example, identification of genetic regulatory mechanisms and competitive pathways, thereby assisting the in silico design of metabolic engineering strategies for improving strain performance. Examples on how systems and synthetic biology brought yeast metabolic engineering closer to industrial biotechnology are described in this review, and these examples should demonstrate the potential of a systems-level approach for fast and efficient generation of yeast cell factories. © 2011 Federation of European Microbiological Societies. Published by Blackwell Publishing Ltd. All rights reserved.

Flight elements: Fault detection and fault management

NASA Technical Reports Server (NTRS)

Lum, H.; Patterson-Hine, A.; Edge, J. T.; Lawler, D.

1990-01-01

Fault management for an intelligent computational system must be developed using a top down integrated engineering approach. An approach proposed includes integrating the overall environment involving sensors and their associated data; design knowledge capture; operations; fault detection, identification, and reconfiguration; testability; causal models including digraph matrix analysis; and overall performance impacts on the hardware and software architecture. Implementation of the concept to achieve a real time intelligent fault detection and management system will be accomplished via the implementation of several objectives, which are: Development of fault tolerant/FDIR requirement and specification from a systems level which will carry through from conceptual design through implementation and mission operations; Implementation of monitoring, diagnosis, and reconfiguration at all system levels providing fault isolation and system integration; Optimize system operations to manage degraded system performance through system integration; and Lower development and operations costs through the implementation of an intelligent real time fault detection and fault management system and an information management system.

Identification errors in the blood transfusion laboratory: a still relevant issue for patient safety.

PubMed

Lippi, Giuseppe; Plebani, Mario

2011-04-01

Remarkable technological advances and increased awareness have both contributed to decrease substantially the uncertainty of the analytical phase, so that the manually intensive preanalytical activities currently represent the leading sources of errors in laboratory and transfusion medicine. Among preanalytical errors, misidentification and mistransfusion are still regarded as a considerable problem, posing serious risks for patient health and carrying huge expenses for the healthcare system. As such, a reliable policy of risk management should be readily implemented, developing through a multifaceted approach to prevent or limit the adverse outcomes related to transfusion reactions from blood incompatibility. This strategy encompasses root cause analysis, compliance with accreditation requirements, strict adherence to standard operating procedures, guidelines and recommendations for specimen collection, use of positive identification devices, rejection of potentially misidentified specimens, informatics data entry, query host communication, automated systems for patient identification and sample labeling and an adequate and safe environment. Copyright © 2011 Elsevier Ltd. All rights reserved.

Expert system for identification of simultaneous and sequential reactor fuel failures with gas tagging

DOEpatents

Gross, K.C.

1994-07-26

Failure of a fuel element in a nuclear reactor core is determined by a gas tagging failure detection system and method. Failures are catalogued and characterized after the event so that samples of the reactor's cover gas are taken at regular intervals and analyzed by mass spectroscopy. Employing a first set of systematic heuristic rules which are applied in a transformed node space allows the number of node combinations which must be processed within a barycentric algorithm to be substantially reduced. A second set of heuristic rules treats the tag nodes of the most recent one or two leakers as background'' gases, further reducing the number of trial node combinations. Lastly, a fuzzy'' set theory formalism minimizes experimental uncertainties in the identification of the most likely volumes of tag gases. This approach allows for the identification of virtually any number of sequential leaks and up to five simultaneous gas leaks from fuel elements. 14 figs.

Detecting synchronization clusters in multivariate time series via coarse-graining of Markov chains.

PubMed

Allefeld, Carsten; Bialonski, Stephan

2007-12-01

Synchronization cluster analysis is an approach to the detection of underlying structures in data sets of multivariate time series, starting from a matrix R of bivariate synchronization indices. A previous method utilized the eigenvectors of R for cluster identification, analogous to several recent attempts at group identification using eigenvectors of the correlation matrix. All of these approaches assumed a one-to-one correspondence of dominant eigenvectors and clusters, which has however been shown to be wrong in important cases. We clarify the usefulness of eigenvalue decomposition for synchronization cluster analysis by translating the problem into the language of stochastic processes, and derive an enhanced clustering method harnessing recent insights from the coarse-graining of finite-state Markov processes. We illustrate the operation of our method using a simulated system of coupled Lorenz oscillators, and we demonstrate its superior performance over the previous approach. Finally we investigate the question of robustness of the algorithm against small sample size, which is important with regard to field applications.

A Noise Trimming and Positional Significance of Transposon Insertion System to Identify Essential Genes in Yersinia pestis

NASA Astrophysics Data System (ADS)

Yang, Zheng Rong; Bullifent, Helen L.; Moore, Karen; Paszkiewicz, Konrad; Saint, Richard J.; Southern, Stephanie J.; Champion, Olivia L.; Senior, Nicola J.; Sarkar-Tyson, Mitali; Oyston, Petra C. F.; Atkins, Timothy P.; Titball, Richard W.

2017-02-01

Massively parallel sequencing technology coupled with saturation mutagenesis has provided new and global insights into gene functions and roles. At a simplistic level, the frequency of mutations within genes can indicate the degree of essentiality. However, this approach neglects to take account of the positional significance of mutations - the function of a gene is less likely to be disrupted by a mutation close to the distal ends. Therefore, a systematic bioinformatics approach to improve the reliability of essential gene identification is desirable. We report here a parametric model which introduces a novel mutation feature together with a noise trimming approach to predict the biological significance of Tn5 mutations. We show improved performance of essential gene prediction in the bacterium Yersinia pestis, the causative agent of plague. This method would have broad applicability to other organisms and to the identification of genes which are essential for competitiveness or survival under a broad range of stresses.

A Noise Trimming and Positional Significance of Transposon Insertion System to Identify Essential Genes in Yersinia pestis

PubMed Central

Yang, Zheng Rong; Bullifent, Helen L.; Moore, Karen; Paszkiewicz, Konrad; Saint, Richard J.; Southern, Stephanie J.; Champion, Olivia L.; Senior, Nicola J.; Sarkar-Tyson, Mitali; Oyston, Petra C. F.; Atkins, Timothy P.; Titball, Richard W.

2017-01-01

Massively parallel sequencing technology coupled with saturation mutagenesis has provided new and global insights into gene functions and roles. At a simplistic level, the frequency of mutations within genes can indicate the degree of essentiality. However, this approach neglects to take account of the positional significance of mutations - the function of a gene is less likely to be disrupted by a mutation close to the distal ends. Therefore, a systematic bioinformatics approach to improve the reliability of essential gene identification is desirable. We report here a parametric model which introduces a novel mutation feature together with a noise trimming approach to predict the biological significance of Tn5 mutations. We show improved performance of essential gene prediction in the bacterium Yersinia pestis, the causative agent of plague. This method would have broad applicability to other organisms and to the identification of genes which are essential for competitiveness or survival under a broad range of stresses. PMID:28165493

Personalized medicine and chronic obstructive pulmonary disease.

PubMed

Wouters, E F M; Wouters, B B R A F; Augustin, I M L; Franssen, F M E

2017-05-01

The current review summarizes ongoing developments in personalized medicine and precision medicine in chronic obstructive pulmonary disease (COPD). Our current approach is far away of personalized management algorithms as current recommendations for COPD are largely based on a reductionist disease description, operationally defined by results of spirometry. Besides precision medicine developments, a personalized medicine approach in COPD is described based on a holistic approach of the patient and considering illness as the consequence of dynamic interactions within and between multiple interacting and self-adjusting systems. Pulmonary rehabilitation is described as a model of personalized medicine. Largely based on current understanding of inflammatory processes in COPD, targeted interventions in COPD are reviewed. Augmentation therapy for α-1-antitrypsine deficiency is described as model of precision medicine in COPD based in profound understanding of the related genetic endotype. Future developments of precision medicine in COPD require identification of relevant endotypes combined with proper identification of phenotypes involved in the complex and heterogeneous manifestations of COPD.

An Efficient Approach to Evaluate Reporter Ion Behavior from MALDI-MS/MS Data for Quantification Studies using Isobaric Tags

PubMed Central

Cologna, Stephanie M.; Crutchfield, Christopher A.; Searle, Brian C.; Blank, Paul S.; Toth, Cynthia L.; Ely, Alexa M.; Picache, Jaqueline A.; Backlund, Peter S.; Wassif, Christopher A.; Porter, Forbes D.; Yergey, Alfred L.

2017-01-01

Protein quantification, identification and abundance determination are important aspects of proteome characterization and are crucial in understanding biological mechanisms and human diseases. Different strategies are available to quantify proteins using mass spectrometric detection, and most are performed at the peptide level and include both targeted and un-targeted methodologies. Discovery-based or un-targeted approaches oftentimes use covalent tagging strategies (i.e., iTRAQ®, TMT™) where reporter ion signals collected in the tandem MS experiment are used for quantification. Herein we investigate the behavior of the iTRAQ 8-plex chemistry using MALDI-TOF/TOF instrumentation. The experimental design and data analysis approach described is simple and straightforward, which allows researchers to optimize data collection and proper analysis within a laboratory. iTRAQ reporter ion signals were normalized within each spectrum to remove peptide biases. An advantage of this approach is that missing reporter ion values can be accepted for purposes of protein identification and quantification with the need for ANOVA analysis. We investigate the distribution of reporter ion peak areas in an equimolar system and a mock biological system and provide recommendations for establishing fold-change cutoff values at the peptide level for iTRAQ datasets. These data provide a unique dataset available to the community for informatics training and analysis. PMID:26288259

Optimal Sensor Scheduling for Multiple Hypothesis Testing

DTIC Science & Technology

1981-09-01

Naval Research, under contract N00014-77-0532 is gratpfully acknowledged. 2 Laboratory for Information and Decision Systems , MIT Room 35-213, Cambridge...treat the more general problem [9,10]. However, two common threads connect these approaches: they obtain feedback laws mapping posterior destributions ...objective of a detection or identification algorithm is to produce correct estimates of the true state of a system . It is also bene- ficial if these

Methods and Apparatus for Reducing Multipath Signal Error Using Deconvolution

NASA Technical Reports Server (NTRS)

Kumar, Rajendra (Inventor); Lau, Kenneth H. (Inventor)

1999-01-01

A deconvolution approach to adaptive signal processing has been applied to the elimination of signal multipath errors as embodied in one preferred embodiment in a global positioning system receiver. The method and receiver of the present invention estimates then compensates for multipath effects in a comprehensive manner. Application of deconvolution, along with other adaptive identification and estimation techniques, results in completely novel GPS (Global Positioning System) receiver architecture.

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.

[Surgical anatomy of the peri-insular association tracts. Part I.The superior longitudinal fascicle system].

PubMed

Bykanov, A E; Pitskhelauri, D I; Batalov, A I; Pronin, I N; Shkarubo, M A; Dobrovol'sky, G F; Kobyakov, G L; Buklina, S B; Puchkov, V L; Zakharova, N E; Smirnov, A S; Sanikidze, A Z; Gol'bin, D A; Pogosbekyan, E L; Kudieva, E S; Shkatova, A M; Potapov, A A

To study the peri-insular association tract anatomy and define the permissible anatomical boundaries for resection of glial insular tumors with allowance for the surgical anatomy of the peri-insular association tracts. In an anatomic study of the superior longitudinal fascicle system (SLF I, SLF II, SLF III, arcuate fascicle), we used 12 anatomical specimens (6 left and 6 right hemispheres) prepared according to the Klingler's fiber dissection technique. To confirm the dissection data, we used MR tractography (HARDI-CSD-tractography) of the conduction tracts, which was performed in two healthy volunteers. Except the SLF I (identified in 7 hemispheres by fiber dissection), all fascicles of the SLF system were found in all investigated hemispheres by both fiber dissection and MR tractography. The transcortical approach to the insula through the frontal and (or) parietal operculum is associated with a significant risk of transverse transection of the SLF III fibers passing in the frontal and parietal opercula. The most optimal area for the transcortical approach to the insula is the anterior third of the superior temporal gyrus that lacks important association tracts and, consequently, a risk of their injury. The superior peri-insular sulcus is an intraoperative landmark for the transsylvian approach, which enables identification of the SLF II and arcuate fascicle in the surgical wound. Detailed knowledge of the peri-insular association tract anatomy is the prerequisite for neurosurgery in the insular region. Our findings facilitate correct identification of both the site for cerebral operculum dissection upon the transcortical approach and the intraoperative landmarks for locating the association tracts in the surgical wound upon the transsylvian approach to the insula.

Integration of low level and ontology derived features for automatic weapon recognition and identification

NASA Astrophysics Data System (ADS)

Sirakov, Nikolay M.; Suh, Sang; Attardo, Salvatore

2011-06-01

This paper presents a further step of a research toward the development of a quick and accurate weapons identification methodology and system. A basic stage of this methodology is the automatic acquisition and updating of weapons ontology as a source of deriving high level weapons information. The present paper outlines the main ideas used to approach the goal. In the next stage, a clustering approach is suggested on the base of hierarchy of concepts. An inherent slot of every node of the proposed ontology is a low level features vector (LLFV), which facilitates the search through the ontology. Part of the LLFV is the information about the object's parts. To partition an object a new approach is presented capable of defining the objects concavities used to mark the end points of weapon parts, considered as convexities. Further an existing matching approach is optimized to determine whether an ontological object matches the objects from an input image. Objects from derived ontological clusters will be considered for the matching process. Image resizing is studied and applied to decrease the runtime of the matching approach and investigate its rotational and scaling invariance. Set of experiments are preformed to validate the theoretical concepts.

Monitoring and control requirement definition study for Dispersed Storage and Generation (DSG). Volume 4, appendix C: Identification from utility visits of present and future approaches to integration of DSG into distribution networks

NASA Technical Reports Server (NTRS)

1980-01-01

Visits to four utilities concerned with the use of DSG power sources on their distribution networks yielded useful impressions of present and future approaches to the integration of DSGs into electrical distribution network. Different approaches to future utility systems with DSG are beginning to take shape. The new DSG sources will be in decentralized locations with some measure of centralized control. The utilities have yet to establish firmly the communication and control means or their organization. For the present, the means for integrating the DSGs and their associated monitoring and control equipment into a unified system have not been decided.

Inferring Time-Varying Network Topologies from Gene Expression Data

PubMed Central

2007-01-01

Most current methods for gene regulatory network identification lead to the inference of steady-state networks, that is, networks prevalent over all times, a hypothesis which has been challenged. There has been a need to infer and represent networks in a dynamic, that is, time-varying fashion, in order to account for different cellular states affecting the interactions amongst genes. In this work, we present an approach, regime-SSM, to understand gene regulatory networks within such a dynamic setting. The approach uses a clustering method based on these underlying dynamics, followed by system identification using a state-space model for each learnt cluster—to infer a network adjacency matrix. We finally indicate our results on the mouse embryonic kidney dataset as well as the T-cell activation-based expression dataset and demonstrate conformity with reported experimental evidence. PMID:18309363

Inferring time-varying network topologies from gene expression data.

PubMed

Rao, Arvind; Hero, Alfred O; States, David J; Engel, James Douglas

2007-01-01

Most current methods for gene regulatory network identification lead to the inference of steady-state networks, that is, networks prevalent over all times, a hypothesis which has been challenged. There has been a need to infer and represent networks in a dynamic, that is, time-varying fashion, in order to account for different cellular states affecting the interactions amongst genes. In this work, we present an approach, regime-SSM, to understand gene regulatory networks within such a dynamic setting. The approach uses a clustering method based on these underlying dynamics, followed by system identification using a state-space model for each learnt cluster--to infer a network adjacency matrix. We finally indicate our results on the mouse embryonic kidney dataset as well as the T-cell activation-based expression dataset and demonstrate conformity with reported experimental evidence.

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.

A Statistics-based Platform for Quantitative N-terminome Analysis and Identification of Protease Cleavage Products*

PubMed Central

auf dem Keller, Ulrich; Prudova, Anna; Gioia, Magda; Butler, Georgina S.; Overall, Christopher M.

2010-01-01

Terminal amine isotopic labeling of substrates (TAILS), our recently introduced platform for quantitative N-terminome analysis, enables wide dynamic range identification of original mature protein N-termini and protease cleavage products. Modifying TAILS by use of isobaric tag for relative and absolute quantification (iTRAQ)-like labels for quantification together with a robust statistical classifier derived from experimental protease cleavage data, we report reliable and statistically valid identification of proteolytic events in complex biological systems in MS2 mode. The statistical classifier is supported by a novel parameter evaluating ion intensity-dependent quantification confidences of single peptide quantifications, the quantification confidence factor (QCF). Furthermore, the isoform assignment score (IAS) is introduced, a new scoring system for the evaluation of single peptide-to-protein assignments based on high confidence protein identifications in the same sample prior to negative selection enrichment of N-terminal peptides. By these approaches, we identified and validated, in addition to known substrates, low abundance novel bioactive MMP-2 targets including the plasminogen receptor S100A10 (p11) and the proinflammatory cytokine proEMAP/p43 that were previously undescribed. PMID:20305283

Identification of potentially emerging food safety issues by analysis of reports published by the European Community's Rapid Alert System for Food and Feed (RASFF) during a four-year period.

PubMed

Kleter, G A; Prandini, A; Filippi, L; Marvin, H J P

2009-05-01

The SAFE FOODS project undertakes to design a new approach towards the early identification of emerging food safety hazards. This study explored the utility of notifications filed through RASFF, the European Commission's Rapid Alert System for Food and Feed, to identify emerging trends in food safety issues. RASFF information and alert notifications published in the four-year period of July 2003-June 2007 were assigned to categories of products and hazards. For chronological trend analysis, a basic time unit of three months was chosen. Data within each hazard category were analyzed for chronological trends, relationships between product and hazard categories, regions of origin, and countries filing the notifications. Conspicuous trends that were observed included a rise in the incidence of food contact substances, particularly 2-isopropyl-thioxanthone, as well as of chemical substances migrating from utensils and fraud-related issues. Temporary increases were noted in the incidences of the unauthorized dye Para Red, genetically modified organisms, the pesticide isophenfos-methyl, and herring worm, Anisakis simplex. National and European authorities themselves have signaled these conspicuous trends and taken measures. It is recommended to add complementary data to RASFF data, including safety assessments, risk management measures, background data on hazards and surveillance patterns, for a holistic approach towards early identification of emerging hazards.

Gyro-based Maximum-Likelihood Thruster Fault Detection and Identification

NASA Technical Reports Server (NTRS)

Wilson, Edward; Lages, Chris; Mah, Robert; Clancy, Daniel (Technical Monitor)

2002-01-01

When building smaller, less expensive spacecraft, there is a need for intelligent fault tolerance vs. increased hardware redundancy. If fault tolerance can be achieved using existing navigation sensors, cost and vehicle complexity can be reduced. A maximum likelihood-based approach to thruster fault detection and identification (FDI) for spacecraft is developed here and applied in simulation to the X-38 space vehicle. The system uses only gyro signals to detect and identify hard, abrupt, single and multiple jet on- and off-failures. Faults are detected within one second and identified within one to five accords,

Septic tank burial: not just another skeleton in the closet.

PubMed

Lew, E O; Bannach, B; Rodriguez, W C

1996-09-01

Backed-up toilets lead to the discovery of a skeleton in the septic tank. Our challenges began with the excavation of this unconventional grave and progressed through recovery and examination of the skeleton, determination of the cause and manner of death, and ultimately, identification of the victim. Main aspects of the septic tank system are summarized, including functional theory, physical design and components, and general comments on use and maintenance. We discuss some basic principles applicable to the excavation and examination of any human skeletal remains, and offer a general approach to identification of the decedent.

A class of least-squares filtering and identification algorithms with systolic array architectures

NASA Technical Reports Server (NTRS)

Kalson, Seth Z.; Yao, Kung

1991-01-01

A unified approach is presented for deriving a large class of new and previously known time- and order-recursive least-squares algorithms with systolic array architectures, suitable for high-throughput-rate and VLSI implementations of space-time filtering and system identification problems. The geometrical derivation given is unique in that no assumption is made concerning the rank of the sample data correlation matrix. This method utilizes and extends the concept of oblique projections, as used previously in the derivations of the least-squares lattice algorithms. Exponentially weighted least-squares criteria are considered for both sliding and growing memory.

Pitfalls of Establishing DNA Barcoding Systems in Protists: The Cryptophyceae as a Test Case

PubMed Central

Hoef-Emden, Kerstin

2012-01-01

A DNA barcode is a preferrably short and highly variable region of DNA supposed to facilitate a rapid identification of species. In many protistan lineages, a lack of species-specific morphological characters hampers an identification of species by light or electron microscopy, and difficulties to perform mating experiments in laboratory cultures also do not allow for an identification of biological species. Thus, testing candidate barcode markers as well as establishment of accurately working species identification systems are more challenging than in multicellular organisms. In cryptic species complexes the performance of a potential barcode marker can not be monitored using morphological characters as a feedback, but an inappropriate choice of DNA region may result in artifactual species trees for several reasons. Therefore a priori knowledge of the systematics of a group is required. In addition to identification of known species, methods for an automatic delimitation of species with DNA barcodes have been proposed. The Cryptophyceae provide a mixture of systematically well characterized as well as badly characterized groups and are used in this study to test the suitability of some of the methods for protists. As species identification method the performance of blast in searches against badly to well-sampled reference databases has been tested with COI-5P and 5′-partial LSU rDNA (domains A to D of the nuclear LSU rRNA gene). In addition the performance of two different methods for automatic species delimitation, fixed thresholds of genetic divergence and the general mixed Yule-coalescent model (GMYC), have been examined. The study demonstrates some pitfalls of barcoding methods that have to be taken care of. Also a best-practice approach towards establishing a DNA barcode system in protists is proposed. PMID:22970104

Pitfalls of establishing DNA barcoding systems in protists: the cryptophyceae as a test case.

PubMed

Hoef-Emden, Kerstin

2012-01-01

A DNA barcode is a preferrably short and highly variable region of DNA supposed to facilitate a rapid identification of species. In many protistan lineages, a lack of species-specific morphological characters hampers an identification of species by light or electron microscopy, and difficulties to perform mating experiments in laboratory cultures also do not allow for an identification of biological species. Thus, testing candidate barcode markers as well as establishment of accurately working species identification systems are more challenging than in multicellular organisms. In cryptic species complexes the performance of a potential barcode marker can not be monitored using morphological characters as a feedback, but an inappropriate choice of DNA region may result in artifactual species trees for several reasons. Therefore a priori knowledge of the systematics of a group is required. In addition to identification of known species, methods for an automatic delimitation of species with DNA barcodes have been proposed. The Cryptophyceae provide a mixture of systematically well characterized as well as badly characterized groups and are used in this study to test the suitability of some of the methods for protists. As species identification method the performance of blast in searches against badly to well-sampled reference databases has been tested with COI-5P and 5'-partial LSU rDNA (domains A to D of the nuclear LSU rRNA gene). In addition the performance of two different methods for automatic species delimitation, fixed thresholds of genetic divergence and the general mixed Yule-coalescent model (GMYC), have been examined. The study demonstrates some pitfalls of barcoding methods that have to be taken care of. Also a best-practice approach towards establishing a DNA barcode system in protists is proposed.

Intelligent demand side management of residential building energy systems

NASA Astrophysics Data System (ADS)

Sinha, Maruti N.

Advent of modern sensing technologies, data processing capabilities and rising cost of energy are driving the implementation of intelligent systems in buildings and houses which constitute 41% of total energy consumption. The primary motivation has been to provide a framework for demand-side management and to improve overall reliability. The entire formulation is to be implemented on NILM (Non-Intrusive Load Monitoring System), a smart meter. This is going to play a vital role in the future of demand side management. Utilities have started deploying smart meters throughout the world which will essentially help to establish communication between utility and consumers. This research is focused on investigation of a suitable thermal model of residential house, building up control system and developing diagnostic and energy usage forecast tool. The present work has considered measurement based approach to pursue. Identification of building thermal parameters is the very first step towards developing performance measurement and controls. The proposed identification technique is PEM (Prediction Error Method) based, discrete state-space model. The two different models have been devised. First model is focused toward energy usage forecast and diagnostics. Here one of the novel idea has been investigated which takes integral of thermal capacity to identify thermal model of house. The purpose of second identification is to build up a model for control strategy. The controller should be able to take into account the weather forecast information, deal with the operating point constraints and at the same time minimize the energy consumption. To design an optimal controller, MPC (Model Predictive Control) scheme has been implemented instead of present thermostatic/hysteretic control. This is a receding horizon approach. Capability of the proposed schemes has also been investigated.

Low-order black-box models for control system design in large power systems

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

Kamwa, I.; Trudel, G.; Gerin-Lajoie, L.

1996-02-01

The paper studies two multi-input multi-output (MIMO) procedures for the identification of low-order state-space models of power systems, by probing the network in open loop with low-energy pulses or random signals. Although such data may result from actual measurements, the development assumes simulated responses from a transient stability program, hence benefiting from the existing large base of stability models. While pulse data is processed using the eigensystem realization algorithm, the analysis of random responses is done by means of subspace identification methods. On a prototype Hydro-Quebec power system, including SVCs, DC lines, series compensation, and more than 1,100 buses, itmore » is verified that the two approaches are equivalent only when strict requirements are imposed on the pulse length and magnitude. The 10th-order equivalent models derived by random-signal probing allow for effective tuning of decentralized power system stabilizers (PSSs) able to damp both local and very slow inter-area modes.« less

Real-Time Parameter Estimation Method Applied to a MIMO Process and its Comparison with an Offline Identification Method

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

Kaplanoglu, Erkan; Safak, Koray K.; Varol, H. Selcuk

2009-01-12

An experiment based method is proposed for parameter estimation of a class of linear multivariable systems. The method was applied to a pressure-level control process. Experimental time domain input/output data was utilized in a gray-box modeling approach. Prior knowledge of the form of the system transfer function matrix elements is assumed to be known. Continuous-time system transfer function matrix parameters were estimated in real-time by the least-squares method. Simulation results of experimentally determined system transfer function matrix compare very well with the experimental results. For comparison and as an alternative to the proposed real-time estimation method, we also implemented anmore » offline identification method using artificial neural networks and obtained fairly good results. The proposed methods can be implemented conveniently on a desktop PC equipped with a data acquisition board for parameter estimation of moderately complex linear multivariable systems.« less

Low-order black-box models for control system design in large power systems

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

Kamwa, I.; Trudel, G.; Gerin-Lajoie, L.

1995-12-31

The paper studies two multi-input multi-output (MIMO) procedures for the identification of low-order state-space models of power systems, by probing the network in open loop with low-energy pulses or random signals. Although such data may result from actual measurements, the development assumes simulated responses from a transient stability program, hence benefiting form the existing large base of stability models. While pulse data is processed using the eigensystem realization algorithm, the analysis of random responses is done by means of subspace identification methods. On a prototype Hydro-Quebec power system, including SVCs, DC lines, series compensation, and more than 1,100 buses, itmore » is verified that the two approaches are equivalent only when strict requirements are imposed on the pulse length and magnitude. The 10th-order equivalent models derived by random-signal probing allow for effective tuning of decentralized power system stabilizers (PSSs) able to damp both local and very slow inter-area modes.« less

Screening and identification of antioxidants in biological samples using high-performance liquid chromatography-mass spectrometry and its application on Salacca edulis Reinw.

PubMed

Shui, Guanghou; Leong, Lai Peng

2005-02-23

In this study, a new approach was developed for screening and identifying antioxidants in biological samples. The approach was based on significant decreases of the intensities of ion peaks obtained from high-performance liquid chromatography (HPLC) coupled with mass spectrometry (MS) upon reaction with 2,2'-azinobis(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) free radicals. HPLC-MS/MS was further applied to elucidate structures of antioxidant peaks characterized in a spiking test. The new approach could also be used to monitor the reactivity of antioxidants in biological sample with free radicals. The approach was successfully applied to the identification of antioxidants in salak (Salacca edulis Reinw), a tropical fruit that is reported to be a very good source of natural antioxidants, but it was still not clear which compounds were responsible for its antioxidant property. The antioxidants in salak were identified to be chlorogenic acid, (-)-epicatechin, and singly linked proanthocyanidins that mainly existed as dimers through hexamers of catechin or epicatechin. In salak, chlorogenic acid was identified to be an antioxidant of the slow reaction type as it reacted with free radicals much more slowly than either (-)-epicatechin or proanthocyanidins. The new approach was proved to be useful for the characterization and identification of antioxidants in biological samples as a mass detector combined with an HPLC separation system not only serves as an ideal tool to monitor free radical active components but also provides their possible chemical structures in a biological sample.

Mass spectrometry-based protein identification by integrating de novo sequencing with database searching.

PubMed

Wang, Penghao; Wilson, Susan R

2013-01-01

Mass spectrometry-based protein identification is a very challenging task. The main identification approaches include de novo sequencing and database searching. Both approaches have shortcomings, so an integrative approach has been developed. The integrative approach firstly infers partial peptide sequences, known as tags, directly from tandem spectra through de novo sequencing, and then puts these sequences into a database search to see if a close peptide match can be found. However the current implementation of this integrative approach has several limitations. Firstly, simplistic de novo sequencing is applied and only very short sequence tags are used. Secondly, most integrative methods apply an algorithm similar to BLAST to search for exact sequence matches and do not accommodate sequence errors well. Thirdly, by applying these methods the integrated de novo sequencing makes a limited contribution to the scoring model which is still largely based on database searching. We have developed a new integrative protein identification method which can integrate de novo sequencing more efficiently into database searching. Evaluated on large real datasets, our method outperforms popular identification methods.

Air pollution source identification

NASA Technical Reports Server (NTRS)

Fordyce, J. S.

1975-01-01

The techniques available for source identification are reviewed: remote sensing, injected tracers, and pollutants themselves as tracers. The use of the large number of trace elements in the ambient airborne particulate matter as a practical means of identifying sources is discussed. Trace constituents are determined by sensitive, inexpensive, nondestructive, multielement analytical methods such as instrumental neutron activation and charged particle X-ray fluorescence. The application to a large data set of pairwise correlation, the more advanced pattern recognition-cluster analysis approach with and without training sets, enrichment factors, and pollutant concentration rose displays for each element is described. It is shown that elemental constituents are related to specific source types: earth crustal, automotive, metallurgical, and more specific industries. A field-ready source identification system based on time and wind direction resolved sampling is described.

Application of statistical process control and process capability analysis procedures in orbiter processing activities at the Kennedy Space Center

NASA Technical Reports Server (NTRS)

Safford, Robert R.; Jackson, Andrew E.; Swart, William W.; Barth, Timothy S.

1994-01-01

Successful ground processing at KSC requires that flight hardware and ground support equipment conform to specifications at tens of thousands of checkpoints. Knowledge of conformance is an essential requirement for launch. That knowledge of conformance at every requisite point does not, however, enable identification of past problems with equipment, or potential problem areas. This paper describes how the introduction of Statistical Process Control and Process Capability Analysis identification procedures into existing shuttle processing procedures can enable identification of potential problem areas and candidates for improvements to increase processing performance measures. Results of a case study describing application of the analysis procedures to Thermal Protection System processing are used to illustrate the benefits of the approaches described in the paper.

An eigensystem realization algorithm using data correlations (ERA/DC) for modal parameter identification

NASA Technical Reports Server (NTRS)

Juang, Jer-Nan; Cooper, J. E.; Wright, J. R.

1987-01-01

A modification to the Eigensystem Realization Algorithm (ERA) for modal parameter identification is presented in this paper. The ERA minimum order realization approach using singular value decomposition is combined with the philosophy of the Correlation Fit method in state space form such that response data correlations rather than actual response values are used for modal parameter identification. This new method, the ERA using data correlations (ERA/DC), reduces bias errors due to noise corruption significantly without the need for model overspecification. This method is tested using simulated five-degree-of-freedom system responses corrupted by measurement noise. It is found for this case that, when model overspecification is permitted and a minimum order solution obtained via singular value truncation, the results from the two methods are of similar quality.

Ab Initio and Monte Carlo Approaches For the MagnetocaloricEffect in Co- and In-Doped Ni-Mn-Ga Heusler Alloys

NASA Astrophysics Data System (ADS)

Sokolovskiy, Vladimir; Grünebohm, Anna; Buchelnikov, Vasiliy; Entel, Peter

2014-09-01

This special issue collects contributions from the participants of the "Information in Dynamical Systems and Complex Systems" workshop, which cover a wide range of important problems and new approaches that lie in the intersection of information theory and dynamical systems. The contributions include theoretical characterization and understanding of the different types of information flow and causality in general stochastic processes, inference and identification of coupling structure and parameters of system dynamics, rigorous coarse-grain modeling of network dynamical systems, and exact statistical testing of fundamental information-theoretic quantities such as the mutual information. The collective efforts reported herein reflect a modern perspective of the intimate connection between dynamical systems and information flow, leading to the promise of better understanding and modeling of natural complex systems and better/optimal design of engineering systems.

Introducing new technologies into Space Station subsystems

NASA Technical Reports Server (NTRS)

Wiskerchen, Michael J.; Mollakarimi, Cindy L.

1989-01-01

A new systems engineering technology has been developed and applied to Shuttle processing. The new engineering approach emphasizes the identification, quantitative assessment, and management of system performance and risk related to the dynamic nature of requirements, technology, and operational concepts. The Space Shuttle Tile Automation System is described as an example of the first application of the new engineering technology. Lessons learned from the Shuttle processing experience are examined, and concepts are presented which are applicable to the design and development of the Space Station Freedom.

Certainty Equivalence M-MRAC for Systems with Unmatched Uncertainties

NASA Technical Reports Server (NTRS)

Stepanyan, Vahram; Krishnakumar, Kalmanje

2012-01-01

The paper presents a certainty equivalence state feedback indirect adaptive control design method for the systems of any relative degree with unmatched uncertainties. The approach is based on the parameter identification (estimation) model, which is completely separated from the control design and is capable of producing parameter estimates as fast as the computing power allows without generating high frequency oscillations. It is shown that the system's input and output tracking errors can be systematically decreased by the proper choice of the design parameters.

Identification of control targets in Boolean molecular network models via computational algebra.

PubMed

Murrugarra, David; Veliz-Cuba, Alan; Aguilar, Boris; Laubenbacher, Reinhard

2016-09-23

Many problems in biomedicine and other areas of the life sciences can be characterized as control problems, with the goal of finding strategies to change a disease or otherwise undesirable state of a biological system into another, more desirable, state through an intervention, such as a drug or other therapeutic treatment. The identification of such strategies is typically based on a mathematical model of the process to be altered through targeted control inputs. This paper focuses on processes at the molecular level that determine the state of an individual cell, involving signaling or gene regulation. The mathematical model type considered is that of Boolean networks. The potential control targets can be represented by a set of nodes and edges that can be manipulated to produce a desired effect on the system. This paper presents a method for the identification of potential intervention targets in Boolean molecular network models using algebraic techniques. The approach exploits an algebraic representation of Boolean networks to encode the control candidates in the network wiring diagram as the solutions of a system of polynomials equations, and then uses computational algebra techniques to find such controllers. The control methods in this paper are validated through the identification of combinatorial interventions in the signaling pathways of previously reported control targets in two well studied systems, a p53-mdm2 network and a blood T cell lymphocyte granular leukemia survival signaling network. Supplementary data is available online and our code in Macaulay2 and Matlab are available via http://www.ms.uky.edu/~dmu228/ControlAlg . This paper presents a novel method for the identification of intervention targets in Boolean network models. The results in this paper show that the proposed methods are useful and efficient for moderately large networks.

Systems Approach to Defeating Maritime Improvised Explosive Devices in U.S. Ports

DTIC Science & Technology

2008-12-01

DETECTION Pfi PROBABILITY OF FALSE IDENTIFICATION PHPK PROBABILITY OF HIT/PROBABILITY OF KILL PMA POST MISSION ANALYSIS PNNL PACIFIC...16 Naval Warfare Publication 27-2(Rev. B), Section 1.8.4.1(unclassified) 42 detection analysis is conducted...NAVAL POSTGRADUATE SCHOOL MONTEREY, CALIFORNIA Approved for public release; distribution is unlimited Prepared

Identification of Absorption, Distribution, Metabolism, and Excretion (ADME) Genes Relevant to Steatosis Using a Systems Biology Approach

EPA Science Inventory

Ensuring chemical safety and sustainability form a main priority of the U.S. Environmental Protection Agency. This entails efforts on multiple fronts to characterize the potential hazard posed by chemicals currently in use and those to be commercialized in the future. The use of ...

Modeling and Analysis Tools for Linear and Nonlinear Mechanical Systems Subjected to Extreme Impulsive Loading

DTIC Science & Technology

2015-03-23

SAMPE, Long Beach, CA, 2008. [28] N Hu and H Fukunaga. A new approach for health monitoring of composite structures through identification of impact...Bernard H Minster . Hysteresis and two- dimensional nonlinear wave propagation in berea sandstone. Journal of Geo- physical Research: Solid Earth (1978–2012

Investing in Education: Analysis of the 1999 World Education Indicators. Education and Skills.

ERIC Educational Resources Information Center

Organisation for Economic Cooperation and Development, Paris (France).

This Organisation for Economic Cooperation and Development report documents the growing demand for learning around the world. A quantitative description of the functioning of education systems allows for international comparisons and the identification of the strengths and weaknesses of various approaches to providing quality education. Chapter 1,…

Implementation of RFID Technology in University of Pune Library

ERIC Educational Resources Information Center

Bansode, Sadanand Y.; Desale, Sanjay K.

2009-01-01

Purpose: The purpose of this paper is to describe the implementation of a radio frequency identification (RFID) system in Pune University Library. Design/methodology/approach: The paper provides a brief overview of background of the project, barriers faced and changes that have been experienced after the implementation of the technology. Findings:…

Multiplex PCR method for use in real-time PCR for identification of fish fillets from grouper (Epinephelus and Mycteroperca species) and common substitute species.

PubMed

Trotta, Michele; Schönhuth, Susana; Pepe, Tiziana; Cortesi, M Luisa; Puyet, Antonio; Bautista, José M

2005-03-23

Mitochondrial 16S rRNA sequences from morphological validated grouper (Epinephelus aeneus, E. caninus, E. costae, and E. marginatus; Mycteroperca fusca and M. rubra), Nile perch (Lates niloticus), and wreck fish (Polyprion americanus) were used to develop an analytical system for group diagnosis based on two alternative Polymerase Chain Reaction (PCR) approaches. The first includes conventional multiplex PCR in which electrophoretic migration of different sizes of bands allowed identification of the fish species. The second approach, involving real-time PCR, produced a single amplicon from each species that showed different Tm values allowing the fish groups to be directly identified. Real-time PCR allows the quick differential diagnosis of the three groups of species and high-throughput screening of multiple samples. Neither PCR system cross-reacted with DNA samples from 41 common marketed fish species, thus conforming to standards for species validation. The use of these two PCR-based methods makes it now possible to discriminate grouper from substitute fish species.

Computerized decision support system for mass identification in breast using digital mammogram: a study on GA-based neuro-fuzzy approaches.

PubMed

Das, Arpita; Bhattacharya, Mahua

2011-01-01

In the present work, authors have developed a treatment planning system implementing genetic based neuro-fuzzy approaches for accurate analysis of shape and margin of tumor masses appearing in breast using digital mammogram. It is obvious that a complicated structure invites the problem of over learning and misclassification. In proposed methodology, genetic algorithm (GA) has been used for searching of effective input feature vectors combined with adaptive neuro-fuzzy model for final classification of different boundaries of tumor masses. The study involves 200 digitized mammograms from MIAS and other databases and has shown 86% correct classification rate.

Modeling of multi-rotor torsional vibrations in rotating machinery using substructuring

NASA Technical Reports Server (NTRS)

Soares, Fola R.

1986-01-01

The application of FEM modeling techniques to the analysis of torsional vibrations in complex rotating systems is described and demonstrated, summarizing results reported by Soares (1985). A substructuring approach is used for determination of torsional natural frequencies and resonant-mode shapes, steady-state frequency-sweep analysis, identification of dynamically unstable speed ranges, and characterization of transient linear and nonlinear systems. Results for several sample problems are presented in diagrams, graphs, and tables. STORV, a computer code based on this approach, is in use as a preliminary design tool for drive-train torsional analysis in the High Altitude Wind Tunnel at NASA Lewis.

Remote monitoring of soldier safety through body posture identification using wearable sensor networks

NASA Astrophysics Data System (ADS)

Biswas, Subir; Quwaider, Muhannad

2008-04-01

The physical safety and well being of the soldiers in a battlefield is the highest priority of Incident Commanders. Currently, the ability to track and monitor soldiers rely on visual and verbal communication which can be somewhat limited in scenarios where the soldiers are deployed inside buildings and enclosed areas that are out of visual range of the commanders. Also, the need for being stealth can often prevent a battling soldier to send verbal clues to a commander about his or her physical well being. Sensor technologies can remotely provide various data about the soldiers including physiological monitoring and personal alert safety system functionality. This paper presents a networked sensing solution in which a body area wireless network of multi-modal sensors can monitor the body movement and other physiological parameters for statistical identification of a soldier's body posture, which can then be indicative of the physical conditions and safety alerts of the soldier in question. The specific concept is to leverage on-body proximity sensing and a Hidden Markov Model (HMM) based mechanism that can be applied for stochastic identification of human body postures using a wearable sensor network. The key idea is to collect relative proximity information between wireless sensors that are strategically placed over a subject's body to monitor the relative movements of the body segments, and then to process that using HMM in order to identify the subject's body postures. The key novelty of this approach is a departure from the traditional accelerometry based approaches in which the individual body segment movements, rather than their relative proximity, is used for activity monitoring and posture detection. Through experiments with body mounted sensors we demonstrate that while the accelerometry based approaches can be used for differentiating activity intensive postures such as walking and running, they are not very effective for identification and differentiation between low activity postures such as sitting and standing. We develop a wearable sensor network that monitors relative proximity using Radio Signal Strength indication (RSSI), and then construct a HMM system for posture identification in the presence of sensing errors. Controlled experiments using human subjects were carried out for evaluating the accuracy of the HMM identified postures compared to a naÃve threshold based mechanism, and its variations over different human subjects. A large spectrum of target human postures, including lie down, sit (straight and reclined), stand, walk, run, sprint and stair climbing, are used for validating the proposed system.

Fault identification of rotor-bearing system based on ensemble empirical mode decomposition and self-zero space projection analysis

NASA Astrophysics Data System (ADS)

Jiang, Fan; Zhu, Zhencai; Li, Wei; Zhou, Gongbo; Chen, Guoan

2014-07-01

Accurately identifying faults in rotor-bearing systems by analyzing vibration signals, which are nonlinear and nonstationary, is challenging. To address this issue, a new approach based on ensemble empirical mode decomposition (EEMD) and self-zero space projection analysis is proposed in this paper. This method seeks to identify faults appearing in a rotor-bearing system using simple algebraic calculations and projection analyses. First, EEMD is applied to decompose the collected vibration signals into a set of intrinsic mode functions (IMFs) for features. Second, these extracted features under various mechanical health conditions are used to design a self-zero space matrix according to space projection analysis. Finally, the so-called projection indicators are calculated to identify the rotor-bearing system's faults with simple decision logic. Experiments are implemented to test the reliability and effectiveness of the proposed approach. The results show that this approach can accurately identify faults in rotor-bearing systems.

Airborne ladar man-in-the-loop operations in tactical environments

NASA Astrophysics Data System (ADS)

Grobmyer, Joseph E., Jr.; Lum, Tommy; Morris, Robert E.; Hard, Sarah J.; Pratt, H. L.; Florence, Tom; Peddycoart, Ed

2004-09-01

The U.S. Army Research, Development and Engineering Command (RDECOM) is developing approaches and processes that will exploit the characteristics of current and future Laser Radar (LADAR) sensor systems for critical man-in-the-loop tactical processes. The importance of timely and accurate target detection, classification, identification, and engagement for future combat systems has been documented and is viewed as a critical enabling factor for FCS survivability and lethality. Recent work has demonstrated the feasibility of using low cost but relatively capable personal computer class systems to exploit the information available in Ladar sensor frames to present the war fighter or analyst with compelling and usable imagery for use in the target identification and engagement processes in near real time. The advantages of LADAR imagery are significant in environments presenting cover for targets and the associated difficulty for automated target recognition (ATR) technologies.

Identification of Patients with Family History of Pancreatic Cancer--Investigation of an NLP System Portability.

PubMed

Mehrabi, Saeed; Krishnan, Anand; Roch, Alexandra M; Schmidt, Heidi; Li, DingCheng; Kesterson, Joe; Beesley, Chris; Dexter, Paul; Schmidt, Max; Palakal, Mathew; Liu, Hongfang

2015-01-01

In this study we have developed a rule-based natural language processing (NLP) system to identify patients with family history of pancreatic cancer. The algorithm was developed in a Unstructured Information Management Architecture (UIMA) framework and consisted of section segmentation, relation discovery, and negation detection. The system was evaluated on data from two institutions. The family history identification precision was consistent across the institutions shifting from 88.9% on Indiana University (IU) dataset to 87.8% on Mayo Clinic dataset. Customizing the algorithm on the the Mayo Clinic data, increased its precision to 88.1%. The family member relation discovery achieved precision, recall, and F-measure of 75.3%, 91.6% and 82.6% respectively. Negation detection resulted in precision of 99.1%. The results show that rule-based NLP approaches for specific information extraction tasks are portable across institutions; however customization of the algorithm on the new dataset improves its performance.

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.

Small UAS-Based Wind Feature Identification System Part 1: Integration and Validation

PubMed Central

Rodriguez Salazar, Leopoldo; Cobano, Jose A.; Ollero, Anibal

2016-01-01

This paper presents a system for identification of wind features, such as gusts and wind shear. These are of particular interest in the context of energy-efficient navigation of Small Unmanned Aerial Systems (UAS). The proposed system generates real-time wind vector estimates and a novel algorithm to generate wind field predictions. Estimations are based on the integration of an off-the-shelf navigation system and airspeed readings in a so-called direct approach. Wind predictions use atmospheric models to characterize the wind field with different statistical analyses. During the prediction stage, the system is able to incorporate, in a big-data approach, wind measurements from previous flights in order to enhance the approximations. Wind estimates are classified and fitted into a Weibull probability density function. A Genetic Algorithm (GA) is utilized to determine the shaping and scale parameters of the distribution, which are employed to determine the most probable wind speed at a certain position. The system uses this information to characterize a wind shear or a discrete gust and also utilizes a Gaussian Process regression to characterize continuous gusts. The knowledge of the wind features is crucial for computing energy-efficient trajectories with low cost and payload. Therefore, the system provides a solution that does not require any additional sensors. The system architecture presents a modular decentralized approach, in which the main parts of the system are separated in modules and the exchange of information is managed by a communication handler to enhance upgradeability and maintainability. Validation is done providing preliminary results of both simulations and Software-In-The-Loop testing. Telemetry data collected from real flights, performed in the Seville Metropolitan Area in Andalusia (Spain), was used for testing. Results show that wind estimation and predictions can be calculated at 1 Hz and a wind map can be updated at 0.4 Hz. Predictions show a convergence time with a 95% confidence interval of approximately 30 s. PMID:28025531

Small UAS-Based Wind Feature Identification System Part 1: Integration and Validation.

PubMed

Rodriguez Salazar, Leopoldo; Cobano, Jose A; Ollero, Anibal

2016-12-23

This paper presents a system for identification of wind features, such as gusts and wind shear. These are of particular interest in the context of energy-efficient navigation of Small Unmanned Aerial Systems (UAS). The proposed system generates real-time wind vector estimates and a novel algorithm to generate wind field predictions. Estimations are based on the integration of an off-the-shelf navigation system and airspeed readings in a so-called direct approach. Wind predictions use atmospheric models to characterize the wind field with different statistical analyses. During the prediction stage, the system is able to incorporate, in a big-data approach, wind measurements from previous flights in order to enhance the approximations. Wind estimates are classified and fitted into a Weibull probability density function. A Genetic Algorithm (GA) is utilized to determine the shaping and scale parameters of the distribution, which are employed to determine the most probable wind speed at a certain position. The system uses this information to characterize a wind shear or a discrete gust and also utilizes a Gaussian Process regression to characterize continuous gusts. The knowledge of the wind features is crucial for computing energy-efficient trajectories with low cost and payload. Therefore, the system provides a solution that does not require any additional sensors. The system architecture presents a modular decentralized approach, in which the main parts of the system are separated in modules and the exchange of information is managed by a communication handler to enhance upgradeability and maintainability. Validation is done providing preliminary results of both simulations and Software-In-The-Loop testing. Telemetry data collected from real flights, performed in the Seville Metropolitan Area in Andalusia (Spain), was used for testing. Results show that wind estimation and predictions can be calculated at 1 Hz and a wind map can be updated at 0.4 Hz . Predictions show a convergence time with a 95% confidence interval of approximately 30 s .

Teacher Justice and Students’ Class Identification: Belief in a Just World and Teacher–Student Relationship as Mediators

PubMed Central

Jiang, Ronghuan; Liu, Ru-De; Ding, Yi; Zhen, Rui; Sun, Yan; Fu, Xinchen

2018-01-01

For school-age adolescents, teacher justice plays an important role in their learning and social outcomes. The present study examined the relation between teacher justice and students’ class identification in 1735 Chinese school-age adolescents by considering belief in a just world (BJW) and teacher–student relationship as mediators. Structure equation modeling (SEM) was used to reveal the direct and indirect effects. The analyses showed that all the direct and indirect effects were significant. These findings indicated that teacher justice had a positive effect on students’ class identification. In addition, teacher justice impacted students’ class identification through students’ just-world belief and teacher–student relationships. These results suggested that for adolescents, teacher justice played an important role in shaping their just-world belief system and their interpersonal relationships with teachers, which in turn affected their sense of belonging and values in relation to their class. Thus, it is important for teachers to be aware that their injustice may negatively impact their relationships with students, students’ belief systems, and their psychological engagement at school. There is a need to develop teacher-training programs to help teachers to establish classroom reward-punishment systems with the consideration of social justice, to communicate with students through an unbiased approach, and to increase student participation in the important decision making of the whole class. PMID:29875726

Teacher Justice and Students' Class Identification: Belief in a Just World and Teacher-Student Relationship as Mediators.

PubMed

Jiang, Ronghuan; Liu, Ru-De; Ding, Yi; Zhen, Rui; Sun, Yan; Fu, Xinchen

2018-01-01

For school-age adolescents, teacher justice plays an important role in their learning and social outcomes. The present study examined the relation between teacher justice and students' class identification in 1735 Chinese school-age adolescents by considering belief in a just world (BJW) and teacher-student relationship as mediators. Structure equation modeling (SEM) was used to reveal the direct and indirect effects. The analyses showed that all the direct and indirect effects were significant. These findings indicated that teacher justice had a positive effect on students' class identification. In addition, teacher justice impacted students' class identification through students' just-world belief and teacher-student relationships. These results suggested that for adolescents, teacher justice played an important role in shaping their just-world belief system and their interpersonal relationships with teachers, which in turn affected their sense of belonging and values in relation to their class. Thus, it is important for teachers to be aware that their injustice may negatively impact their relationships with students, students' belief systems, and their psychological engagement at school. There is a need to develop teacher-training programs to help teachers to establish classroom reward-punishment systems with the consideration of social justice, to communicate with students through an unbiased approach, and to increase student participation in the important decision making of the whole class.

A knock to the system: A new sociotechnical systems approach to sport-related concussion.

PubMed

Clacy, Amanda; Goode, Natassia; Sharman, Rachael; Lovell, Geoff P; Salmon, Paul M

2017-11-01

Broader contextual factors that influence concussion management have tended to be overlooked. To address this, the present study used a sociotechnical systems approach to identify perceived responsibilities and applied strategies for three domains of concussion management (i.e., prevention, identification and treatment). Participants were 118 members of the community rugby union system in Australia (69.2% male). Participants from throughout the rugby system (e.g., players, parents, coaches, club management) were asked open-ended questions about their perceived responsibilities and the strategies they use for concussion management. It was found that (a) proper training, technique correction and education were recurrent prevention themes; (b) the majority of key stakeholders felt that they could consistently identify concussion; however, medical aids (medics) were the only system actors who stated a responsibility to use standardised concussion assessment measures and (c) less than one third of the respondents indicated their involvement in treating concussion. This study identifies specific junctures in the system that prevents effective concussion management strategies. A sociotechnical systems approach improves the understanding of concussion prevention, and management beliefs and behaviours.

Bionic models for identification of biological systems

NASA Astrophysics Data System (ADS)

Gerget, O. M.

2017-01-01

This article proposes a clinical decision support system that processes biomedical data. For this purpose a bionic model has been designed based on neural networks, genetic algorithms and immune systems. The developed system has been tested on data from pregnant women. The paper focuses on the approach to enable selection of control actions that can minimize the risk of adverse outcome. The control actions (hyperparameters of a new type) are further used as an additional input signal. Its values are defined by a hyperparameter optimization method. A software developed with Python is briefly described.

Exome-first approach identified a novel gloss deletion associated with Lowe syndrome.

PubMed

Watanabe, Miki; Nakagawa, Ryuji; Kohmoto, Tomohiro; Naruto, Takuya; Suga, Ken-Ichi; Goji, Aya; Horikawa, Hideaki; Masuda, Kiyoshi; Kagami, Shoji; Imoto, Issei

2016-01-01

Lowe syndrome (LS) is an X-linked disorder affecting the eyes, nervous system and kidneys, typically caused by missense or nonsense/frameshift OCRL mutations. We report a 6-month-old male clinically suspected to have LS, but without the Fanconi-type renal dysfunction. Using a targeted-exome sequencing-first approach, LS was diagnosed by the identification of a deletion involving 1.7 Mb at Xq25-q26.1, encompassing the entire OCRL gene and neighboring loci.

Exome-first approach identified a novel gloss deletion associated with Lowe syndrome

PubMed Central

Watanabe, Miki; Nakagawa, Ryuji; Kohmoto, Tomohiro; Naruto, Takuya; Suga, Ken-ichi; Goji, Aya; Horikawa, Hideaki; Masuda, Kiyoshi; Kagami, Shoji; Imoto, Issei

2016-01-01

Lowe syndrome (LS) is an X-linked disorder affecting the eyes, nervous system and kidneys, typically caused by missense or nonsense/frameshift OCRL mutations. We report a 6-month-old male clinically suspected to have LS, but without the Fanconi-type renal dysfunction. Using a targeted-exome sequencing-first approach, LS was diagnosed by the identification of a deletion involving 1.7 Mb at Xq25-q26.1, encompassing the entire OCRL gene and neighboring loci. PMID:27867521

Evaluation of an On-Farm Culture System (Accumast) for Fast Identification of Milk Pathogens Associated with Clinical Mastitis in Dairy Cows.

PubMed

Ganda, Erika Korzune; Bisinotto, Rafael Sisconeto; Decter, Dean Harrison; Bicalho, Rodrigo Carvalho

2016-01-01

The present study aimed evaluate an on-farm culture system for identification of milk pathogens associated with clinical mastitis in dairy cows using two different gold standard approaches: standard laboratory culture in study 1 and 16S rRNA sequencing in study 2. In study 1, milk from mastitic quarters (i.e. presence of flakes, clots, or serous milk; n = 538) was cultured on-farm using a single plate containing three selective chromogenic media (Accumast-FERA Animal Health LCC, Ithaca, NY) and in a reference laboratory using standard culture methods, which was considered the gold standard. In study 2, mastitic milk was cultured on-farm and analyzed through 16S rRNA sequencing (n = 214). In both studies, plates were cultured aerobically at 37°C for 24 h and read by a single technician masked to gold standard results. Accuracy, sensitivity, specificity, positive (PPV) and negative predictive value (NPV) were calculated based on standard laboratory culture in study 1, and PPV was calculated based on sequencing results in study 2. Overall accuracy of Accumast was 84.9%. Likewise, accuracy for identification of Gram-negative bacteria, Staphylococcus sp., and Streptococcus sp. was 96.4%, 93.8%, and 91.5%, respectively. Sensitivity, specificity, PPV, and NPV were 75.0%, 97.9%, 79.6%, and 97.3% for identification of E. coli, 100.0%, 99.8%, 87.5%, and 100.0% for S. aureus, 70.0%, 95.0%, 45.7%, and 98.1% for other Staphylococcus sp., and 90.0%, 92.9%, 91.8%, and 91.2% for Streptococcus sp. In study 2, Accumast PPV was 96.7% for E. coli, 100.0% for Enterococcus sp., 100.0% for Other Gram-negatives, 88.2% for Staphylococcus sp., and 95.0% for Streptococcus sp., respectively. In conclusion, Accumast is a unique approach for on-farm identification pathogens associated with mastitis, presenting overall sensitivity and specificity of 82.3% and 89.9% respectively.

Evaluation of an On-Farm Culture System (Accumast) for Fast Identification of Milk Pathogens Associated with Clinical Mastitis in Dairy Cows

PubMed Central

Ganda, Erika Korzune; Bisinotto, Rafael Sisconeto; Decter, Dean Harrison; Bicalho, Rodrigo Carvalho

2016-01-01

The present study aimed evaluate an on-farm culture system for identification of milk pathogens associated with clinical mastitis in dairy cows using two different gold standard approaches: standard laboratory culture in study 1 and 16S rRNA sequencing in study 2. In study 1, milk from mastitic quarters (i.e. presence of flakes, clots, or serous milk; n = 538) was cultured on-farm using a single plate containing three selective chromogenic media (Accumast—FERA Animal Health LCC, Ithaca, NY) and in a reference laboratory using standard culture methods, which was considered the gold standard. In study 2, mastitic milk was cultured on-farm and analyzed through 16S rRNA sequencing (n = 214). In both studies, plates were cultured aerobically at 37°C for 24 h and read by a single technician masked to gold standard results. Accuracy, sensitivity, specificity, positive (PPV) and negative predictive value (NPV) were calculated based on standard laboratory culture in study 1, and PPV was calculated based on sequencing results in study 2. Overall accuracy of Accumast was 84.9%. Likewise, accuracy for identification of Gram-negative bacteria, Staphylococcus sp., and Streptococcus sp. was 96.4%, 93.8%, and 91.5%, respectively. Sensitivity, specificity, PPV, and NPV were 75.0%, 97.9%, 79.6%, and 97.3% for identification of E. coli, 100.0%, 99.8%, 87.5%, and 100.0% for S. aureus, 70.0%, 95.0%, 45.7%, and 98.1% for other Staphylococcus sp., and 90.0%, 92.9%, 91.8%, and 91.2% for Streptococcus sp. In study 2, Accumast PPV was 96.7% for E. coli, 100.0% for Enterococcus sp., 100.0% for Other Gram-negatives, 88.2% for Staphylococcus sp., and 95.0% for Streptococcus sp., respectively. In conclusion, Accumast is a unique approach for on-farm identification pathogens associated with mastitis, presenting overall sensitivity and specificity of 82.3% and 89.9% respectively. PMID:27176216

Photovoltaic power systems workshop

NASA Technical Reports Server (NTRS)

Killian, H. J.; Given, R. W.

1978-01-01

Discussions are presented on apparent deficiencies in NASA planning and technology development relating to a standard power module (25-35 kW) and to future photovoltaic power systems in general. Topics of discussion consider the following: (1) adequate studies on power systems; (2) whether a standard power system module should be developed from a standard spacecraft; (3) identification of proper approaches to cost reduction; (4) energy storage avoidance; (5) attitude control; (6) thermal effects of heat rejection on solar array configuration stability; (7) assembly of large power systems in space; and (8) factoring terrestrial photovoltaic work into space power systems for possible payoff.

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

Proceedings of Interservice/Industry Training Systems and Education Conference (16th) Held on 28 November -1 December 1994.

DTIC Science & Technology

1994-12-01

be INTRODUCTION familiar: best value source selection, processes and metrics In simplified terms, acquisition and continuous improvement ; of a training ...pro- continuous improvement , MIL-STD- posed processes and metrics are 1379D, the systems approach to placed in the contract in a training , concurrent...identification and 5 Continuous Process Improvement correction of errors are critical to software product 6 Training correctness and quality. Correcting

Theoretical Models for Aircraft Availability: Classical Approach to Identification of Trends, Seasonality, and System Constraints in the Development of Realized Models

DTIC Science & Technology

2004-03-01

predicting future events ( Heizer and Render , 1999). Forecasting techniques fall into two major categories, qualitative and quantitative methods...Globemaster III.” Excerpt from website. www.globalsecurity.org/military /systems/ aircraft/c-17-history.htm. 2003. Heizer , Jay, and Barry Render ...of the past data used to make the forecast ( Heizer , et. al., 1999). Explanatory forecasting models assume that the variable being forecasted

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.

Surgical instrument similarity metrics and tray analysis for multi-sensor instrument identification

NASA Astrophysics Data System (ADS)

Glaser, Bernhard; Schellenberg, Tobias; Franke, Stefan; Dänzer, Stefan; Neumuth, Thomas

2015-03-01

A robust identification of the instrument currently used by the surgeon is crucial for the automatic modeling and analysis of surgical procedures. Various approaches for intra-operative surgical instrument identification have been presented, mostly based on radio-frequency identification (RFID) or endoscopic video analysis. A novel approach is to identify the instruments on the instrument table of the scrub nurse with a combination of video and weight information. In a previous article, we successfully followed this approach and applied it to multiple instances of an ear, nose and throat (ENT) procedure and the surgical tray used therein. In this article, we present a metric for the suitability of the instruments of a surgical tray for identification by video and weight analysis and apply it to twelve trays of four different surgical domains (abdominal surgery, neurosurgery, orthopedics and urology). The used trays were digitized at the central sterile services department of the hospital. The results illustrate that surgical trays differ in their suitability for the approach. In general, additional weight information can significantly contribute to the successful identification of surgical instruments. Additionally, for ten different surgical instruments, ten exemplars of each instrument were tested for their weight differences. The samples indicate high weight variability in instruments with identical brand and model number. The results present a new metric for approaches aiming towards intra-operative surgical instrument detection and imply consequences for algorithms exploiting video and weight information for identification purposes.

System Guidelines for EMC Safety-Critical Circuits: Design, Selection, and Margin Demonstration

NASA Technical Reports Server (NTRS)

Lawton, R. M.

1996-01-01

Demonstration of required safety margins on critical electrical/electronic circuits in large complex systems has become an implementation and cost problem. These margins are the difference between the activation level of the circuit and the electrical noise on the circuit in the actual operating environment. This document discusses the origin of the requirement and gives a detailed process flow for the identification of the system electromagnetic compatibility (EMC) critical circuit list. The process flow discusses the roles of engineering disciplines such as systems engineering, safety, and EMC. Design and analysis guidelines are provided to assist the designer in assuring the system design has a high probability of meeting the margin requirements. Examples of approaches used on actual programs (Skylab and Space Shuttle Solid Rocket Booster) are provided to show how variations of the approach can be used successfully.

A system identification approach for developing model predictive controllers of antibody quality attributes in cell culture processes.

PubMed

Downey, Brandon; Schmitt, John; Beller, Justin; Russell, Brian; Quach, Anthony; Hermann, Elizabeth; Lyon, David; Breit, Jeffrey

2017-11-01

As the biopharmaceutical industry evolves to include more diverse protein formats and processes, more robust control of Critical Quality Attributes (CQAs) is needed to maintain processing flexibility without compromising quality. Active control of CQAs has been demonstrated using model predictive control techniques, which allow development of processes which are robust against disturbances associated with raw material variability and other potentially flexible operating conditions. Wide adoption of model predictive control in biopharmaceutical cell culture processes has been hampered, however, in part due to the large amount of data and expertise required to make a predictive model of controlled CQAs, a requirement for model predictive control. Here we developed a highly automated, perfusion apparatus to systematically and efficiently generate predictive models using application of system identification approaches. We successfully created a predictive model of %galactosylation using data obtained by manipulating galactose concentration in the perfusion apparatus in serialized step change experiments. We then demonstrated the use of the model in a model predictive controller in a simulated control scenario to successfully achieve a %galactosylation set point in a simulated fed-batch culture. The automated model identification approach demonstrated here can potentially be generalized to many CQAs, and could be a more efficient, faster, and highly automated alternative to batch experiments for developing predictive models in cell culture processes, and allow the wider adoption of model predictive control in biopharmaceutical processes. © 2017 The Authors Biotechnology Progress published by Wiley Periodicals, Inc. on behalf of American Institute of Chemical Engineers Biotechnol. Prog., 33:1647-1661, 2017. © 2017 The Authors Biotechnology Progress published by Wiley Periodicals, Inc. on behalf of American Institute of Chemical Engineers.

Identification of Lactobacillus from the Saliva of Adult Patients with Caries Using Matrix-Assisted Laser Desorption/Ionization Time-Of-Flight Mass Spectrometry

PubMed Central

Ma, Qingwei; Song, Yeqing; Zhang, Qian; Wang, Xiaoyan; Chen, Feng

2014-01-01

Matrix-assisted laser desorption/ionization (MALDI) time-of-flight (TOF) mass spectrometry (MS) has been presented as a superior method for the detection of microorganisms in body fluid samples (e.g., blood, saliva, pus, etc.) However, the performance of MALDI-TOF MS in routine identification of caries-related Lactobacillus isolates from saliva of adult patients with caries has not been determined. In the present study, we introduced a new MALDI-TOF MS system for identification of lactobacilli. Saliva samples were collected from 120 subjects with caries. Bacteria were isolated and cultured, and each isolate was identified by both 16S rRNA sequencing and MALDI-TOF MS. The identification results obtained by MALDI-TOF MS were concordant at the genus level with those of conventional 16S rRNA-based sequencing for 88.6% of lactobacilli (62/70) and 95.5% of non-lactobacilli (21/22). Up to 96 results could be obtained in parallel on a single MALDI target, suggesting that this is a reliable high-throughput approach for routine identification of lactobacilli. However, additional reference strains are necessary to increase the sensitivity and specificity of species-level identification. PMID:25166027

Constraints in distortion-invariant target recognition system simulation

NASA Astrophysics Data System (ADS)

Iftekharuddin, Khan M.; Razzaque, Md A.

2000-11-01

Automatic target recognition (ATR) is a mature but active research area. In an earlier paper, we proposed a novel ATR approach for recognition of targets varying in fine details, rotation, and translation using a Learning Vector Quantization (LVQ) Neural Network (NN). The proposed approach performed segmentation of multiple objects and the identification of the objects using LVQNN. In this current paper, we extend the previous approach for recognition of targets varying in rotation, translation, scale, and combination of all three distortions. We obtain the analytical results of the system level design to show that the approach performs well with some constraints. The first constraint determines the size of the input images and input filters. The second constraint shows the limits on amount of rotation, translation, and scale of input objects. We present the simulation verification of the constraints using DARPA's Moving and Stationary Target Recognition (MSTAR) images with different depression and pose angles. The simulation results using MSTAR images verify the analytical constraints of the system level design.

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.

Nonlinear damage identification of breathing cracks in Truss system

NASA Astrophysics Data System (ADS)

Zhao, Jie; DeSmidt, Hans

2014-03-01

The breathing cracks in truss system are detected by Frequency Response Function (FRF) based damage identification method. This method utilizes damage-induced changes of frequency response functions to estimate the severity and location of structural damage. This approach enables the possibility of arbitrary interrogation frequency and multiple inputs/outputs which greatly enrich the dataset for damage identification. The dynamical model of truss system is built using the finite element method and the crack model is based on fracture mechanics. Since the crack is driven by tensional and compressive forces of truss member, only one damage parameter is needed to represent the stiffness reduction of each truss member. Assuming that the crack constantly breathes with the exciting frequency, the linear damage detection algorithm is developed in frequency/time domain using Least Square and Newton Raphson methods. Then, the dynamic response of the truss system with breathing cracks is simulated in the time domain and meanwhile the crack breathing status for each member is determined by the feedback from real-time displacements of member's nodes. Harmonic Fourier Coefficients (HFCs) of dynamical response are computed by processing the data through convolution and moving average filters. Finally, the results show the effectiveness of linear damage detection algorithm in identifying the nonlinear breathing cracks using different combinations of HFCs and sensors.

Engineering Lessons Learned and Systems Engineering Applications

NASA Technical Reports Server (NTRS)

Gill, Paul S.; Garcia, Danny; Vaughan, William W.

2005-01-01

Systems Engineering is fundamental to good engineering, which in turn depends on the integration and application of engineering lessons learned and technical standards. Thus, good Systems Engineering also depends on systems engineering lessons learned from within the aerospace industry being documented and applied. About ten percent of the engineering lessons learned documented in the NASA Lessons Learned Information System are directly related to Systems Engineering. A key issue associated with lessons learned datasets is the communication and incorporation of this information into engineering processes. Systems Engineering has been defined (EINIS-632) as "an interdisciplinary approach encompassing the entire technical effort to evolve and verify an integrated and life-cycle balanced set of system people, product, and process solutions that satisfy customer needs". Designing reliable space-based systems has always been a goal for NASA, and many painful lessons have been learned along the way. One of the continuing functions of a system engineer is to compile development and operations "lessons learned" documents and ensure their integration into future systems development activities. They can produce insights and information for risk identification identification and characterization. on a new project. Lessons learned files from previous projects are especially valuable in risk

Identification of immunodominant proteins from Mannheimia haemolytica and Histophilus somni by an immunoproteomic approach.

PubMed

Alvarez, Angel H; Gutiérrez-Ortega, Abel; Hernández-Gutiérrez, Rodolfo

2015-10-01

Mannheimia haemolytica and Histophilus somni are frequently isolated from diseased cattle with bovine respiratory disease (BRD). They compromise animal lung function and the immune responses generated are not sufficient to limit infection. Identification of specific immunogenic antigens for vaccine development represents a great challenge. Immunogenic proteins were identified by immunoproteomic approach with sera from cattle immunized with a commercial cellular vaccine of M. haemolytica and H. somni. Proteins of M. haemolytica were identified as solute ABC transporter, iron-binding protein, and hypothetical protein of capsular biosynthesis. Histophilus somni proteins correspond to porin, amino acid ABC transporter, hypothetical outer membrane protein, cysteine synthase, and outer membrane protein P6. Although these antigens share strong similarities with other proteins from animal pathogens, the ABC system proteins have been associated with virulence and these proteins could be considered as potential vaccine candidates for BRD.

Identification of immunodominant proteins from Mannheimia haemolytica and Histophilus somni by an immunoproteomic approach

PubMed Central

Alvarez, Angel H.; Gutiérrez-Ortega, Abel; Hernández-Gutiérrez, Rodolfo

2015-01-01

Mannheimia haemolytica and Histophilus somni are frequently isolated from diseased cattle with bovine respiratory disease (BRD). They compromise animal lung function and the immune responses generated are not sufficient to limit infection. Identification of specific immunogenic antigens for vaccine development represents a great challenge. Immunogenic proteins were identified by immunoproteomic approach with sera from cattle immunized with a commercial cellular vaccine of M. haemolytica and H. somni. Proteins of M. haemolytica were identified as solute ABC transporter, iron-binding protein, and hypothetical protein of capsular biosynthesis. Histophilus somni proteins correspond to porin, amino acid ABC transporter, hypothetical outer membrane protein, cysteine synthase, and outer membrane protein P6. Although these antigens share strong similarities with other proteins from animal pathogens, the ABC system proteins have been associated with virulence and these proteins could be considered as potential vaccine candidates for BRD. PMID:26424916

Relative quantification of biomarkers using mixed-isotope labeling coupled with MS

PubMed Central

Chapman, Heidi M; Schutt, Katherine L; Dieter, Emily M; Lamos, Shane M

2013-01-01

The identification and quantification of important biomarkers is a critical first step in the elucidation of biological systems. Biomarkers take many forms as cellular responses to stimuli and can be manifested during transcription, translation, and/or metabolic processing. Increasingly, researchers have relied upon mixed-isotope labeling (MIL) coupled with MS to perform relative quantification of biomarkers between two or more biological samples. MIL effectively tags biomarkers of interest for ease of identification and quantification within the mass spectrometer by using isotopic labels that introduce a heavy and light form of the tag. In addition to MIL coupled with MS, a number of other approaches have been used to quantify biomarkers including protein gel staining, enzymatic labeling, metabolic labeling, and several label-free approaches that generate quantitative data from the MS signal response. This review focuses on MIL techniques coupled with MS for the quantification of protein and small-molecule biomarkers. PMID:23157360

An ensemble-based approach for breast mass classification in mammography images

NASA Astrophysics Data System (ADS)

Ribeiro, Patricia B.; Papa, João. P.; Romero, Roseli A. F.

2017-03-01

Mammography analysis is an important tool that helps detecting breast cancer at the very early stages of the disease, thus increasing the quality of life of hundreds of thousands of patients worldwide. In Computer-Aided Detection systems, the identification of mammograms with and without masses (without clinical findings) is highly needed to reduce the false positive rates regarding the automatic selection of regions of interest that may contain some suspicious content. In this work, the introduce a variant of the Optimum-Path Forest (OPF) classifier for breast mass identification, as well as we employed an ensemble-based approach that can enhance the effectiveness of individual classifiers aiming at dealing with the aforementioned purpose. The experimental results also comprise the naïve OPF and a traditional neural network, being the most accurate results obtained through the ensemble of classifiers, with an accuracy nearly to 86%.

Driving profile modeling and recognition based on soft computing approach.

PubMed

Wahab, Abdul; Quek, Chai; Tan, Chin Keong; Takeda, Kazuya

2009-04-01

Advancements in biometrics-based authentication have led to its increasing prominence and are being incorporated into everyday tasks. Existing vehicle security systems rely only on alarms or smart card as forms of protection. A biometric driver recognition system utilizing driving behaviors is a highly novel and personalized approach and could be incorporated into existing vehicle security system to form a multimodal identification system and offer a greater degree of multilevel protection. In this paper, detailed studies have been conducted to model individual driving behavior in order to identify features that may be efficiently and effectively used to profile each driver. Feature extraction techniques based on Gaussian mixture models (GMMs) are proposed and implemented. Features extracted from the accelerator and brake pedal pressure were then used as inputs to a fuzzy neural network (FNN) system to ascertain the identity of the driver. Two fuzzy neural networks, namely, the evolving fuzzy neural network (EFuNN) and the adaptive network-based fuzzy inference system (ANFIS), are used to demonstrate the viability of the two proposed feature extraction techniques. The performances were compared against an artificial neural network (NN) implementation using the multilayer perceptron (MLP) network and a statistical method based on the GMM. Extensive testing was conducted and the results show great potential in the use of the FNN for real-time driver identification and verification. In addition, the profiling of driver behaviors has numerous other potential applications for use by law enforcement and companies dealing with buses and truck drivers.

Sociotechnical attributes of safe and unsafe work systems.

PubMed

Kleiner, Brian M; Hettinger, Lawrence J; DeJoy, David M; Huang, Yuang-Hsiang; Love, Peter E D

2015-01-01

Theoretical and practical approaches to safety based on sociotechnical systems principles place heavy emphasis on the intersections between social-organisational and technical-work process factors. Within this perspective, work system design emphasises factors such as the joint optimisation of social and technical processes, a focus on reliable human-system performance and safety metrics as design and analysis criteria, the maintenance of a realistic and consistent set of safety objectives and policies, and regular access to the expertise and input of workers. We discuss three current approaches to the analysis and design of complex sociotechnical systems: human-systems integration, macroergonomics and safety climate. Each approach emphasises key sociotechnical systems themes, and each prescribes a more holistic perspective on work systems than do traditional theories and methods. We contrast these perspectives with historical precedents such as system safety and traditional human factors and ergonomics, and describe potential future directions for their application in research and practice. The identification of factors that can reliably distinguish between safe and unsafe work systems is an important concern for ergonomists and other safety professionals. This paper presents a variety of sociotechnical systems perspectives on intersections between social--organisational and technology--work process factors as they impact work system analysis, design and operation.

Maritime domain awareness community of interest net centric information sharing

NASA Astrophysics Data System (ADS)

Andress, Mark; Freeman, Brian; Rhiddlehover, Trey; Shea, John

2007-04-01

This paper highlights the approach taken by the Maritime Domain Awareness (MDA) Community of Interest (COI) in establishing an approach to data sharing that seeks to overcome many of the obstacles to sharing both within the federal government and with international and private sector partners. The approach uses the DOD Net Centric Data Strategy employed through Net Centric Enterprise Services (NCES) Service Oriented Architecture (SOA) foundation provided by Defense Information Systems Agency (DISA), but is unique in that the community is made up of more than just Defense agencies. For the first pilot project, the MDA COI demonstrated how four agencies from DOD, the Intelligence Community, Department of Homeland Security (DHS), and Department of Transportation (DOT) could share Automatic Identification System (AIS) data in a common format using shared enterprise service components.

Clone tag detection in distributed RFID systems

PubMed Central

Kamaludin, Hazalila; Mahdin, Hairulnizam

2018-01-01

Although Radio Frequency Identification (RFID) is poised to displace barcodes, security vulnerabilities pose serious challenges for global adoption of the RFID technology. Specifically, RFID tags are prone to basic cloning and counterfeiting security attacks. A successful cloning of the RFID tags in many commercial applications can lead to many serious problems such as financial losses, brand damage, safety and health of the public. With many industries such as pharmaceutical and businesses deploying RFID technology with a variety of products, it is important to tackle RFID tag cloning problem and improve the resistance of the RFID systems. To this end, we propose an approach for detecting cloned RFID tags in RFID systems with high detection accuracy and minimal overhead thus overcoming practical challenges in existing approaches. The proposed approach is based on consistency of dual hash collisions and modified count-min sketch vector. We evaluated the proposed approach through extensive experiments and compared it with existing baseline approaches in terms of execution time and detection accuracy under varying RFID tag cloning ratio. The results of the experiments show that the proposed approach outperforms the baseline approaches in cloned RFID tag detection accuracy. PMID:29565982

Remodeling of legacy systems in health care using UML.

PubMed

Garde, Sebastian; Knaup, Petra; Herold, Ralf

2002-01-01

Research projects in the field of Medical Informatics often involve the development of application systems. Usually they are developed over a longer period of time, so that at a certain point of time a systematically planned reimplementation is necessary. The first step of reimplementation should be a systematic and comprehensive remodeling. When using UML for this task a systematic approach for remodeling activities is missing. Therefore, we developed a method for remodeling of legacy systems (Qumquad) and applied it to DOSPO, a documentation and therapy planning system for pediatric oncology. Qumquad helps to systematically carry out three steps: the modeling of the current actual state of the application system, the systematic identification of weak points and the development of a target concept for reimplementation considering the identified weak points. Results show that this approach is valuable and feasible and could be applied to various application systems in health care.

Intelligent Chemistry Management System (ICMS)--A new approach to steam generator chemistry control

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

Barto, R.J.; Farrell, D.M.; Noto, F.A.

1986-04-01

The Intelligent Chemistry Management System (ICMS) is a new tool which assists in steam generator chemistry control. Utilizing diagnostic capabilities, the ICMS will provide utility and industrial boiler operators, system chemists, and plant engineers with a tool for monitoring, diagnosing, and controlling steam generator system chemistry. By reducing the number of forced outages through early identification of potentially detrimental conditions, suggestion of possible causes, and execution of corrective actions, improvements in unit availability and reliability will result. The system monitors water and steam quality at a number of critical locations in the plant.

Frequency Response Function Based Damage Identification for Aerospace Structures

NASA Astrophysics Data System (ADS)

Oliver, Joseph Acton

Structural health monitoring technologies continue to be pursued for aerospace structures in the interests of increased safety and, when combined with health prognosis, efficiency in life-cycle management. The current dissertation develops and validates damage identification technology as a critical component for structural health monitoring of aerospace structures and, in particular, composite unmanned aerial vehicles. The primary innovation is a statistical least-squares damage identification algorithm based in concepts of parameter estimation and model update. The algorithm uses frequency response function based residual force vectors derived from distributed vibration measurements to update a structural finite element model through statistically weighted least-squares minimization producing location and quantification of the damage, estimation uncertainty, and an updated model. Advantages compared to other approaches include robust applicability to systems which are heavily damped, large, and noisy, with a relatively low number of distributed measurement points compared to the number of analytical degrees-of-freedom of an associated analytical structural model (e.g., modal finite element model). Motivation, research objectives, and a dissertation summary are discussed in Chapter 1 followed by a literature review in Chapter 2. Chapter 3 gives background theory and the damage identification algorithm derivation followed by a study of fundamental algorithm behavior on a two degree-of-freedom mass-spring system with generalized damping. Chapter 4 investigates the impact of noise then successfully proves the algorithm against competing methods using an analytical eight degree-of-freedom mass-spring system with non-proportional structural damping. Chapter 5 extends use of the algorithm to finite element models, including solutions for numerical issues, approaches for modeling damping approximately in reduced coordinates, and analytical validation using a composite sandwich plate model. Chapter 6 presents the final extension to experimental systems-including methods for initial baseline correlation and data reduction-and validates the algorithm on an experimental composite plate with impact damage. The final chapter deviates from development and validation of the primary algorithm to discuss development of an experimental scaled-wing test bed as part of a collaborative effort for developing structural health monitoring and prognosis technology. The dissertation concludes with an overview of technical conclusions and recommendations for future work.

Development of a novel scoring system for identifying emerging chemical risks in the food chain.

PubMed

Oltmanns, J; Licht, O; Bitsch, A; Bohlen, M-L; Escher, S E; Silano, V; MacLeod, M; Serafimova, R; Kass, G E N; Merten, C

2018-02-21

The European Food Safety Authority (EFSA) is responsible for risk assessment of all aspects of food safety, including the establishment of procedures aimed at the identification of emerging risks to food safety. Here, a scoring system was developed for identifying chemicals registered under the European REACH Regulation that could be of potential concern in the food chain using the following parameters: (i) environmental release based on maximum aggregated tonnages and environmental release categories; (ii) biodegradation in the environment; (iii) bioaccumulation and in vivo and in vitro toxicity. The screening approach was tested on 100 data-rich chemicals registered under the REACH Regulation at aggregated volumes of at least 1000 tonnes per annum. The results show that substance-specific data generated under the REACH Regulation can be used to identify potential emerging risks in the food chain. After application of the screening procedure, priority chemicals can be identified as potentially emerging risk chemicals through the integration of exposure, environmental fate and toxicity. The default approach is to generate a single total score for each substance using a predefined weighting scenario. However, it is also possible to use a pivot table approach to combine the individual scores in different ways that reflect user-defined priorities, which enables a very flexible, iterative definition of screening criteria. Possible applications of the approaches are discussed using illustrative examples. Either approach can then be followed by in-depth evaluation of priority substances to ensure the identification of substances that present a real emerging chemical risk in the food chain.

Hotspots identification and ranking for road safety improvement: an alternative approach.

PubMed

Coll, Bronagh; Moutari, Salissou; Marshall, Adele H

2013-10-01

During the last decade, the concept of composite performance index, brought from economic and business statistics, has become a popular practice in the field of road safety, namely for the identification and classification of worst performing areas or time slots also known as hotspots. The overall quality of a composite index depends upon the complexity of phenomena of interest as well as the relevance of the methodological approach used to aggregate the various indicators into a single composite index. However, current aggregation methods used to estimate the composite road safety performance index suffer from various deficiencies at both the theoretical and operational level; these include the correlation and compensability between indicators, the weighting of the indicators as well as their high "degree of freedom" which enables one to readily manipulate them to produce desired outcomes (Munda and Nardo, 2003, 2005, 2009). The objective of this study is to contribute to the ongoing research effort on the estimation of road safety composite index for hotspots' identification and ranking. The aggregation method for constructing the composite road safety performance index introduced in this paper, strives to minimize the aforementioned deficiencies of the current approaches. Furthermore, this new method can be viewed as an intelligent decision support system for road safety performance evaluation, in order to prioritize interventions for road safety improvement. Copyright © 2013 Elsevier Ltd. All rights reserved.

Patient and Sample Identification. Out of the Maze?

PubMed

Lippi, Giuseppe; Chiozza, Laura; Mattiuzzi, Camilla; Plebani, Mario

2017-04-01

Patient and sample misidentification may cause significant harm or discomfort to the patients, especially when incorrect data is used for performing specific healthcare activities. It is hence obvious that efficient and quality care can only start from accurate patient identification. There are many opportunities for misidentification in healthcare and laboratory medicine, including homonymy, incorrect patient registration, reliance on wrong patient data, mistakes in order entry, collection of biological specimens from wrong patients, inappropriate sample labeling and inaccurate entry or erroneous transmission of test results through the laboratory information system. Many ongoing efforts are made to prevent this important healthcare problem, entailing streamlined strategies for identifying patients throughout the healthcare industry by means of traditional and innovative identifiers, as well as using technologic tools that may enhance both the quality and efficiency of blood tubes labeling. The aim of this article is to provide an overview about the liability of identification errors in healthcare, thus providing a pragmatic approach for diverging the so-called patient identification crisis.

Unambiguous metabolite identification in high-throughput metabolomics by hybrid 1D 1 H NMR/ESI MS 1 approach: Hybrid 1D 1 H NMR/ESI MS 1 metabolomics method

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

Walker, Lawrence R.; Hoyt, David W.; Walker, S. Michael

We present a novel approach to improve accuracy of metabolite identification by combining direct infusion ESI MS1 with 1D 1H NMR spectroscopy. The new approach first applies standard 1D 1H NMR metabolite identification protocol by matching the chemical shift, J-coupling and intensity information of experimental NMR signals against the NMR signals of standard metabolites in metabolomics library. This generates a list of candidate metabolites. The list contains false positive and ambiguous identifications. Next, we constrained the list with the chemical formulas derived from high-resolution direct infusion ESI MS1 spectrum of the same sample. Detection of the signals of a metabolitemore » both in NMR and MS significantly improves the confidence of identification and eliminates false positive identification. 1D 1H NMR and direct infusion ESI MS1 spectra of a sample can be acquired in parallel in several minutes. This is highly beneficial for rapid and accurate screening of hundreds of samples in high-throughput metabolomics studies. In order to make this approach practical, we developed a software tool, which is integrated to Chenomx NMR Suite. The approach is demonstrated on a model mixture, tomato and Arabidopsis thaliana metabolite extracts, and human urine.« less

The Rules Grid: Helping Children with Social Communication and Interaction Needs Manage Social Complexity

ERIC Educational Resources Information Center

Devlin, Niall

2009-01-01

This article introduces a new practical visual approach, the Rules Grid, to support children who have social communication and interaction needs. The Rules Grid involves a system whereby behaviours of concern can be broken down into smaller behavioural manifestations which in turn lead not only to problem identification and specification, but…

Talent Development as an Ecology of Games: A Case Study of Norwegian Handball

ERIC Educational Resources Information Center

Bjørndal, Christian Thue; Ronglan, Lars Tore; Andersen, Svein S.

2017-01-01

Structured talent identification and development, it has been argued, is one of the foundations of international sporting success and many modern elite sport systems have applied normative talent development (TD) models. The success of Norwegian handball, however, is based on an alternative approach to TD. Norwegian handball is characterized by a…

Effects of weighting schemes on the identification of wildlife corridors generated with least-cost methods

Treesearch

Sean A. Parks; Kevin S. McKelvey; Michael K. Schwartz

2012-01-01

The importance of movement corridors for maintaining connectivity within metapopulations of wild animals is a cornerstone of conservation. One common approach for determining corridor locations is least-cost corridor (LCC) modeling, which uses algorithms within a geographic information system to search for routes with the lowest cumulative resistance between target...

Creating Community Change to Improve Youth Development: The Communities That Care System

ERIC Educational Resources Information Center

Brooke-Weiss, Blaire; Haggerty, Kevin P.; Fagan, Abigail A.; Hawkins, J. David; Cady, Rick

2008-01-01

Advances in prevention science in the past 25 years provide important direction for neighborhood and community efforts to improve the lives of youth. Prevention scientists have applied a public health approach to youth problems such as drug use and delinquency, and have shown that these problems can be prevented. The identification of risk and…

Activity Theory Approach to Developing Context-Aware Mobile Learning Systems for Understanding Scientific Phenomenon and Theories

ERIC Educational Resources Information Center

Uden, Lorna; Hwang, Gwo-Jen

2013-01-01

Mobile computing offers potential opportunities for students' learning especially when it combines a sensing device such as RFID (Radio Frequency Identification). Researchers have indicated that a key feature of in-field learning supported by mobile devices and technology is context awareness, with which context and functionality provided by…

Rule-driven defect detection in CT images of hardwood logs

Treesearch

Erol Sarigul; A. Lynn Abbott; Daniel L. Schmoldt

2000-01-01

This paper deals with automated detection and identification of internal defects in hardwood logs using computed tomography (CT) images. We have developed a system that employs artificial neural networks to perform tentative classification of logs on a pixel-by-pixel basis. This approach achieves a high level of classification accuracy for several hardwood species (...

Air Force Manufacturing Technology. Year 2000 Project Book

DTIC Science & Technology

2000-01-01

Electronic Warfare Component Manufacturing 13 National Center for Manufacturing Science 14 Product Research Market Analysis System 15 Electronics Acoustic...other agile organizations that can respond to rapidly changing market demands. Approach This program demonstrated and evaluated the advanced design...production worker contact with customers and suppliers; shopfloor identification of new technologies, markets , and products; and strategic planning to assure

An adaptive deep learning approach for PPG-based identification.

PubMed

Jindal, V; Birjandtalab, J; Pouyan, M Baran; Nourani, M

2016-08-01

Wearable biosensors have become increasingly popular in healthcare due to their capabilities for low cost and long term biosignal monitoring. This paper presents a novel two-stage technique to offer biometric identification using these biosensors through Deep Belief Networks and Restricted Boltzman Machines. Our identification approach improves robustness in current monitoring procedures within clinical, e-health and fitness environments using Photoplethysmography (PPG) signals through deep learning classification models. The approach is tested on TROIKA dataset using 10-fold cross validation and achieved an accuracy of 96.1%.

Biometric identification based on novel frequency domain facial asymmetry measures

NASA Astrophysics Data System (ADS)

Mitra, Sinjini; Savvides, Marios; Vijaya Kumar, B. V. K.

2005-03-01

In the modern world, the ever-growing need to ensure a system's security has spurred the growth of the newly emerging technology of biometric identification. The present paper introduces a novel set of facial biometrics based on quantified facial asymmetry measures in the frequency domain. In particular, we show that these biometrics work well for face images showing expression variations and have the potential to do so in presence of illumination variations as well. A comparison of the recognition rates with those obtained from spatial domain asymmetry measures based on raw intensity values suggests that the frequency domain representation is more robust to intra-personal distortions and is a novel approach for performing biometric identification. In addition, some feature analysis based on statistical methods comparing the asymmetry measures across different individuals and across different expressions is presented.

Polluter identification with spaceborne radar imagery, AIS and forward drift modeling.

PubMed

Longépé, N; Mouche, A A; Goacolou, M; Granier, N; Carrere, L; Lebras, J Y; Lozach, P; Besnard, S

2015-12-30

This study defines and assesses a new operational concept to identify the origin of pollution at sea, based on Synthetic Aperture Radar, Automatic Identification System, and a forward drift model. As opposed to traditional methodologies where the SAR detected pollution is backtracked in the past, our approach assumes that all the vessels pollute all along their way. Based on all the AIS data flows, the forward-tracked simulated pollutions are then compared to the detected pollution, and the potential polluter can be finally identified. Case studies are presented to showcase its usefulness in a variety of maritime situations with a focus on orphan pollutions in a dense traffic area. Out of the identification of the suspected polluters, the age and eventually the type of the pollution can be retrieved. Copyright © 2015 Elsevier Ltd. All rights reserved.

Prioritizing causal disease genes using unbiased genomic features.

PubMed

Deo, Rahul C; Musso, Gabriel; Tasan, Murat; Tang, Paul; Poon, Annie; Yuan, Christiana; Felix, Janine F; Vasan, Ramachandran S; Beroukhim, Rameen; De Marco, Teresa; Kwok, Pui-Yan; MacRae, Calum A; Roth, Frederick P

2014-12-03

Cardiovascular disease (CVD) is the leading cause of death in the developed world. Human genetic studies, including genome-wide sequencing and SNP-array approaches, promise to reveal disease genes and mechanisms representing new therapeutic targets. In practice, however, identification of the actual genes contributing to disease pathogenesis has lagged behind identification of associated loci, thus limiting the clinical benefits. To aid in localizing causal genes, we develop a machine learning approach, Objective Prioritization for Enhanced Novelty (OPEN), which quantitatively prioritizes gene-disease associations based on a diverse group of genomic features. This approach uses only unbiased predictive features and thus is not hampered by a preference towards previously well-characterized genes. We demonstrate success in identifying genetic determinants for CVD-related traits, including cholesterol levels, blood pressure, and conduction system and cardiomyopathy phenotypes. Using OPEN, we prioritize genes, including FLNC, for association with increased left ventricular diameter, which is a defining feature of a prevalent cardiovascular disorder, dilated cardiomyopathy or DCM. Using a zebrafish model, we experimentally validate FLNC and identify a novel FLNC splice-site mutation in a patient with severe DCM. Our approach stands to assist interpretation of large-scale genetic studies without compromising their fundamentally unbiased nature.

Application of multi-target phytotherapeutic concept in malaria drug discovery: a systems biology approach in biomarker identification.

PubMed

Tarkang, Protus Arrey; Appiah-Opong, Regina; Ofori, Michael F; Ayong, Lawrence S; Nyarko, Alexander K

2016-01-01

There is an urgent need for new anti-malaria drugs with broad therapeutic potential and novel mode of action, for effective treatment and to overcome emerging drug resistance. Plant-derived anti-malarials remain a significant source of bioactive molecules in this regard. The multicomponent formulation forms the basis of phytotherapy. Mechanistic reasons for the poly-pharmacological effects of plants constitute increased bioavailability, interference with cellular transport processes, activation of pro-drugs/deactivation of active compounds to inactive metabolites and action of synergistic partners at different points of the same signaling cascade. These effects are known as the multi-target concept. However, due to the intrinsic complexity of natural products-based drug discovery, there is need to rethink the approaches toward understanding their therapeutic effect. This review discusses the multi-target phytotherapeutic concept and its application in biomarker identification using the modified reverse pharmacology - systems biology approach. Considerations include the generation of a product library, high throughput screening (HTS) techniques for efficacy and interaction assessment, High Performance Liquid Chromatography (HPLC)-based anti-malarial profiling and animal pharmacology. This approach is an integrated interdisciplinary implementation of tailored technology platforms coupled to miniaturized biological assays, to track and characterize the multi-target bioactive components of botanicals as well as identify potential biomarkers. While preserving biodiversity, this will serve as a primary step towards the development of standardized phytomedicines, as well as facilitate lead discovery for chemical prioritization and downstream clinical development.

Searching for the elusive gift: advances in talent identification in sport.

PubMed

Mann, David L; Dehghansai, Nima; Baker, Joseph

2017-08-01

The incentives for sport organizations to identify talented athletes from a young age continue to grow, yet effective talent identification remains a challenging task. This opinion paper examines recent advances in talent identification, focusing in particular on the emergence of new approaches that may offer promise to identify talent (e.g., small-sided games, genetic testing, and advanced statistical analyses). We appraise new multi-disciplinary and large-scale population studies of talent identification, provide a consideration of the most recent psychological predictors of performance, examine the emergence of new approaches that strive to diminish biases in talent identification, and look at the rise in interest in talent identification in Paralympic sport. Crown Copyright © 2017. Published by Elsevier Ltd. All rights reserved.

An integrated high resolution mass spectrometric and informatics approach for the rapid identification of phenolics in plant extract

USDA-ARS?s Scientific Manuscript database

An integrated approach based on high resolution MS analysis (orbitrap), database (db) searching and MS/MS fragmentation prediction for the rapid identification of plant phenols is reported. The approach was firstly validated by using a mixture of phenolic standards (phenolic acids, flavones, flavono...

Dissipative rendering and neural network control system design

NASA Technical Reports Server (NTRS)

Gonzalez, Oscar R.

1995-01-01

Model-based control system designs are limited by the accuracy of the models of the plant, plant uncertainty, and exogenous signals. Although better models can be obtained with system identification, the models and control designs still have limitations. One approach to reduce the dependency on particular models is to design a set of compensators that will guarantee robust stability to a set of plants. Optimization over the compensator parameters can then be used to get the desired performance. Conservativeness of this approach can be reduced by integrating fundamental properties of the plant models. This is the approach of dissipative control design. Dissipative control designs are based on several variations of the Passivity Theorem, which have been proven for nonlinear/linear and continuous-time/discrete-time systems. These theorems depend not on a specific model of a plant, but on its general dissipative properties. Dissipative control design has found wide applicability in flexible space structures and robotic systems that can be configured to be dissipative. Currently, there is ongoing research to improve the performance of dissipative control designs. For aircraft systems that are not dissipative active control may be used to make them dissipative and then a dissipative control design technique can be used. It is also possible that rendering a system dissipative and dissipative control design may be combined into one step. Furthermore, the transformation of a non-dissipative system to dissipative can be done robustly. One sequential design procedure for finite dimensional linear time-invariant systems has been developed. For nonlinear plants that cannot be controlled adequately with a single linear controller, model-based techniques have additional problems. Nonlinear system identification is still a research topic. Lacking analytical models for model-based design, artificial neural network algorithms have recently received considerable attention. Using their universal approximation property, neural networks have been introduced into nonlinear control designs in several ways. Unfortunately, little work has appeared that analyzes neural network control systems and establishes margins for stability and performance. One approach for this analysis is to set up neural network control systems in the framework presented above. For example, one neural network could be used to render a system to be dissipative, a second strictly dissipative neural network controller could be used to guarantee robust stability.

Road sign recognition using Viapix module and correlation

NASA Astrophysics Data System (ADS)

Ouerhani, Y.; Desthieux, M.; Alfalou, A.

2015-03-01

In this paper, we propose and validate a new system used to explore road assets. In this work we are interested on the vertical road signs. To do this, we are based on the combination of road signs detection, recognition and identification using data provides by sensors. The proposed approach consists on using panoramic views provided by the innovative device, VIAPIX®1, developed by our company ACTRIS2. We are based also on the optimized correlation technique for road signs recognition and identification on pictures. Obtained results shows the interest on using panoramic views compared to results obtained using images provided using only one camera.

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).

Affordable proteomics: the two-hybrid systems.

PubMed

Gillespie, Marc

2003-06-01

Numerous proteomic methodologies exist, but most require a heavy investment in expertise and technology. This puts these approaches out of reach for many laboratories and small companies, rarely allowing proteomics to be used as a pilot approach for biomarker or target identification. Two proteomic approaches, 2D gel electrophoresis and the two-hybrid systems, are currently available to most researchers. The two-hybrid systems, though accommodating to large-scale experiments, were originally designed as practical screens, that by comparison to current proteomics tools were small-scale, affordable and technically feasible. The screens rapidly generated data, identifying protein interactions that were previously uncharacterized. The foundation for a two-hybrid proteomic investigation can be purchased as separate kits from a number of companies. The true power of the technique lies not in its affordability, but rather in its portability. The two-hybrid system puts proteomics back into laboratories where the output of the screens can be evaluated by researchers with experience in the particular fields of basic research, cancer biology, toxicology or drug development.

AGARD Flight Test Techniques Series. Volume 3. Identification of Dynamic Systems - Applications to Aircraft. Part 1. The Output Error Approach

DTIC Science & Technology

1986-12-01

poorly written problem statements. We decline to artificially create difficulties for experimentation. Others have encountered these issues and treated...you lose some of the weaning. The method also does not extend well to nonlinear or time-varying system (sometimes it can be don#. but it creates ...thereby introduced creates problems and solves nothing. For variable-geometry aircraft, some projects establish reference geometry values that change as

Systems Genetics as a Tool to Identify Master Genetic Regulators in Complex Disease.

PubMed

Moreno-Moral, Aida; Pesce, Francesco; Behmoaras, Jacques; Petretto, Enrico

2017-01-01

Systems genetics stems from systems biology and similarly employs integrative modeling approaches to describe the perturbations and phenotypic effects observed in a complex system. However, in the case of systems genetics the main source of perturbation is naturally occurring genetic variation, which can be analyzed at the systems-level to explain the observed variation in phenotypic traits. In contrast with conventional single-variant association approaches, the success of systems genetics has been in the identification of gene networks and molecular pathways that underlie complex disease. In addition, systems genetics has proven useful in the discovery of master trans-acting genetic regulators of functional networks and pathways, which in many cases revealed unexpected gene targets for disease. Here we detail the central components of a fully integrated systems genetics approach to complex disease, starting from assessment of genetic and gene expression variation, linking DNA sequence variation to mRNA (expression QTL mapping), gene regulatory network analysis and mapping the genetic control of regulatory networks. By summarizing a few illustrative (and successful) examples, we highlight how different data-modeling strategies can be effectively integrated in a systems genetics study.

A data recipient centered de-identification method to retain statistical attributes.

PubMed

Gal, Tamas S; Tucker, Thomas C; Gangopadhyay, Aryya; Chen, Zhiyuan

2014-08-01

Privacy has always been a great concern of patients and medical service providers. As a result of the recent advances in information technology and the government's push for the use of Electronic Health Record (EHR) systems, a large amount of medical data is collected and stored electronically. This data needs to be made available for analysis but at the same time patient privacy has to be protected through de-identification. Although biomedical researchers often describe their research plans when they request anonymized data, most existing anonymization methods do not use this information when de-identifying the data. As a result, the anonymized data may not be useful for the planned research project. This paper proposes a data recipient centered approach to tailor the de-identification method based on input from the recipient of the data. We demonstrate our approach through an anonymization project for biomedical researchers with specific goals to improve the utility of the anonymized data for statistical models used for their research project. The selected algorithm improves a privacy protection method called Condensation by Aggarwal et al. Our methods were tested and validated on real cancer surveillance data provided by the Kentucky Cancer Registry. Copyright © 2014 Elsevier Inc. All rights reserved.

Leaf epidermis images for robust identification of plants

PubMed Central

da Silva, Núbia Rosa; Oliveira, Marcos William da Silva; Filho, Humberto Antunes de Almeida; Pinheiro, Luiz Felipe Souza; Rossatto, Davi Rodrigo; Kolb, Rosana Marta; Bruno, Odemir Martinez

2016-01-01

This paper proposes a methodology for plant analysis and identification based on extracting texture features from microscopic images of leaf epidermis. All the experiments were carried out using 32 plant species with 309 epidermal samples captured by an optical microscope coupled to a digital camera. The results of the computational methods using texture features were compared to the conventional approach, where quantitative measurements of stomatal traits (density, length and width) were manually obtained. Epidermis image classification using texture has achieved a success rate of over 96%, while success rate was around 60% for quantitative measurements taken manually. Furthermore, we verified the robustness of our method accounting for natural phenotypic plasticity of stomata, analysing samples from the same species grown in different environments. Texture methods were robust even when considering phenotypic plasticity of stomatal traits with a decrease of 20% in the success rate, as quantitative measurements proved to be fully sensitive with a decrease of 77%. Results from the comparison between the computational approach and the conventional quantitative measurements lead us to discover how computational systems are advantageous and promising in terms of solving problems related to Botany, such as species identification. PMID:27217018

Variational Bayesian identification and prediction of stochastic nonlinear dynamic causal models.

PubMed

Daunizeau, J; Friston, K J; Kiebel, S J

2009-11-01

In this paper, we describe a general variational Bayesian approach for approximate inference on nonlinear stochastic dynamic models. This scheme extends established approximate inference on hidden-states to cover: (i) nonlinear evolution and observation functions, (ii) unknown parameters and (precision) hyperparameters and (iii) model comparison and prediction under uncertainty. Model identification or inversion entails the estimation of the marginal likelihood or evidence of a model. This difficult integration problem can be finessed by optimising a free-energy bound on the evidence using results from variational calculus. This yields a deterministic update scheme that optimises an approximation to the posterior density on the unknown model variables. We derive such a variational Bayesian scheme in the context of nonlinear stochastic dynamic hierarchical models, for both model identification and time-series prediction. The computational complexity of the scheme is comparable to that of an extended Kalman filter, which is critical when inverting high dimensional models or long time-series. Using Monte-Carlo simulations, we assess the estimation efficiency of this variational Bayesian approach using three stochastic variants of chaotic dynamic systems. We also demonstrate the model comparison capabilities of the method, its self-consistency and its predictive power.

A systematic identification of species-specific protein succinylation sites using joint element features information.

PubMed

Hasan, Md Mehedi; Khatun, Mst Shamima; Mollah, Md Nurul Haque; Yong, Cao; Guo, Dianjing

2017-01-01

Lysine succinylation, an important type of protein posttranslational modification, plays significant roles in many cellular processes. Accurate identification of succinylation sites can facilitate our understanding about the molecular mechanism and potential roles of lysine succinylation. However, even in well-studied systems, a majority of the succinylation sites remain undetected because the traditional experimental approaches to succinylation site identification are often costly, time-consuming, and laborious. In silico approach, on the other hand, is potentially an alternative strategy to predict succinylation substrates. In this paper, a novel computational predictor SuccinSite2.0 was developed for predicting generic and species-specific protein succinylation sites. This predictor takes the composition of profile-based amino acid and orthogonal binary features, which were used to train a random forest classifier. We demonstrated that the proposed SuccinSite2.0 predictor outperformed other currently existing implementations on a complementarily independent dataset. Furthermore, the important features that make visible contributions to species-specific and cross-species-specific prediction of protein succinylation site were analyzed. The proposed predictor is anticipated to be a useful computational resource for lysine succinylation site prediction. The integrated species-specific online tool of SuccinSite2.0 is publicly accessible.

Performance of Kiestra Total Laboratory Automation Combined with MS in Clinical Microbiology Practice

PubMed Central

Hodiamont, Caspar J.; de Jong, Menno D.; Overmeijer, Hendri P. J.; van den Boogaard, Mandy; Visser, Caroline E.

2014-01-01

Background Microbiological laboratories seek technologically innovative solutions to cope with large numbers of samples and limited personnel and financial resources. One platform that has recently become available is the Kiestra Total Laboratory Automation (TLA) system (BD Kiestra B.V., the Netherlands). This fully automated sample processing system, equipped with digital imaging technology, allows superior detection of microbial growth. Combining this approach with matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MS) (Bruker Daltonik, Germany) is expected to enable more rapid identification of pathogens. Methods Early growth detection by digital imaging using Kiestra TLA combined with MS was compared to conventional methods (CM) of detection. Accuracy and time taken for microbial identification were evaluated for the two methods in 219 clinical blood culture isolates. The possible clinical impact of earlier microbial identification was assessed according to antibiotic treatment prescription. Results Pathogen identification using Kiestra TLA combined with MS resulted in a 30.6 hr time gain per isolate compared to CM. Pathogens were successfully identified in 98.4% (249/253) of all tested isolates. Early microbial identification without susceptibility testing led to an adjustment of antibiotic regimen in 12% (24/200) of patients. Conclusions The requisite 24 hr incubation time for microbial pathogens to reach sufficient growth for susceptibility testing and identification would be shortened by the implementation of Kiestra TLA in combination with MS, compared to the use of CM. Not only can this method optimize workflow and reduce costs, but it can allow potentially life-saving switches in antibiotic regimen to be initiated sooner. PMID:24624346

Indel-seq: a fast-forward genetics approach for identification of trait-associated putative candidate genomic regions and its application in pigeonpea (Cajanus cajan).

PubMed

Singh, Vikas K; Khan, Aamir W; Saxena, Rachit K; Sinha, Pallavi; Kale, Sandip M; Parupalli, Swathi; Kumar, Vinay; Chitikineni, Annapurna; Vechalapu, Suryanarayana; Sameer Kumar, Chanda Venkata; Sharma, Mamta; Ghanta, Anuradha; Yamini, Kalinati Narasimhan; Muniswamy, Sonnappa; Varshney, Rajeev K

2017-07-01

Identification of candidate genomic regions associated with target traits using conventional mapping methods is challenging and time-consuming. In recent years, a number of single nucleotide polymorphism (SNP)-based mapping approaches have been developed and used for identification of candidate/putative genomic regions. However, in the majority of these studies, insertion-deletion (Indel) were largely ignored. For efficient use of Indels in mapping target traits, we propose Indel-seq approach, which is a combination of whole-genome resequencing (WGRS) and bulked segregant analysis (BSA) and relies on the Indel frequencies in extreme bulks. Deployment of Indel-seq approach for identification of candidate genomic regions associated with fusarium wilt (FW) and sterility mosaic disease (SMD) resistance in pigeonpea has identified 16 Indels affecting 26 putative candidate genes. Of these 26 affected putative candidate genes, 24 genes showed effect in the upstream/downstream of the genic region and two genes showed effect in the genes. Validation of these 16 candidate Indels in other FW- and SMD-resistant and FW- and SMD-susceptible genotypes revealed a significant association of five Indels (three for FW and two for SMD resistance). Comparative analysis of Indel-seq with other genetic mapping approaches highlighted the importance of the approach in identification of significant genomic regions associated with target traits. Therefore, the Indel-seq approach can be used for quick and precise identification of candidate genomic regions for any target traits in any crop species. © 2016 The Authors. Plant Biotechnology Journal published by Society for Experimental Biology and The Association of Applied Biologists and John Wiley & Sons Ltd.

Automatic de-identification of textual documents in the electronic health record: a review of recent research

PubMed Central

2010-01-01

Background In the United States, the Health Insurance Portability and Accountability Act (HIPAA) protects the confidentiality of patient data and requires the informed consent of the patient and approval of the Internal Review Board to use data for research purposes, but these requirements can be waived if data is de-identified. For clinical data to be considered de-identified, the HIPAA "Safe Harbor" technique requires 18 data elements (called PHI: Protected Health Information) to be removed. The de-identification of narrative text documents is often realized manually, and requires significant resources. Well aware of these issues, several authors have investigated automated de-identification of narrative text documents from the electronic health record, and a review of recent research in this domain is presented here. Methods This review focuses on recently published research (after 1995), and includes relevant publications from bibliographic queries in PubMed, conference proceedings, the ACM Digital Library, and interesting publications referenced in already included papers. Results The literature search returned more than 200 publications. The majority focused only on structured data de-identification instead of narrative text, on image de-identification, or described manual de-identification, and were therefore excluded. Finally, 18 publications describing automated text de-identification were selected for detailed analysis of the architecture and methods used, the types of PHI detected and removed, the external resources used, and the types of clinical documents targeted. All text de-identification systems aimed to identify and remove person names, and many included other types of PHI. Most systems used only one or two specific clinical document types, and were mostly based on two different groups of methodologies: pattern matching and machine learning. Many systems combined both approaches for different types of PHI, but the majority relied only on pattern matching, rules, and dictionaries. Conclusions In general, methods based on dictionaries performed better with PHI that is rarely mentioned in clinical text, but are more difficult to generalize. Methods based on machine learning tend to perform better, especially with PHI that is not mentioned in the dictionaries used. Finally, the issues of anonymization, sufficient performance, and "over-scrubbing" are discussed in this publication. PMID:20678228

Automatic de-identification of textual documents in the electronic health record: a review of recent research.

PubMed

Meystre, Stephane M; Friedlin, F Jeffrey; South, Brett R; Shen, Shuying; Samore, Matthew H

2010-08-02

In the United States, the Health Insurance Portability and Accountability Act (HIPAA) protects the confidentiality of patient data and requires the informed consent of the patient and approval of the Internal Review Board to use data for research purposes, but these requirements can be waived if data is de-identified. For clinical data to be considered de-identified, the HIPAA "Safe Harbor" technique requires 18 data elements (called PHI: Protected Health Information) to be removed. The de-identification of narrative text documents is often realized manually, and requires significant resources. Well aware of these issues, several authors have investigated automated de-identification of narrative text documents from the electronic health record, and a review of recent research in this domain is presented here. This review focuses on recently published research (after 1995), and includes relevant publications from bibliographic queries in PubMed, conference proceedings, the ACM Digital Library, and interesting publications referenced in already included papers. The literature search returned more than 200 publications. The majority focused only on structured data de-identification instead of narrative text, on image de-identification, or described manual de-identification, and were therefore excluded. Finally, 18 publications describing automated text de-identification were selected for detailed analysis of the architecture and methods used, the types of PHI detected and removed, the external resources used, and the types of clinical documents targeted. All text de-identification systems aimed to identify and remove person names, and many included other types of PHI. Most systems used only one or two specific clinical document types, and were mostly based on two different groups of methodologies: pattern matching and machine learning. Many systems combined both approaches for different types of PHI, but the majority relied only on pattern matching, rules, and dictionaries. In general, methods based on dictionaries performed better with PHI that is rarely mentioned in clinical text, but are more difficult to generalize. Methods based on machine learning tend to perform better, especially with PHI that is not mentioned in the dictionaries used. Finally, the issues of anonymization, sufficient performance, and "over-scrubbing" are discussed in this publication.

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.

Roll-to-Roll Screen Printed Radio Frequency Identification Transponder Antennas for Vehicle Tracking Systems

NASA Astrophysics Data System (ADS)

Zichner, Ralf; Baumann, Reinhard R.

2013-05-01

Vehicle tracking systems based on ultra high frequency (UHF) radio frequency identification (RFID) technology are already introduced to control the access to car parks and corporate premises. For this field of application so-called Windshield RFID transponder labels are used, which are applied to the inside of the windshield. State of the art for manufacturing these transponder antennas is the traditional lithography/etching approach. Furthermore the performance of these transponders is limited to a reading distance of approximately 5 m which results in car speed limit of 5 km/h for identification. However, to achieve improved performance compared to existing all-purpose transponders and a dramatic cost reduction, an optimized antenna design is needed which takes into account the special dielectric and in particular metallic car environment of the tag and an roll-to-roll (R2R) printing manufacturing process. In this paper we focus on the development of a customized UHF RFID transponder antenna design, which is adopted for vehicle geometry as well as R2R screen printing manufacturing processes.

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.

Structural system identification based on variational mode decomposition

NASA Astrophysics Data System (ADS)

Bagheri, Abdollah; Ozbulut, Osman E.; Harris, Devin K.

2018-03-01

In this paper, a new structural identification method is proposed to identify the modal properties of engineering structures based on dynamic response decomposition using the variational mode decomposition (VMD). The VMD approach is a decomposition algorithm that has been developed as a means to overcome some of the drawbacks and limitations of the empirical mode decomposition method. The VMD-based modal identification algorithm decomposes the acceleration signal into a series of distinct modal responses and their respective center frequencies, such that when combined their cumulative modal responses reproduce the original acceleration response. The decaying amplitude of the extracted modal responses is then used to identify the modal damping ratios using a linear fitting function on modal response data. Finally, after extracting modal responses from available sensors, the mode shape vector for each of the decomposed modes in the system is identified from all obtained modal response data. To demonstrate the efficiency of the algorithm, a series of numerical, laboratory, and field case studies were evaluated. The laboratory case study utilized the vibration response of a three-story shear frame, whereas the field study leveraged the ambient vibration response of a pedestrian bridge to characterize the modal properties of the structure. The modal properties of the shear frame were computed using analytical approach for a comparison with the experimental modal frequencies. Results from these case studies demonstrated that the proposed method is efficient and accurate in identifying modal data of the structures.

Predictive teratology: teratogenic risk-hazard identification partnered in the discovery process.

PubMed

Augustine-Rauch, K A

2008-11-01

Unexpected teratogenicity is ranked as one of the most prevalent causes for toxicity-related attrition of drug candidates. Without proactive assessment, the liability tends to be identified relatively late in drug development, following significant investment in compound and engagement in pre clinical and clinical studies. When unexpected teratogenicity occurs in pre-clinical development, three principle questions arise: Can clinical trials that include women of child bearing populations be initiated? Will all compounds in this pharmacological class produce the same liability? Could this effect be related to the chemical structure resulting in undesirable off-target adverse effects? The first question is typically addressed at the time of the unexpected finding and involves considering the nature of the teratogenicity, whether or not maternal toxicity could have had a role in onset, human exposure margins and therapeutic indication. The latter two questions can be addressed proactively, earlier in the discovery process as drug target profiling and lead compound optimization is taking place. Such proactive approaches include thorough assessment of the literature for identification of potential liabilities and follow-up work that can be conducted on the level of target expression and functional characterization using molecular biology and developmental model systems. Developmental model systems can also be applied in the form of in vitro teratogenicity screens, and show potential for effective hazard identification or issue resolution on the level of characterizing teratogenic mechanism. This review discusses approaches that can be applied for proactive assessment of compounds for teratogenic liability.

INTERPOL survey of the use of speaker identification by law enforcement agencies.

PubMed

Morrison, Geoffrey Stewart; Sahito, Farhan Hyder; Jardine, Gaëlle; Djokic, Djordje; Clavet, Sophie; Berghs, Sabine; Goemans Dorny, Caroline

2016-06-01

A survey was conducted of the use of speaker identification by law enforcement agencies around the world. A questionnaire was circulated to law enforcement agencies in the 190 member countries of INTERPOL. 91 responses were received from 69 countries. 44 respondents reported that they had speaker identification capabilities in house or via external laboratories. Half of these came from Europe. 28 respondents reported that they had databases of audio recordings of speakers. The clearest pattern in the responses was that of diversity. A variety of different approaches to speaker identification were used: The human-supervised-automatic approach was the most popular in North America, the auditory-acoustic-phonetic approach was the most popular in Europe, and the spectrographic/auditory-spectrographic approach was the most popular in Africa, Asia, the Middle East, and South and Central America. Globally, and in Europe, the most popular framework for reporting conclusions was identification/exclusion/inconclusive. In Europe, the second most popular framework was the use of verbal likelihood ratio scales. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

[Measures to prevent patient identification errors in blood collection/physiological function testing utilizing a laboratory information system].

PubMed

Shimazu, Chisato; Hoshino, Satoshi; Furukawa, Taiji

2013-08-01

We constructed an integrated personal identification workflow chart using both bar code reading and an all in-one laboratory information system. The information system not only handles test data but also the information needed for patient guidance in the laboratory department. The reception terminals at the entrance, displays for patient guidance and patient identification tools at blood-sampling booths are all controlled by the information system. The number of patient identification errors was greatly reduced by the system. However, identification errors have not been abolished in the ultrasound department. After re-evaluation of the patient identification process in this department, we recognized that the major reason for the errors came from excessive identification workflow. Ordinarily, an ultrasound test requires patient identification 3 times, because 3 different systems are required during the entire test process, i.e. ultrasound modality system, laboratory information system and a system for producing reports. We are trying to connect the 3 different systems to develop a one-time identification workflow, but it is not a simple task and has not been completed yet. Utilization of the laboratory information system is effective, but is not yet perfect for patient identification. The most fundamental procedure for patient identification is to ask a person's name even today. Everyday checks in the ordinary workflow and everyone's participation in safety-management activity are important for the prevention of patient identification errors.

Real-time energy-saving metro train rescheduling with primary delay identification

PubMed Central

Li, Keping; Schonfeld, Paul

2018-01-01

This paper aims to reschedule online metro trains in delay scenarios. A graph representation and a mixed integer programming model are proposed to formulate the optimization problem. The solution approach is a two-stage optimization method. In the first stage, based on a proposed train state graph and system analysis, the primary and flow-on delays are specifically analyzed and identified with a critical path algorithm. For the second stage a hybrid genetic algorithm is designed to optimize the schedule, with the delay identification results as input. Then, based on the infrastructure data of Beijing Subway Line 4 of China, case studies are presented to demonstrate the effectiveness and efficiency of the solution approach. The results show that the algorithm can quickly and accurately identify primary delays among different types of delays. The economic cost of energy consumption and total delay is considerably reduced (by more than 10% in each case). The computation time of the Hybrid-GA is low enough for rescheduling online. Sensitivity analyses further demonstrate that the proposed approach can be used as a decision-making support tool for operators. PMID:29474471

Calibration of Reduced Dynamic Models of Power Systems using Phasor Measurement Unit (PMU) Data

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

Zhou, Ning; Lu, Shuai; Singh, Ruchi

2011-09-23

Accuracy of a power system dynamic model is essential to the secure and efficient operation of the system. Lower confidence on model accuracy usually leads to conservative operation and lowers asset usage. To improve model accuracy, identification algorithms have been developed to calibrate parameters of individual components using measurement data from staged tests. To facilitate online dynamic studies for large power system interconnections, this paper proposes a model reduction and calibration approach using phasor measurement unit (PMU) data. First, a model reduction method is used to reduce the number of dynamic components. Then, a calibration algorithm is developed to estimatemore » parameters of the reduced model. This approach will help to maintain an accurate dynamic model suitable for online dynamic studies. The performance of the proposed method is verified through simulation studies.« less

Phase modulated 2D HSQC-TOCSY for unambiguous assignment of overlapping spin systems

NASA Astrophysics Data System (ADS)

Singh, Amrinder; Dubey, Abhinav; Adiga, Satish K.; Atreya, Hanudatta S.

2018-01-01

We present a new method that allows one to unambiguously resolve overlapping spin systems often encountered in biomolecular systems such as peptides and proteins or in samples containing a mixture of different molecules such as in metabolomics. We address this problem using the recently proposed phase modulation approach. By evolving the 1H chemical shifts in a conventional two dimensional (2D) HSQC-TOCSY experiment for a fixed delay period, the phase/intensity of set of cross peaks belonging to one spin system are modulated differentially relative to those of its overlapping counterpart, resulting in their discrimination and recognition. The method thus accelerates the process of identification and resonance assignment of individual compounds in complex mixtures. This approach facilitated the assignment of molecules in the embryo culture medium used in human assisted reproductive technology.

Approximation methods for inverse problems involving the vibration of beams with tip bodies

NASA Technical Reports Server (NTRS)

Rosen, I. G.

1984-01-01

Two cubic spline based approximation schemes for the estimation of structural parameters associated with the transverse vibration of flexible beams with tip appendages are outlined. The identification problem is formulated as a least squares fit to data subject to the system dynamics which are given by a hybrid system of coupled ordinary and partial differential equations. The first approximation scheme is based upon an abstract semigroup formulation of the state equation while a weak/variational form is the basis for the second. Cubic spline based subspaces together with a Rayleigh-Ritz-Galerkin approach were used to construct sequences of easily solved finite dimensional approximating identification problems. Convergence results are briefly discussed and a numerical example demonstrating the feasibility of the schemes and exhibiting their relative performance for purposes of comparison is provided.

Non-whole beat correlation method for the identification of an unbalance response of a dual-rotor system with a slight rotating speed difference

NASA Astrophysics Data System (ADS)

Zhang, Z. X.; Wang, L. Z.; Jin, Z. J.; Zhang, Q.; Li, X. L.

2013-08-01

The efficient identification of the unbalanced responses in the inner and outer rotors from the beat vibration is the key step in the dynamic balancing of a dual-rotor system with a slight rotating speed difference. This paper proposes a non-whole beat correlation method to identify the unbalance responses whose integral time is shorter than the whole beat correlation method. The principle, algorithm and parameter selection of the proposed method is emphatically demonstrated in this paper. From the numerical simulation and balancing experiment conducted on horizontal decanter centrifuge, conclusions can be drawn that the proposed approach is feasible and practicable. This method makes important sense in developing the field balancing equipment based on portable Single Chip Microcomputer (SCMC) with low expense.

Machine-learning approach for local classification of crystalline structures in multiphase systems

NASA Astrophysics Data System (ADS)

Dietz, C.; Kretz, T.; Thoma, M. H.

2017-07-01

Machine learning is one of the most popular fields in computer science and has a vast number of applications. In this work we will propose a method that will use a neural network to locally identify crystal structures in a mixed phase Yukawa system consisting of fcc, hcp, and bcc clusters and disordered particles similar to plasma crystals. We compare our approach to already used methods and show that the quality of identification increases significantly. The technique works very well for highly disturbed lattices and shows a flexible and robust way to classify crystalline structures that can be used by only providing particle positions. This leads to insights into highly disturbed crystalline structures.

Automated synthetic scene generation

NASA Astrophysics Data System (ADS)

Givens, Ryan N.

Physics-based simulations generate synthetic imagery to help organizations anticipate system performance of proposed remote sensing systems. However, manually constructing synthetic scenes which are sophisticated enough to capture the complexity of real-world sites can take days to months depending on the size of the site and desired fidelity of the scene. This research, sponsored by the Air Force Research Laboratory's Sensors Directorate, successfully developed an automated approach to fuse high-resolution RGB imagery, lidar data, and hyperspectral imagery and then extract the necessary scene components. The method greatly reduces the time and money required to generate realistic synthetic scenes and developed new approaches to improve material identification using information from all three of the input datasets.

Identification of flame transfer functions in the presence of intrinsic thermoacoustic feedback and noise

NASA Astrophysics Data System (ADS)

Jaensch, Stefan; Merk, Malte; Emmert, Thomas; Polifke, Wolfgang

2018-05-01

The Large Eddy Simulation/System Identification (LES/SI) approach is a general and efficient numerical method for deducing a Flame Transfer Function (FTF) from the LES of turbulent reacting flow. The method may be summarised as follows: a simulated flame is forced with a broadband excitation signal. The resulting fluctuations of the reference velocity and of the global heat release rate are post-processed via SI techniques in order to estimate a low-order model of the flame dynamics. The FTF is readily deduced from the low-order model. The SI method most frequently applied in aero- and thermo-acoustics has been Wiener-Hopf Inversion (WHI). This method is known to yield biased estimates in situations with feedback, thus it was assumed that non-reflective boundary conditions are required to generate accurate results with the LES/SI approach. Recent research has shown that the FTF is part of the so-called Intrinsic ThermoAcoustic (ITA) feedback loop. Hence, identifying an FTF from a compressible LES is always a closed-loop problem, and consequently one should expect that the WHI would yield biased results. However, several studies proved that WHI results compare favourably with validation data. To resolve this apparent contradiction, a variety of identification methods are compared against each other, including models designed for closed-loop identification. In agreement with theory, we show that the estimate given by WHI does not converge to the actual FTF. Fortunately, the error made is small if excitation amplitudes can be set such that the signal-to-noise ratio is large, but not large enough to trigger nonlinear flame dynamics. Furthermore, we conclude that non-reflective boundary conditions are not essentially necessary to apply the LES/SI approach.