A fuzzy Petri-net-based mode identification algorithm for fault diagnosis of complex systems

NASA Astrophysics Data System (ADS)

Propes, Nicholas C.; Vachtsevanos, George

2003-08-01

Complex dynamical systems such as aircraft, manufacturing systems, chillers, motor vehicles, submarines, etc. exhibit continuous and event-driven dynamics. These systems undergo several discrete operating modes from startup to shutdown. For example, a certain shipboard system may be operating at half load or full load or may be at start-up or shutdown. Of particular interest are extreme or "shock" operating conditions, which tend to severely impact fault diagnosis or the progression of a fault leading to a failure. Fault conditions are strongly dependent on the operating mode. Therefore, it is essential that in any diagnostic/prognostic architecture, the operating mode be identified as accurately as possible so that such functions as feature extraction, diagnostics, prognostics, etc. can be correlated with the predominant operating conditions. This paper introduces a mode identification methodology that incorporates both time- and event-driven information about the process. A fuzzy Petri net is used to represent the possible successive mode transitions and to detect events from processed sensor signals signifying a mode change. The operating mode is initialized and verified by analysis of the time-driven dynamics through a fuzzy logic classifier. An evidence combiner module is used to combine the results from both the fuzzy Petri net and the fuzzy logic classifier to determine the mode. Unlike most event-driven mode identifiers, this architecture will provide automatic mode initialization through the fuzzy logic classifier and robustness through the combining of evidence of the two algorithms. The mode identification methodology is applied to an AC Plant typically found as a component of a shipboard system.

A data-driven, knowledge-based approach to biomarker discovery: application to circulating microRNA markers of colorectal cancer prognosis.

PubMed

Vafaee, Fatemeh; Diakos, Connie; Kirschner, Michaela B; Reid, Glen; Michael, Michael Z; Horvath, Lisa G; Alinejad-Rokny, Hamid; Cheng, Zhangkai Jason; Kuncic, Zdenka; Clarke, Stephen

2018-01-01

Recent advances in high-throughput technologies have provided an unprecedented opportunity to identify molecular markers of disease processes. This plethora of complex-omics data has simultaneously complicated the problem of extracting meaningful molecular signatures and opened up new opportunities for more sophisticated integrative and holistic approaches. In this era, effective integration of data-driven and knowledge-based approaches for biomarker identification has been recognised as key to improving the identification of high-performance biomarkers, and necessary for translational applications. Here, we have evaluated the role of circulating microRNA as a means of predicting the prognosis of patients with colorectal cancer, which is the second leading cause of cancer-related death worldwide. We have developed a multi-objective optimisation method that effectively integrates a data-driven approach with the knowledge obtained from the microRNA-mediated regulatory network to identify robust plasma microRNA signatures which are reliable in terms of predictive power as well as functional relevance. The proposed multi-objective framework has the capacity to adjust for conflicting biomarker objectives and to incorporate heterogeneous information facilitating systems approaches to biomarker discovery. We have found a prognostic signature of colorectal cancer comprising 11 circulating microRNAs. The identified signature predicts the patients' survival outcome and targets pathways underlying colorectal cancer progression. The altered expression of the identified microRNAs was confirmed in an independent public data set of plasma samples of patients in early stage vs advanced colorectal cancer. Furthermore, the generality of the proposed method was demonstrated across three publicly available miRNA data sets associated with biomarker studies in other diseases.

A robust firearm identification algorithm of forensic ballistics specimens

NASA Astrophysics Data System (ADS)

Chuan, Z. L.; Jemain, A. A.; Liong, C.-Y.; Ghani, N. A. M.; Tan, L. K.

2017-09-01

There are several inherent difficulties in the existing firearm identification algorithms, include requiring the physical interpretation and time consuming. Therefore, the aim of this study is to propose a robust algorithm for a firearm identification based on extracting a set of informative features from the segmented region of interest (ROI) using the simulated noisy center-firing pin impression images. The proposed algorithm comprises Laplacian sharpening filter, clustering-based threshold selection, unweighted least square estimator, and segment a square ROI from the noisy images. A total of 250 simulated noisy images collected from five different pistols of the same make, model and caliber are used to evaluate the robustness of the proposed algorithm. This study found that the proposed algorithm is able to perform the identical task on the noisy images with noise levels as high as 70%, while maintaining a firearm identification accuracy rate of over 90%.

Working memory-driven attention improves spatial resolution: Support for perceptual enhancement.

PubMed

Pan, Yi; Luo, Qianying; Cheng, Min

2016-08-01

Previous research has indicated that attention can be biased toward those stimuli matching the contents of working memory and thereby facilitates visual processing at the location of the memory-matching stimuli. However, whether this working memory-driven attentional modulation takes place on early perceptual processes remains unclear. Our present results showed that working memory-driven attention improved identification of a brief Landolt target presented alone in the visual field. Because the suprathreshold target appeared without any external noise added (i.e., no distractors or masks), the results suggest that working memory-driven attention enhances the target signal at early perceptual stages of visual processing. Furthermore, given that performance in the Landolt target identification task indexes spatial resolution, this attentional facilitation indicates that working memory-driven attention can boost early perceptual processing via enhancement of spatial resolution at the attended location.

Robust finger vein ROI localization based on flexible segmentation.

PubMed

Lu, Yu; Xie, Shan Juan; Yoon, Sook; Yang, Jucheng; Park, Dong Sun

2013-10-24

Finger veins have been proved to be an effective biometric for personal identification in the recent years. However, finger vein images are easily affected by influences such as image translation, orientation, scale, scattering, finger structure, complicated background, uneven illumination, and collection posture. All these factors may contribute to inaccurate region of interest (ROI) definition, and so degrade the performance of finger vein identification system. To improve this problem, in this paper, we propose a finger vein ROI localization method that has high effectiveness and robustness against the above factors. The proposed method consists of a set of steps to localize ROIs accurately, namely segmentation, orientation correction, and ROI detection. Accurate finger region segmentation and correct calculated orientation can support each other to produce higher accuracy in localizing ROIs. Extensive experiments have been performed on the finger vein image database, MMCBNU_6000, to verify the robustness of the proposed method. The proposed method shows the segmentation accuracy of 100%. Furthermore, the average processing time of the proposed method is 22 ms for an acquired image, which satisfies the criterion of a real-time finger vein identification system.

Robust Finger Vein ROI Localization Based on Flexible Segmentation

PubMed Central

Lu, Yu; Xie, Shan Juan; Yoon, Sook; Yang, Jucheng; Park, Dong Sun

2013-01-01

Finger veins have been proved to be an effective biometric for personal identification in the recent years. However, finger vein images are easily affected by influences such as image translation, orientation, scale, scattering, finger structure, complicated background, uneven illumination, and collection posture. All these factors may contribute to inaccurate region of interest (ROI) definition, and so degrade the performance of finger vein identification system. To improve this problem, in this paper, we propose a finger vein ROI localization method that has high effectiveness and robustness against the above factors. The proposed method consists of a set of steps to localize ROIs accurately, namely segmentation, orientation correction, and ROI detection. Accurate finger region segmentation and correct calculated orientation can support each other to produce higher accuracy in localizing ROIs. Extensive experiments have been performed on the finger vein image database, MMCBNU_6000, to verify the robustness of the proposed method. The proposed method shows the segmentation accuracy of 100%. Furthermore, the average processing time of the proposed method is 22 ms for an acquired image, which satisfies the criterion of a real-time finger vein identification system. PMID:24284769

FusionAnalyser: a new graphical, event-driven tool for fusion rearrangements discovery

PubMed Central

Piazza, Rocco; Pirola, Alessandra; Spinelli, Roberta; Valletta, Simona; Redaelli, Sara; Magistroni, Vera; Gambacorti-Passerini, Carlo

2012-01-01

Gene fusions are common driver events in leukaemias and solid tumours; here we present FusionAnalyser, a tool dedicated to the identification of driver fusion rearrangements in human cancer through the analysis of paired-end high-throughput transcriptome sequencing data. We initially tested FusionAnalyser by using a set of in silico randomly generated sequencing data from 20 known human translocations occurring in cancer and subsequently using transcriptome data from three chronic and three acute myeloid leukaemia samples. in all the cases our tool was invariably able to detect the presence of the correct driver fusion event(s) with high specificity. In one of the acute myeloid leukaemia samples, FusionAnalyser identified a novel, cryptic, in-frame ETS2–ERG fusion. A fully event-driven graphical interface and a flexible filtering system allow complex analyses to be run in the absence of any a priori programming or scripting knowledge. Therefore, we propose FusionAnalyser as an efficient and robust graphical tool for the identification of functional rearrangements in the context of high-throughput transcriptome sequencing data. PMID:22570408

FusionAnalyser: a new graphical, event-driven tool for fusion rearrangements discovery.

PubMed

Piazza, Rocco; Pirola, Alessandra; Spinelli, Roberta; Valletta, Simona; Redaelli, Sara; Magistroni, Vera; Gambacorti-Passerini, Carlo

2012-09-01

Gene fusions are common driver events in leukaemias and solid tumours; here we present FusionAnalyser, a tool dedicated to the identification of driver fusion rearrangements in human cancer through the analysis of paired-end high-throughput transcriptome sequencing data. We initially tested FusionAnalyser by using a set of in silico randomly generated sequencing data from 20 known human translocations occurring in cancer and subsequently using transcriptome data from three chronic and three acute myeloid leukaemia samples. in all the cases our tool was invariably able to detect the presence of the correct driver fusion event(s) with high specificity. In one of the acute myeloid leukaemia samples, FusionAnalyser identified a novel, cryptic, in-frame ETS2-ERG fusion. A fully event-driven graphical interface and a flexible filtering system allow complex analyses to be run in the absence of any a priori programming or scripting knowledge. Therefore, we propose FusionAnalyser as an efficient and robust graphical tool for the identification of functional rearrangements in the context of high-throughput transcriptome sequencing data.

Reliability Assessment for Low-cost Unmanned Aerial Vehicles

NASA Astrophysics Data System (ADS)

Freeman, Paul Michael

Existing low-cost unmanned aerospace systems are unreliable, and engineers must blend reliability analysis with fault-tolerant control in novel ways. This dissertation introduces the University of Minnesota unmanned aerial vehicle flight research platform, a comprehensive simulation and flight test facility for reliability and fault-tolerance research. An industry-standard reliability assessment technique, the failure modes and effects analysis, is performed for an unmanned aircraft. Particular attention is afforded to the control surface and servo-actuation subsystem. Maintaining effector health is essential for safe flight; failures may lead to loss of control incidents. Failure likelihood, severity, and risk are qualitatively assessed for several effector failure modes. Design changes are recommended to improve aircraft reliability based on this analysis. Most notably, the control surfaces are split, providing independent actuation and dual-redundancy. The simulation models for control surface aerodynamic effects are updated to reflect the split surfaces using a first-principles geometric analysis. The failure modes and effects analysis is extended by using a high-fidelity nonlinear aircraft simulation. A trim state discovery is performed to identify the achievable steady, wings-level flight envelope of the healthy and damaged vehicle. Tolerance of elevator actuator failures is studied using familiar tools from linear systems analysis. This analysis reveals significant inherent performance limitations for candidate adaptive/reconfigurable control algorithms used for the vehicle. Moreover, it demonstrates how these tools can be applied in a design feedback loop to make safety-critical unmanned systems more reliable. Control surface impairments that do occur must be quickly and accurately detected. This dissertation also considers fault detection and identification for an unmanned aerial vehicle using model-based and model-free approaches and applies those algorithms to experimental faulted and unfaulted flight test data. Flight tests are conducted with actuator faults that affect the plant input and sensor faults that affect the vehicle state measurements. A model-based detection strategy is designed and uses robust linear filtering methods to reject exogenous disturbances, e.g. wind, while providing robustness to model variation. A data-driven algorithm is developed to operate exclusively on raw flight test data without physical model knowledge. The fault detection and identification performance of these complementary but different methods is compared. Together, enhanced reliability assessment and multi-pronged fault detection and identification techniques can help to bring about the next generation of reliable low-cost unmanned aircraft.

Performance study of LMS based adaptive algorithms for unknown system identification

NASA Astrophysics Data System (ADS)

Javed, Shazia; Ahmad, Noor Atinah

2014-07-01

Adaptive filtering techniques have gained much popularity in the modeling of unknown system identification problem. These techniques can be classified as either iterative or direct. Iterative techniques include stochastic descent method and its improved versions in affine space. In this paper we present a comparative study of the least mean square (LMS) algorithm and some improved versions of LMS, more precisely the normalized LMS (NLMS), LMS-Newton, transform domain LMS (TDLMS) and affine projection algorithm (APA). The performance evaluation of these algorithms is carried out using adaptive system identification (ASI) model with random input signals, in which the unknown (measured) signal is assumed to be contaminated by output noise. Simulation results are recorded to compare the performance in terms of convergence speed, robustness, misalignment, and their sensitivity to the spectral properties of input signals. Main objective of this comparative study is to observe the effects of fast convergence rate of improved versions of LMS algorithms on their robustness and misalignment.

Performance study of LMS based adaptive algorithms for unknown system identification

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

Javed, Shazia; Ahmad, Noor Atinah

Adaptive filtering techniques have gained much popularity in the modeling of unknown system identification problem. These techniques can be classified as either iterative or direct. Iterative techniques include stochastic descent method and its improved versions in affine space. In this paper we present a comparative study of the least mean square (LMS) algorithm and some improved versions of LMS, more precisely the normalized LMS (NLMS), LMS-Newton, transform domain LMS (TDLMS) and affine projection algorithm (APA). The performance evaluation of these algorithms is carried out using adaptive system identification (ASI) model with random input signals, in which the unknown (measured) signalmore » is assumed to be contaminated by output noise. Simulation results are recorded to compare the performance in terms of convergence speed, robustness, misalignment, and their sensitivity to the spectral properties of input signals. Main objective of this comparative study is to observe the effects of fast convergence rate of improved versions of LMS algorithms on their robustness and misalignment.« less

A voting-based star identification algorithm utilizing local and global distribution

NASA Astrophysics Data System (ADS)

Fan, Qiaoyun; Zhong, Xuyang; Sun, Junhua

2018-03-01

A novel star identification algorithm based on voting scheme is presented in this paper. In the proposed algorithm, the global distribution and local distribution of sensor stars are fully utilized, and the stratified voting scheme is adopted to obtain the candidates for sensor stars. The database optimization is employed to reduce its memory requirement and improve the robustness of the proposed algorithm. The simulation shows that the proposed algorithm exhibits 99.81% identification rate with 2-pixel standard deviations of positional noises and 0.322-Mv magnitude noises. Compared with two similar algorithms, the proposed algorithm is more robust towards noise, and the average identification time and required memory is less. Furthermore, the real sky test shows that the proposed algorithm performs well on the real star images.

Structured Uncertainty Bound Determination From Data for Control and Performance Validation

NASA Technical Reports Server (NTRS)

Lim, Kyong B.

2003-01-01

This report attempts to document the broad scope of issues that must be satisfactorily resolved before one can expect to methodically obtain, with a reasonable confidence, a near-optimal robust closed loop performance in physical applications. These include elements of signal processing, noise identification, system identification, model validation, and uncertainty modeling. Based on a recently developed methodology involving a parameterization of all model validating uncertainty sets for a given linear fractional transformation (LFT) structure and noise allowance, a new software, Uncertainty Bound Identification (UBID) toolbox, which conveniently executes model validation tests and determine uncertainty bounds from data, has been designed and is currently available. This toolbox also serves to benchmark the current state-of-the-art in uncertainty bound determination and in turn facilitate benchmarking of robust control technology. To help clarify the methodology and use of the new software, two tutorial examples are provided. The first involves the uncertainty characterization of a flexible structure dynamics, and the second example involves a closed loop performance validation of a ducted fan based on an uncertainty bound from data. These examples, along with other simulation and experimental results, also help describe the many factors and assumptions that determine the degree of success in applying robust control theory to practical problems.

Robust time and frequency domain estimation methods in adaptive control

NASA Technical Reports Server (NTRS)

Lamaire, Richard Orville

1987-01-01

A robust identification method was developed for use in an adaptive control system. The type of estimator is called the robust estimator, since it is robust to the effects of both unmodeled dynamics and an unmeasurable disturbance. The development of the robust estimator was motivated by a need to provide guarantees in the identification part of an adaptive controller. To enable the design of a robust control system, a nominal model as well as a frequency-domain bounding function on the modeling uncertainty associated with this nominal model must be provided. Two estimation methods are presented for finding parameter estimates, and, hence, a nominal model. One of these methods is based on the well developed field of time-domain parameter estimation. In a second method of finding parameter estimates, a type of weighted least-squares fitting to a frequency-domain estimated model is used. The frequency-domain estimator is shown to perform better, in general, than the time-domain parameter estimator. In addition, a methodology for finding a frequency-domain bounding function on the disturbance is used to compute a frequency-domain bounding function on the additive modeling error due to the effects of the disturbance and the use of finite-length data. The performance of the robust estimator in both open-loop and closed-loop situations is examined through the use of simulations.

Advancements in robust algorithm formulation for speaker identification of whispered speech

NASA Astrophysics Data System (ADS)

Fan, Xing

Whispered speech is an alternative speech production mode from neutral speech, which is used by talkers intentionally in natural conversational scenarios to protect privacy and to avoid certain content from being overheard/made public. Due to the profound differences between whispered and neutral speech in production mechanism and the absence of whispered adaptation data, the performance of speaker identification systems trained with neutral speech degrades significantly. This dissertation therefore focuses on developing a robust closed-set speaker recognition system for whispered speech by using no or limited whispered adaptation data from non-target speakers. This dissertation proposes the concept of "High''/"Low'' performance whispered data for the purpose of speaker identification. A variety of acoustic properties are identified that contribute to the quality of whispered data. An acoustic analysis is also conducted to compare the phoneme/speaker dependency of the differences between whispered and neutral data in the feature domain. The observations from those acoustic analysis are new in this area and also serve as a guidance for developing robust speaker identification systems for whispered speech. This dissertation further proposes two systems for speaker identification of whispered speech. One system focuses on front-end processing. A two-dimensional feature space is proposed to search for "Low''-quality performance based whispered utterances and separate feature mapping functions are applied to vowels and consonants respectively in order to retain the speaker's information shared between whispered and neutral speech. The other system focuses on speech-mode-independent model training. The proposed method generates pseudo whispered features from neutral features by using the statistical information contained in a whispered Universal Background model (UBM) trained from extra collected whispered data from non-target speakers. Four modeling methods are proposed for the transformation estimation in order to generate the pseudo whispered features. Both of the above two systems demonstrate a significant improvement over the baseline system on the evaluation data. This dissertation has therefore contributed to providing a scientific understanding of the differences between whispered and neutral speech as well as improved front-end processing and modeling method for speaker identification of whispered speech. Such advancements will ultimately contribute to improve the robustness of speech processing systems.

Robust Control of a Cable-Driven Soft Exoskeleton Joint for Intrinsic Human-Robot Interaction.

PubMed

Jarrett, C; McDaid, A J

2017-07-01

A novel, cable-driven soft joint is presented for use in robotic rehabilitation exoskeletons to provide intrinsic, comfortable human-robot interaction. The torque-displacement characteristics of the soft elastomeric core contained within the joint are modeled. This knowledge is used in conjunction with a dynamic system model to derive a sliding mode controller (SMC) to implement low-level torque control of the joint. The SMC controller is experimentally compared with a baseline feedback-linearised proportional-derivative controller across a range of conditions and shown to be robust to un-modeled disturbances. The torque controller is then tested with six healthy subjects while they perform a selection of activities of daily living, which has validated its range of performance. Finally, a case study with a participant with spastic cerebral palsy is presented to illustrate the potential of both the joint and controller to be used in a physiotherapy setting to assist clinical populations.

Identifying Seizure Onset Zone From the Causal Connectivity Inferred Using Directed Information

NASA Astrophysics Data System (ADS)

Malladi, Rakesh; Kalamangalam, Giridhar; Tandon, Nitin; Aazhang, Behnaam

2016-10-01

In this paper, we developed a model-based and a data-driven estimator for directed information (DI) to infer the causal connectivity graph between electrocorticographic (ECoG) signals recorded from brain and to identify the seizure onset zone (SOZ) in epileptic patients. Directed information, an information theoretic quantity, is a general metric to infer causal connectivity between time-series and is not restricted to a particular class of models unlike the popular metrics based on Granger causality or transfer entropy. The proposed estimators are shown to be almost surely convergent. Causal connectivity between ECoG electrodes in five epileptic patients is inferred using the proposed DI estimators, after validating their performance on simulated data. We then proposed a model-based and a data-driven SOZ identification algorithm to identify SOZ from the causal connectivity inferred using model-based and data-driven DI estimators respectively. The data-driven SOZ identification outperforms the model-based SOZ identification algorithm when benchmarked against visual analysis by neurologist, the current clinical gold standard. The causal connectivity analysis presented here is the first step towards developing novel non-surgical treatments for epilepsy.

High Performance Automatic Character Skinning Based on Projection Distance

NASA Astrophysics Data System (ADS)

Li, Jun; Lin, Feng; Liu, Xiuling; Wang, Hongrui

2018-03-01

Skeleton-driven-deformation methods have been commonly used in the character deformations. The process of painting skin weights for character deformation is a long-winded task requiring manual tweaking. We present a novel method to calculate skinning weights automatically from 3D human geometric model and corresponding skeleton. The method first, groups each mesh vertex of 3D human model to a skeleton bone by the minimum distance from a mesh vertex to each bone. Secondly, calculates each vertex's weights to the adjacent bones by the vertex's projection point distance to the bone joints. Our method's output can not only be applied to any kind of skeleton-driven deformation, but also to motion capture driven (mocap-driven) deformation. Experiments results show that our method not only has strong generality and robustness, but also has high performance.

Optics measurement and correction for the Relativistic Heavy Ion Collider

NASA Astrophysics Data System (ADS)

Shen, Xiaozhe

The quality of beam optics is of great importance for the performance of a high energy accelerator like the Relativistic Heavy Ion Collider (RHIC). The turn-by-turn (TBT) beam position monitor (BPM) data can be used to derive beam optics. However, the accuracy of the derived beam optics is often limited by the performance and imperfections of instruments as well as measurement methods and conditions. Therefore, a robust and model-independent data analysis method is highly desired to extract noise-free information from TBT BPM data. As a robust signal-processing technique, an independent component analysis (ICA) algorithm called second order blind identification (SOBI) has been proven to be particularly efficient in extracting physical beam signals from TBT BPM data even in the presence of instrument's noise and error. We applied the SOBI ICA algorithm to RHIC during the 2013 polarized proton operation to extract accurate linear optics from TBT BPM data of AC dipole driven coherent beam oscillation. From the same data, a first systematic estimation of RHIC BPM noise performance was also obtained by the SOBI ICA algorithm, and showed a good agreement with the RHIC BPM configurations. Based on the accurate linear optics measurement, a beta-beat response matrix correction method and a scheme of using horizontal closed orbit bumps at sextupoles for arc beta-beat correction were successfully applied to reach a record-low beam optics error at RHIC. This thesis presents principles of the SOBI ICA algorithm and theory as well as experimental results of optics measurement and correction at RHIC.

Outperforming whom? A multilevel study of performance-prove goal orientation, performance, and the moderating role of shared team identification.

PubMed

Dietz, Bart; van Knippenberg, Daan; Hirst, Giles; Restubog, Simon Lloyd D

2015-11-01

Performance-prove goal orientation affects performance because it drives people to try to outperform others. A proper understanding of the performance-motivating potential of performance-prove goal orientation requires, however, that we consider the question of whom people desire to outperform. In a multilevel analysis of this issue, we propose that the shared team identification of a team plays an important moderating role here, directing the performance-motivating influence of performance-prove goal orientation to either the team level or the individual level of performance. A multilevel study of salespeople nested in teams supports this proposition, showing that performance-prove goal orientation motivates team performance more with higher shared team identification, whereas performance-prove goal orientation motivates individual performance more with lower shared team identification. Establishing the robustness of these findings, a second study replicates them with individual and team performance in an educational context. (c) 2015 APA, all rights reserved).

Re-identification of persons in multi-camera surveillance under varying viewpoints and illumination

NASA Astrophysics Data System (ADS)

Bouma, Henri; Borsboom, Sander; den Hollander, Richard J. M.; Landsmeer, Sander H.; Worring, Marcel

2012-06-01

The capability to track individuals in CCTV cameras is important for surveillance and forensics alike. However, it is laborious to do over multiple cameras. Therefore, an automated system is desirable. In literature several methods have been proposed, but their robustness against varying viewpoints and illumination is limited. Hence performance in realistic settings is also limited. In this paper, we present a novel method for the automatic re-identification of persons in video from surveillance cameras in a realistic setting. The method is computationally efficient, robust to a wide variety of viewpoints and illumination, simple to implement and it requires no training. We compare the performance of our method to several state-of-the-art methods on a publically available dataset that contains the variety of viewpoints and illumination to allow benchmarking. The results indicate that our method shows good performance and enables a human operator to track persons five times faster.

Criminal Prohibition of Wrongful Re‑identification: Legal Solution or Minefield for Big Data?

PubMed

Phillips, Mark; Dove, Edward S; Knoppers, Bartha M

2017-12-01

The collapse of confidence in anonymization (sometimes also known as de-identification) as a robust approach for preserving the privacy of personal data has incited an outpouring of new approaches that aim to fill the resulting trifecta of technical, organizational, and regulatory privacy gaps left in its wake. In the latter category, and in large part due to the growth of Big Data-driven biomedical research, falls a growing chorus of calls for criminal and penal offences to sanction wrongful re-identification of "anonymized" data. This chorus cuts across the fault lines of polarized privacy law scholarship that at times seems to advocate privacy protection at the expense of Big Data research or vice versa. Focusing on Big Data in the context of biomedicine, this article surveys the approaches that criminal or penal law might take toward wrongful re-identification of health data. It contextualizes the strategies within their respective legal regimes as well as in relation to emerging privacy debates focusing on personal data use and data linkage and assesses the relative merit of criminalization. We conclude that this approach suffers from several flaws and that alternative social and legal strategies to deter wrongful re-identification may be preferable.

Integrated direct/indirect adaptive robust motion trajectory tracking control of pneumatic cylinders

NASA Astrophysics Data System (ADS)

Meng, Deyuan; Tao, Guoliang; Zhu, Xiaocong

2013-09-01

This paper studies the precision motion trajectory tracking control of a pneumatic cylinder driven by a proportional-directional control valve. An integrated direct/indirect adaptive robust controller is proposed. The controller employs a physical model based indirect-type parameter estimation to obtain reliable estimates of unknown model parameters, and utilises a robust control method with dynamic compensation type fast adaptation to attenuate the effects of parameter estimation errors, unmodelled dynamics and disturbances. Due to the use of projection mapping, the robust control law and the parameter adaption algorithm can be designed separately. Since the system model uncertainties are unmatched, the recursive backstepping technology is adopted to design the robust control law. Extensive comparative experimental results are presented to illustrate the effectiveness of the proposed controller and its performance robustness to parameter variations and sudden disturbances.

Design and experiment of data-driven modeling and flutter control of a prototype wing

NASA Astrophysics Data System (ADS)

Lum, Kai-Yew; Xu, Cai-Lin; Lu, Zhenbo; Lai, Kwok-Leung; Cui, Yongdong

2017-06-01

This paper presents an approach for data-driven modeling of aeroelasticity and its application to flutter control design of a wind-tunnel wing model. Modeling is centered on system identification of unsteady aerodynamic loads using computational fluid dynamics data, and adopts a nonlinear multivariable extension of the Hammerstein-Wiener system. The formulation is in modal coordinates of the elastic structure, and yields a reduced-order model of the aeroelastic feedback loop that is parametrized by airspeed. Flutter suppression is thus cast as a robust stabilization problem over uncertain airspeed, for which a low-order H∞ controller is computed. The paper discusses in detail parameter sensitivity and observability of the model, the former to justify the chosen model structure, and the latter to provide a criterion for physical sensor placement. Wind tunnel experiments confirm the validity of the modeling approach and the effectiveness of the control design.

Data-Driven Neural Network Model for Robust Reconstruction of Automobile Casting

NASA Astrophysics Data System (ADS)

Lin, Jinhua; Wang, Yanjie; Li, Xin; Wang, Lu

2017-09-01

In computer vision system, it is a challenging task to robustly reconstruct complex 3D geometries of automobile castings. However, 3D scanning data is usually interfered by noises, the scanning resolution is low, these effects normally lead to incomplete matching and drift phenomenon. In order to solve these problems, a data-driven local geometric learning model is proposed to achieve robust reconstruction of automobile casting. In order to relieve the interference of sensor noise and to be compatible with incomplete scanning data, a 3D convolution neural network is established to match the local geometric features of automobile casting. The proposed neural network combines the geometric feature representation with the correlation metric function to robustly match the local correspondence. We use the truncated distance field(TDF) around the key point to represent the 3D surface of casting geometry, so that the model can be directly embedded into the 3D space to learn the geometric feature representation; Finally, the training labels is automatically generated for depth learning based on the existing RGB-D reconstruction algorithm, which accesses to the same global key matching descriptor. The experimental results show that the matching accuracy of our network is 92.2% for automobile castings, the closed loop rate is about 74.0% when the matching tolerance threshold τ is 0.2. The matching descriptors performed well and retained 81.6% matching accuracy at 95% closed loop. For the sparse geometric castings with initial matching failure, the 3D matching object can be reconstructed robustly by training the key descriptors. Our method performs 3D reconstruction robustly for complex automobile castings.

A hierarchical anatomical classification schema for prediction of phenotypic side effects

PubMed Central

Kanji, Rakesh

2018-01-01

Prediction of adverse drug reactions is an important problem in drug discovery endeavors which can be addressed with data-driven strategies. SIDER is one of the most reliable and frequently used datasets for identification of key features as well as building machine learning models for side effects prediction. The inherently unbalanced nature of this data presents with a difficult multi-label multi-class problem towards prediction of drug side effects. We highlight the intrinsic issue with SIDER data and methodological flaws in relying on performance measures such as AUC while attempting to predict side effects.We argue for the use of metrics that are robust to class imbalance for evaluation of classifiers. Importantly, we present a ‘hierarchical anatomical classification schema’ which aggregates side effects into organs, sub-systems, and systems. With the help of a weighted performance measure, using 5-fold cross-validation we show that this strategy facilitates biologically meaningful side effects prediction at different levels of anatomical hierarchy. By implementing various machine learning classifiers we show that Random Forest model yields best classification accuracy at each level of coarse-graining. The manually curated, hierarchical schema for side effects can also serve as the basis of future studies towards prediction of adverse reactions and identification of key features linked to specific organ systems. Our study provides a strategy for hierarchical classification of side effects rooted in the anatomy and can pave the way for calibrated expert systems for multi-level prediction of side effects. PMID:29494708

A hierarchical anatomical classification schema for prediction of phenotypic side effects.

PubMed

Wadhwa, Somin; Gupta, Aishwarya; Dokania, Shubham; Kanji, Rakesh; Bagler, Ganesh

2018-01-01

Prediction of adverse drug reactions is an important problem in drug discovery endeavors which can be addressed with data-driven strategies. SIDER is one of the most reliable and frequently used datasets for identification of key features as well as building machine learning models for side effects prediction. The inherently unbalanced nature of this data presents with a difficult multi-label multi-class problem towards prediction of drug side effects. We highlight the intrinsic issue with SIDER data and methodological flaws in relying on performance measures such as AUC while attempting to predict side effects.We argue for the use of metrics that are robust to class imbalance for evaluation of classifiers. Importantly, we present a 'hierarchical anatomical classification schema' which aggregates side effects into organs, sub-systems, and systems. With the help of a weighted performance measure, using 5-fold cross-validation we show that this strategy facilitates biologically meaningful side effects prediction at different levels of anatomical hierarchy. By implementing various machine learning classifiers we show that Random Forest model yields best classification accuracy at each level of coarse-graining. The manually curated, hierarchical schema for side effects can also serve as the basis of future studies towards prediction of adverse reactions and identification of key features linked to specific organ systems. Our study provides a strategy for hierarchical classification of side effects rooted in the anatomy and can pave the way for calibrated expert systems for multi-level prediction of side effects.

Design and experimental evaluation of robust controllers for a two-wheeled robot

NASA Astrophysics Data System (ADS)

Kralev, J.; Slavov, Ts.; Petkov, P.

2016-11-01

The paper presents the design and experimental evaluation of two alternative μ-controllers for robust vertical stabilisation of a two-wheeled self-balancing robot. The controllers design is based on models derived by identification from closed-loop experimental data. In the first design, a signal-based uncertainty representation obtained directly from the identification procedure is used, which leads to a controller of order 29. In the second design the signal uncertainty is approximated by an input multiplicative uncertainty, which leads to a controller of order 50, subsequently reduced to 30. The performance of the two μ-controllers is compared with the performance of a conventional linear quadratic controller with 17th-order Kalman filter. A proportional-integral controller of the rotational motion around the vertical axis is implemented as well. The control code is generated using Simulink® controller models and is embedded in a digital signal processor. Results from the simulation of the closed-loop system as well as experimental results obtained during the real-time implementation of the designed controllers are given. The theoretical investigation and experimental results confirm that the closed-loop system achieves robust performance in respect to the uncertainties related to the identified robot model.

Evaluation of Anomaly Detection Capability for Ground-Based Pre-Launch Shuttle Operations. Chapter 8

NASA Technical Reports Server (NTRS)

Martin, Rodney Alexander

2010-01-01

This chapter will provide a thorough end-to-end description of the process for evaluation of three different data-driven algorithms for anomaly detection to select the best candidate for deployment as part of a suite of IVHM (Integrated Vehicle Health Management) technologies. These algorithms were deemed to be sufficiently mature enough to be considered viable candidates for deployment in support of the maiden launch of Ares I-X, the successor to the Space Shuttle for NASA's Constellation program. Data-driven algorithms are just one of three different types being deployed. The other two types of algorithms being deployed include a "nile-based" expert system, and a "model-based" system. Within these two categories, the deployable candidates have already been selected based upon qualitative factors such as flight heritage. For the rule-based system, SHINE (Spacecraft High-speed Inference Engine) has been selected for deployment, which is a component of BEAM (Beacon-based Exception Analysis for Multimissions), a patented technology developed at NASA's JPL (Jet Propulsion Laboratory) and serves to aid in the management and identification of operational modes. For the "model-based" system, a commercially available package developed by QSI (Qualtech Systems, Inc.), TEAMS (Testability Engineering and Maintenance System) has been selected for deployment to aid in diagnosis. In the context of this particular deployment, distinctions among the use of the terms "data-driven," "rule-based," and "model-based," can be found in. Although there are three different categories of algorithms that have been selected for deployment, our main focus in this chapter will be on the evaluation of three candidates for data-driven anomaly detection. These algorithms will be evaluated upon their capability for robustly detecting incipient faults or failures in the ground-based phase of pre-launch space shuttle operations, rather than based oil heritage as performed in previous studies. Robust detection will allow for the achievement of pre-specified minimum false alarm and/or missed detection rates in the selection of alert thresholds. All algorithms will also be optimized with respect to an aggregation of these same criteria. Our study relies upon the use of Shuttle data to act as was a proxy for and in preparation for application to Ares I-X data, which uses a very similar hardware platform for the subsystems that are being targeted (TVC - Thrust Vector Control subsystem for the SRB (Solid Rocket Booster)).

High performance data acquisition, identification, and monitoring for active magnetic bearings

NASA Technical Reports Server (NTRS)

Herzog, Raoul; Siegwart, Roland

1994-01-01

Future active magnetic bearing systems (AMB) must feature easier on-site tuning, higher stiffness and damping, better robustness with respect to undesirable vibrations in housing and foundation, and enhanced monitoring and identification abilities. To get closer to these goals we developed a fast parallel link from the digitally controlled AMB to Matlab, which is used on a host computer for data processing, identification, and controller layout. This enables the magnetic bearing to take its frequency responses without using any additional measurement equipment. These measurements can be used for AMB identification.

Comparative analysis of different weight matrices in subspace system identification for structural health monitoring

NASA Astrophysics Data System (ADS)

Shokravi, H.; Bakhary, NH

2017-11-01

Subspace System Identification (SSI) is considered as one of the most reliable tools for identification of system parameters. Performance of a SSI scheme is considerably affected by the structure of the associated identification algorithm. Weight matrix is a variable in SSI that is used to reduce the dimensionality of the state-space equation. Generally one of the weight matrices of Principle Component (PC), Unweighted Principle Component (UPC) and Canonical Variate Analysis (CVA) are used in the structure of a SSI algorithm. An increasing number of studies in the field of structural health monitoring are using SSI for damage identification. However, studies that evaluate the performance of the weight matrices particularly in association with accuracy, noise resistance, and time complexity properties are very limited. In this study, the accuracy, noise-robustness, and time-efficiency of the weight matrices are compared using different qualitative and quantitative metrics. Three evaluation metrics of pole analysis, fit values and elapsed time are used in the assessment process. A numerical model of a mass-spring-dashpot and operational data is used in this research paper. It is observed that the principal components obtained using PC algorithms are more robust against noise uncertainty and give more stable results for the pole distribution. Furthermore, higher estimation accuracy is achieved using UPC algorithm. CVA had the worst performance for pole analysis and time efficiency analysis. The superior performance of the UPC algorithm in the elapsed time is attributed to using unit weight matrices. The obtained results demonstrated that the process of reducing dimensionality in CVA and PC has not enhanced the time efficiency but yield an improved modal identification in PC.

Robust H(∞) positional control of 2-DOF robotic arm driven by electro-hydraulic servo system.

PubMed

Guo, Qing; Yu, Tian; Jiang, Dan

2015-11-01

In this paper an H∞ positional feedback controller is developed to improve the robust performance under structural and parametric uncertainty disturbance in electro-hydraulic servo system (EHSS). The robust control model is described as the linear state-space equation by upper linear fractional transformation. According to the solution of H∞ sub-optimal control problem, the robust controller is designed and simplified to lower order linear model which is easily realized in EHSS. The simulation and experimental results can validate the robustness of this proposed method. The comparison result with PI control shows that the robust controller is suitable for this EHSS under the critical condition where the desired system bandwidth is higher and the external load of the hydraulic actuator is closed to its limited capability. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

Data-Driven Robust M-LS-SVR-Based NARX Modeling for Estimation and Control of Molten Iron Quality Indices in Blast Furnace Ironmaking.

PubMed

Zhou, Ping; Guo, Dongwei; Wang, Hong; Chai, Tianyou

2017-09-29

Optimal operation of an industrial blast furnace (BF) ironmaking process largely depends on a reliable measurement of molten iron quality (MIQ) indices, which are not feasible using the conventional sensors. This paper proposes a novel data-driven robust modeling method for the online estimation and control of MIQ indices. First, a nonlinear autoregressive exogenous (NARX) model is constructed for the MIQ indices to completely capture the nonlinear dynamics of the BF process. Then, considering that the standard least-squares support vector regression (LS-SVR) cannot directly cope with the multioutput problem, a multitask transfer learning is proposed to design a novel multioutput LS-SVR (M-LS-SVR) for the learning of the NARX model. Furthermore, a novel M-estimator is proposed to reduce the interference of outliers and improve the robustness of the M-LS-SVR model. Since the weights of different outlier data are properly given by the weight function, their corresponding contributions on modeling can properly be distinguished, thus a robust modeling result can be achieved. Finally, a novel multiobjective evaluation index on the modeling performance is developed by comprehensively considering the root-mean-square error of modeling and the correlation coefficient on trend fitting, based on which the nondominated sorting genetic algorithm II is used to globally optimize the model parameters. Both experiments using industrial data and industrial applications illustrate that the proposed method can eliminate the adverse effect caused by the fluctuation of data in BF process efficiently. This indicates its stronger robustness and higher accuracy. Moreover, control testing shows that the developed model can be well applied to realize data-driven control of the BF process.

Data-Driven Robust M-LS-SVR-Based NARX Modeling for Estimation and Control of Molten Iron Quality Indices in Blast Furnace Ironmaking

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

Zhou, Ping; Guo, Dongwei; Wang, Hong

Optimal operation of an industrial blast furnace (BF) ironmaking process largely depends on a reliable measurement of molten iron quality (MIQ) indices, which are not feasible using the conventional sensors. This paper proposes a novel data-driven robust modeling method for the online estimation and control of MIQ indices. First, a nonlinear autoregressive exogenous (NARX) model is constructed for the MIQ indices to completely capture the nonlinear dynamics of the BF process. Then, considering that the standard least-squares support vector regression (LS-SVR) cannot directly cope with the multioutput problem, a multitask transfer learning is proposed to design a novel multioutput LS-SVRmore » (M-LS-SVR) for the learning of the NARX model. Furthermore, a novel M-estimator is proposed to reduce the interference of outliers and improve the robustness of the M-LS-SVR model. Since the weights of different outlier data are properly given by the weight function, their corresponding contributions on modeling can properly be distinguished, thus a robust modeling result can be achieved. Finally, a novel multiobjective evaluation index on the modeling performance is developed by comprehensively considering the root-mean-square error of modeling and the correlation coefficient on trend fitting, based on which the nondominated sorting genetic algorithm II is used to globally optimize the model parameters. Both experiments using industrial data and industrial applications illustrate that the proposed method can eliminate the adverse effect caused by the fluctuation of data in BF process efficiently. In conclusion, this indicates its stronger robustness and higher accuracy. Moreover, control testing shows that the developed model can be well applied to realize data-driven control of the BF process.« less

Data-Driven Robust M-LS-SVR-Based NARX Modeling for Estimation and Control of Molten Iron Quality Indices in Blast Furnace Ironmaking

DOE PAGES

Zhou, Ping; Guo, Dongwei; Wang, Hong; ...

2017-09-29

Optimal operation of an industrial blast furnace (BF) ironmaking process largely depends on a reliable measurement of molten iron quality (MIQ) indices, which are not feasible using the conventional sensors. This paper proposes a novel data-driven robust modeling method for the online estimation and control of MIQ indices. First, a nonlinear autoregressive exogenous (NARX) model is constructed for the MIQ indices to completely capture the nonlinear dynamics of the BF process. Then, considering that the standard least-squares support vector regression (LS-SVR) cannot directly cope with the multioutput problem, a multitask transfer learning is proposed to design a novel multioutput LS-SVRmore » (M-LS-SVR) for the learning of the NARX model. Furthermore, a novel M-estimator is proposed to reduce the interference of outliers and improve the robustness of the M-LS-SVR model. Since the weights of different outlier data are properly given by the weight function, their corresponding contributions on modeling can properly be distinguished, thus a robust modeling result can be achieved. Finally, a novel multiobjective evaluation index on the modeling performance is developed by comprehensively considering the root-mean-square error of modeling and the correlation coefficient on trend fitting, based on which the nondominated sorting genetic algorithm II is used to globally optimize the model parameters. Both experiments using industrial data and industrial applications illustrate that the proposed method can eliminate the adverse effect caused by the fluctuation of data in BF process efficiently. In conclusion, this indicates its stronger robustness and higher accuracy. Moreover, control testing shows that the developed model can be well applied to realize data-driven control of the BF process.« less

Video fingerprinting for copy identification: from research to industry applications

NASA Astrophysics Data System (ADS)

Lu, Jian

2009-02-01

Research that began a decade ago in video copy detection has developed into a technology known as "video fingerprinting". Today, video fingerprinting is an essential and enabling tool adopted by the industry for video content identification and management in online video distribution. This paper provides a comprehensive review of video fingerprinting technology and its applications in identifying, tracking, and managing copyrighted content on the Internet. The review includes a survey on video fingerprinting algorithms and some fundamental design considerations, such as robustness, discriminability, and compactness. It also discusses fingerprint matching algorithms, including complexity analysis, and approximation and optimization for fast fingerprint matching. On the application side, it provides an overview of a number of industry-driven applications that rely on video fingerprinting. Examples are given based on real-world systems and workflows to demonstrate applications in detecting and managing copyrighted content, and in monitoring and tracking video distribution on the Internet.

Performance analysis of robust road sign identification

NASA Astrophysics Data System (ADS)

Ali, Nursabillilah M.; Mustafah, Y. M.; Rashid, N. K. A. M.

2013-12-01

This study describes performance analysis of a robust system for road sign identification that incorporated two stages of different algorithms. The proposed algorithms consist of HSV color filtering and PCA techniques respectively in detection and recognition stages. The proposed algorithms are able to detect the three standard types of colored images namely Red, Yellow and Blue. The hypothesis of the study is that road sign images can be used to detect and identify signs that are involved with the existence of occlusions and rotational changes. PCA is known as feature extraction technique that reduces dimensional size. The sign image can be easily recognized and identified by the PCA method as is has been used in many application areas. Based on the experimental result, it shows that the HSV is robust in road sign detection with minimum of 88% and 77% successful rate for non-partial and partial occlusions images. For successful recognition rates using PCA can be achieved in the range of 94-98%. The occurrences of all classes are recognized successfully is between 5% and 10% level of occlusions.

Exploration of available feature detection and identification systems and their performance on radiographs

NASA Astrophysics Data System (ADS)

Wantuch, Andrew C.; Vita, Joshua A.; Jimenez, Edward S.; Bray, Iliana E.

2016-10-01

Despite object detection, recognition, and identification being very active areas of computer vision research, many of the available tools to aid in these processes are designed with only photographs in mind. Although some algorithms used specifically for feature detection and identification may not take explicit advantage of the colors available in the image, they still under-perform on radiographs, which are grayscale images. We are especially interested in the robustness of these algorithms, specifically their performance on a preexisting database of X-ray radiographs in compressed JPEG form, with multiple ways of describing pixel information. We will review various aspects of the performance of available feature detection and identification systems, including MATLABs Computer Vision toolbox, VLFeat, and OpenCV on our non-ideal database. In the process, we will explore possible reasons for the algorithms' lessened ability to detect and identify features from the X-ray radiographs.

Identification of Terrestrial Reflectance From Remote Sensing

NASA Technical Reports Server (NTRS)

Alter-Gartenberg, Rachel; Nolf, Scott R.; Stacy, Kathryn (Technical Monitor)

2000-01-01

Correcting for atmospheric effects is an essential part of surface-reflectance recovery from radiance measurements. Model-based atmospheric correction techniques enable an accurate identification and classification of terrestrial reflectances from multi-spectral imagery. Successful and efficient removal of atmospheric effects from remote-sensing data is a key factor in the success of Earth observation missions. This report assesses the performance, robustness and sensitivity of two atmospheric-correction and reflectance-recovery techniques as part of an end-to-end simulation of hyper-spectral acquisition, identification and classification.

Closed-Loop Evaluation of an Integrated Failure Identification and Fault Tolerant Control System for a Transport Aircraft

NASA Technical Reports Server (NTRS)

Shin, Jong-Yeob; Belcastro, Christine; Khong, thuan

2006-01-01

Formal robustness analysis of aircraft control upset prevention and recovery systems could play an important role in their validation and ultimate certification. Such systems developed for failure detection, identification, and reconfiguration, as well as upset recovery, need to be evaluated over broad regions of the flight envelope or under extreme flight conditions, and should include various sources of uncertainty. To apply formal robustness analysis, formulation of linear fractional transformation (LFT) models of complex parameter-dependent systems is required, which represent system uncertainty due to parameter uncertainty and actuator faults. This paper describes a detailed LFT model formulation procedure from the nonlinear model of a transport aircraft by using a preliminary LFT modeling software tool developed at the NASA Langley Research Center, which utilizes a matrix-based computational approach. The closed-loop system is evaluated over the entire flight envelope based on the generated LFT model which can cover nonlinear dynamics. The robustness analysis results of the closed-loop fault tolerant control system of a transport aircraft are presented. A reliable flight envelope (safe flight regime) is also calculated from the robust performance analysis results, over which the closed-loop system can achieve the desired performance of command tracking and failure detection.

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.

Implementation of facial recognition with Microsoft Kinect v2 sensor for patient verification.

PubMed

Silverstein, Evan; Snyder, Michael

2017-06-01

The aim of this study was to present a straightforward implementation of facial recognition using the Microsoft Kinect v2 sensor for patient identification in a radiotherapy setting. A facial recognition system was created with the Microsoft Kinect v2 using a facial mapping library distributed with the Kinect v2 SDK as a basis for the algorithm. The system extracts 31 fiducial points representing various facial landmarks which are used in both the creation of a reference data set and subsequent evaluations of real-time sensor data in the matching algorithm. To test the algorithm, a database of 39 faces was created, each with 465 vectors derived from the fiducial points, and a one-to-one matching procedure was performed to obtain sensitivity and specificity data of the facial identification system. ROC curves were plotted to display system performance and identify thresholds for match determination. In addition, system performance as a function of ambient light intensity was tested. Using optimized parameters in the matching algorithm, the sensitivity of the system for 5299 trials was 96.5% and the specificity was 96.7%. The results indicate a fairly robust methodology for verifying, in real-time, a specific face through comparison from a precollected reference data set. In its current implementation, the process of data collection for each face and subsequent matching session averaged approximately 30 s, which may be too onerous to provide a realistic supplement to patient identification in a clinical setting. Despite the time commitment, the data collection process was well tolerated by all participants and most robust when consistent ambient light conditions were maintained across both the reference recording session and subsequent real-time identification sessions. A facial recognition system can be implemented for patient identification using the Microsoft Kinect v2 sensor and the distributed SDK. In its present form, the system is accurate-if time consuming-and further iterations of the method could provide a robust, easy to implement, and cost-effective supplement to traditional patient identification methods. © 2017 American Association of Physicists in Medicine.

A multi-center study benchmarks software tools for label-free proteome quantification

PubMed Central

Gillet, Ludovic C; Bernhardt, Oliver M.; MacLean, Brendan; Röst, Hannes L.; Tate, Stephen A.; Tsou, Chih-Chiang; Reiter, Lukas; Distler, Ute; Rosenberger, George; Perez-Riverol, Yasset; Nesvizhskii, Alexey I.; Aebersold, Ruedi; Tenzer, Stefan

2016-01-01

The consistent and accurate quantification of proteins by mass spectrometry (MS)-based proteomics depends on the performance of instruments, acquisition methods and data analysis software. In collaboration with the software developers, we evaluated OpenSWATH, SWATH2.0, Skyline, Spectronaut and DIA-Umpire, five of the most widely used software methods for processing data from SWATH-MS (sequential window acquisition of all theoretical fragment ion spectra), a method that uses data-independent acquisition (DIA) for label-free protein quantification. We analyzed high-complexity test datasets from hybrid proteome samples of defined quantitative composition acquired on two different MS instruments using different SWATH isolation windows setups. For consistent evaluation we developed LFQbench, an R-package to calculate metrics of precision and accuracy in label-free quantitative MS, and report the identification performance, robustness and specificity of each software tool. Our reference datasets enabled developers to improve their software tools. After optimization, all tools provided highly convergent identification and reliable quantification performance, underscoring their robustness for label-free quantitative proteomics. PMID:27701404

A multicenter study benchmarks software tools for label-free proteome quantification.

PubMed

Navarro, Pedro; Kuharev, Jörg; Gillet, Ludovic C; Bernhardt, Oliver M; MacLean, Brendan; Röst, Hannes L; Tate, Stephen A; Tsou, Chih-Chiang; Reiter, Lukas; Distler, Ute; Rosenberger, George; Perez-Riverol, Yasset; Nesvizhskii, Alexey I; Aebersold, Ruedi; Tenzer, Stefan

2016-11-01

Consistent and accurate quantification of proteins by mass spectrometry (MS)-based proteomics depends on the performance of instruments, acquisition methods and data analysis software. In collaboration with the software developers, we evaluated OpenSWATH, SWATH 2.0, Skyline, Spectronaut and DIA-Umpire, five of the most widely used software methods for processing data from sequential window acquisition of all theoretical fragment-ion spectra (SWATH)-MS, which uses data-independent acquisition (DIA) for label-free protein quantification. We analyzed high-complexity test data sets from hybrid proteome samples of defined quantitative composition acquired on two different MS instruments using different SWATH isolation-window setups. For consistent evaluation, we developed LFQbench, an R package, to calculate metrics of precision and accuracy in label-free quantitative MS and report the identification performance, robustness and specificity of each software tool. Our reference data sets enabled developers to improve their software tools. After optimization, all tools provided highly convergent identification and reliable quantification performance, underscoring their robustness for label-free quantitative proteomics.

Robust inertia-free attitude takeover control of postcapture combined spacecraft with guaranteed prescribed performance.

PubMed

Luo, Jianjun; Wei, Caisheng; Dai, Honghua; Yin, Zeyang; Wei, Xing; Yuan, Jianping

2018-03-01

In this paper, a robust inertia-free attitude takeover control scheme with guaranteed prescribed performance is investigated for postcapture combined spacecraft with consideration of unmeasurable states, unknown inertial property and external disturbance torque. Firstly, to estimate the unavailable angular velocity of combination accurately, a novel finite-time-convergent tracking differentiator is developed with a quite computationally achievable structure free from the unknown nonlinear dynamics of combined spacecraft. Then, a robust inertia-free prescribed performance control scheme is proposed, wherein, the transient and steady-state performance of combined spacecraft is first quantitatively studied by stabilizing the filtered attitude tracking errors. Compared with the existing works, the prominent advantage is that no parameter identifications and no neural or fuzzy nonlinear approximations are needed, which decreases the complexity of robust controller design dramatically. Moreover, the prescribed performance of combined spacecraft is guaranteed a priori without resorting to repeated regulations of the controller parameters. Finally, four illustrative examples are employed to validate the effectiveness of the proposed control scheme and tracking differentiator. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

Data-Driven Anomaly Detection Performance for the Ares I-X Ground Diagnostic Prototype

NASA Technical Reports Server (NTRS)

Martin, Rodney A.; Schwabacher, Mark A.; Matthews, Bryan L.

2010-01-01

In this paper, we will assess the performance of a data-driven anomaly detection algorithm, the Inductive Monitoring System (IMS), which can be used to detect simulated Thrust Vector Control (TVC) system failures. However, the ability of IMS to detect these failures in a true operational setting may be related to the realistic nature of how they are simulated. As such, we will investigate both a low fidelity and high fidelity approach to simulating such failures, with the latter based upon the underlying physics. Furthermore, the ability of IMS to detect anomalies that were previously unknown and not previously simulated will be studied in earnest, as well as apparent deficiencies or misapplications that result from using the data-driven paradigm. Our conclusions indicate that robust detection performance of simulated failures using IMS is not appreciably affected by the use of a high fidelity simulation. However, we have found that the inclusion of a data-driven algorithm such as IMS into a suite of deployable health management technologies does add significant value.

Robust volcano plot: identification of differential metabolites in the presence of outliers.

PubMed

Kumar, Nishith; Hoque, Md Aminul; Sugimoto, Masahiro

2018-04-11

The identification of differential metabolites in metabolomics is still a big challenge and plays a prominent role in metabolomics data analyses. Metabolomics datasets often contain outliers because of analytical, experimental, and biological ambiguity, but the currently available differential metabolite identification techniques are sensitive to outliers. We propose a kernel weight based outlier-robust volcano plot for identifying differential metabolites from noisy metabolomics datasets. Two numerical experiments are used to evaluate the performance of the proposed technique against nine existing techniques, including the t-test and the Kruskal-Wallis test. Artificially generated data with outliers reveal that the proposed method results in a lower misclassification error rate and a greater area under the receiver operating characteristic curve compared with existing methods. An experimentally measured breast cancer dataset to which outliers were artificially added reveals that our proposed method produces only two non-overlapping differential metabolites whereas the other nine methods produced between seven and 57 non-overlapping differential metabolites. Our data analyses show that the performance of the proposed differential metabolite identification technique is better than that of existing methods. Thus, the proposed method can contribute to analysis of metabolomics data with outliers. The R package and user manual of the proposed method are available at https://github.com/nishithkumarpaul/Rvolcano .

Robustness of waves with a high phase velocity

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

Tajima, T., E-mail: ttajima@uci.edu; Tri Alpha Energy, Inc., P.O. Box 7010, Rancho Santa Margarita, CA 92688; Necas, A., E-mail: anecas@trialphaenergy.com

Norman Rostoker pioneered research of (1) plasma-driven accelerators and (2) beam-driven fusion reactors. The collective acceleration, coined by Veksler, advocates to drive above-ionization plasma waves by an electron beam to accelerate ions. The research on this, among others, by the Rostoker group incubated the idea that eventually led to the birth of the laser wakefield acceleration (LWFA), by which a large and robust accelerating collective fields may be generated in plasma in which plasma remains robust and undisrupted. Besides the emergence of LWFA, the Rostoker research spawned our lessons learned on the importance of adiabatic acceleration of ions in collectivemore » accelerators, including the recent rebirth in laser-driven ion acceleration efforts in a smooth adiabatic fashion by a variety of ingenious methods. Following Rostoker’s research in (2), the beam-driven Field Reversed Configuration (FRC) has accomplished breakthroughs in recent years. The beam-driven kinetic plasma instabilities have been found to drive the reactivity of deuteron-deuteron fusion beyond the thermonuclear yield in C-2U plasma that Rostoker started. This remarkable result in FRCs as well as the above mentioned LWFA may be understood with the aid of the newly introduced idea of the “robustness hypothesis of waves with a high phase velocity”. It posits that when the wave driven by a particle beam (or laser pulse) has a high phase velocity, its amplitude is high without disrupting the supporting bulk plasma. This hypothesis may guide us into more robust and efficient fusion reactors and more compact accelerators.« less

Identification and differentiation of food-related bacteria: A comparison of FTIR spectroscopy and MALDI-TOF mass spectrometry.

PubMed

Wenning, Mareike; Breitenwieser, Franziska; Konrad, Regina; Huber, Ingrid; Busch, Ulrich; Scherer, Siegfried

2014-08-01

The food industry requires easy, accurate, and cost-effective techniques for microbial identification to ensure safe products and identify microbial contaminations. In this work, FTIR spectroscopy and MALDI-TOF mass spectrometry were assessed for their suitability and applicability for routine microbial diagnostics of food-related microorganisms by analyzing their robustness according to changes in incubation time and medium, identification accuracy and their ability to differentiate isolates down to the strain level. Changes in the protocol lead to a significantly impaired performance of FTIR spectroscopy, whereas they had only little effects on MALDI-TOF MS. Identification accuracy was tested using 174 food-related bacteria (93 species) from an in-house strain collection and 40 fresh isolates from routine food analyses. For MALDI-TOF MS, weaknesses in the identification of bacilli and pseudomonads were observed; FTIR spectroscopy had most difficulties in identifying pseudomonads and enterobacteria. In general, MALDI-TOF MS obtained better results (52-85% correct at species level), since the analysis of mainly ribosomal proteins is more robust and seems to be more reliable. FTIR spectroscopy suffers from the fact that it generates a whole-cell fingerprint and intraspecies diversity may lead to overlapping species borders which complicates identification. In the present study values between 56% and 67% correct species identification were obtained. On the opposite, this high sensitivity offers the opportunity of typing below the species level which was not possible using MALDI-TOF MS. Using fresh isolates from routine diagnostics, both techniques performed well with 88% (MALDI-TOF) and 75% (FTIR) correct identifications at species level, respectively. Copyright © 2014 Elsevier B.V. All rights reserved.

Advanced driver assistance system: Road sign identification using VIAPIX system and a correlation technique

NASA Astrophysics Data System (ADS)

Ouerhani, Y.; Alfalou, A.; Desthieux, M.; Brosseau, C.

2017-02-01

We present a three-step approach based on the commercial VIAPIX® module for road traffic sign recognition and identification. Firstly, detection in a scene of all objects having characteristics of traffic signs is performed. This is followed by a first-level recognition based on correlation which consists in making a comparison between each detected object with a set of reference images of a database. Finally, a second level of identification allows us to confirm or correct the previous identification. In this study, we perform a correlation-based analysis by combining and adapting the Vander Lugt correlator with the nonlinear joint transformation correlator (JTC). Of particular significance, this approach permits to make a reliable decision on road traffic sign identification. We further discuss a robust scheme allowing us to track a detected road traffic sign in a video sequence for the purpose of increasing the decision performance of our system. This approach can have broad practical applications in the maintenance and rehabilitation of transportation infrastructure, or for drive assistance.

Data-driven robust approximate optimal tracking control for unknown general nonlinear systems using adaptive dynamic programming method.

PubMed

Zhang, Huaguang; Cui, Lili; Zhang, Xin; Luo, Yanhong

2011-12-01

In this paper, a novel data-driven robust approximate optimal tracking control scheme is proposed for unknown general nonlinear systems by using the adaptive dynamic programming (ADP) method. In the design of the controller, only available input-output data is required instead of known system dynamics. A data-driven model is established by a recurrent neural network (NN) to reconstruct the unknown system dynamics using available input-output data. By adding a novel adjustable term related to the modeling error, the resultant modeling error is first guaranteed to converge to zero. Then, based on the obtained data-driven model, the ADP method is utilized to design the approximate optimal tracking controller, which consists of the steady-state controller and the optimal feedback controller. Further, a robustifying term is developed to compensate for the NN approximation errors introduced by implementing the ADP method. Based on Lyapunov approach, stability analysis of the closed-loop system is performed to show that the proposed controller guarantees the system state asymptotically tracking the desired trajectory. Additionally, the obtained control input is proven to be close to the optimal control input within a small bound. Finally, two numerical examples are used to demonstrate the effectiveness of the proposed control scheme.

Design and Experimental Evaluation of a Robust Position Controller for an Electrohydrostatic Actuator Using Adaptive Antiwindup Sliding Mode Scheme

PubMed Central

Lee, Ji Min; Park, Sung Hwan; Kim, Jong Shik

2013-01-01

A robust control scheme is proposed for the position control of the electrohydrostatic actuator (EHA) when considering hardware saturation, load disturbance, and lumped system uncertainties and nonlinearities. To reduce overshoot due to a saturation of electric motor and to realize robustness against load disturbance and lumped system uncertainties such as varying parameters and modeling error, this paper proposes an adaptive antiwindup PID sliding mode scheme as a robust position controller for the EHA system. An optimal PID controller and an optimal anti-windup PID controller are also designed to compare control performance. An EHA prototype is developed, carrying out system modeling and parameter identification in designing the position controller. The simply identified linear model serves as the basis for the design of the position controllers, while the robustness of the control systems is compared by experiments. The adaptive anti-windup PID sliding mode controller has been found to have the desired performance and become robust against hardware saturation, load disturbance, and lumped system uncertainties and nonlinearities. PMID:23983640

Failure detection system design methodology. Ph.D. Thesis

NASA Technical Reports Server (NTRS)

Chow, E. Y.

1980-01-01

The design of a failure detection and identification system consists of designing a robust residual generation process and a high performance decision making process. The design of these two processes are examined separately. Residual generation is based on analytical redundancy. Redundancy relations that are insensitive to modelling errors and noise effects are important for designing robust residual generation processes. The characterization of the concept of analytical redundancy in terms of a generalized parity space provides a framework in which a systematic approach to the determination of robust redundancy relations are developed. The Bayesian approach is adopted for the design of high performance decision processes. The FDI decision problem is formulated as a Bayes sequential decision problem. Since the optimal decision rule is incomputable, a methodology for designing suboptimal rules is proposed. A numerical algorithm is developed to facilitate the design and performance evaluation of suboptimal rules.

Combining Knowledge and Data Driven Insights for Identifying Risk Factors using Electronic Health Records

PubMed Central

Sun, Jimeng; Hu, Jianying; Luo, Dijun; Markatou, Marianthi; Wang, Fei; Edabollahi, Shahram; Steinhubl, Steven E.; Daar, Zahra; Stewart, Walter F.

2012-01-01

Background: The ability to identify the risk factors related to an adverse condition, e.g., heart failures (HF) diagnosis, is very important for improving care quality and reducing cost. Existing approaches for risk factor identification are either knowledge driven (from guidelines or literatures) or data driven (from observational data). No existing method provides a model to effectively combine expert knowledge with data driven insight for risk factor identification. Methods: We present a systematic approach to enhance known knowledge-based risk factors with additional potential risk factors derived from data. The core of our approach is a sparse regression model with regularization terms that correspond to both knowledge and data driven risk factors. Results: The approach is validated using a large dataset containing 4,644 heart failure cases and 45,981 controls. The outpatient electronic health records (EHRs) for these patients include diagnosis, medication, lab results from 2003–2010. We demonstrate that the proposed method can identify complementary risk factors that are not in the existing known factors and can better predict the onset of HF. We quantitatively compare different sets of risk factors in the context of predicting onset of HF using the performance metric, the Area Under the ROC Curve (AUC). The combined risk factors between knowledge and data significantly outperform knowledge-based risk factors alone. Furthermore, those additional risk factors are confirmed to be clinically meaningful by a cardiologist. Conclusion: We present a systematic framework for combining knowledge and data driven insights for risk factor identification. We demonstrate the power of this framework in the context of predicting onset of HF, where our approach can successfully identify intuitive and predictive risk factors beyond a set of known HF risk factors. PMID:23304365

Fluctuation-driven price dynamics and investment strategies

PubMed Central

Li, Yan; Zheng, Bo; Chen, Ting-Ting; Jiang, Xiong-Fei

2017-01-01

Investigation of the driven mechanism of the price dynamics in complex financial systems is important and challenging. In this paper, we propose an investment strategy to study how dynamic fluctuations drive the price movements. The strategy is successfully applied to different stock markets in the world, and the result indicates that the driving effect of the dynamic fluctuations is rather robust. We investigate how the strategy performance is influenced by the market states and optimize the strategy performance by introducing two parameters. The strategy is also compared with several typical technical trading rules. Our findings not only provide an investment strategy which extends investors’ profits, but also offer a useful method to look into the dynamic properties of complex financial systems. PMID:29240783

Fluctuation-driven price dynamics and investment strategies.

PubMed

Li, Yan; Zheng, Bo; Chen, Ting-Ting; Jiang, Xiong-Fei

2017-01-01

Investigation of the driven mechanism of the price dynamics in complex financial systems is important and challenging. In this paper, we propose an investment strategy to study how dynamic fluctuations drive the price movements. The strategy is successfully applied to different stock markets in the world, and the result indicates that the driving effect of the dynamic fluctuations is rather robust. We investigate how the strategy performance is influenced by the market states and optimize the strategy performance by introducing two parameters. The strategy is also compared with several typical technical trading rules. Our findings not only provide an investment strategy which extends investors' profits, but also offer a useful method to look into the dynamic properties of complex financial systems.

Development and experimental verification of a robust active noise control system for a diesel engine in submarines

NASA Astrophysics Data System (ADS)

Sachau, D.; Jukkert, S.; Hövelmann, N.

2016-08-01

This paper presents the development and experimental validation of an ANC (active noise control)-system designed for a particular application in the exhaust line of a submarine. Thereby, tonal components of the exhaust noise in the frequency band from 75 Hz to 120 Hz are reduced by more than 30 dB. The ANC-system is based on the feedforward leaky FxLMS-algorithm. The observability of the sound pressure in standing wave field is ensured by using two error microphones. The noninvasive online plant identification method is used to increase the robustness of the controller. Online plant identification is extended by a time-varying convergence gain to improve the performance in the presence of slight error in the frequency of the reference signal.

Data-driven inference of network connectivity for modeling the dynamics of neural codes in the insect antennal lobe

PubMed Central

Shlizerman, Eli; Riffell, Jeffrey A.; Kutz, J. Nathan

2014-01-01

The antennal lobe (AL), olfactory processing center in insects, is able to process stimuli into distinct neural activity patterns, called olfactory neural codes. To model their dynamics we perform multichannel recordings from the projection neurons in the AL driven by different odorants. We then derive a dynamic neuronal network from the electrophysiological data. The network consists of lateral-inhibitory neurons and excitatory neurons (modeled as firing-rate units), and is capable of producing unique olfactory neural codes for the tested odorants. To construct the network, we (1) design a projection, an odor space, for the neural recording from the AL, which discriminates between distinct odorants trajectories (2) characterize scent recognition, i.e., decision-making based on olfactory signals and (3) infer the wiring of the neural circuit, the connectome of the AL. We show that the constructed model is consistent with biological observations, such as contrast enhancement and robustness to noise. The study suggests a data-driven approach to answer a key biological question in identifying how lateral inhibitory neurons can be wired to excitatory neurons to permit robust activity patterns. PMID:25165442

Gaze Behavior Consistency among Older and Younger Adults When Looking at Emotional Faces

PubMed Central

Chaby, Laurence; Hupont, Isabelle; Avril, Marie; Luherne-du Boullay, Viviane; Chetouani, Mohamed

2017-01-01

The identification of non-verbal emotional signals, and especially of facial expressions, is essential for successful social communication among humans. Previous research has reported an age-related decline in facial emotion identification, and argued for socio-emotional or aging-brain model explanations. However, more perceptual differences in the gaze strategies that accompany facial emotional processing with advancing age have been under-explored yet. In this study, 22 young (22.2 years) and 22 older (70.4 years) adults were instructed to look at basic facial expressions while their gaze movements were recorded by an eye-tracker. Participants were then asked to identify each emotion, and the unbiased hit rate was applied as performance measure. Gaze data were first analyzed using traditional measures of fixations over two preferential regions of the face (upper and lower areas) for each emotion. Then, to better capture core gaze changes with advancing age, spatio-temporal gaze behaviors were deeper examined using data-driven analysis (dimension reduction, clustering). Results first confirmed that older adults performed worse than younger adults at identifying facial expressions, except for “joy” and “disgust,” and this was accompanied by a gaze preference toward the lower-face. Interestingly, this phenomenon was maintained during the whole time course of stimulus presentation. More importantly, trials corresponding to older adults were more tightly clustered, suggesting that the gaze behavior patterns of older adults are more consistent than those of younger adults. This study demonstrates that, confronted to emotional faces, younger and older adults do not prioritize or ignore the same facial areas. Older adults mainly adopted a focused-gaze strategy, consisting in focusing only on the lower part of the face throughout the whole stimuli display time. This consistency may constitute a robust and distinctive “social signature” of emotional identification in aging. Younger adults, however, were more dispersed in terms of gaze behavior and used a more exploratory-gaze strategy, consisting in repeatedly visiting both facial areas. PMID:28450841

Vehicle logo recognition using multi-level fusion model

NASA Astrophysics Data System (ADS)

Ming, Wei; Xiao, Jianli

2018-04-01

Vehicle logo recognition plays an important role in manufacturer identification and vehicle recognition. This paper proposes a new vehicle logo recognition algorithm. It has a hierarchical framework, which consists of two fusion levels. At the first level, a feature fusion model is employed to map the original features to a higher dimension feature space. In this space, the vehicle logos become more recognizable. At the second level, a weighted voting strategy is proposed to promote the accuracy and the robustness of the recognition results. To evaluate the performance of the proposed algorithm, extensive experiments are performed, which demonstrate that the proposed algorithm can achieve high recognition accuracy and work robustly.

Robust polygon recognition method with similarity invariants applied to star identification

NASA Astrophysics Data System (ADS)

Hernández, E. Antonio; Alonso, Miguel A.; Chávez, Edgar; Covarrubias, David H.; Conte, Roberto

2017-02-01

In the star identification process the goal is to recognize a star by using the celestial bodies in its vicinity as context. An additional requirement is to avoid having to perform an exhaustive scan of the star database. In this paper we present a novel approach to star identification using similarity invariants. More specifically, the proposed algorithm defines a polygon for each star, using the neighboring celestial bodies in the field of view as vertices. The mapping is insensitive to similarity transformation; that is, the image of the polygon under the transformation is not affected by rotation, scaling or translations. Each polygon is associated with an essentially unique complex number. We perform an exhaustive experimental validation of the proposed algorithm using synthetic data generated from the star catalog with uniformly-distributed positional noise introduced to each star. The star identification method that we present is proven to be robust, achieving a recognition rate of 99.68% when noise levels of up to ± 424 μ radians are introduced to the location of the stars. In our tests the proposed algorithm proves that if a polygon match is found, it always corresponds to the star under analysis; no mismatches are found. In its present form our method cannot identify polygons in cases where there exist missing or false stars in the analyzed images, in those situations it only indicates that no match was found.

Semiconductor lasers driven by self-sustained chaotic electronic oscillators and applications to optical chaos cryptography.

PubMed

Kingni, Sifeu Takougang; Mbé, Jimmi Hervé Talla; Woafo, Paul

2012-09-01

In this work, we numerically study the dynamics of vertical cavity surface emitting laser (VCSEL) firstly when it is driven by Chua's oscillator, secondly in case where it is driven by a broad frequency spectral bandwidth chaotic oscillator developed by Nana et al. [Commun. Nonlinear Sci. Numer. Simul. 14, 2266 (2009)]. We demonstrated that the VCSEL generated robust chaotic dynamics compared to the ones found in VCSEL subject to a sinusoidally modulated current and therefore it is more suitable for chaos encryption techniques. The synchronization characteristics and the communication performances of unidirectional coupled VCSEL driven by the broad frequency spectral bandwidth chaotic oscillators are investigated numerically. The results show that high-quality synchronization and transmission of messages can be realized for suitable system parameters. Chaos shift keying method is successfully applied to encrypt a message at a high bitrate.

Robust nonlinear system identification: Bayesian mixture of experts using the t-distribution

NASA Astrophysics Data System (ADS)

Baldacchino, Tara; Worden, Keith; Rowson, Jennifer

2017-02-01

A novel variational Bayesian mixture of experts model for robust regression of bifurcating and piece-wise continuous processes is introduced. The mixture of experts model is a powerful model which probabilistically splits the input space allowing different models to operate in the separate regions. However, current methods have no fail-safe against outliers. In this paper, a robust mixture of experts model is proposed which consists of Student-t mixture models at the gates and Student-t distributed experts, trained via Bayesian inference. The Student-t distribution has heavier tails than the Gaussian distribution, and so it is more robust to outliers, noise and non-normality in the data. Using both simulated data and real data obtained from the Z24 bridge this robust mixture of experts performs better than its Gaussian counterpart when outliers are present. In particular, it provides robustness to outliers in two forms: unbiased parameter regression models, and robustness to overfitting/complex models.

Adjustment of Adaptive Gain with Bounded Linear Stability Analysis to Improve Time-Delay Margin for Metrics-Driven Adaptive Control

NASA Technical Reports Server (NTRS)

Bakhtiari-Nejad, Maryam; Nguyen, Nhan T.; Krishnakumar, Kalmanje Srinvas

2009-01-01

This paper presents the application of Bounded Linear Stability Analysis (BLSA) method for metrics driven adaptive control. The bounded linear stability analysis method is used for analyzing stability of adaptive control models, without linearizing the adaptive laws. Metrics-driven adaptive control introduces a notion that adaptation should be driven by some stability metrics to achieve robustness. By the application of bounded linear stability analysis method the adaptive gain is adjusted during the adaptation in order to meet certain phase margin requirements. Analysis of metrics-driven adaptive control is evaluated for a linear damaged twin-engine generic transport model of aircraft. The analysis shows that the system with the adjusted adaptive gain becomes more robust to unmodeled dynamics or time delay.

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.

Nonlinear Tracking Control of a Conductive Supercoiled Polymer Actuator.

PubMed

Luong, Tuan Anh; Cho, Kyeong Ho; Song, Min Geun; Koo, Ja Choon; Choi, Hyouk Ryeol; Moon, Hyungpil

2018-04-01

Artificial muscle actuators made from commercial nylon fishing lines have been recently introduced and shown as a new type of actuator with high performance. However, the actuators also exhibit significant nonlinearities, which make them difficult to control, especially in precise trajectory-tracking applications. In this article, we present a nonlinear mathematical model of a conductive supercoiled polymer (SCP) actuator driven by Joule heating for model-based feedback controls. Our efforts include modeling of the hysteresis behavior of the actuator. Based on nonlinear modeling, we design a sliding mode controller for SCP actuator-driven manipulators. The system with proposed control law is proven to be asymptotically stable using the Lyapunov theory. The control performance of the proposed method is evaluated experimentally and compared with that of a proportional-integral-derivative (PID) controller through one-degree-of-freedom SCP actuator-driven manipulators. Experimental results show that the proposed controller's performance is superior to that of a PID controller, such as the tracking errors are nearly 10 times smaller compared with those of a PID controller, and it is more robust to external disturbances such as sensor noise and actuator modeling error.

Data-Driven Robust RVFLNs Modeling of a Blast Furnace Iron-Making Process Using Cauchy Distribution Weighted M-Estimation

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

Zhou, Ping; Lv, Youbin; Wang, Hong

Optimal operation of a practical blast furnace (BF) ironmaking process depends largely on a good measurement of molten iron quality (MIQ) indices. However, measuring the MIQ online is not feasible using the available techniques. In this paper, a novel data-driven robust modeling is proposed for online estimation of MIQ using improved random vector functional-link networks (RVFLNs). Since the output weights of traditional RVFLNs are obtained by the least squares approach, a robustness problem may occur when the training dataset is contaminated with outliers. This affects the modeling accuracy of RVFLNs. To solve this problem, a Cauchy distribution weighted M-estimation basedmore » robust RFVLNs is proposed. Since the weights of different outlier data are properly determined by the Cauchy distribution, their corresponding contribution on modeling can be properly distinguished. Thus robust and better modeling results can be achieved. Moreover, given that the BF is a complex nonlinear system with numerous coupling variables, the data-driven canonical correlation analysis is employed to identify the most influential components from multitudinous factors that affect the MIQ indices to reduce the model dimension. Finally, experiments using industrial data and comparative studies have demonstrated that the obtained model produces a better modeling and estimating accuracy and stronger robustness than other modeling methods.« less

Quantitative Story Telling: Initial steps towards bridging perspectives and tools for a robust nexus assessment

NASA Astrophysics Data System (ADS)

Cabello, Violeta

2017-04-01

This communication will present the advancement of an innovative analytical framework for the analysis of Water-Energy-Food-Climate Nexus termed Quantitative Story Telling (QST). The methodology is currently under development within the H2020 project MAGIC - Moving Towards Adaptive Governance in Complexity: Informing Nexus Security (www.magic-nexus.eu). The key innovation of QST is that it bridges qualitative and quantitative analytical tools into an iterative research process in which each step is built and validated in interaction with stakeholders. The qualitative analysis focusses on the identification of the narratives behind the development of relevant WEFC-Nexus policies and innovations. The quantitative engine is the Multi-Scale Analysis of Societal and Ecosystem Metabolism (MuSIASEM), a resource accounting toolkit capable of integrating multiple analytical dimensions at different scales through relational analysis. Although QST may not be labelled a data-driven but a story-driven approach, I will argue that improving models per se may not lead to an improved understanding of WEF-Nexus problems unless we are capable of generating more robust narratives to frame them. The communication will cover an introduction to MAGIC project, the basic concepts of QST and a case study focussed on agricultural production in a semi-arid region in Southern Spain. Data requirements for this case study and the limitations to find, access or estimate them will be presented alongside a reflection on the relation between analytical scales and data availability.

Integrated structural control design of large space structures

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

Allen, J.J.; Lauffer, J.P.

1995-01-01

Active control of structures has been under intensive development for the last ten years. Reference 2 reviews much of the identification and control technology for structural control developed during this time. The technology was initially focused on space structure and weapon applications; however, recently the technology is also being directed toward applications in manufacturing and transportation. Much of this technology focused on multiple-input/multiple-output (MIMO) identification and control methodology because many of the applications require a coordinated control involving multiple disturbances and control objectives where multiple actuators and sensors are necessary for high performance. There have been many optimal robust controlmore » methods developed for the design of MIMO robust control laws; however, there appears to be a significant gap between the theoretical development and experimental evaluation of control and identification methods to address structural control applications. Many methods have been developed for MIMO identification and control of structures, such as the Eigensystem Realization Algorithm (ERA), Q-Markov Covariance Equivalent Realization (Q-Markov COVER) for identification; and, Linear Quadratic Gaussian (LQG), Frequency Weighted LQG and H-/ii-synthesis methods for control. Upon implementation, many of the identification and control methods have shown limitations such as the excitation of unmodelled dynamics and sensitivity to system parameter variations. As a result, research on methods which address these problems have been conducted.« less

A robust star identification algorithm with star shortlisting

NASA Astrophysics Data System (ADS)

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

2018-05-01

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

Rapid Identification of Microorganisms from Positive Blood Culture by MALDI-TOF MS After Short-Term Incubation on Solid Medium.

PubMed

Curtoni, Antonio; Cipriani, Raffaella; Marra, Elisa Simona; Barbui, Anna Maria; Cavallo, Rossana; Costa, Cristina

2017-01-01

Matrix-assisted laser-desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry (MS) is a useful tool for rapid identification of microorganisms. Unfortunately, its direct application to positive blood culture is still lacking standardized procedures. In this study, we evaluated an easy- and rapid-to-perform protocol for MALDI-TOF MS direct identification of microorganisms from positive blood culture after a short-term incubation on solid medium. This protocol was used to evaluate direct identification of microorganisms from 162 positive monomicrobial blood cultures; at different incubation times (3, 5, 24 h), MALDI-TOF MS assay was performed from the growing microorganism patina. Overall, MALDI-TOF MS concordance with conventional methods at species level was 60.5, 80.2, and 93.8% at 3, 5, and 24 h, respectively. Considering only bacteria, the identification performances at species level were 64.1, 85.0, and 94.1% at 3, 5, and 24 h, respectively. This protocol applied to a commercially available MS typing system may represent, a fast and powerful diagnostic tool for pathogen direct identification and for a promptly and pathogen-driven antimicrobial therapy in selected cases.

Robust nonlinear control of vectored thrust aircraft

NASA Technical Reports Server (NTRS)

Doyle, John C.; Murray, Richard; Morris, John

1993-01-01

An interdisciplinary program in robust control for nonlinear systems with applications to a variety of engineering problems is outlined. Major emphasis will be placed on flight control, with both experimental and analytical studies. This program builds on recent new results in control theory for stability, stabilization, robust stability, robust performance, synthesis, and model reduction in a unified framework using Linear Fractional Transformations (LFT's), Linear Matrix Inequalities (LMI's), and the structured singular value micron. Most of these new advances have been accomplished by the Caltech controls group independently or in collaboration with researchers in other institutions. These recent results offer a new and remarkably unified framework for all aspects of robust control, but what is particularly important for this program is that they also have important implications for system identification and control of nonlinear systems. This combines well with Caltech's expertise in nonlinear control theory, both in geometric methods and methods for systems with constraints and saturations.

Comparison between traditional strategies and classification technique (SIMCA) in the identification of old proteinaceous binders.

PubMed

Checa-Moreno, R; Manzano, E; Mirón, G; Capitan-Vallvey, L F

2008-05-15

In this paper, we performed a comparison between commonly used strategies amino acid ratios (Aa ratios), two-dimensional ratio plots (2D-Plot) and statistical correlation factor (SCF) and a classification technique, soft independent modelling of class analogy (SIMCA), to identify protein binders present in old artwork samples. To do this, we used a natural standard collection of proteinaceous binders prepared in our laboratory using old recipes and eleven samples coming from Cultural Heritage, such as mural and easel paintings, manuscripts and polychrome sculptures from the 15-18th centuries. Protein binder samples were hydrolyzed and their constitutive amino acids were determined as PITC-derivatives using HPLC-DAD. Amino acid profile data were used to perform the comparison between the four different strategies mentioned above. Traditional strategies can lead to ambiguous or non-conclusive results. With SIMCA, it is possible to provide a more robust and less subjective identification knowing the confidence level of identification. As a standard, we used proteinaceous albumin (whole egg, yolk and glair); casein (goat, cow and sheep) and collagen (mammalian and fish). The process results in a more robust understanding of proteinaceous binding media in old artworks that makes it possible to distinguish them according to their origin.

Highly efficient and robust molecular ruthenium catalysts for water oxidation.

PubMed

Duan, Lele; Araujo, Carlos Moyses; Ahlquist, Mårten S G; Sun, Licheng

2012-09-25

Water oxidation catalysts are essential components of light-driven water splitting systems, which could convert water to H(2) driven by solar radiation (H(2)O + hν → 1/2O(2) + H(2)). The oxidation of water (H(2)O → 1/2O(2) + 2H(+) + 2e(-)) provides protons and electrons for the production of dihydrogen (2H(+) + 2e(-) → H(2)), a clean-burning and high-capacity energy carrier. One of the obstacles now is the lack of effective and robust water oxidation catalysts. Aiming at developing robust molecular Ru-bda (H(2)bda = 2,2'-bipyridine-6,6'-dicarboxylic acid) water oxidation catalysts, we carried out density functional theory studies, correlated the robustness of catalysts against hydration with the highest occupied molecular orbital levels of a set of ligands, and successfully directed the synthesis of robust Ru-bda water oxidation catalysts. A series of mononuclear ruthenium complexes [Ru(bda)L(2)] (L = pyridazine, pyrimidine, and phthalazine) were subsequently synthesized and shown to effectively catalyze Ce(IV)-driven [Ce(IV) = Ce(NH(4))(2)(NO(3))(6)] water oxidation with high oxygen production rates up to 286 s(-1) and high turnover numbers up to 55,400.

Robustness analysis of superpixel algorithms to image blur, additive Gaussian noise, and impulse noise

NASA Astrophysics Data System (ADS)

Brekhna, Brekhna; Mahmood, Arif; Zhou, Yuanfeng; Zhang, Caiming

2017-11-01

Superpixels have gradually become popular in computer vision and image processing applications. However, no comprehensive study has been performed to evaluate the robustness of superpixel algorithms in regard to common forms of noise in natural images. We evaluated the robustness of 11 recently proposed algorithms to different types of noise. The images were corrupted with various degrees of Gaussian blur, additive white Gaussian noise, and impulse noise that either made the object boundaries weak or added extra information to it. We performed a robustness analysis of simple linear iterative clustering (SLIC), Voronoi Cells (VCells), flooding-based superpixel generation (FCCS), bilateral geodesic distance (Bilateral-G), superpixel via geodesic distance (SSS-G), manifold SLIC (M-SLIC), Turbopixels, superpixels extracted via energy-driven sampling (SEEDS), lazy random walk (LRW), real-time superpixel segmentation by DBSCAN clustering, and video supervoxels using partially absorbing random walks (PARW) algorithms. The evaluation process was carried out both qualitatively and quantitatively. For quantitative performance comparison, we used achievable segmentation accuracy (ASA), compactness, under-segmentation error (USE), and boundary recall (BR) on the Berkeley image database. The results demonstrated that all algorithms suffered performance degradation due to noise. For Gaussian blur, Bilateral-G exhibited optimal results for ASA and USE measures, SLIC yielded optimal compactness, whereas FCCS and DBSCAN remained optimal for BR. For the case of additive Gaussian and impulse noises, FCCS exhibited optimal results for ASA, USE, and BR, whereas Bilateral-G remained a close competitor in ASA and USE for Gaussian noise only. Additionally, Turbopixel demonstrated optimal performance for compactness for both types of noise. Thus, no single algorithm was able to yield optimal results for all three types of noise across all performance measures. Conclusively, to solve real-world problems effectively, more robust superpixel algorithms must be developed.

Note: Model-based identification method of a cable-driven wearable device for arm rehabilitation

NASA Astrophysics Data System (ADS)

Cui, Xiang; Chen, Weihai; Zhang, Jianbin; Wang, Jianhua

2015-09-01

Cable-driven exoskeletons have used active cables to actuate the system and are worn on subjects to provide motion assistance. However, this kind of wearable devices usually contains uncertain kinematic parameters. In this paper, a model-based identification method has been proposed for a cable-driven arm exoskeleton to estimate its uncertainties. The identification method is based on the linearized error model derived from the kinematics of the exoskeleton. Experiment has been conducted to demonstrate the feasibility of the proposed model-based method in practical application.

DARPA super resolution vision system (SRVS) robust turbulence data collection and analysis

NASA Astrophysics Data System (ADS)

Espinola, Richard L.; Leonard, Kevin R.; Thompson, Roger; Tofsted, David; D'Arcy, Sean

2014-05-01

Atmospheric turbulence degrades the range performance of military imaging systems, specifically those intended for long range, ground-to-ground target identification. The recent Defense Advanced Research Projects Agency (DARPA) Super Resolution Vision System (SRVS) program developed novel post-processing system components to mitigate turbulence effects on visible and infrared sensor systems. As part of the program, the US Army RDECOM CERDEC NVESD and the US Army Research Laboratory Computational & Information Sciences Directorate (CISD) collaborated on a field collection and atmospheric characterization of a two-handed weapon identification dataset through a diurnal cycle for a variety of ranges and sensor systems. The robust dataset is useful in developing new models and simulations of turbulence, as well for providing as a standard baseline for comparison of sensor systems in the presence of turbulence degradation and mitigation. In this paper, we describe the field collection and atmospheric characterization and present the robust dataset to the defense, sensing, and security community. In addition, we present an expanded model validation of turbulence degradation using the field collected video sequences.

Data for Renewable Energy Planning, Policy, and Investment

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

Cox, Sarah L

Reliable, robust, and validated data are critical for informed planning, policy development, and investment in the clean energy sector. The Renewable Energy (RE) Explorer was developed to support data-driven renewable energy analysis that can inform key renewable energy decisions globally. This document presents the types of geospatial and other data at the core of renewable energy analysis and decision making. Individual data sets used to inform decisions vary in relation to spatial and temporal resolution, quality, and overall usefulness. From Data to Decisions, a complementary geospatial data and analysis decision guide, provides an in-depth view of these and other considerationsmore » to enable data-driven planning, policymaking, and investment. Data support a wide variety of renewable energy analyses and decisions, including technical and economic potential assessment, renewable energy zone analysis, grid integration, risk and resiliency identification, electrification, and distributed solar photovoltaic potential. This fact sheet provides information on the types of data that are important for renewable energy decision making using the RE Data Explorer or similar types of geospatial analysis tools.« less

Statistical analysis of RHIC beam position monitors performance

NASA Astrophysics Data System (ADS)

Calaga, R.; Tomás, R.

2004-04-01

A detailed statistical analysis of beam position monitors (BPM) performance at RHIC is a critical factor in improving regular operations and future runs. Robust identification of malfunctioning BPMs plays an important role in any orbit or turn-by-turn analysis. Singular value decomposition and Fourier transform methods, which have evolved as powerful numerical techniques in signal processing, will aid in such identification from BPM data. This is the first attempt at RHIC to use a large set of data to statistically enhance the capability of these two techniques and determine BPM performance. A comparison from run 2003 data shows striking agreement between the two methods and hence can be used to improve BPM functioning at RHIC and possibly other accelerators.

Control-based method to identify underlying delays of a nonlinear dynamical system.

PubMed

Yu, Dongchuan; Frasca, Mattia; Liu, Fang

2008-10-01

We suggest several stationary state control-based delay identification methods which do not require any structural information about the controlled systems and are applicable to systems described by delayed ordinary differential equations. This proposed technique includes three steps: (i) driving a system to a steady state; (ii) perturbing the control signal for shifting the steady state; and (iii) identifying all delays by detecting the time that the system is abruptly drawn out of stationarity. Some aspects especially important for applications are discussed as well, including interaction delay identification, stationary state convergence speed, performance comparison, and the influence of noise on delay identification. Several examples are presented to illustrate the reliability and robustness of all delay identification methods suggested.

Racial bias in implicit danger associations generalizes to older male targets.

PubMed

Lundberg, Gustav J W; Neel, Rebecca; Lassetter, Bethany; Todd, Andrew R

2018-01-01

Across two experiments, we examined whether implicit stereotypes linking younger (~28-year-old) Black versus White men with violence and criminality extend to older (~68-year-old) Black versus White men. In Experiment 1, participants completed a sequential priming task wherein they categorized objects as guns or tools after seeing briefly-presented facial images of men who varied in age (younger versus older) and race (Black versus White). In Experiment 2, we used different face primes of younger and older Black and White men, and participants categorized words as 'threatening' or 'safe.' Results consistently revealed robust racial biases in object and word identification: Dangerous objects and words were identified more easily (faster response times, lower error rates), and non-dangerous objects and words were identified less easily, after seeing Black face primes than after seeing White face primes. Process dissociation procedure analyses, which aim to isolate the unique contributions of automatic and controlled processes to task performance, further indicated that these effects were driven entirely by racial biases in automatic processing. In neither experiment did prime age moderate racial bias, suggesting that the implicit danger associations commonly evoked by younger Black versus White men appear to generalize to older Black versus White men.

Efficient exploration of pan-cancer networks by generalized covariance selection and interactive web content

PubMed Central

Kling, Teresia; Johansson, Patrik; Sanchez, José; Marinescu, Voichita D.; Jörnsten, Rebecka; Nelander, Sven

2015-01-01

Statistical network modeling techniques are increasingly important tools to analyze cancer genomics data. However, current tools and resources are not designed to work across multiple diagnoses and technical platforms, thus limiting their applicability to comprehensive pan-cancer datasets such as The Cancer Genome Atlas (TCGA). To address this, we describe a new data driven modeling method, based on generalized Sparse Inverse Covariance Selection (SICS). The method integrates genetic, epigenetic and transcriptional data from multiple cancers, to define links that are present in multiple cancers, a subset of cancers, or a single cancer. It is shown to be statistically robust and effective at detecting direct pathway links in data from TCGA. To facilitate interpretation of the results, we introduce a publicly accessible tool (cancerlandscapes.org), in which the derived networks are explored as interactive web content, linked to several pathway and pharmacological databases. To evaluate the performance of the method, we constructed a model for eight TCGA cancers, using data from 3900 patients. The model rediscovered known mechanisms and contained interesting predictions. Possible applications include prediction of regulatory relationships, comparison of network modules across multiple forms of cancer and identification of drug targets. PMID:25953855

Structural damage detection based on stochastic subspace identification and statistical pattern recognition: I. Theory

NASA Astrophysics Data System (ADS)

Ren, W. X.; Lin, Y. Q.; Fang, S. E.

2011-11-01

One of the key issues in vibration-based structural health monitoring is to extract the damage-sensitive but environment-insensitive features from sampled dynamic response measurements and to carry out the statistical analysis of these features for structural damage detection. A new damage feature is proposed in this paper by using the system matrices of the forward innovation model based on the covariance-driven stochastic subspace identification of a vibrating system. To overcome the variations of the system matrices, a non-singularity transposition matrix is introduced so that the system matrices are normalized to their standard forms. For reducing the effects of modeling errors, noise and environmental variations on measured structural responses, a statistical pattern recognition paradigm is incorporated into the proposed method. The Mahalanobis and Euclidean distance decision functions of the damage feature vector are adopted by defining a statistics-based damage index. The proposed structural damage detection method is verified against one numerical signal and two numerical beams. It is demonstrated that the proposed statistics-based damage index is sensitive to damage and shows some robustness to the noise and false estimation of the system ranks. The method is capable of locating damage of the beam structures under different types of excitations. The robustness of the proposed damage detection method to the variations in environmental temperature is further validated in a companion paper by a reinforced concrete beam tested in the laboratory and a full-scale arch bridge tested in the field.

Constraint Network Analysis (CNA): a Python software package for efficiently linking biomacromolecular structure, flexibility, (thermo-)stability, and function.

PubMed

Pfleger, Christopher; Rathi, Prakash Chandra; Klein, Doris L; Radestock, Sebastian; Gohlke, Holger

2013-04-22

For deriving maximal advantage from information on biomacromolecular flexibility and rigidity, results from rigidity analyses must be linked to biologically relevant characteristics of a structure. Here, we describe the Python-based software package Constraint Network Analysis (CNA) developed for this task. CNA functions as a front- and backend to the graph-based rigidity analysis software FIRST. CNA goes beyond the mere identification of flexible and rigid regions in a biomacromolecule in that it (I) provides a refined modeling of thermal unfolding simulations that also considers the temperature-dependence of hydrophobic tethers, (II) allows performing rigidity analyses on ensembles of network topologies, either generated from structural ensembles or by using the concept of fuzzy noncovalent constraints, and (III) computes a set of global and local indices for quantifying biomacromolecular stability. This leads to more robust results from rigidity analyses and extends the application domain of rigidity analyses in that phase transition points ("melting points") and unfolding nuclei ("structural weak spots") are determined automatically. Furthermore, CNA robustly handles small-molecule ligands in general. Such advancements are important for applying rigidity analysis to data-driven protein engineering and for estimating the influence of ligand molecules on biomacromolecular stability. CNA maintains the efficiency of FIRST such that the analysis of a single protein structure takes a few seconds for systems of several hundred residues on a single core. These features make CNA an interesting tool for linking biomacromolecular structure, flexibility, (thermo-)stability, and function. CNA is available from http://cpclab.uni-duesseldorf.de/software for nonprofit organizations.

From the patient to the clinical mycology laboratory: how can we optimise microscopy and culture methods for mould identification?

PubMed

Vyzantiadis, Timoleon-Achilleas A; Johnson, Elizabeth M; Kibbler, Christopher C

2012-06-01

The identification of fungi relies mainly on morphological criteria. However, there is a need for robust and definitive phenotypic identification procedures in order to evaluate continuously evolving molecular methods. For the future, there is an emerging consensus that a combined (phenotypic and molecular) approach is more powerful for fungal identification, especially for moulds. Most of the procedures used for phenotypic identification are based on experience rather than comparative studies of effectiveness or performance and there is a need for standardisation among mycology laboratories. This review summarises and evaluates the evidence for the major existing phenotypic identification procedures for the predominant causes of opportunistic mould infection. We have concentrated mainly on Aspergillus, Fusarium and mucoraceous mould species, as these are the most important clinically and the ones for which there are the most molecular taxonomic data.

Application of Bounded Linear Stability Analysis Method for Metrics-Driven Adaptive Control

NASA Technical Reports Server (NTRS)

Bakhtiari-Nejad, Maryam; Nguyen, Nhan T.; Krishnakumar, Kalmanje

2009-01-01

This paper presents the application of Bounded Linear Stability Analysis (BLSA) method for metrics-driven adaptive control. The bounded linear stability analysis method is used for analyzing stability of adaptive control models, without linearizing the adaptive laws. Metrics-driven adaptive control introduces a notion that adaptation should be driven by some stability metrics to achieve robustness. By the application of bounded linear stability analysis method the adaptive gain is adjusted during the adaptation in order to meet certain phase margin requirements. Analysis of metrics-driven adaptive control is evaluated for a second order system that represents a pitch attitude control of a generic transport aircraft. The analysis shows that the system with the metrics-conforming variable adaptive gain becomes more robust to unmodeled dynamics or time delay. The effect of analysis time-window for BLSA is also evaluated in order to meet the stability margin criteria.

Development of a Robust star identification technique for use in attitude determination of the ACE spacecraft

NASA Technical Reports Server (NTRS)

Woodard, Mark; Rohrbaugh, Dave

1995-01-01

The Advanced Composition Explorer (ACE) spacecraft is designed to fly in a spin-stabilized attitude. The spacecraft will carry two attitude sensors - a digital fine Sun sensor and a charge coupled device (CCD) star tracker - to allow ground-based determination of the spacecraft attitude and spin rate. Part of the processing that must be performed on the CCD star tracker data is the star identification. Star data received from the spacecraft must be matched with star information in the SKYMAP catalog to determine exactly which stars the sensor is tracking. This information, along with the Sun vector measured by the Sun sensor, is used to determine the spacecraft attitude. Several existing star identification (star ID) systems were examined to determine whether they could be modified for use on the ACE mission. Star ID systems which exist for three-axis stabilized spacecraft tend to be complex in nature and many require fairly good knowledge of the spacecraft attitude, making their use for ACE excessive. Star ID systems used for spinners carrying traditional slit star sensors would have to be modified to model the CCD star tracker. The ACE star ID algorithm must also be robust, in that it will be able to correctly identify stars even though the attitude is not known to a high degree of accuracy, and must be very efficient to allow real-time star identification. The paper presents the star ID algorithm that was developed for ACE. Results from prototype testing are also presented to demonstrate the efficiency, accuracy, and robustness of the algorithm.

Shape detection of Gaborized outline versions of everyday objects

PubMed Central

Sassi, Michaël; Machilsen, Bart; Wagemans, Johan

2012-01-01

We previously tested the identifiability of six versions of Gaborized outlines of everyday objects, differing in the orientations assigned to elements inside and outside the outline. We found significant differences in identifiability between the versions, and related a number of stimulus metrics to identifiability [Sassi, M., Vancleef, K., Machilsen, B., Panis, S., & Wagemans, J. (2010). Identification of everyday objects on the basis of Gaborized outline versions. i-Perception, 1(3), 121–142]. In this study, after retesting the identifiability of new variants of three of the stimulus versions, we tested their robustness to local orientation jitter in a detection experiment. In general, our results replicated the key findings from the previous study, and allowed us to substantiate our earlier interpretations of the effects of our stimulus metrics and of the performance differences between the different stimulus versions. The results of the detection task revealed a different ranking order of stimulus versions than the identification task. By examining the parallels and differences between the effects of our stimulus metrics in the two tasks, we found evidence for a trade-off between shape detectability and identifiability. The generally simple and smooth shapes that yield the strongest contour integration and most robust detectability tend to lack the distinguishing features necessary for clear-cut identification. Conversely, contours that do contain such identifying features tend to be inherently more complex and, therefore, yield weaker integration and less robust detectability. PMID:23483752

Control Oriented Modeling and Validation of Aeroservoelastic Systems

NASA Technical Reports Server (NTRS)

Crowder, Marianne; deCallafon, Raymond (Principal Investigator)

2002-01-01

Lightweight aircraft design emphasizes the reduction of structural weight to maximize aircraft efficiency and agility at the cost of increasing the likelihood of structural dynamic instabilities. To ensure flight safety, extensive flight testing and active structural servo control strategies are required to explore and expand the boundary of the flight envelope. Aeroservoelastic (ASE) models can provide online flight monitoring of dynamic instabilities to reduce flight time testing and increase flight safety. The success of ASE models is determined by the ability to take into account varying flight conditions and the possibility to perform flight monitoring under the presence of active structural servo control strategies. In this continued study, these aspects are addressed by developing specific methodologies and algorithms for control relevant robust identification and model validation of aeroservoelastic structures. The closed-loop model robust identification and model validation are based on a fractional model approach where the model uncertainties are characterized in a closed-loop relevant way.

Adaptive integral robust control and application to electromechanical servo systems.

PubMed

Deng, Wenxiang; Yao, Jianyong

2017-03-01

This paper proposes a continuous adaptive integral robust control with robust integral of the sign of the error (RISE) feedback for a class of uncertain nonlinear systems, in which the RISE feedback gain is adapted online to ensure the robustness against disturbances without the prior bound knowledge of the additive disturbances. In addition, an adaptive compensation integrated with the proposed adaptive RISE feedback term is also constructed to further reduce design conservatism when the system also exists parametric uncertainties. Lyapunov analysis reveals the proposed controllers could guarantee the tracking errors are asymptotically converging to zero with continuous control efforts. To illustrate the high performance nature of the developed controllers, numerical simulations are provided. At the end, an application case of an actual electromechanical servo system driven by motor is also studied, with some specific design consideration, and comparative experimental results are obtained to verify the effectiveness of the proposed controllers. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

A Robust Control of Two-Wheeled Mobile Manipulator with Underactuated Joint by Nonlinear Backstepping Method

NASA Astrophysics Data System (ADS)

Acar, Cihan; Murakami, Toshiyuki

In this paper, a robust control of two-wheeled mobile manipulator with underactuated joint is considered. Two-wheeled mobile manipulators are dynamically balanced two-wheeled driven systems that do not have any caster or extra wheels to stabilize their body. Two-wheeled mobile manipulators mainly have an important feature that makes them more flexible and agile than the statically stable mobile manipulators. However, two-wheeled mobile manipulator is an underactuated system due to its two-wheeled structure. Therefore, it is required to stabilize the underactuated passive body and, at the same time, control the position of the center of gravity (CoG) of the manipulator in this system. To realize this, nonlinear backstepping based control method with virtual double inverted pendulum model is proposed in this paper. Backstepping is used with sliding mode to increase the robustness of the system against modeling errors and other perturbations. Then robust acceleration control is also achieved by utilizing disturbance observer. Performance of the proposed method is evaluated by several experiments.

Automated identification of the lung contours in positron emission tomography

NASA Astrophysics Data System (ADS)

Nery, F.; Silvestre Silva, J.; Ferreira, N. C.; Caramelo, F. J.; Faustino, R.

2013-03-01

Positron Emission Tomography (PET) is a nuclear medicine imaging technique that permits to analyze, in three dimensions, the physiological processes in vivo. One of the areas where PET has demonstrated its advantages is in the staging of lung cancer, where it offers better sensitivity and specificity than other techniques such as CT. On the other hand, accurate segmentation, an important procedure for Computer Aided Diagnostics (CAD) and automated image analysis, is a challenging task given the low spatial resolution and the high noise that are intrinsic characteristics of PET images. This work presents an algorithm for the segmentation of lungs in PET images, to be used in CAD and group analysis in a large patient database. The lung boundaries are automatically extracted from a PET volume through the application of a marker-driven watershed segmentation procedure which is robust to the noise. In order to test the effectiveness of the proposed method, we compared the segmentation results in several slices using our approach with the results obtained from manual delineation. The manual delineation was performed by nuclear medicine physicians that used a software routine that we developed specifically for this task. To quantify the similarity between the contours obtained from the two methods, we used figures of merit based on region and also on contour definitions. Results show that the performance of the algorithm was similar to the performance of human physicians. Additionally, we found that the algorithm-physician agreement is similar (statistically significant) to the inter-physician agreement.

High Performance, Robust Control of Flexible Space Structures: MSFC Center Director's Discretionary Fund

NASA Technical Reports Server (NTRS)

Whorton, M. S.

1998-01-01

Many spacecraft systems have ambitious objectives that place stringent requirements on control systems. Achievable performance is often limited because of difficulty of obtaining accurate models for flexible space structures. To achieve sufficiently high performance to accomplish mission objectives may require the ability to refine the control design model based on closed-loop test data and tune the controller based on the refined model. A control system design procedure is developed based on mixed H2/H(infinity) optimization to synthesize a set of controllers explicitly trading between nominal performance and robust stability. A homotopy algorithm is presented which generates a trajectory of gains that may be implemented to determine maximum achievable performance for a given model error bound. Examples show that a better balance between robustness and performance is obtained using the mixed H2/H(infinity) design method than either H2 or mu-synthesis control design. A second contribution is a new procedure for closed-loop system identification which refines parameters of a control design model in a canonical realization. Examples demonstrate convergence of the parameter estimation and improved performance realized by using the refined model for controller redesign. These developments result in an effective mechanism for achieving high-performance control of flexible space structures.

Synchrony and entrainment properties of robust circadian oscillators

PubMed Central

Bagheri, Neda; Taylor, Stephanie R.; Meeker, Kirsten; Petzold, Linda R.; Doyle, Francis J.

2008-01-01

Systems theoretic tools (i.e. mathematical modelling, control, and feedback design) advance the understanding of robust performance in complex biological networks. We highlight phase entrainment as a key performance measure used to investigate dynamics of a single deterministic circadian oscillator for the purpose of generating insight into the behaviour of a population of (synchronized) oscillators. More specifically, the analysis of phase characteristics may facilitate the identification of appropriate coupling mechanisms for the ensemble of noisy (stochastic) circadian clocks. Phase also serves as a critical control objective to correct mismatch between the biological clock and its environment. Thus, we introduce methods of investigating synchrony and entrainment in both stochastic and deterministic frameworks, and as a property of a single oscillator or population of coupled oscillators. PMID:18426774

Robust evaluation of time series classification algorithms for structural health monitoring

NASA Astrophysics Data System (ADS)

Harvey, Dustin Y.; Worden, Keith; Todd, Michael D.

2014-03-01

Structural health monitoring (SHM) systems provide real-time damage and performance information for civil, aerospace, and mechanical infrastructure through analysis of structural response measurements. The supervised learning methodology for data-driven SHM involves computation of low-dimensional, damage-sensitive features from raw measurement data that are then used in conjunction with machine learning algorithms to detect, classify, and quantify damage states. However, these systems often suffer from performance degradation in real-world applications due to varying operational and environmental conditions. Probabilistic approaches to robust SHM system design suffer from incomplete knowledge of all conditions a system will experience over its lifetime. Info-gap decision theory enables nonprobabilistic evaluation of the robustness of competing models and systems in a variety of decision making applications. Previous work employed info-gap models to handle feature uncertainty when selecting various components of a supervised learning system, namely features from a pre-selected family and classifiers. In this work, the info-gap framework is extended to robust feature design and classifier selection for general time series classification through an efficient, interval arithmetic implementation of an info-gap data model. Experimental results are presented for a damage type classification problem on a ball bearing in a rotating machine. The info-gap framework in conjunction with an evolutionary feature design system allows for fully automated design of a time series classifier to meet performance requirements under maximum allowable uncertainty.

Robust Kriged Kalman Filtering

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

Baingana, Brian; Dall'Anese, Emiliano; Mateos, Gonzalo

2015-11-11

Although the kriged Kalman filter (KKF) has well-documented merits for prediction of spatial-temporal processes, its performance degrades in the presence of outliers due to anomalous events, or measurement equipment failures. This paper proposes a robust KKF model that explicitly accounts for presence of measurement outliers. Exploiting outlier sparsity, a novel l1-regularized estimator that jointly predicts the spatial-temporal process at unmonitored locations, while identifying measurement outliers is put forth. Numerical tests are conducted on a synthetic Internet protocol (IP) network, and real transformer load data. Test results corroborate the effectiveness of the novel estimator in joint spatial prediction and outlier identification.

Robust PLS approach for KPI-related prediction and diagnosis against outliers and missing data

NASA Astrophysics Data System (ADS)

Yin, Shen; Wang, Guang; Yang, Xu

2014-07-01

In practical industrial applications, the key performance indicator (KPI)-related prediction and diagnosis are quite important for the product quality and economic benefits. To meet these requirements, many advanced prediction and monitoring approaches have been developed which can be classified into model-based or data-driven techniques. Among these approaches, partial least squares (PLS) is one of the most popular data-driven methods due to its simplicity and easy implementation in large-scale industrial process. As PLS is totally based on the measured process data, the characteristics of the process data are critical for the success of PLS. Outliers and missing values are two common characteristics of the measured data which can severely affect the effectiveness of PLS. To ensure the applicability of PLS in practical industrial applications, this paper introduces a robust version of PLS to deal with outliers and missing values, simultaneously. The effectiveness of the proposed method is finally demonstrated by the application results of the KPI-related prediction and diagnosis on an industrial benchmark of Tennessee Eastman process.

Experiments with a pressure-driven Stirling refrigerator with flexible chambers

NASA Astrophysics Data System (ADS)

McFarlane, Patrick; Suire, Jonathan; Sen, Mihir; Semperlotti, Fabio

2014-06-01

We report on the design and experimental testing of a Stirling refrigerator that uses air as the working fluid, and where the conventional piston-cylinder assemblies are replaced by pressure-driven flexible chambers. The two chambers are periodically compressed by pneumatic actuators resulting in airflow through the regenerator and in a net temperature difference between the chambers. An experimental setup is used to investigate the performance of the refrigerator under different operating conditions with particular attention to actuation frequencies, driving pressure differences, and phase angles between the two inputs. The time constant of the temperature difference between the two chambers is determined, and the temperature difference is measured as a function of the system parameters. The results of several tests conducted under different operating conditions show that the refrigerating effect is very robust and allows good performance even for modulated inputs. The frequency response is radically different from that of a traditional motion-driven device. This work suggests that mechanical to thermal energy conversion devices based on this principle can be successfully powered by human motion.

Heterogeneous Bimetallic Phosphide/Sulfide Nanocomposite for Efficient Solar-Energy-Driven Overall Water Splitting.

PubMed

Xin, Yanmei; Kan, Xiang; Gan, Li-Yong; Zhang, Zhonghai

2017-10-24

Solar-driven overall water splitting is highly desirable for hydrogen generation with sustainable energy sources, which need efficient, earth-abundant, robust, and bifunctional electrocatalysts for both oxygen evolution reaction (OER) and hydrogen evolution reaction (HER). Herein, we propose a heterogeneous bimetallic phosphide/sulfide nanocomposite electrocatalyst of NiFeSP on nickel foam (NiFeSP/NF), which shows superior electrocatalytic activity of low overpotentials of 91 mV at -10 mA cm -2 for HER and of 240 mV at 50 mA cm -2 for OER in 1 M KOH solution. In addition, the NiFeSP/NF presents excellent overall water splitting performance with a cell voltage as low as 1.58 V at a current density of 10 mA cm -2 . Combining with a photovoltaic device of a Si solar cell or integrating into photoelectrochemical (PEC) systems, the bifunctional NiFeSP/NF electrocatalyst implements unassisted solar-driven water splitting with a solar-to-hydrogen conversion efficiency of ∼9.2% and significantly enhanced PEC performance, respectively.

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.

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

Baker, Kyri; Dall'Anese, Emiliano; Summers, Tyler

This paper outlines a data-driven, distributionally robust approach to solve chance-constrained AC optimal power flow problems in distribution networks. Uncertain forecasts for loads and power generated by photovoltaic (PV) systems are considered, with the goal of minimizing PV curtailment while meeting power flow and voltage regulation constraints. A data- driven approach is utilized to develop a distributionally robust conservative convex approximation of the chance-constraints; particularly, the mean and covariance matrix of the forecast errors are updated online, and leveraged to enforce voltage regulation with predetermined probability via Chebyshev-based bounds. By combining an accurate linear approximation of the AC power flowmore » equations with the distributionally robust chance constraint reformulation, the resulting optimization problem becomes convex and computationally tractable.« less

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 (...

Autonomous frequency domain identification: Theory and experiment

NASA Technical Reports Server (NTRS)

Yam, Yeung; Bayard, D. S.; Hadaegh, F. Y.; Mettler, E.; Milman, M. H.; Scheid, R. E.

1989-01-01

The analysis, design, and on-orbit tuning of robust controllers require more information about the plant than simply a nominal estimate of the plant transfer function. Information is also required concerning the uncertainty in the nominal estimate, or more generally, the identification of a model set within which the true plant is known to lie. The identification methodology that was developed and experimentally demonstrated makes use of a simple but useful characterization of the model uncertainty based on the output error. This is a characterization of the additive uncertainty in the plant model, which has found considerable use in many robust control analysis and synthesis techniques. The identification process is initiated by a stochastic input u which is applied to the plant p giving rise to the output. Spectral estimation (h = P sub uy/P sub uu) is used as an estimate of p and the model order is estimated using the produce moment matrix (PMM) method. A parametric model unit direction vector p is then determined by curve fitting the spectral estimate to a rational transfer function. The additive uncertainty delta sub m = p - unit direction vector p is then estimated by the cross spectral estimate delta = P sub ue/P sub uu where e = y - unit direction vectory y is the output error, and unit direction vector y = unit direction vector pu is the computed output of the parametric model subjected to the actual input u. The experimental results demonstrate the curve fitting algorithm produces the reduced-order plant model which minimizes the additive uncertainty. The nominal transfer function estimate unit direction vector p and the estimate delta of the additive uncertainty delta sub m are subsequently available to be used for optimization of robust controller performance and stability.

Robust identification of transcriptional regulatory networks using a Gibbs sampler on outlier sum statistic.

PubMed

Gu, Jinghua; Xuan, Jianhua; Riggins, Rebecca B; Chen, Li; Wang, Yue; Clarke, Robert

2012-08-01

Identification of transcriptional regulatory networks (TRNs) is of significant importance in computational biology for cancer research, providing a critical building block to unravel disease pathways. However, existing methods for TRN identification suffer from the inclusion of excessive 'noise' in microarray data and false-positives in binding data, especially when applied to human tumor-derived cell line studies. More robust methods that can counteract the imperfection of data sources are therefore needed for reliable identification of TRNs in this context. In this article, we propose to establish a link between the quality of one target gene to represent its regulator and the uncertainty of its expression to represent other target genes. Specifically, an outlier sum statistic was used to measure the aggregated evidence for regulation events between target genes and their corresponding transcription factors. A Gibbs sampling method was then developed to estimate the marginal distribution of the outlier sum statistic, hence, to uncover underlying regulatory relationships. To evaluate the effectiveness of our proposed method, we compared its performance with that of an existing sampling-based method using both simulation data and yeast cell cycle data. The experimental results show that our method consistently outperforms the competing method in different settings of signal-to-noise ratio and network topology, indicating its robustness for biological applications. Finally, we applied our method to breast cancer cell line data and demonstrated its ability to extract biologically meaningful regulatory modules related to estrogen signaling and action in breast cancer. The Gibbs sampler MATLAB package is freely available at http://www.cbil.ece.vt.edu/software.htm. xuan@vt.edu Supplementary data are available at Bioinformatics online.

Robust identification of transcriptional regulatory networks using a Gibbs sampler on outlier sum statistic

PubMed Central

Gu, Jinghua; Xuan, Jianhua; Riggins, Rebecca B.; Chen, Li; Wang, Yue; Clarke, Robert

2012-01-01

Motivation: Identification of transcriptional regulatory networks (TRNs) is of significant importance in computational biology for cancer research, providing a critical building block to unravel disease pathways. However, existing methods for TRN identification suffer from the inclusion of excessive ‘noise’ in microarray data and false-positives in binding data, especially when applied to human tumor-derived cell line studies. More robust methods that can counteract the imperfection of data sources are therefore needed for reliable identification of TRNs in this context. Results: In this article, we propose to establish a link between the quality of one target gene to represent its regulator and the uncertainty of its expression to represent other target genes. Specifically, an outlier sum statistic was used to measure the aggregated evidence for regulation events between target genes and their corresponding transcription factors. A Gibbs sampling method was then developed to estimate the marginal distribution of the outlier sum statistic, hence, to uncover underlying regulatory relationships. To evaluate the effectiveness of our proposed method, we compared its performance with that of an existing sampling-based method using both simulation data and yeast cell cycle data. The experimental results show that our method consistently outperforms the competing method in different settings of signal-to-noise ratio and network topology, indicating its robustness for biological applications. Finally, we applied our method to breast cancer cell line data and demonstrated its ability to extract biologically meaningful regulatory modules related to estrogen signaling and action in breast cancer. Availability and implementation: The Gibbs sampler MATLAB package is freely available at http://www.cbil.ece.vt.edu/software.htm. Contact: xuan@vt.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:22595208

Highly efficient and robust molecular ruthenium catalysts for water oxidation

PubMed Central

Duan, Lele; Araujo, Carlos Moyses; Ahlquist, Mårten S.G.; Sun, Licheng

2012-01-01

Water oxidation catalysts are essential components of light-driven water splitting systems, which could convert water to H2 driven by solar radiation (H2O + hν → 1/2O2 + H2). The oxidation of water (H2O → 1/2O2 + 2H+ + 2e-) provides protons and electrons for the production of dihydrogen (2H+ + 2e- → H2), a clean-burning and high-capacity energy carrier. One of the obstacles now is the lack of effective and robust water oxidation catalysts. Aiming at developing robust molecular Ru-bda (H2bda = 2,2′-bipyridine-6,6′-dicarboxylic acid) water oxidation catalysts, we carried out density functional theory studies, correlated the robustness of catalysts against hydration with the highest occupied molecular orbital levels of a set of ligands, and successfully directed the synthesis of robust Ru-bda water oxidation catalysts. A series of mononuclear ruthenium complexes [Ru(bda)L2] (L = pyridazine, pyrimidine, and phthalazine) were subsequently synthesized and shown to effectively catalyze CeIV-driven [CeIV = Ce(NH4)2(NO3)6] water oxidation with high oxygen production rates up to 286 s-1 and high turnover numbers up to 55,400. PMID:22753518

Olfactory Performance Is Predicted by Individual Sex-Atypicality, but Not Sexual Orientation

PubMed Central

Nováková, Lenka; Varella Valentová, Jaroslava; Havlíček, Jan

2013-01-01

Many previous studies have reported robust sex differences in olfactory perception. However, both men and women can be expected to vary in the degree to which they exhibit olfactory performance considered typical of their own or the opposite sex. Sex-atypicality is often described in terms of childhood gender nonconformity, which, however, is not a perfect correlate of non-heterosexual orientation. Here we explored intrasexual variability in psychophysical olfactory performance in a sample of 156 individuals (83 non-heterosexual) and found the lowest odor identification scores in heterosexual men. However, when childhood gender nonconformity was entered in the model along with sexual orientation, better odor identification scores were exhibited by gender-nonconforming men, and greater olfactory sensitivity by gender-conforming women, irrespective of their sexual orientation. Thus, sex-atypicality, but not sexual orientation predicts olfactory performance, and we propose that this might not be limited to olfaction, but represent a more general phenomenon. PMID:24244657

Olfactory performance is predicted by individual sex-atypicality, but not sexual orientation.

PubMed

Nováková, Lenka; Varella Valentová, Jaroslava; Havlíček, Jan

2013-01-01

Many previous studies have reported robust sex differences in olfactory perception. However, both men and women can be expected to vary in the degree to which they exhibit olfactory performance considered typical of their own or the opposite sex. Sex-atypicality is often described in terms of childhood gender nonconformity, which, however, is not a perfect correlate of non-heterosexual orientation. Here we explored intrasexual variability in psychophysical olfactory performance in a sample of 156 individuals (83 non-heterosexual) and found the lowest odor identification scores in heterosexual men. However, when childhood gender nonconformity was entered in the model along with sexual orientation, better odor identification scores were exhibited by gender-nonconforming men, and greater olfactory sensitivity by gender-conforming women, irrespective of their sexual orientation. Thus, sex-atypicality, but not sexual orientation predicts olfactory performance, and we propose that this might not be limited to olfaction, but represent a more general phenomenon.

Probing the prostate tumour microenvironment II: Impact of hypoxia on a cell model of prostate cancer progression

PubMed Central

Tonry, Claire; Armstrong, John; Pennington, Stephen

2017-01-01

Approximately one in six men are diagnosed with Prostate Cancer every year in the Western world. Although it can be well managed and non-life threatening in the early stages, over time many patients cease to respond to treatment and develop castrate resistant prostate cancer (CRPC). CRPC represents a clinically challenging and lethal form of prostate cancer. Progression of CRPC is, in part, driven by the ability of cancer cells to alter their metabolic profile during the course of tumourgenesis and metastasis so that they can survive in oxygen and nutrient-poor environments and even withstand treatment. This work was carried out as a continuation of a study aimed towards gaining greater mechanistic understanding of how conditions within the tumour microenvironment impact on both androgen sensitive (LNCaP) and androgen independent (LNCaP-abl and LNCaP-abl-Hof) prostate cancer cell lines. Here we have applied technically robust and reproducible label-free liquid chromatography mass spectrometry analysis for comprehensive proteomic profiling of prostate cancer cell lines under hypoxic conditions. This led to the identification of over 4,000 proteins – one of the largest protein datasets for prostate cancer cell lines established to date. The biological and clinical significance of proteins showing a significant change in expression as result of hypoxic conditions was established. Novel, intuitive workflows were subsequently implemented to enable robust, reproducible and high throughput verification of selected proteins of interest. Overall, these data suggest that this strategy supports identification of protein biomarkers of prostate cancer progression and potential therapeutic targets for CRPC. PMID:28410543

Performance Evaluation of a UWB-RFID System for Potential Space Applications

NASA Technical Reports Server (NTRS)

Phan, Chan T.; Arndt, D.; Ngo, P.; Gross, J.; Ni, Jianjun; Rafford, Melinda

2006-01-01

This talk presents a brief overview of the ultra-wideband (UWB) RFID system with emphasis on the performance evaluation of a commercially available UWB-RFID system. There are many RFID systems available today, but many provide just basic identification for auditing and inventory tracking. For applications that require high precision real time tracking, UWB technology has been shown to be a viable solution. The use of extremely short bursts of RF pulses offers high immunity to interference from other RF systems, precise tracking due to sub-nanosecond time resolution, and robust performance in multipath environments. The UWB-RFID system Sapphire DART (Digital Active RFID & Tracking) will be introduced in this talk. Laboratory testing using Sapphire DART is performed to evaluate its capability such as coverage area, accuracy, ease of operation, and robustness. Performance evaluation of this system in an operational environment (a receiving warehouse) for inventory tracking is also conducted. Concepts of using the UWB-RFID technology to track astronauts and assets are being proposed for space exploration.

Method for star identification using neural networks

NASA Astrophysics Data System (ADS)

Lindsey, Clark S.; Lindblad, Thomas; Eide, Age J.

1997-04-01

Identification of star constellations with an onboard star tracker provides the highest precision of all attitude determination techniques for spacecraft. A method for identification of star constellations inspired by neural network (NNW) techniques is presented. It compares feature vectors derived from histograms of distances to multiple stars around the unknown star. The NNW method appears most robust with respect to position noise and would require a smaller database than conventional methods, especially for small fields of view. The neural network method is quite slow when performed on a sequential (serial) processor, but would provide very high speed if implemented in special hardware. Such hardware solutions could also yield lower low weight and low power consumption, both important features for small satellites.

Robust automated mass spectra interpretation and chemical formula calculation using mixed integer linear programming.

PubMed

Baran, Richard; Northen, Trent R

2013-10-15

Untargeted metabolite profiling using liquid chromatography and mass spectrometry coupled via electrospray ionization is a powerful tool for the discovery of novel natural products, metabolic capabilities, and biomarkers. However, the elucidation of the identities of uncharacterized metabolites from spectral features remains challenging. A critical step in the metabolite identification workflow is the assignment of redundant spectral features (adducts, fragments, multimers) and calculation of the underlying chemical formula. Inspection of the data by experts using computational tools solving partial problems (e.g., chemical formula calculation for individual ions) can be performed to disambiguate alternative solutions and provide reliable results. However, manual curation is tedious and not readily scalable or standardized. Here we describe an automated procedure for the robust automated mass spectra interpretation and chemical formula calculation using mixed integer linear programming optimization (RAMSI). Chemical rules among related ions are expressed as linear constraints and both the spectra interpretation and chemical formula calculation are performed in a single optimization step. This approach is unbiased in that it does not require predefined sets of neutral losses and positive and negative polarity spectra can be combined in a single optimization. The procedure was evaluated with 30 experimental mass spectra and was found to effectively identify the protonated or deprotonated molecule ([M + H](+) or [M - H](-)) while being robust to the presence of background ions. RAMSI provides a much-needed standardized tool for interpreting ions for subsequent identification in untargeted metabolomics workflows.

Species Identification and Design Value Estimation of Wooden Members in Covered Bridges

Treesearch

Alex C. Wiedenhoeft; David E. Kretschmann

2014-01-01

Covered timber bridges are historic structures with unique aesthetic value. To preserve this value and maintain bridges in service, robust evaluation of their performance and safety is necessary. The strength of the timber found in covered bridges can vary considerably, not only because of age and condition, but also because of species and grade. For the practicing...

A Robust Outlier Approach to Prevent Type I Error Inflation in Differential Item Functioning

ERIC Educational Resources Information Center

Magis, David; De Boeck, Paul

2012-01-01

The identification of differential item functioning (DIF) is often performed by means of statistical approaches that consider the raw scores as proxies for the ability trait level. One of the most popular approaches, the Mantel-Haenszel (MH) method, belongs to this category. However, replacing the ability level by the simple raw score is a source…

Identify High-Quality Protein Structural Models by Enhanced K-Means.

PubMed

Wu, Hongjie; Li, Haiou; Jiang, Min; Chen, Cheng; Lv, Qiang; Wu, Chuang

2017-01-01

Background. One critical issue in protein three-dimensional structure prediction using either ab initio or comparative modeling involves identification of high-quality protein structural models from generated decoys. Currently, clustering algorithms are widely used to identify near-native models; however, their performance is dependent upon different conformational decoys, and, for some algorithms, the accuracy declines when the decoy population increases. Results. Here, we proposed two enhanced K -means clustering algorithms capable of robustly identifying high-quality protein structural models. The first one employs the clustering algorithm SPICKER to determine the initial centroids for basic K -means clustering ( SK -means), whereas the other employs squared distance to optimize the initial centroids ( K -means++). Our results showed that SK -means and K -means++ were more robust as compared with SPICKER alone, detecting 33 (59%) and 42 (75%) of 56 targets, respectively, with template modeling scores better than or equal to those of SPICKER. Conclusions. We observed that the classic K -means algorithm showed a similar performance to that of SPICKER, which is a widely used algorithm for protein-structure identification. Both SK -means and K -means++ demonstrated substantial improvements relative to results from SPICKER and classical K -means.

Identify High-Quality Protein Structural Models by Enhanced K-Means

PubMed Central

Li, Haiou; Chen, Cheng; Lv, Qiang; Wu, Chuang

2017-01-01

Background. One critical issue in protein three-dimensional structure prediction using either ab initio or comparative modeling involves identification of high-quality protein structural models from generated decoys. Currently, clustering algorithms are widely used to identify near-native models; however, their performance is dependent upon different conformational decoys, and, for some algorithms, the accuracy declines when the decoy population increases. Results. Here, we proposed two enhanced K-means clustering algorithms capable of robustly identifying high-quality protein structural models. The first one employs the clustering algorithm SPICKER to determine the initial centroids for basic K-means clustering (SK-means), whereas the other employs squared distance to optimize the initial centroids (K-means++). Our results showed that SK-means and K-means++ were more robust as compared with SPICKER alone, detecting 33 (59%) and 42 (75%) of 56 targets, respectively, with template modeling scores better than or equal to those of SPICKER. Conclusions. We observed that the classic K-means algorithm showed a similar performance to that of SPICKER, which is a widely used algorithm for protein-structure identification. Both SK-means and K-means++ demonstrated substantial improvements relative to results from SPICKER and classical K-means. PMID:28421198

Of taps and toilets: quasi-experimental protocol for evaluating community-demand-driven projects.

PubMed

Pattanayak, Subhrendu K; Poulos, Christine; Yang, Jui-Chen; Patil, Sumeet R; Wendland, Kelly J

2009-09-01

Sustainable and equitable access to safe water and adequate sanitation are widely acknowledged as vital, yet neglected, development goals. Water supply and sanitation (WSS) policies are justified because of the usual efficiency criteria, but also major equity concerns. Yet, to date there are few scientific impact evaluations showing that WSS policies are effective in delivering social welfare outcomes. This lack of an evaluation culture is partly because WSS policies are characterized by diverse mechanisms, broad goals and the increasing importance of decentralized delivery, and partly because programme administrators are unaware of appropriate methods. We describe a protocol for a quasi-experimental evaluation of a community-demand-driven programme for water and sanitation in rural India, which addresses several evaluation challenges. After briefly reviewing policy and implementation issues in the sector, we describe key features of our protocol, including control group identification, pre-post measurement, programme theory, sample sufficiency and robust indicators. At its core, our protocol proposes to combine propensity score matching and difference-in-difference estimation. We conclude by briefly summarizing how quasi-experimental impact evaluations can address key issues in WSS policy design and when such evaluations are needed.

A Two-Step Method to Identify Positive Deviant Physician Organizations of Accountable Care Organizations with Robust Performance Management Systems.

PubMed

Pimperl, Alexander F; Rodriguez, Hector P; Schmittdiel, Julie A; Shortell, Stephen M

2018-06-01

To identify positive deviant (PD) physician organizations of Accountable Care Organizations (ACOs) with robust performance management systems (PMSYS). Third National Survey of Physician Organizations (NSPO3, n = 1,398). Organizational and external factors from NSPO3 were analyzed. Linear regression estimated the association of internal and contextual factors on PMSYS. Two cutpoints (75th/90th percentiles) identified PDs with the largest residuals and highest PMSYS scores. A total of 65 and 41 PDs were identified using 75th and 90th percentiles cutpoints, respectively. The 90th percentile more strongly differentiated PDs from non-PDs. Having a high proportion of vulnerable patients appears to constrain PMSYS development. Our PD identification method increases the likelihood that PD organizations selected for in-depth inquiry are high-performing organizations that exceed expectations. © Health Research and Educational Trust.

GPU-accelerated automatic identification of robust beam setups for proton and carbon-ion radiotherapy

NASA Astrophysics Data System (ADS)

Ammazzalorso, F.; Bednarz, T.; Jelen, U.

2014-03-01

We demonstrate acceleration on graphic processing units (GPU) of automatic identification of robust particle therapy beam setups, minimizing negative dosimetric effects of Bragg peak displacement caused by treatment-time patient positioning errors. Our particle therapy research toolkit, RobuR, was extended with OpenCL support and used to implement calculation on GPU of the Port Homogeneity Index, a metric scoring irradiation port robustness through analysis of tissue density patterns prior to dose optimization and computation. Results were benchmarked against an independent native CPU implementation. Numerical results were in agreement between the GPU implementation and native CPU implementation. For 10 skull base cases, the GPU-accelerated implementation was employed to select beam setups for proton and carbon ion treatment plans, which proved to be dosimetrically robust, when recomputed in presence of various simulated positioning errors. From the point of view of performance, average running time on the GPU decreased by at least one order of magnitude compared to the CPU, rendering the GPU-accelerated analysis a feasible step in a clinical treatment planning interactive session. In conclusion, selection of robust particle therapy beam setups can be effectively accelerated on a GPU and become an unintrusive part of the particle therapy treatment planning workflow. Additionally, the speed gain opens new usage scenarios, like interactive analysis manipulation (e.g. constraining of some setup) and re-execution. Finally, through OpenCL portable parallelism, the new implementation is suitable also for CPU-only use, taking advantage of multiple cores, and can potentially exploit types of accelerators other than GPUs.

Curtailing the high rate of late-stage attrition of investigational therapeutics against unprecedented targets in patients with lung and other malignancies.

PubMed

Rowinsky, Eric K

2004-06-15

A greater understanding of the pathogenesis and biology of cancer coupled with major advances in biotechnology has resulted in the identification of rationally designed, target-based (RDTB) anticancer therapeutics, ushering in new therapeutic opportunities and high expectations for the future as well as developmental challenges. Because these agents appear to principally target malignant cells, it is expected that they will produce less toxicity at clinically effective doses than nonspecific cytotoxic agents, but their target requirements are likely to be much more stringent. The innate complexity of the networks that contain elements targeted by these agents also decreases the probability that any single therapeutic manipulation will result in robust clinical activity and success when used alone, particularly in patients with solid malignancies that have multiple relevant signaling aberrations. In contrast, proof of principle and robust antitumor activity may be most efficiently demonstrated in nonrandomized evaluations involving tumors that are principally driven by aberrations of the specific target. The predominant therapeutic manifestation of RDTB agents in preclinical studies is due to decreased tumor growth rates and will likely be similar in the clinic; however, such manifestations are not readily detectable and quantifiable using nonrandomized clinical evaluations. To curtail the increasing rate of late-stage attrition of RDTB agents, which, if maintained, will stymie progress in cancer therapy, the design of initial nonrandomized evaluations, particularly the selection of tumors and patients, must be guided by the principal biological features of the agents. Next, evaluations, some of which must be randomized, can be performed in a wide range of tumor types, depending on the presence and relevance of the target. To validate the concept of RDTB therapeutics and to realize their full potential, radically different development, evaluation, and regulatory paradigms must be adopted.

A Study on the Requirements for Fast Active Turbine Tip Clearance Control Systems

NASA Technical Reports Server (NTRS)

DeCastro, Jonathan A.; Melcher, Kevin J.

2004-01-01

This paper addresses the requirements of a control system for active turbine tip clearance control in a generic commercial turbofan engine through design and analysis. The control objective is to articulate the shroud in the high pressure turbine section in order to maintain a certain clearance set point given several possible engine transient events. The system must also exhibit reasonable robustness to modeling uncertainties and reasonable noise rejection properties. Two actuators were chosen to fulfill such a requirement, both of which possess different levels of technological readiness: electrohydraulic servovalves and piezoelectric stacks. Identification of design constraints, desired actuator parameters, and actuator limitations are addressed in depth; all of which are intimately tied with the hardware and controller design process. Analytical demonstrations of the performance and robustness characteristics of the two axisymmetric LQG clearance control systems are presented. Takeoff simulation results show that both actuators are capable of maintaining the clearance within acceptable bounds and demonstrate robustness to parameter uncertainty. The present model-based control strategy was employed to demonstrate the tradeoff between performance, control effort, and robustness and to implement optimal state estimation in a noisy engine environment with intent to eliminate ad hoc methods for designing reliable control systems.

Robust Speaker Authentication Based on Combined Speech and Voiceprint Recognition

NASA Astrophysics Data System (ADS)

Malcangi, Mario

2009-08-01

Personal authentication is becoming increasingly important in many applications that have to protect proprietary data. Passwords and personal identification numbers (PINs) prove not to be robust enough to ensure that unauthorized people do not use them. Biometric authentication technology may offer a secure, convenient, accurate solution but sometimes fails due to its intrinsically fuzzy nature. This research aims to demonstrate that combining two basic speech processing methods, voiceprint identification and speech recognition, can provide a very high degree of robustness, especially if fuzzy decision logic is used.

Harnessing NGS and Big Data Optimally: Comparison of miRNA Prediction from Assembled versus Non-assembled Sequencing Data--The Case of the Grass Aegilops tauschii Complex Genome.

PubMed

Budak, Hikmet; Kantar, Melda

2015-07-01

MicroRNAs (miRNAs) are small, endogenous, non-coding RNA molecules that regulate gene expression at the post-transcriptional level. As high-throughput next generation sequencing (NGS) and Big Data rapidly accumulate for various species, efforts for in silico identification of miRNAs intensify. Surprisingly, the effect of the input genomics sequence on the robustness of miRNA prediction was not evaluated in detail to date. In the present study, we performed a homology-based miRNA and isomiRNA prediction of the 5D chromosome of bread wheat progenitor, Aegilops tauschii, using two distinct sequence data sets as input: (1) raw sequence reads obtained from 454-GS FLX Titanium sequencing platform and (2) an assembly constructed from these reads. We also compared this method with a number of available plant sequence datasets. We report here the identification of 62 and 22 miRNAs from raw reads and the assembly, respectively, of which 16 were predicted with high confidence from both datasets. While raw reads promoted sensitivity with the high number of miRNAs predicted, 55% (12 out of 22) of the assembly-based predictions were supported by previous observations, bringing specificity forward compared to the read-based predictions, of which only 37% were supported. Importantly, raw reads could identify several repeat-related miRNAs that could not be detected with the assembly. However, raw reads could not capture 6 miRNAs, for which the stem-loops could only be covered by the relatively longer sequences from the assembly. In summary, the comparison of miRNA datasets obtained by these two strategies revealed that utilization of raw reads, as well as assemblies for in silico prediction, have distinct advantages and disadvantages. Consideration of these important nuances can benefit future miRNA identification efforts in the current age of NGS and Big Data driven life sciences innovation.

Nursing expertise: a course of ambiguity and evolution in a concept.

PubMed

Hutchinson, Marie; Higson, Mary; Cleary, Michelle; Jackson, Debra

2016-12-01

In this article, we clarify and describe the nature of nursing expertise and provide a framework to guide its identification and further development. To have utility and rigour, concept-driven research and theories of practice require underlying concepts that are robust, valid and reliable. Advancing understanding of a concept requires careful attention to explicating its knowledge, metaphors and conceptual meaning. Examining the concepts and metaphors of nursing expertise, and how they have been interpreted into the nursing discourse, we aimed to synthesise definitions and similarities between concepts and elicit the defining characteristics and properties of nursing expertise. In clarifying the concept, we sought to move beyond the ambiguity that currently surrounds expertise in nursing and unravel it to make explicit the characteristics of nursing expertise from published peer-reviewed studies and structured literature synthesis. Findings indicate a lack of clarity surrounding the use of the term expertise. Traditional reliance upon intuition as a way of explaining expert performance is slowly evolving. Emerging from the analysis is a picture of expertise as the relationship between networks of contextual reasoning, understanding and practice. Striking absences in the discourse include limited explication of ethical reasoning and theorising a broader interpretation of expertise reflective of contemporary forms of nursing. © 2016 John Wiley & Sons Ltd.

Determination of psychostimulants and their metabolites by electrochemistry linked on-line to flowing atmospheric pressure afterglow mass spectrometry.

PubMed

Smoluch, Marek; Mielczarek, Przemyslaw; Reszke, Edward; Hieftje, Gary M; Silberring, Jerzy

2014-09-07

The flowing atmospheric pressure afterglow (FAPA) ion source operates in the ambient atmosphere and has been proven to be a promising tool for direct and rapid determination of numerous compounds. Here we linked a FAPA-MS system to an electrochemical flow cell for the identification of drug metabolites generated electrochemically in order to study simulated metabolic pathways. Psychostimulants and their metabolites produced by electrochemistry (EC) were detected on-line by FAPA-MS. The FAPA source has never been used before for an on-line connection with liquid flow, neither for identification of products generated in an electrochemical flow cell. The system was optimized to achieve the highest ionization efficiency by adjusting several parameters, including distances and angles between the ion source and the outlet of the EC system, the high voltage for plasma generation, flow-rates, and EC parameters. Simulated metabolites from tested compounds [methamphetamine (MAF), para-methoxy-N-methylamphetamine (PMMA), dextromethorphan (DXM), and benzydamine (BAM)] were formed in the EC cell at various pH levels. In all cases the main products were oxidized substrates and compounds after N-demethylation. Generation of such products and their thorough on-line identification confirm that the cytochrome P450 - driven metabolism of pharmaceuticals can be efficiently simulated in an electrochemical cell; this approach may serve as a step towards predictive pharmacology using a fast and robust design.

Molecular diagnosis of bloodstream infections: planning to (physically) reach the bedside.

PubMed

Leggieri, N; Rida, A; François, P; Schrenzel, Jacques

2010-08-01

Faster identification of infecting microorganisms and treatment options is a first-ranking priority in the infectious disease area, in order to prevent inappropriate treatment and overuse of broad-spectrum antibiotics. Standard bacterial identification is intrinsically time-consuming, and very recently there has been a burst in the number of commercially available nonphenotype-based techniques and in the documentation of a possible clinical impact of these techniques. Matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) is now a standard diagnostic procedure on cultures and hold promises on spiked blood. Meanwhile, commercial PCR-based techniques have improved with the use of bacterial DNA enrichment methods, the diversity of amplicon analysis techniques (melting curve analysis, microarrays, gel electrophoresis, sequencing and analysis by mass spectrometry) leading to the ability to challenge bacterial culture as the gold standard for providing earlier diagnosis with a better 'clinical' sensitivity and additional prognostic information. Laboratory practice has already changed with MALDI-TOF MS, but a change in clinical practice, driven by emergent nucleic acid-based techniques, will need the demonstration of real-life applicability as well as robust clinical-impact-oriented studies.

McGurk Effect in Gender Identification: Vision Trumps Audition in Voice Judgments.

PubMed

Peynircioǧlu, Zehra F; Brent, William; Tatz, Joshua R; Wyatt, Jordan

2017-01-01

Demonstrations of non-speech McGurk effects are rare, mostly limited to emotion identification, and sometimes not considered true analogues. We presented videos of males and females singing a single syllable on the same pitch and asked participants to indicate the true range of the voice-soprano, alto, tenor, or bass. For one group of participants, the gender shown on the video matched the gender of the voice heard, and for the other group they were mismatched. Soprano or alto responses were interpreted as "female voice" decisions and tenor or bass responses as "male voice" decisions. Identification of the voice gender was 100% correct in the preceding audio-only condition. However, whereas performance was also 100% correct in the matched video/audio condition, it was only 31% correct in the mismatched video/audio condition. Thus, the visual gender information overrode the voice gender identification, showing a robust non-speech McGurk effect.

Topic Identification and Categorization of Public Information in Community-Based Social Media

NASA Astrophysics Data System (ADS)

Kusumawardani, RP; Basri, MH

2017-01-01

This paper presents a work on a semi-supervised method for topic identification and classification of short texts in the social media, and its application on tweets containing dialogues in a large community of dwellers in a city, written mostly in Indonesian. These dialogues comprise a wealth of information about the city, shared in real-time. We found that despite the high irregularity of the language used, and the scarcity of suitable linguistic resources, a meaningful identification of topics could be performed by clustering the tweets using the K-Means algorithm. The resulting clusters are found to be robust enough to be the basis of a classification. On three grouping schemes derived from the clusters, we get accuracy of 95.52%, 95.51%, and 96.7 using linear SVMs, reflecting the applicability of applying this method for generating topic identification and classification on such data.

Safety features of subcritical fluid fueled systems

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

Bell, C.R.

1995-10-01

Accelerator-driven transmutation technology has been under study at Los Alamos for several years for application to nuclear waste treatment, tritium production, energy generation, and recently, to the disposition of excess weapons plutonium. Studies and evaluations performed to date at Los Alamos have led to a current focus on a fluid-fuel, fission system operating in a neutron source-supported subcritical mode, using molten salt reactor technology and accelerator-driven proton-neutron spallation. In this paper, the safety features and characteristics of such systems are explored from the perspective of the fundamental nuclear safety objectives that any reactor-type system should address. This exploration is qualitativemore » in nature and uses current vintage solid-fueled reactors as a baseline for comparison. Based on the safety perspectives presented, such systems should be capable of meeting the fundamental nuclear safety objectives. In addition, they should be able to provide the safety robustness desired for advanced reactors. However, the manner in which safety objectives and robustness are achieved is very different from that associated with conventional reactors. Also, there are a number of safety design and operational challenges that will have to be addressed for the safety potential of such systems to be credible.« less

Development and Validation of a Novel Dual Luciferase Reporter Gene Assay to Quantify Ebola Virus VP24 Inhibition of IFN Signaling

PubMed Central

Frau, Aldo; Sgarbanti, Marco; Orsatti, Roberto

2018-01-01

The interferon (IFN) system is the first line of defense against viral infections. Evasion of IFN signaling by Ebola viral protein 24 (VP24) is a critical event in the pathogenesis of the infection and, hence, VP24 is a potential target for drug development. Since no drugs target VP24, the identification of molecules able to inhibit VP24, restoring and possibly enhancing the IFN response, is a goal of concern. Accordingly, we developed a dual signal firefly and Renilla luciferase cell-based drug screening assay able to quantify IFN-mediated induction of Interferon Stimulated Genes (ISGs) and its inhibition by VP24. Human Embryonic Kidney 293T (HEK293T) cells were transiently transfected with a luciferase reporter gene construct driven by the promoter of ISGs, Interferon-Stimulated Response Element (ISRE). Stimulation of cells with IFN-α activated the IFN cascade leading to the expression of ISRE. Cotransfection of cells with a plasmid expressing VP24 cloned from a virus isolated during the last 2014 outbreak led to the inhibition of ISRE transcription, quantified by a luminescent signal. To adapt this system to test a large number of compounds, we performed it in 96-well plates; optimized the assay analyzing different parameters; and validated the system by calculating the Z′- and Z-factor, which showed values of 0.62 and 0.53 for IFN-α stimulation assay and VP24 inhibition assay, respectively, indicative of robust assay performance. PMID:29495311

Development and Validation of a Novel Dual Luciferase Reporter Gene Assay to Quantify Ebola Virus VP24 Inhibition of IFN Signaling.

PubMed

Fanunza, Elisa; Frau, Aldo; Sgarbanti, Marco; Orsatti, Roberto; Corona, Angela; Tramontano, Enzo

2018-02-24

The interferon (IFN) system is the first line of defense against viral infections. Evasion of IFN signaling by Ebola viral protein 24 (VP24) is a critical event in the pathogenesis of the infection and, hence, VP24 is a potential target for drug development. Since no drugs target VP24, the identification of molecules able to inhibit VP24, restoring and possibly enhancing the IFN response, is a goal of concern. Accordingly, we developed a dual signal firefly and Renilla luciferase cell-based drug screening assay able to quantify IFN-mediated induction of Interferon Stimulated Genes (ISGs) and its inhibition by VP24. Human Embryonic Kidney 293T (HEK293T) cells were transiently transfected with a luciferase reporter gene construct driven by the promoter of ISGs, Interferon-Stimulated Response Element (ISRE). Stimulation of cells with IFN-α activated the IFN cascade leading to the expression of ISRE. Cotransfection of cells with a plasmid expressing VP24 cloned from a virus isolated during the last 2014 outbreak led to the inhibition of ISRE transcription, quantified by a luminescent signal. To adapt this system to test a large number of compounds, we performed it in 96-well plates; optimized the assay analyzing different parameters; and validated the system by calculating the Z'- and Z-factor, which showed values of 0.62 and 0.53 for IFN-α stimulation assay and VP24 inhibition assay, respectively, indicative of robust assay performance.

Strong genetic influences on measures of behavioral-regulation among inbred rat strains

PubMed Central

Richards, Jerry B.; Lloyd, David R.; Kuehlewind, Brandon; Militello, Leah; Paredez, Marita; Solberg -Woods, Leah; Palmer, Abraham A.

2013-01-01

A fundamental challenge for any complex nervous system is to regulate behavior in response to environmental challenges. Three measures of behavioral regulation were tested in a panel of 8 inbred rat strains. These measures were; 1) sensation seeking as assessed by locomotor response to novelty and the sensory reinforcing effects of light onset, 2) attention and impulsivity, as measured by a choice reaction time task, and 3) impulsivity as measured by a delay discounting task. Deficient behavioral regulation has been linked to a number of psychopathologies, including ADHD, Schizophrenia, Autism, drug abuse and eating disorders. Eight inbred rat strains (August Copenhagen Irish, Brown Norway, Buffalo, Fischer 344, Wistar Kyoto, Spontaneous Hypertensive Rat, Lewis, Dahl Salt Sensitive) were tested. With n=9 for each strain, we observed robust strain differences for all tasks; heritability was estimated between 0.43 and 0.66. Performance of the 8 inbred rat strains on the choice reaction time task was compared to the performance of out bred Sprague Dawley (n=28) and Heterogeneous strain rats (n=48). The results indicate a strong genetic influence on complex tasks related to behavioral regulation and indicate that some of measures tap common genetically-driven processes. Furthermore, our results establish the potential for future studies aimed at identifying specific alleles that influence variability for these traits. Identification of such alleles could contribute to our understanding of the molecular genetic basis of behavioral regulation, which is of fundamental importance and likely contributes to multiple psychiatric disorders. PMID:23710681

Distribution-Agnostic Stochastic Optimal Power Flow for Distribution Grids: Preprint

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

Baker, Kyri; Dall'Anese, Emiliano; Summers, Tyler

2016-09-01

This paper outlines a data-driven, distributionally robust approach to solve chance-constrained AC optimal power flow problems in distribution networks. Uncertain forecasts for loads and power generated by photovoltaic (PV) systems are considered, with the goal of minimizing PV curtailment while meeting power flow and voltage regulation constraints. A data- driven approach is utilized to develop a distributionally robust conservative convex approximation of the chance-constraints; particularly, the mean and covariance matrix of the forecast errors are updated online, and leveraged to enforce voltage regulation with predetermined probability via Chebyshev-based bounds. By combining an accurate linear approximation of the AC power flowmore » equations with the distributionally robust chance constraint reformulation, the resulting optimization problem becomes convex and computationally tractable.« less

Noise Robust Speech Recognition Applied to Voice-Driven Wheelchair

NASA Astrophysics Data System (ADS)

Sasou, Akira; Kojima, Hiroaki

2009-12-01

Conventional voice-driven wheelchairs usually employ headset microphones that are capable of achieving sufficient recognition accuracy, even in the presence of surrounding noise. However, such interfaces require users to wear sensors such as a headset microphone, which can be an impediment, especially for the hand disabled. Conversely, it is also well known that the speech recognition accuracy drastically degrades when the microphone is placed far from the user. In this paper, we develop a noise robust speech recognition system for a voice-driven wheelchair. This system can achieve almost the same recognition accuracy as the headset microphone without wearing sensors. We verified the effectiveness of our system in experiments in different environments, and confirmed that our system can achieve almost the same recognition accuracy as the headset microphone without wearing sensors.

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.

Taguchi experimental design to determine the taste quality characteristic of candied carrot

NASA Astrophysics Data System (ADS)

Ekawati, Y.; Hapsari, A. A.

2018-03-01

Robust parameter design is used to design product that is robust to noise factors so the product’s performance fits the target and delivers a better quality. In the process of designing and developing the innovative product of candied carrot, robust parameter design is carried out using Taguchi Method. The method is used to determine an optimal quality design. The optimal quality design is based on the process and the composition of product ingredients that are in accordance with consumer needs and requirements. According to the identification of consumer needs from the previous research, quality dimensions that need to be assessed are the taste and texture of the product. The quality dimension assessed in this research is limited to the taste dimension. Organoleptic testing is used for this assessment, specifically hedonic testing that makes assessment based on consumer preferences. The data processing uses mean and signal to noise ratio calculation and optimal level setting to determine the optimal process/composition of product ingredients. The optimal value is analyzed using confirmation experiments to prove that proposed product match consumer needs and requirements. The result of this research is identification of factors that affect the product taste and the optimal quality of product according to Taguchi Method.

Robust and efficient estimation with weighted composite quantile regression

NASA Astrophysics Data System (ADS)

Jiang, Xuejun; Li, Jingzhi; Xia, Tian; Yan, Wanfeng

2016-09-01

In this paper we introduce a weighted composite quantile regression (CQR) estimation approach and study its application in nonlinear models such as exponential models and ARCH-type models. The weighted CQR is augmented by using a data-driven weighting scheme. With the error distribution unspecified, the proposed estimators share robustness from quantile regression and achieve nearly the same efficiency as the oracle maximum likelihood estimator (MLE) for a variety of error distributions including the normal, mixed-normal, Student's t, Cauchy distributions, etc. We also suggest an algorithm for the fast implementation of the proposed methodology. Simulations are carried out to compare the performance of different estimators, and the proposed approach is used to analyze the daily S&P 500 Composite index, which verifies the effectiveness and efficiency of our theoretical results.

HIFU Transducer Characterization Using a Robust Needle Hydrophone

NASA Astrophysics Data System (ADS)

Howard, Samuel M.; Zanelli, Claudio I.

2007-05-01

A robust needle hydrophone has been developed for HIFU transducer characterization and reported on earlier. After a brief review of the hydrophone design and performance, we demonstrate its use to characterize a 1.5 MHz, 10 cm diameter, F-number 1.5 spherically focused source driven to exceed an intensity of 1400 W/cm2at its focus. Quantitative characterization of this source at high powers is assisted by deconvolving the hydrophone's calibrated frequency response in order to accurately reflect the contribution of harmonics generated by nonlinear propagation in the water testing environment. Results are compared to measurements with a membrane hydrophone at 0.3% duty cycle and to theoretical calculations, using measurements of the field at the source's radiating surface as input to a numerical solution of the KZK equation.

Application of stakeholder-based and modelling approaches for supporting robust adaptation decision making under future climatic uncertainty and changing urban-agricultural water demand

NASA Astrophysics Data System (ADS)

Bhave, Ajay; Dessai, Suraje; Conway, Declan; Stainforth, David

2016-04-01

Deep uncertainty in future climate change and socio-economic conditions necessitates the use of assess-risk-of-policy approaches over predict-then-act approaches for adaptation decision making. Robust Decision Making (RDM) approaches embody this principle and help evaluate the ability of adaptation options to satisfy stakeholder preferences under wide-ranging future conditions. This study involves the simultaneous application of two RDM approaches; qualitative and quantitative, in the Cauvery River Basin in Karnataka (population ~23 million), India. The study aims to (a) determine robust water resources adaptation options for the 2030s and 2050s and (b) compare the usefulness of a qualitative stakeholder-driven approach with a quantitative modelling approach. For developing a large set of future scenarios a combination of climate narratives and socio-economic narratives was used. Using structured expert elicitation with a group of climate experts in the Indian Summer Monsoon, climatic narratives were developed. Socio-economic narratives were developed to reflect potential future urban and agricultural water demand. In the qualitative RDM approach, a stakeholder workshop helped elicit key vulnerabilities, water resources adaptation options and performance criteria for evaluating options. During a second workshop, stakeholders discussed and evaluated adaptation options against the performance criteria for a large number of scenarios of climatic and socio-economic change in the basin. In the quantitative RDM approach, a Water Evaluation And Planning (WEAP) model was forced by precipitation and evapotranspiration data, coherent with the climatic narratives, together with water demand data based on socio-economic narratives. We find that compared to business-as-usual conditions options addressing urban water demand satisfy performance criteria across scenarios and provide co-benefits like energy savings and reduction in groundwater depletion, while options reducing agricultural water demand significantly affect downstream water availability. Water demand options demonstrate potential to improve environmental flow conditions and satisfy legal water supply requirements for downstream riparian states. On the other hand, currently planned large scale infrastructural projects demonstrate reduced value in certain scenarios, illustrating the impacts of lock-in effects of large scale infrastructure. From a methodological perspective, we find that while the stakeholder-driven approach revealed robust options in a resource-light manner and helped initiate much needed interaction amongst stakeholders, the modelling approach provides complementary quantitative information. The study reveals robust adaptation options for this important basin and provides a strong methodological basis for carrying out future studies that support adaptation decision making.

Extreme learning machine for reduced order modeling of turbulent geophysical flows.

PubMed

San, Omer; Maulik, Romit

2018-04-01

We investigate the application of artificial neural networks to stabilize proper orthogonal decomposition-based reduced order models for quasistationary geophysical turbulent flows. An extreme learning machine concept is introduced for computing an eddy-viscosity closure dynamically to incorporate the effects of the truncated modes. We consider a four-gyre wind-driven ocean circulation problem as our prototype setting to assess the performance of the proposed data-driven approach. Our framework provides a significant reduction in computational time and effectively retains the dynamics of the full-order model during the forward simulation period beyond the training data set. Furthermore, we show that the method is robust for larger choices of time steps and can be used as an efficient and reliable tool for long time integration of general circulation models.

Extreme learning machine for reduced order modeling of turbulent geophysical flows

NASA Astrophysics Data System (ADS)

San, Omer; Maulik, Romit

2018-04-01

We investigate the application of artificial neural networks to stabilize proper orthogonal decomposition-based reduced order models for quasistationary geophysical turbulent flows. An extreme learning machine concept is introduced for computing an eddy-viscosity closure dynamically to incorporate the effects of the truncated modes. We consider a four-gyre wind-driven ocean circulation problem as our prototype setting to assess the performance of the proposed data-driven approach. Our framework provides a significant reduction in computational time and effectively retains the dynamics of the full-order model during the forward simulation period beyond the training data set. Furthermore, we show that the method is robust for larger choices of time steps and can be used as an efficient and reliable tool for long time integration of general circulation models.

Inferring Binary and Trinary Stellar Populations in Photometric and Astrometric Surveys

NASA Astrophysics Data System (ADS)

Widmark, Axel; Leistedt, Boris; Hogg, David W.

2018-04-01

Multiple stellar systems are ubiquitous in the Milky Way but are often unresolved and seen as single objects in spectroscopic, photometric, and astrometric surveys. However, modeling them is essential for developing a full understanding of large surveys such as Gaia and connecting them to stellar and Galactic models. In this paper, we address this problem by jointly fitting the Gaia and Two Micron All Sky Survey photometric and astrometric data using a data-driven Bayesian hierarchical model that includes populations of binary and trinary systems. This allows us to classify observations into singles, binaries, and trinaries, in a robust and efficient manner, without resorting to external models. We are able to identify multiple systems and, in some cases, make strong predictions for the properties of their unresolved stars. We will be able to compare such predictions with Gaia Data Release 4, which will contain astrometric identification and analysis of binary systems.

A vibration-based health monitoring program for a large and seismically vulnerable masonry dome

NASA Astrophysics Data System (ADS)

Pecorelli, M. L.; Ceravolo, R.; De Lucia, G.; Epicoco, R.

2017-05-01

Vibration-based health monitoring of monumental structures must rely on efficient and, as far as possible, automatic modal analysis procedures. Relatively low excitation energy provided by traffic, wind and other sources is usually sufficient to detect structural changes, as those produced by earthquakes and extreme events. Above all, in-operation modal analysis is a non-invasive diagnostic technique that can support optimal strategies for the preservation of architectural heritage, especially if complemented by model-driven procedures. In this paper, the preliminary steps towards a fully automated vibration-based monitoring of the world’s largest masonry oval dome (internal axes of 37.23 by 24.89 m) are presented. More specifically, the paper reports on signal treatment operations conducted to set up the permanent dynamic monitoring system of the dome and to realise a robust automatic identification procedure. Preliminary considerations on the effects of temperature on dynamic parameters are finally reported.

Detecting outliers when fitting data with nonlinear regression – a new method based on robust nonlinear regression and the false discovery rate

PubMed Central

Motulsky, Harvey J; Brown, Ronald E

2006-01-01

Background Nonlinear regression, like linear regression, assumes that the scatter of data around the ideal curve follows a Gaussian or normal distribution. This assumption leads to the familiar goal of regression: to minimize the sum of the squares of the vertical or Y-value distances between the points and the curve. Outliers can dominate the sum-of-the-squares calculation, and lead to misleading results. However, we know of no practical method for routinely identifying outliers when fitting curves with nonlinear regression. Results We describe a new method for identifying outliers when fitting data with nonlinear regression. We first fit the data using a robust form of nonlinear regression, based on the assumption that scatter follows a Lorentzian distribution. We devised a new adaptive method that gradually becomes more robust as the method proceeds. To define outliers, we adapted the false discovery rate approach to handling multiple comparisons. We then remove the outliers, and analyze the data using ordinary least-squares regression. Because the method combines robust regression and outlier removal, we call it the ROUT method. When analyzing simulated data, where all scatter is Gaussian, our method detects (falsely) one or more outlier in only about 1–3% of experiments. When analyzing data contaminated with one or several outliers, the ROUT method performs well at outlier identification, with an average False Discovery Rate less than 1%. Conclusion Our method, which combines a new method of robust nonlinear regression with a new method of outlier identification, identifies outliers from nonlinear curve fits with reasonable power and few false positives. PMID:16526949

Prony Ringdown GUI (CERTS Prony Ringdown, part of the DSI Tool Box)

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

Tuffner, Francis; Marinovici, PNNL Laurentiu; Hauer, PNNL John

2014-02-21

The PNNL Prony Ringdown graphical user interface is one analysis tool included in the Dynamic System Identification toolbox (DSI Toolbox). The Dynamic System Identification toolbox is a MATLAB-based collection of tools for parsing and analyzing phasor measurement unit data, especially in regards to small signal stability. It includes tools to read the data, preprocess it, and perform small signal analysis. 5. Method of Solution: The Dynamic System Identification Toolbox (DSI Toolbox) is designed to provide a research environment for examining phasor measurement unit data and performing small signal stability analysis. The software uses a series of text-driven menus to helpmore » guide users and organize the toolbox features. Methods for reading in populate phasor measurement unit data are provided, with appropriate preprocessing options for small-signal-stability analysis. The toolbox includes the Prony Ringdown GUI and basic algorithms to estimate information on oscillatory modes of the system, such as modal frequency and damping ratio.« less

An Autonomous Star Identification Algorithm Based on One-Dimensional Vector Pattern for Star Sensors

PubMed Central

Luo, Liyan; Xu, Luping; Zhang, Hua

2015-01-01

In order to enhance the robustness and accelerate the recognition speed of star identification, an autonomous star identification algorithm for star sensors is proposed based on the one-dimensional vector pattern (one_DVP). In the proposed algorithm, the space geometry information of the observed stars is used to form the one-dimensional vector pattern of the observed star. The one-dimensional vector pattern of the same observed star remains unchanged when the stellar image rotates, so the problem of star identification is simplified as the comparison of the two feature vectors. The one-dimensional vector pattern is adopted to build the feature vector of the star pattern, which makes it possible to identify the observed stars robustly. The characteristics of the feature vector and the proposed search strategy for the matching pattern make it possible to achieve the recognition result as quickly as possible. The simulation results demonstrate that the proposed algorithm can effectively accelerate the star identification. Moreover, the recognition accuracy and robustness by the proposed algorithm are better than those by the pyramid algorithm, the modified grid algorithm, and the LPT algorithm. The theoretical analysis and experimental results show that the proposed algorithm outperforms the other three star identification algorithms. PMID:26198233

An Autonomous Star Identification Algorithm Based on One-Dimensional Vector Pattern for Star Sensors.

PubMed

Luo, Liyan; Xu, Luping; Zhang, Hua

2015-07-07

In order to enhance the robustness and accelerate the recognition speed of star identification, an autonomous star identification algorithm for star sensors is proposed based on the one-dimensional vector pattern (one_DVP). In the proposed algorithm, the space geometry information of the observed stars is used to form the one-dimensional vector pattern of the observed star. The one-dimensional vector pattern of the same observed star remains unchanged when the stellar image rotates, so the problem of star identification is simplified as the comparison of the two feature vectors. The one-dimensional vector pattern is adopted to build the feature vector of the star pattern, which makes it possible to identify the observed stars robustly. The characteristics of the feature vector and the proposed search strategy for the matching pattern make it possible to achieve the recognition result as quickly as possible. The simulation results demonstrate that the proposed algorithm can effectively accelerate the star identification. Moreover, the recognition accuracy and robustness by the proposed algorithm are better than those by the pyramid algorithm, the modified grid algorithm, and the LPT algorithm. The theoretical analysis and experimental results show that the proposed algorithm outperforms the other three star identification algorithms.

Enhanced echolocation via robust statistics and super-resolution of sonar images

NASA Astrophysics Data System (ADS)

Kim, Kio

Echolocation is a process in which an animal uses acoustic signals to exchange information with environments. In a recent study, Neretti et al. have shown that the use of robust statistics can significantly improve the resiliency of echolocation against noise and enhance its accuracy by suppressing the development of sidelobes in the processing of an echo signal. In this research, the use of robust statistics is extended to problems in underwater explorations. The dissertation consists of two parts. Part I describes how robust statistics can enhance the identification of target objects, which in this case are cylindrical containers filled with four different liquids. Particularly, this work employs a variation of an existing robust estimator called an L-estimator, which was first suggested by Koenker and Bassett. As pointed out by Au et al.; a 'highlight interval' is an important feature, and it is closely related with many other important features that are known to be crucial for dolphin echolocation. A varied L-estimator described in this text is used to enhance the detection of highlight intervals, which eventually leads to a successful classification of echo signals. Part II extends the problem into 2 dimensions. Thanks to the advances in material and computer technology, various sonar imaging modalities are available on the market. By registering acoustic images from such video sequences, one can extract more information on the region of interest. Computer vision and image processing allowed application of robust statistics to the acoustic images produced by forward looking sonar systems, such as Dual-frequency Identification Sonar and ProViewer. The first use of robust statistics for sonar image enhancement in this text is in image registration. Random Sampling Consensus (RANSAC) is widely used for image registration. The registration algorithm using RANSAC is optimized for sonar image registration, and the performance is studied. The second use of robust statistics is in fusing the images. It is shown that the maximum a posteriori fusion method can be formulated in a Kalman filter-like manner, and also that the resulting expression is identical to a W-estimator with a specific weight function.

Kennard-Stone combined with least square support vector machine method for noncontact discriminating human blood species

NASA Astrophysics Data System (ADS)

Zhang, Linna; Li, Gang; Sun, Meixiu; Li, Hongxiao; Wang, Zhennan; Li, Yingxin; Lin, Ling

2017-11-01

Identifying whole bloods to be either human or nonhuman is an important responsibility for import-export ports and inspection and quarantine departments. Analytical methods and DNA testing methods are usually destructive. Previous studies demonstrated that visible diffuse reflectance spectroscopy method can realize noncontact human and nonhuman blood discrimination. An appropriate method for calibration set selection was very important for a robust quantitative model. In this paper, Random Selection (RS) method and Kennard-Stone (KS) method was applied in selecting samples for calibration set. Moreover, proper stoichiometry method can be greatly beneficial for improving the performance of classification model or quantification model. Partial Least Square Discrimination Analysis (PLSDA) method was commonly used in identification of blood species with spectroscopy methods. Least Square Support Vector Machine (LSSVM) was proved to be perfect for discrimination analysis. In this research, PLSDA method and LSSVM method was used for human blood discrimination. Compared with the results of PLSDA method, this method could enhance the performance of identified models. The overall results convinced that LSSVM method was more feasible for identifying human and animal blood species, and sufficiently demonstrated LSSVM method was a reliable and robust method for human blood identification, and can be more effective and accurate.

ARIANE 5 upper-stage ignition conditions improvement, and return to operation with ''Envisat'' payload

NASA Astrophysics Data System (ADS)

Dutheil, J. Ph.; Langel, G.

2003-08-01

ARIANE 5 experienced a flight anomaly with the 10 th model mission (F 510), having placed its both satellites in a lower orbit than the planned GTO. Only one satellite (Artemis) could be retrieved due to its own propulsion systems. Arianespace, CNES and Astrium-GmbH (former DaimlerChrysler Aerospace Dasa) immediately set up a recovery team, combining forces for carrying deep and schedule-driven investigations, and later qualifying recovery measures. A failure in such an important program: is immediately triggering a large "post-shock" reaction from the ARIANE community implied in the relevant business and technology. The investigation fields are summarised in the following chapters, showing how failure analysis, engineering investigations and basic research have been combined in order to have a schedule and methodic efficient approach. The combination of all available European resources in space vehicle design has been implemented, involving industry, agency technical centers and research laboratories. The investigation methodology applied has been driven by the particular situation of a flight anomaly investigation, which has to take into account the reduced amount of measurement available in flight and the necessary combination with ground test data for building a strategy to reach identification of possible failure scenario. From the investigations and from extensive sensitivity characterisation test of EPS engine (AESTUS) ignition transient, stability margins have been deeply investigated and introduced in the post-anomaly upgraded stage design. The identification and implementation of recovery measures, extended as well to - potential ignition margin reduction factors even beyond the observed flight anomaly allowed to establish a robust complementary qualification status, thus allowing resuming of operational flight, starting with the valuable "Envisat" payload of European Space Agency, dedicated to earth and climate monitoring, on flight 511, the 28/02/2002, from Kourou Spaceport.

Subscale HDC implosions driven at high radiation temperature using advanced hohlraums

NASA Astrophysics Data System (ADS)

Ho, D.; Amendt, P.; Jones, O.; Berzak Hopkins, L.; Le Pape, S.

2017-10-01

Implosions using HDC ablators have received increased attention because of shorter pulse length and can access higher implosion velocity than CH ablators. Recent HDC midscale (979 m radius) implosion experiments have achieved DT neutron yields of 1.5e16. Our 2D simulations show that subscale (890 m radius) HDC capsules can achieve robust high-yield performance if driven at high enough radiation temperature 330 eV, because the penalty for less fuel mass can be offset by higher implosion velocity. To achieve 330 eV will likely require the use of innovative hohlraum concepts, e.g., subscale rugby-shaped hohlraum using 1.3 MJ of laser energy without incurring a risk of high laser backscatter. Radiation symmetry is currently under study. Confidence in our modeling of HDC implosions is high in part because our 2D modeling of recent HDC implosions experiments show good agreement with data. Work performed under auspices of U.S. DOE by LLNL under 15-ERD-058.

Optimal Design of Cable-Driven Manipulators Using Particle Swarm Optimization.

PubMed

Bryson, Joshua T; Jin, Xin; Agrawal, Sunil K

2016-08-01

The design of cable-driven manipulators is complicated by the unidirectional nature of the cables, which results in extra actuators and limited workspaces. Furthermore, the particular arrangement of the cables and the geometry of the robot pose have a significant effect on the cable tension required to effect a desired joint torque. For a sufficiently complex robot, the identification of a satisfactory cable architecture can be difficult and can result in multiply redundant actuators and performance limitations based on workspace size and cable tensions. This work leverages previous research into the workspace analysis of cable systems combined with stochastic optimization to develop a generalized methodology for designing optimized cable routings for a given robot and desired task. A cable-driven robot leg performing a walking-gait motion is used as a motivating example to illustrate the methodology application. The components of the methodology are described, and the process is applied to the example problem. An optimal cable routing is identified, which provides the necessary controllable workspace to perform the desired task and enables the robot to perform that task with minimal cable tensions. A robot leg is constructed according to this routing and used to validate the theoretical model and to demonstrate the effectiveness of the resulting cable architecture.

Virtual DRI dataset development

NASA Astrophysics Data System (ADS)

Hixson, Jonathan G.; Teaney, Brian P.; May, Christopher; Maurer, Tana; Nelson, Michael B.; Pham, Justin R.

2017-05-01

The U.S. Army RDECOM CERDEC NVESD MSD's target acquisition models have been used for many years by the military analysis community for sensor design, trade studies, and field performance prediction. This paper analyzes the results of perception tests performed to compare the results of a field DRI (Detection, Recognition, and Identification Test) performed in 2009 to current Soldier performance viewing the same imagery in a laboratory environment and simulated imagery of the same data set. The purpose of the experiment is to build a robust data set for use in the virtual prototyping of infrared sensors. This data set will provide a strong foundation relating, model predictions, field DRI results and simulated imagery.

Multi-Directional Multi-Level Dual-Cross Patterns for Robust Face Recognition.

PubMed

Ding, Changxing; Choi, Jonghyun; Tao, Dacheng; Davis, Larry S

2016-03-01

To perform unconstrained face recognition robust to variations in illumination, pose and expression, this paper presents a new scheme to extract "Multi-Directional Multi-Level Dual-Cross Patterns" (MDML-DCPs) from face images. Specifically, the MDML-DCPs scheme exploits the first derivative of Gaussian operator to reduce the impact of differences in illumination and then computes the DCP feature at both the holistic and component levels. DCP is a novel face image descriptor inspired by the unique textural structure of human faces. It is computationally efficient and only doubles the cost of computing local binary patterns, yet is extremely robust to pose and expression variations. MDML-DCPs comprehensively yet efficiently encodes the invariant characteristics of a face image from multiple levels into patterns that are highly discriminative of inter-personal differences but robust to intra-personal variations. Experimental results on the FERET, CAS-PERL-R1, FRGC 2.0, and LFW databases indicate that DCP outperforms the state-of-the-art local descriptors (e.g., LBP, LTP, LPQ, POEM, tLBP, and LGXP) for both face identification and face verification tasks. More impressively, the best performance is achieved on the challenging LFW and FRGC 2.0 databases by deploying MDML-DCPs in a simple recognition scheme.

Noise-robust speech triage.

PubMed

Bartos, Anthony L; Cipr, Tomas; Nelson, Douglas J; Schwarz, Petr; Banowetz, John; Jerabek, Ladislav

2018-04-01

A method is presented in which conventional speech algorithms are applied, with no modifications, to improve their performance in extremely noisy environments. It has been demonstrated that, for eigen-channel algorithms, pre-training multiple speaker identification (SID) models at a lattice of signal-to-noise-ratio (SNR) levels and then performing SID using the appropriate SNR dependent model was successful in mitigating noise at all SNR levels. In those tests, it was found that SID performance was optimized when the SNR of the testing and training data were close or identical. In this current effort multiple i-vector algorithms were used, greatly improving both processing throughput and equal error rate classification accuracy. Using identical approaches in the same noisy environment, performance of SID, language identification, gender identification, and diarization were significantly improved. A critical factor in this improvement is speech activity detection (SAD) that performs reliably in extremely noisy environments, where the speech itself is barely audible. To optimize SAD operation at all SNR levels, two algorithms were employed. The first maximized detection probability at low levels (-10 dB ≤ SNR < +10 dB) using just the voiced speech envelope, and the second exploited features extracted from the original speech to improve overall accuracy at higher quality levels (SNR ≥ +10 dB).

Comparative study of performance of neutral axis tracking based damage detection

NASA Astrophysics Data System (ADS)

Soman, R.; Malinowski, P.; Ostachowicz, W.

2015-07-01

This paper presents a comparative study of a novel SHM technique for damage isolation. The performance of the Neutral Axis (NA) tracking based damage detection strategy is compared to other popularly used vibration based damage detection methods viz. ECOMAC, Mode Shape Curvature Method and Strain Flexibility Index Method. The sensitivity of the novel method is compared under changing ambient temperature conditions and in the presence of measurement noise. Finite Element Analysis (FEA) of the DTU 10 MW Wind Turbine was conducted to compare the local damage identification capability of each method and the results are presented. Under the conditions examined, the proposed method was found to be robust to ambient condition changes and measurement noise. The damage identification in some is either at par with the methods mentioned in the literature or better under the investigated damage scenarios.

UWE-3, in-orbit performance and lessons learned of a modular and flexible satellite bus for future pico-satellite formations

NASA Astrophysics Data System (ADS)

Busch, S.; Bangert, P.; Dombrovski, S.; Schilling, K.

2015-12-01

Formations of small satellites offer promising perspectives due to improved temporal and spatial coverage and resolution at reasonable costs. The UWE-program addresses in-orbit demonstrations of key technologies to enable formations of cooperating distributed spacecraft at pico-satellite level. In this context, the CubeSat UWE-3 addresses experiments for evaluation of real-time attitude determination and control. UWE-3 introduces also a modular and flexible pico-satellite bus as a robust and extensible base for future missions. Technical objective was a very low power consumption of the COTS-based system, nevertheless providing a robust performance of this miniature satellite by advanced microprocessor redundancy and fault detection, identification and recovery software. This contribution addresses the UWE-3 design and mission results with emphasis on the operational experiences of the attitude determination and control system.

Autonomous facial recognition system inspired by human visual system based logarithmical image visualization technique

NASA Astrophysics Data System (ADS)

Wan, Qianwen; Panetta, Karen; Agaian, Sos

2017-05-01

Autonomous facial recognition system is widely used in real-life applications, such as homeland border security, law enforcement identification and authentication, and video-based surveillance analysis. Issues like low image quality, non-uniform illumination as well as variations in poses and facial expressions can impair the performance of recognition systems. To address the non-uniform illumination challenge, we present a novel robust autonomous facial recognition system inspired by the human visual system based, so called, logarithmical image visualization technique. In this paper, the proposed method, for the first time, utilizes the logarithmical image visualization technique coupled with the local binary pattern to perform discriminative feature extraction for facial recognition system. The Yale database, the Yale-B database and the ATT database are used for computer simulation accuracy and efficiency testing. The extensive computer simulation demonstrates the method's efficiency, accuracy, and robustness of illumination invariance for facial recognition.

An optimal baseline selection methodology for data-driven damage detection and temperature compensation in acousto-ultrasonics

NASA Astrophysics Data System (ADS)

Torres-Arredondo, M.-A.; Sierra-Pérez, Julián; Cabanes, Guénaël

2016-05-01

The process of measuring and analysing the data from a distributed sensor network all over a structural system in order to quantify its condition is known as structural health monitoring (SHM). For the design of a trustworthy health monitoring system, a vast amount of information regarding the inherent physical characteristics of the sources and their propagation and interaction across the structure is crucial. Moreover, any SHM system which is expected to transition to field operation must take into account the influence of environmental and operational changes which cause modifications in the stiffness and damping of the structure and consequently modify its dynamic behaviour. On that account, special attention is paid in this paper to the development of an efficient SHM methodology where robust signal processing and pattern recognition techniques are integrated for the correct interpretation of complex ultrasonic waves within the context of damage detection and identification. The methodology is based on an acousto-ultrasonics technique where the discrete wavelet transform is evaluated for feature extraction and selection, linear principal component analysis for data-driven modelling and self-organising maps for a two-level clustering under the principle of local density. At the end, the methodology is experimentally demonstrated and results show that all the damages were detectable and identifiable.

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.

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.

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.

What Is Robustness?: Problem Framing Challenges for Water Systems Planning Under Change

NASA Astrophysics Data System (ADS)

Herman, J. D.; Reed, P. M.; Zeff, H. B.; Characklis, G. W.

2014-12-01

Water systems planners have long recognized the need for robust solutions capable of withstanding deviations from the conditions for which they were designed. Faced with a set of alternatives to choose from—for example, resulting from a multi-objective optimization—existing analysis frameworks offer competing definitions of robustness under change. Robustness analyses have moved from expected utility to exploratory "bottom-up" approaches in which vulnerable scenarios are identified prior to assigning likelihoods; examples include Robust Decision Making (RDM), Decision Scaling, Info-Gap, and Many-Objective Robust Decision Making (MORDM). We propose a taxonomy of robustness frameworks to compare and contrast these approaches, based on their methods of (1) alternative selection, (2) sampling of states of the world, (3) quantification of robustness measures, and (4) identification of key uncertainties using sensitivity analysis. Using model simulations from recent work in multi-objective urban water supply portfolio planning, we illustrate the decision-relevant consequences that emerge from each of these choices. Results indicate that the methodological choices in the taxonomy lead to substantially different planning alternatives, underscoring the importance of an informed definition of robustness. We conclude with a set of recommendations for problem framing: that alternatives should be searched rather than prespecified; dominant uncertainties should be discovered rather than assumed; and that a multivariate satisficing measure of robustness allows stakeholders to achieve their problem-specific performance requirements. This work highlights the importance of careful problem formulation, and provides a common vocabulary to link the robustness frameworks widely used in the field of water systems planning.

Comparative evaluation of matrix-assisted laser desorption ionisation-time of flight mass spectrometry and conventional phenotypic-based methods for identification of clinically important yeasts in a UK-based medical microbiology laboratory.

PubMed

Fatania, Nita; Fraser, Mark; Savage, Mike; Hart, Jason; Abdolrasouli, Alireza

2015-12-01

Performance of matrix-assisted laser desorption ionisation-time of flight mass spectrometry (MALDI-TOF MS) was compared in a side-by side-analysis with conventional phenotypic methods currently in use in our laboratory for identification of yeasts in a routine diagnostic setting. A diverse collection of 200 clinically important yeasts (19 species, five genera) were identified by both methods using standard protocols. Discordant or unreliable identifications were resolved by sequencing of the internal transcribed spacer region of the rRNA gene. MALDI-TOF and conventional methods were in agreement for 182 isolates (91%) with correct identification to species level. Eighteen discordant results (9%) were due to rarely encountered species, hence the difficulty in their identification using traditional phenotypic methods. MALDI-TOF MS enabled rapid, reliable and accurate identification of clinically important yeasts in a routine diagnostic microbiology laboratory. Isolates with rare, unusual or low probability identifications should be confirmed using robust molecular methods. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

Set-membership fault detection under noisy environment with application to the detection of abnormal aircraft control surface positions

NASA Astrophysics Data System (ADS)

El Houda Thabet, Rihab; Combastel, Christophe; Raïssi, Tarek; Zolghadri, Ali

2015-09-01

The paper develops a set membership detection methodology which is applied to the detection of abnormal positions of aircraft control surfaces. Robust and early detection of such abnormal positions is an important issue for early system reconfiguration and overall optimisation of aircraft design. In order to improve fault sensitivity while ensuring a high level of robustness, the method combines a data-driven characterisation of noise and a model-driven approach based on interval prediction. The efficiency of the proposed methodology is illustrated through simulation results obtained based on data recorded in several flight scenarios of a highly representative aircraft benchmark.

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

NASA Astrophysics Data System (ADS)

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

2018-02-01

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

Robust Smoothing: Smoothing Parameter Selection and Applications to Fluorescence Spectroscopy∂

PubMed Central

Lee, Jong Soo; Cox, Dennis D.

2009-01-01

Fluorescence spectroscopy has emerged in recent years as an effective way to detect cervical cancer. Investigation of the data preprocessing stage uncovered a need for a robust smoothing to extract the signal from the noise. Various robust smoothing methods for estimating fluorescence emission spectra are compared and data driven methods for the selection of smoothing parameter are suggested. The methods currently implemented in R for smoothing parameter selection proved to be unsatisfactory, and a computationally efficient procedure that approximates robust leave-one-out cross validation is presented. PMID:20729976

Parameters Identification for Motorcycle Simulator's Platform Characterization

NASA Astrophysics Data System (ADS)

Nehaoua, L.; Arioui, H.

2008-06-01

This paper presents the dynamics modeling and parameters identification of a motorcycle simulator's platform. This model begins with some suppositions which consider that the leg dynamics can be neglected with respect to the mobile platform one. The objectif is to synthesis a simplified control scheme, adapted to driving simulation application, minimising dealys and without loss of tracking performance. Electronic system of platform actuation is described. It's based on a CAN BUS communication which offers a large transmission robustness and error handling. Despite some disadvanteges, we adapted a control solution which overcome these inconvenients and preserve the quality of tracking trajectory. A bref description of the simulator's platform is given and results are shown and justified according to our specifications.

Practical and Efficient Searching in Proteomics: A Cross Engine Comparison

PubMed Central

Paulo, Joao A.

2014-01-01

Background Analysis of large datasets produced by mass spectrometry-based proteomics relies on database search algorithms to sequence peptides and identify proteins. Several such scoring methods are available, each based on different statistical foundations and thereby not producing identical results. Here, the aim is to compare peptide and protein identifications using multiple search engines and examine the additional proteins gained by increasing the number of technical replicate analyses. Methods A HeLa whole cell lysate was analyzed on an Orbitrap mass spectrometer for 10 technical replicates. The data were combined and searched using Mascot, SEQUEST, and Andromeda. Comparisons were made of peptide and protein identifications among the search engines. In addition, searches using each engine were performed with incrementing number of technical replicates. Results The number and identity of peptides and proteins differed across search engines. For all three search engines, the differences in proteins identifications were greater than the differences in peptide identifications indicating that the major source of the disparity may be at the protein inference grouping level. The data also revealed that analysis of 2 technical replicates can increase protein identifications by up to 10-15%, while a third replicate results in an additional 4-5%. Conclusions The data emphasize two practical methods of increasing the robustness of mass spectrometry data analysis. The data show that 1) using multiple search engines can expand the number of identified proteins (union) and validate protein identifications (intersection), and 2) analysis of 2 or 3 technical replicates can substantially expand protein identifications. Moreover, information can be extracted from a dataset by performing database searching with different engines and performing technical repeats, which requires no additional sample preparation and effectively utilizes research time and effort. PMID:25346847

Practical and Efficient Searching in Proteomics: A Cross Engine Comparison.

PubMed

Paulo, Joao A

2013-10-01

Analysis of large datasets produced by mass spectrometry-based proteomics relies on database search algorithms to sequence peptides and identify proteins. Several such scoring methods are available, each based on different statistical foundations and thereby not producing identical results. Here, the aim is to compare peptide and protein identifications using multiple search engines and examine the additional proteins gained by increasing the number of technical replicate analyses. A HeLa whole cell lysate was analyzed on an Orbitrap mass spectrometer for 10 technical replicates. The data were combined and searched using Mascot, SEQUEST, and Andromeda. Comparisons were made of peptide and protein identifications among the search engines. In addition, searches using each engine were performed with incrementing number of technical replicates. The number and identity of peptides and proteins differed across search engines. For all three search engines, the differences in proteins identifications were greater than the differences in peptide identifications indicating that the major source of the disparity may be at the protein inference grouping level. The data also revealed that analysis of 2 technical replicates can increase protein identifications by up to 10-15%, while a third replicate results in an additional 4-5%. The data emphasize two practical methods of increasing the robustness of mass spectrometry data analysis. The data show that 1) using multiple search engines can expand the number of identified proteins (union) and validate protein identifications (intersection), and 2) analysis of 2 or 3 technical replicates can substantially expand protein identifications. Moreover, information can be extracted from a dataset by performing database searching with different engines and performing technical repeats, which requires no additional sample preparation and effectively utilizes research time and effort.

Vertebra identification using template matching modelmp and K-means clustering.

PubMed

Larhmam, Mohamed Amine; Benjelloun, Mohammed; Mahmoudi, Saïd

2014-03-01

Accurate vertebra detection and segmentation are essential steps for automating the diagnosis of spinal disorders. This study is dedicated to vertebra alignment measurement, the first step in a computer-aided diagnosis tool for cervical spine trauma. Automated vertebral segment alignment determination is a challenging task due to low contrast imaging and noise. A software tool for segmenting vertebrae and detecting subluxations has clinical significance. A robust method was developed and tested for cervical vertebra identification and segmentation that extracts parameters used for vertebra alignment measurement. Our contribution involves a novel combination of a template matching method and an unsupervised clustering algorithm. In this method, we build a geometric vertebra mean model. To achieve vertebra detection, manual selection of the region of interest is performed initially on the input image. Subsequent preprocessing is done to enhance image contrast and detect edges. Candidate vertebra localization is then carried out by using a modified generalized Hough transform (GHT). Next, an adapted cost function is used to compute local voted centers and filter boundary data. Thereafter, a K-means clustering algorithm is applied to obtain clusters distribution corresponding to the targeted vertebrae. These clusters are combined with the vote parameters to detect vertebra centers. Rigid segmentation is then carried out by using GHT parameters. Finally, cervical spine curves are extracted to measure vertebra alignment. The proposed approach was successfully applied to a set of 66 high-resolution X-ray images. Robust detection was achieved in 97.5 % of the 330 tested cervical vertebrae. An automated vertebral identification method was developed and demonstrated to be robust to noise and occlusion. This work presents a first step toward an automated computer-aided diagnosis system for cervical spine trauma detection.

Sliding-Mode Control Applied for Robust Control of a Highly Unstable Aircraft

NASA Technical Reports Server (NTRS)

Vetter, Travis Kenneth

2002-01-01

An investigation into the application of an observer based sliding mode controller for robust control of a highly unstable aircraft and methods of compensating for actuator dynamics is performed. After a brief overview of some reconfigurable controllers, sliding mode control (SMC) is selected because of its invariance properties and lack of need for parameter identification. SMC is reviewed and issues with parasitic dynamics, which cause system instability, are addressed. Utilizing sliding manifold boundary layers, the nonlinear control is converted to a linear control and sliding manifold design is performed in the frequency domain. An additional feedback form of model reference hedging is employed which is similar to a prefilter and has large benefits to system performance. The effects of inclusion of actuator dynamics into the designed plant is heavily investigated. Multiple Simulink models of the full longitudinal dynamics and wing deflection modes of the forward swept aero elastic vehicle (FSAV) are constructed. Additionally a linear state space models to analyze effects from various system parameters. The FSAV has a pole at +7 rad/sec and is non-minimum phase. The use of 'model actuators' in the feedback path, and varying there design, is heavily investigated for the resulting effects on plant robustness and tolerance to actuator failure. The use of redundant actuators is also explored and improved robustness is shown. All models are simulated with severe failure and excellent tracking, and task dependent handling qualities, and low pilot induced oscillation tendency is shown.

A sensory-driven controller for quadruped locomotion.

PubMed

Ferreira, César; Santos, Cristina P

2017-02-01

Locomotion of quadruped robots has not yet achieved the harmony, flexibility, efficiency and robustness of its biological counterparts. Biological research showed that spinal reflexes are crucial for a successful locomotion in the most varied terrains. In this context, the development of bio-inspired controllers seems to be a good way to move toward an efficient and robust robotic locomotion, by mimicking their biological counterparts. This contribution presents a sensory-driven controller designed for the simulated Oncilla quadruped robot. In the proposed reflex controller, movement is generated through the robot's interactions with the environment, and therefore, the controller is solely dependent on sensory information. The results show that the reflex controller is capable of producing stable quadruped locomotion with a regular stepping pattern. Furthermore, it is capable of dealing with slopes without changing the parameters and with small obstacles, overcoming them successfully. Finally, system robustness was verified by adding noise to sensors and actuators and also delays.

Survey Definitions of Gout for Epidemiologic Studies: Comparison With Crystal Identification as the Gold Standard.

PubMed

Dalbeth, Nicola; Schumacher, H Ralph; Fransen, Jaap; Neogi, Tuhina; Jansen, Tim L; Brown, Melanie; Louthrenoo, Worawit; Vazquez-Mellado, Janitzia; Eliseev, Maxim; McCarthy, Geraldine; Stamp, Lisa K; Perez-Ruiz, Fernando; Sivera, Francisca; Ea, Hang-Korng; Gerritsen, Martijn; Scire, Carlo A; Cavagna, Lorenzo; Lin, Chingtsai; Chou, Yin-Yi; Tausche, Anne-Kathrin; da Rocha Castelar-Pinheiro, Geraldo; Janssen, Matthijs; Chen, Jiunn-Horng; Cimmino, Marco A; Uhlig, Till; Taylor, William J

2016-12-01

To identify the best-performing survey definition of gout from items commonly available in epidemiologic studies. Survey definitions of gout were identified from 34 epidemiologic studies contributing to the Global Urate Genetics Consortium (GUGC) genome-wide association study. Data from the Study for Updated Gout Classification Criteria (SUGAR) were randomly divided into development and test data sets. A data-driven case definition was formed using logistic regression in the development data set. This definition, along with definitions used in GUGC studies and the 2015 American College of Rheumatology (ACR)/European League Against Rheumatism (EULAR) gout classification criteria were applied to the test data set, using monosodium urate crystal identification as the gold standard. For all tested GUGC definitions, the simple definition of "self-report of gout or urate-lowering therapy use" had the best test performance characteristics (sensitivity 82%, specificity 72%). The simple definition had similar performance to a SUGAR data-driven case definition with 5 weighted items: self-report, self-report of doctor diagnosis, colchicine use, urate-lowering therapy use, and hyperuricemia (sensitivity 87%, specificity 70%). Both of these definitions performed better than the 1977 American Rheumatism Association survey criteria (sensitivity 82%, specificity 67%). Of all tested definitions, the 2015 ACR/EULAR criteria had the best performance (sensitivity 92%, specificity 89%). A simple definition of "self-report of gout or urate-lowering therapy use" has the best test performance characteristics of existing definitions that use routinely available data. A more complex combination of features is more sensitive, but still lacks good specificity. If a more accurate case definition is required for a particular study, the 2015 ACR/EULAR gout classification criteria should be considered. © 2016, American College of Rheumatology.

Discriminative and robust zero-watermarking scheme based on completed local binary pattern for authentication and copyright identification of medical images

NASA Astrophysics Data System (ADS)

Liu, Xiyao; Lou, Jieting; Wang, Yifan; Du, Jingyu; Zou, Beiji; Chen, Yan

2018-03-01

Authentication and copyright identification are two critical security issues for medical images. Although zerowatermarking schemes can provide durable, reliable and distortion-free protection for medical images, the existing zerowatermarking schemes for medical images still face two problems. On one hand, they rarely considered the distinguishability for medical images, which is critical because different medical images are sometimes similar to each other. On the other hand, their robustness against geometric attacks, such as cropping, rotation and flipping, is insufficient. In this study, a novel discriminative and robust zero-watermarking (DRZW) is proposed to address these two problems. In DRZW, content-based features of medical images are first extracted based on completed local binary pattern (CLBP) operator to ensure the distinguishability and robustness, especially against geometric attacks. Then, master shares and ownership shares are generated from the content-based features and watermark according to (2,2) visual cryptography. Finally, the ownership shares are stored for authentication and copyright identification. For queried medical images, their content-based features are extracted and master shares are generated. Their watermarks for authentication and copyright identification are recovered by stacking the generated master shares and stored ownership shares. 200 different medical images of 5 types are collected as the testing data and our experimental results demonstrate that DRZW ensures both the accuracy and reliability of authentication and copyright identification. When fixing the false positive rate to 1.00%, the average value of false negative rates by using DRZW is only 1.75% under 20 common attacks with different parameters.

A novel shape from focus method based on 3D steerable filters for improved performance on treating textureless region

NASA Astrophysics Data System (ADS)

Fan, Tiantian; Yu, Hongbin

2018-03-01

A novel shape from focus method combining 3D steerable filter for improved performance on treating textureless region was proposed in this paper. Different from conventional spatial methods focusing on the search of maximum edges' response to estimate the depth map, the currently proposed method took both of the edges' response and the axial imaging blur degree into consideration during treatment. As a result, more robust and accurate identification for the focused location can be achieved, especially when treating textureless objects. Improved performance in depth measurement has been successfully demonstrated from both of the simulation and experiment results.

What are the most effective strategies for improving quality and safety of health care?

PubMed

Scott, I

2009-06-01

There is now a plethora of different quality improvement strategies (QIS) for optimizing health care, some clinician/patient driven, others manager/policy-maker driven. Which of these are most effective remains unclear despite expressed concerns about potential for QIS-related patient harm and wasting of resources. The objective of this study was to review published literature assessing the relative effectiveness of different QIS. Data sources comprising PubMed Clinical Queries, Cochrane Library and its Effective Practice and Organization of Care database, and HealthStar were searched for studies of QIS between January 1985 and February 2008 using search terms based on an a priori QIS classification suggested by experts. Systematic reviews of controlled trials were selected in determining effect sizes for specific QIS, which were compared as a narrative meta-review. Clinician/patient driven QIS were associated with stronger evidence of efficacy and larger effect sizes than manager/policy-maker driven QIS. The most effective strategies (>10% absolute increase in appropriate care or equivalent measure) included clinician-directed audit and feedback cycles, clinical decision support systems, specialty outreach programmes, chronic disease management programmes, continuing professional education based on interactive small-group case discussions, and patient-mediated clinician reminders. Pay-for-performance schemes directed to clinician groups and organizational process redesign were modestly effective. Other manager/policy-maker driven QIS including continuous quality improvement programmes, risk and safety management systems, public scorecards and performance reports, external accreditation, and clinical governance arrangements have not been adequately evaluated with regard to effectiveness. QIS are heterogeneous and methodological flaws in much of the evaluative literature limit validity and generalizability of results. Based on current best available evidence, clinician/patient driven QIS appear to be more effective than manager/policy-maker driven QIS although the latter have, in many instances, attracted insufficient robust evaluations to accurately determine their comparative effectiveness.

Direct identification of bacteria from positive BacT/ALERT blood culture bottles using matrix-assisted laser desorption ionization-time-of-flight mass spectrometry.

PubMed

Mestas, Javier; Felsenstein, Susanna; Bard, Jennifer Dien

2014-11-01

Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry is a fast and robust method for the identification of bacteria. In this study, we evaluate the performance of a laboratory-developed lysis method (LDT) for the rapid identification of bacteria from positive BacT/ALERT blood culture bottles. Of the 168 positive bottles tested, 159 were monomicrobial, the majority of which were Gram-positive organisms (61.0% versus 39.0%). Using a cut-off score of ≥1.7, 80.4% of the organisms were correctly identified to the species level, and the identification rate of Gram-negative organisms (90.3%) was found to be significantly greater than that of Gram-positive organisms (78.4%). The simplicity and cost-effectiveness of the LDT enable it to be fully integrated into the routine workflow of the clinical microbiology laboratory, allowing for rapid identification of Gram-positive and Gram-negative bacteria within an hour of blood culture positivity. Copyright © 2014 Elsevier Inc. All rights reserved.

The design and testing of a novel mechanomyogram-driven switch controlled by small eyebrow movements

PubMed Central

2010-01-01

Background Individuals with severe physical disabilities and minimal motor behaviour may be unable to use conventional mechanical switches for access. These persons may benefit from access technologies that harness the volitional activity of muscles. In this study, we describe the design and demonstrate the performance of a binary switch controlled by mechanomyogram (MMG) signals recorded from the frontalis muscle during eyebrow movements. Methods Muscle contractions, detected in real-time with a continuous wavelet transform algorithm, were used to control a binary switch for computer access. The automatic selection of scale-specific thresholds reduced the effect of artefact, such as eye blinks and head movement, on the performance of the switch. Switch performance was estimated by cued response-tests performed by eleven participants (one with severe physical disabilities). Results The average sensitivity and specificity of the switch was 99.7 ± 0.4% and 99.9 ± 0.1%, respectively. The algorithm performance was robust against typical participant movement. Conclusions The results suggest that the frontalis muscle is a suitable site for controlling the MMG-driven switch. The high accuracies combined with the minimal requisite effort and training show that MMG is a promising binary control signal. Further investigation of the potential benefits of MMG-control for the target population is warranted. PMID:20492680

The design and testing of a novel mechanomyogram-driven switch controlled by small eyebrow movements.

PubMed

Alves, Natasha; Chau, Tom

2010-05-21

Individuals with severe physical disabilities and minimal motor behaviour may be unable to use conventional mechanical switches for access. These persons may benefit from access technologies that harness the volitional activity of muscles. In this study, we describe the design and demonstrate the performance of a binary switch controlled by mechanomyogram (MMG) signals recorded from the frontalis muscle during eyebrow movements. Muscle contractions, detected in real-time with a continuous wavelet transform algorithm, were used to control a binary switch for computer access. The automatic selection of scale-specific thresholds reduced the effect of artefact, such as eye blinks and head movement, on the performance of the switch. Switch performance was estimated by cued response-tests performed by eleven participants (one with severe physical disabilities). The average sensitivity and specificity of the switch was 99.7 +/- 0.4% and 99.9 +/- 0.1%, respectively. The algorithm performance was robust against typical participant movement. The results suggest that the frontalis muscle is a suitable site for controlling the MMG-driven switch. The high accuracies combined with the minimal requisite effort and training show that MMG is a promising binary control signal. Further investigation of the potential benefits of MMG-control for the target population is warranted.

System identification for modeling for control of flexible structures

NASA Technical Reports Server (NTRS)

Mettler, Edward; Milman, Mark

1986-01-01

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

Increased robustness of single-molecule counting with microfluidics, digital isothermal amplification, and a mobile phone versus real-time kinetic measurements.

PubMed

Selck, David A; Karymov, Mikhail A; Sun, Bing; Ismagilov, Rustem F

2013-11-19

Quantitative bioanalytical measurements are commonly performed in a kinetic format and are known to not be robust to perturbation that affects the kinetics itself or the measurement of kinetics. We hypothesized that the same measurements performed in a "digital" (single-molecule) format would show increased robustness to such perturbations. Here, we investigated the robustness of an amplification reaction (reverse-transcription loop-mediated amplification, RT-LAMP) in the context of fluctuations in temperature and time when this reaction is used for quantitative measurements of HIV-1 RNA molecules under limited-resource settings (LRS). The digital format that counts molecules using dRT-LAMP chemistry detected a 2-fold change in concentration of HIV-1 RNA despite a 6 °C temperature variation (p-value = 6.7 × 10(-7)), whereas the traditional kinetic (real-time) format did not (p-value = 0.25). Digital analysis was also robust to a 20 min change in reaction time, to poor imaging conditions obtained with a consumer cell-phone camera, and to automated cloud-based processing of these images (R(2) = 0.9997 vs true counts over a 100-fold dynamic range). Fluorescent output of multiplexed PCR amplification could also be imaged with the cell phone camera using flash as the excitation source. Many nonlinear amplification schemes based on organic, inorganic, and biochemical reactions have been developed, but their robustness is not well understood. This work implies that these chemistries may be significantly more robust in the digital, rather than kinetic, format. It also calls for theoretical studies to predict robustness of these chemistries and, more generally, to design robust reaction architectures. The SlipChip that we used here and other digital microfluidic technologies already exist to enable testing of these predictions. Such work may lead to identification or creation of robust amplification chemistries that enable rapid and precise quantitative molecular measurements under LRS. Furthermore, it may provide more general principles describing robustness of chemical and biological networks in digital formats.

Competitive region orientation code for palmprint verification and identification

NASA Astrophysics Data System (ADS)

Tang, Wenliang

2015-11-01

Orientation features of the palmprint have been widely investigated in coding-based palmprint-recognition methods. Conventional orientation-based coding methods usually used discrete filters to extract the orientation feature of palmprint. However, in real operations, the orientations of the filter usually are not consistent with the lines of the palmprint. We thus propose a competitive region orientation-based coding method. Furthermore, an effective weighted balance scheme is proposed to improve the accuracy of the extracted region orientation. Compared with conventional methods, the region orientation of the palmprint extracted using the proposed method can precisely and robustly describe the orientation feature of the palmprint. Extensive experiments on the baseline PolyU and multispectral palmprint databases are performed and the results show that the proposed method achieves a promising performance in comparison to conventional state-of-the-art orientation-based coding methods in both palmprint verification and identification.

Lateralization of spatial rather than temporal attention underlies the left hemifield advantage in rapid serial visual presentation.

PubMed

Asanowicz, Dariusz; Kruse, Lena; Śmigasiewicz, Kamila; Verleger, Rolf

2017-11-01

In bilateral rapid serial visual presentation (RSVP), the second of two targets, T1 and T2, is better identified in the left visual field (LVF) than in the right visual field (RVF). This LVF advantage may reflect hemispheric asymmetry in temporal attention or/and in spatial orienting of attention. Participants performed two tasks: the "standard" bilateral RSVP task (Exp.1) and its unilateral variant (Exp.1 & 2). In the bilateral task, spatial location was uncertain, thus target identification involved stimulus-driven spatial orienting. In the unilateral task, the targets were presented block-wise in the LVF or RVF only, such that no spatial orienting was needed for target identification. Temporal attention was manipulated in both tasks by varying the T1-T2 lag. The results showed that the LVF advantage disappeared when involvement of stimulus-driven spatial orienting was eliminated, whereas the manipulation of temporal attention had no effect on the asymmetry. In conclusion, the results do not support the hypothesis of hemispheric asymmetry in temporal attention, and provide further evidence that the LVF advantage reflects right hemisphere predominance in stimulus-driven orienting of spatial attention. These conclusions fit evidence that temporal attention is implemented by bilateral parietal areas and spatial attention by the right-lateralized ventral frontoparietal network. Copyright © 2017 Elsevier Inc. All rights reserved.

Aeroservoelastic Uncertainty Model Identification from Flight Data

NASA Technical Reports Server (NTRS)

Brenner, Martin J.

2001-01-01

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

Powering a wireless sensor node with a vibration-driven piezoelectric energy harvester

NASA Astrophysics Data System (ADS)

Reilly, Elizabeth K.; Burghardt, Fred; Fain, Romy; Wright, Paul

2011-12-01

This paper discusses the direct application of scavenged energy to power a wireless sensor platform. A trapezoidal piezoelectric harvester was designed for a specific machine tool application and tested for robustness and longevity as well as performance. The design focused on resonant performance and distributed strain concentrations at a given resonant frequency and acceleration. Critical issues of power coupling and conditioning between harvester and wireless platform were addressed. The wireless platform consisted of a sensor, controller, power conditioning circuitry, and a custom low power radio. The system transmitted a sensor sample once every 10 s in a scavenging environment of 0.25 g and 100 Hz for a system duty cycle of approximately 0.2%.

Identification of inelastic parameters based on deep drawing forming operations using a global-local hybrid Particle Swarm approach

NASA Astrophysics Data System (ADS)

Vaz, Miguel; Luersen, Marco A.; Muñoz-Rojas, Pablo A.; Trentin, Robson G.

2016-04-01

Application of optimization techniques to the identification of inelastic material parameters has substantially increased in recent years. The complex stress-strain paths and high nonlinearity, typical of this class of problems, require the development of robust and efficient techniques for inverse problems able to account for an irregular topography of the fitness surface. Within this framework, this work investigates the application of the gradient-based Sequential Quadratic Programming method, of the Nelder-Mead downhill simplex algorithm, of Particle Swarm Optimization (PSO), and of a global-local PSO-Nelder-Mead hybrid scheme to the identification of inelastic parameters based on a deep drawing operation. The hybrid technique has shown to be the best strategy by combining the good PSO performance to approach the global minimum basin of attraction with the efficiency demonstrated by the Nelder-Mead algorithm to obtain the minimum itself.

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.

Fast-moving soft electronic fish.

PubMed

Li, Tiefeng; Li, Guorui; Liang, Yiming; Cheng, Tingyu; Dai, Jing; Yang, Xuxu; Liu, Bangyuan; Zeng, Zedong; Huang, Zhilong; Luo, Yingwu; Xie, Tao; Yang, Wei

2017-04-01

Soft robots driven by stimuli-responsive materials have unique advantages over conventional rigid robots, especially in their high adaptability for field exploration and seamless interaction with humans. The grand challenge lies in achieving self-powered soft robots with high mobility, environmental tolerance, and long endurance. We are able to advance a soft electronic fish with a fully integrated onboard system for power and remote control. Without any motor, the fish is driven solely by a soft electroactive structure made of dielectric elastomer and ionically conductive hydrogel. The electronic fish can swim at a speed of 6.4 cm/s (0.69 body length per second), which is much faster than previously reported untethered soft robotic fish driven by soft responsive materials. The fish shows consistent performance in a wide temperature range and permits stealth sailing due to its nearly transparent nature. Furthermore, the fish is robust, as it uses the surrounding water as the electric ground and can operate for 3 hours with one single charge. The design principle can be potentially extended to a variety of flexible devices and soft robots.

Fast-moving soft electronic fish

PubMed Central

Li, Tiefeng; Li, Guorui; Liang, Yiming; Cheng, Tingyu; Dai, Jing; Yang, Xuxu; Liu, Bangyuan; Zeng, Zedong; Huang, Zhilong; Luo, Yingwu; Xie, Tao; Yang, Wei

2017-01-01

Soft robots driven by stimuli-responsive materials have unique advantages over conventional rigid robots, especially in their high adaptability for field exploration and seamless interaction with humans. The grand challenge lies in achieving self-powered soft robots with high mobility, environmental tolerance, and long endurance. We are able to advance a soft electronic fish with a fully integrated onboard system for power and remote control. Without any motor, the fish is driven solely by a soft electroactive structure made of dielectric elastomer and ionically conductive hydrogel. The electronic fish can swim at a speed of 6.4 cm/s (0.69 body length per second), which is much faster than previously reported untethered soft robotic fish driven by soft responsive materials. The fish shows consistent performance in a wide temperature range and permits stealth sailing due to its nearly transparent nature. Furthermore, the fish is robust, as it uses the surrounding water as the electric ground and can operate for 3 hours with one single charge. The design principle can be potentially extended to a variety of flexible devices and soft robots. PMID:28435879

An Economical Multifactor within-Subject Design Robust against Trend and Carryover Effects.

DTIC Science & Technology

1985-10-17

ORGANIZATION REPORT NUMBER (S) S. MONIT ,,M.,,...---. 6a. NAME OF PERFORMING ORGANIZATION 6b. OFFICE SYMBOL 7a. NAME OF MONITORING ORGANIZATION Essex...Road Orlando, FL 32813 Orlando, FL 32803 Ba. NAME OF FUNDING/SPONSORING " Sb. OFFICE SYMBOL 9. PROCUREMENT INSTRUMENT IDENTIFICATION NUMBER ...ORGANIZATION (If applicable) S6~1332- &/. 0.-/195𔃺 Sc. ADDRESS (City, State, and ZIP Code) 10. SOURCE OF FUNDING NUMBERS PROGRAM PROJECT TASK WORK UNIT ELEMENT

An experimental study of nonlinear dynamic system identification

NASA Technical Reports Server (NTRS)

Stry, Greselda I.; Mook, D. Joseph

1990-01-01

A technique for robust identification of nonlinear dynamic systems is developed and illustrated using both simulations and analog experiments. The technique is based on the Minimum Model Error 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 of the current work is the ability to identify nonlinear dynamic systems without prior assumptions regarding the form of the nonlinearities, in constrast to existing nonlinear identification approaches which usually require detailed assumptions of the nonlinearities. 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.

Basic concepts and architectural details of the Delphi trigger system

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

Bocci, V.; Booth, P.S.L.; Bozzo, M.

1995-08-01

Delphi (DEtector with Lepton, Photon and Hadron Identification) is one of the four experiments of the LEP (Large Electron Positron) collider at CERN. The detector is laid out to provide a nearly 4 {pi} coverage for charged particle tracking, electromagnetic, hadronic calorimetry and extended particle identification. The trigger system consists of four levels. The first two are synchronous with the BCO (Beam Cross Over) and rely on hardwired control units, while the last two are performed asynchronously with respect to the BCO and are driven by the Delphi host computers. The aim of this paper is to give a comprehensivemore » global view of the trigger system architecture, presenting in detail the first two levels, their various hardware components and the latest modifications introduced in order to improve their performance and make more user friendly the whole software user interface.« less

m6A-Driver: Identifying Context-Specific mRNA m6A Methylation-Driven Gene Interaction Networks

PubMed Central

Zhang, Song-Yao; Zhang, Shao-Wu; Liu, Lian; Huang, Yufei

2016-01-01

As the most prevalent mammalian mRNA epigenetic modification, N6-methyladenosine (m6A) has been shown to possess important post-transcriptional regulatory functions. However, the regulatory mechanisms and functional circuits of m6A are still largely elusive. To help unveil the regulatory circuitry mediated by mRNA m6A methylation, we develop here m6A-Driver, an algorithm for predicting m6A-driven genes and associated networks, whose functional interactions are likely to be actively modulated by m6A methylation under a specific condition. Specifically, m6A-Driver integrates the PPI network and the predicted differential m6A methylation sites from methylated RNA immunoprecipitation sequencing (MeRIP-Seq) data using a Random Walk with Restart (RWR) algorithm and then builds a consensus m6A-driven network of m6A-driven genes. To evaluate the performance, we applied m6A-Driver to build the context-specific m6A-driven networks for 4 known m6A (de)methylases, i.e., FTO, METTL3, METTL14 and WTAP. Our results suggest that m6A-Driver can robustly and efficiently identify m6A-driven genes that are functionally more enriched and associated with higher degree of differential expression than differential m6A methylated genes. Pathway analysis of the constructed context-specific m6A-driven gene networks further revealed the regulatory circuitry underlying the dynamic interplays between the methyltransferases and demethylase at the epitranscriptomic layer of gene regulation. PMID:28027310

Engineering the quantum states of light in a Kerr-nonlinear resonator by two-photon driving

NASA Astrophysics Data System (ADS)

Puri, Shruti; Boutin, Samuel; Blais, Alexandre

2017-04-01

Photonic cat states stored in high-Q resonators show great promise for hardware efficient universal quantum computing. We propose an approach to efficiently prepare such cat states in a Kerr-nonlinear resonator by the use of a two-photon drive. Significantly, we show that this preparation is robust against single-photon loss. An outcome of this observation is that a two-photon drive can eliminate undesirable phase evolution induced by a Kerr nonlinearity. By exploiting the concept of transitionless quantum driving, we moreover demonstrate how non-adiabatic initialization of cat states is possible. Finally, we present a universal set of quantum logical gates that can be performed on the engineered eigenspace of such a two-photon driven resonator and discuss a possible realization using superconducting circuits. The robustness of the engineered subspace to higher-order circuit nonlinearities makes this implementation favorable for scalable quantum computation.

High-density carbon ablator ignition path with low-density gas-filled rugby hohlraum

NASA Astrophysics Data System (ADS)

Amendt, Peter; Ho, Darwin D.; Jones, Ogden S.

2015-04-01

A recent low gas-fill density (0.6 mg/cc 4He) cylindrical hohlraum experiment on the National Ignition Facility has shown high laser-coupling efficiency (>96%), reduced phenomenological laser drive corrections, and improved high-density carbon capsule implosion symmetry [Jones et al., Bull. Am. Phys. Soc. 59(15), 66 (2014)]. In this Letter, an ignition design using a large rugby-shaped hohlraum [Amendt et al., Phys. Plasmas 21, 112703 (2014)] for high energetics efficiency and symmetry control with the same low gas-fill density (0.6 mg/cc 4He) is developed as a potentially robust platform for demonstrating thermonuclear burn. The companion high-density carbon capsule for this hohlraum design is driven by an adiabat-shaped [Betti et al., Phys. Plasmas 9, 2277 (2002)] 4-shock drive profile for robust high gain (>10) 1-D ignition performance and large margin to 2-D perturbation growth.

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.

Application of morphological associative memories and Fourier descriptors for classification of noisy subsurface signatures

NASA Astrophysics Data System (ADS)

Ortiz, Jorge L.; Parsiani, Hamed; Tolstoy, Leonid

2004-02-01

This paper presents a method for recognition of Noisy Subsurface Images using Morphological Associative Memories (MAM). MAM are type of associative memories that use a new kind of neural networks based in the algebra system known as semi-ring. The operations performed in this algebraic system are highly nonlinear providing additional strength when compared to other transformations. Morphological associative memories are a new kind of neural networks that provide a robust performance with noisy inputs. Two representations of morphological associative memories are used called M and W matrices. M associative memory provides a robust association with input patterns corrupted by dilative random noise, while the W associative matrix performs a robust recognition in patterns corrupted with erosive random noise. The robust performance of MAM is used in combination of the Fourier descriptors for the recognition of underground objects in Ground Penetrating Radar (GPR) images. Multiple 2-D GPR images of a site are made available by NASA-SSC center. The buried objects in these images appear in the form of hyperbolas which are the results of radar backscatter from the artifacts or objects. The Fourier descriptors of the prototype hyperbola-like and shapes from non-hyperbola shapes in the sub-surface images are used to make these shapes scale-, shift-, and rotation-invariant. Typical hyperbola-like and non-hyperbola shapes are used to calculate the morphological associative memories. The trained MAMs are used to process other noisy images to detect the presence of these underground objects. The outputs from the MAM using the noisy patterns may be equal to the training prototypes, providing a positive identification of the artifacts. The results are images with recognized hyperbolas which indicate the presence of buried artifacts. A model using MATLAB has been developed and results are presented.

Laboratory for Engineering Man/Machine Systems (LEMS): System identification, model reduction and deconvolution filtering using Fourier based modulating signals and high order statistics

NASA Technical Reports Server (NTRS)

Pan, Jianqiang

1992-01-01

Several important problems in the fields of signal processing and model identification, such as system structure identification, frequency response determination, high order model reduction, high resolution frequency analysis, deconvolution filtering, and etc. Each of these topics involves a wide range of applications and has received considerable attention. Using the Fourier based sinusoidal modulating signals, it is shown that a discrete autoregressive model can be constructed for the least squares identification of continuous systems. Some identification algorithms are presented for both SISO and MIMO systems frequency response determination using only transient data. Also, several new schemes for model reduction were developed. Based upon the complex sinusoidal modulating signals, a parametric least squares algorithm for high resolution frequency estimation is proposed. Numerical examples show that the proposed algorithm gives better performance than the usual. Also, the problem was studied of deconvolution and parameter identification of a general noncausal nonminimum phase ARMA system driven by non-Gaussian stationary random processes. Algorithms are introduced for inverse cumulant estimation, both in the frequency domain via the FFT algorithms and in the domain via the least squares algorithm.

Impurity transport and bulk ion flow in a mixed collisionality stellarator plasma

NASA Astrophysics Data System (ADS)

Newton, S. L.; Helander, P.; Mollén, A.; Smith, H. M.

2017-10-01

The accumulation of impurities in the core of magnetically confined plasmas, resulting from standard collisional transport mechanisms, is a known threat to their performance as fusion energy sources. Whilst the axisymmetric tokamak systems have been shown to benefit from the effect of temperature screening, that is an outward flux of impurities driven by the temperature gradient, impurity accumulation in stellarators was thought to be inevitable, driven robustly by the inward pointing electric field characteristic of hot fusion plasmas. We have shown in Helander et al. (Phys. Rev. Lett., vol. 118, 2017a, 155002) that such screening can in principle also appear in stellarators, in the experimentally relevant mixed collisionality regime, where a highly collisional impurity species is present in a low collisionality bulk plasma. Details of the analytic calculation are presented here, along with the effect of the impurity on the bulk ion flow, which will ultimately affect the bulk contribution to the bootstrap current.

Network-level accident-mapping: Distance based pattern matching using artificial neural network.

PubMed

Deka, Lipika; Quddus, Mohammed

2014-04-01

The objective of an accident-mapping algorithm is to snap traffic accidents onto the correct road segments. Assigning accidents onto the correct segments facilitate to robustly carry out some key analyses in accident research including the identification of accident hot-spots, network-level risk mapping and segment-level accident risk modelling. Existing risk mapping algorithms have some severe limitations: (i) they are not easily 'transferable' as the algorithms are specific to given accident datasets; (ii) they do not perform well in all road-network environments such as in areas of dense road network; and (iii) the methods used do not perform well in addressing inaccuracies inherent in and type of road environment. The purpose of this paper is to develop a new accident mapping algorithm based on the common variables observed in most accident databases (e.g. road name and type, direction of vehicle movement before the accident and recorded accident location). The challenges here are to: (i) develop a method that takes into account uncertainties inherent to the recorded traffic accident data and the underlying digital road network data, (ii) accurately determine the type and proportion of inaccuracies, and (iii) develop a robust algorithm that can be adapted for any accident set and road network of varying complexity. In order to overcome these challenges, a distance based pattern-matching approach is used to identify the correct road segment. This is based on vectors containing feature values that are common in the accident data and the network data. Since each feature does not contribute equally towards the identification of the correct road segments, an ANN approach using the single-layer perceptron is used to assist in "learning" the relative importance of each feature in the distance calculation and hence the correct link identification. The performance of the developed algorithm was evaluated based on a reference accident dataset from the UK confirming that the accuracy is much better than other methods. Crown Copyright © 2014. Published by Elsevier Ltd. All rights reserved.

On Lack of Robustness in Hydrological Model Development Due to Absence of Guidelines for Selecting Calibration and Evaluation Data: Demonstration for Data-Driven Models

NASA Astrophysics Data System (ADS)

Zheng, Feifei; Maier, Holger R.; Wu, Wenyan; Dandy, Graeme C.; Gupta, Hoshin V.; Zhang, Tuqiao

2018-02-01

Hydrological models are used for a wide variety of engineering purposes, including streamflow forecasting and flood-risk estimation. To develop such models, it is common to allocate the available data to calibration and evaluation data subsets. Surprisingly, the issue of how this allocation can affect model evaluation performance has been largely ignored in the research literature. This paper discusses the evaluation performance bias that can arise from how available data are allocated to calibration and evaluation subsets. As a first step to assessing this issue in a statistically rigorous fashion, we present a comprehensive investigation of the influence of data allocation on the development of data-driven artificial neural network (ANN) models of streamflow. Four well-known formal data splitting methods are applied to 754 catchments from Australia and the U.S. to develop 902,483 ANN models. Results clearly show that the choice of the method used for data allocation has a significant impact on model performance, particularly for runoff data that are more highly skewed, highlighting the importance of considering the impact of data splitting when developing hydrological models. The statistical behavior of the data splitting methods investigated is discussed and guidance is offered on the selection of the most appropriate data splitting methods to achieve representative evaluation performance for streamflow data with different statistical properties. Although our results are obtained for data-driven models, they highlight the fact that this issue is likely to have a significant impact on all types of hydrological models, especially conceptual rainfall-runoff models.

Molecular identification of python species: development and validation of a novel assay for forensic investigations.

PubMed

Ciavaglia, Sherryn A; Tobe, Shanan S; Donnellan, Stephen C; Henry, Julianne M; Linacre, Adrian M T

2015-05-01

Python snake species are often encountered in illegal activities and the question of species identity can be pertinent to such criminal investigations. Morphological identification of species of pythons can be confounded by many issues and molecular examination by DNA analysis can provide an alternative and objective means of identification. Our paper reports on the development and validation of a PCR primer pair that amplifies a segment of the mitochondrial cytochrome b gene that has been suggested previously as a good candidate locus for differentiating python species. We used this DNA region to perform species identification of pythons, even when the template DNA was of poor quality, as might be the case with forensic evidentiary items. Validation tests are presented to demonstrate the characteristics of the assay. Tests involved the cross-species amplification of this marker in non-target species, minimum amount of DNA template required, effects of degradation on product amplification and a blind trial to simulate a casework scenario that provided 100% correct identity. Our results demonstrate that this assay performs reliably and robustly on pythons and can be applied directly to forensic investigations where the presence of a species of python is in question. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Modeling the energy performance of event-driven wireless sensor network by using static sink and mobile sink.

PubMed

Chen, Jiehui; Salim, Mariam B; Matsumoto, Mitsuji

2010-01-01

Wireless Sensor Networks (WSNs) designed for mission-critical applications suffer from limited sensing capacities, particularly fast energy depletion. Regarding this, mobile sinks can be used to balance the energy consumption in WSNs, but the frequent location updates of the mobile sinks can lead to data collisions and rapid energy consumption for some specific sensors. This paper explores an optimal barrier coverage based sensor deployment for event driven WSNs where a dual-sink model was designed to evaluate the energy performance of not only static sensors, but Static Sink (SS) and Mobile Sinks (MSs) simultaneously, based on parameters such as sensor transmission range r and the velocity of the mobile sink v, etc. Moreover, a MS mobility model was developed to enable SS and MSs to effectively collaborate, while achieving spatiotemporal energy performance efficiency by using the knowledge of the cumulative density function (cdf), Poisson process and M/G/1 queue. The simulation results verified that the improved energy performance of the whole network was demonstrated clearly and our eDSA algorithm is more efficient than the static-sink model, reducing energy consumption approximately in half. Moreover, we demonstrate that our results are robust to realistic sensing models and also validate the correctness of our results through extensive simulations.

Modeling the Energy Performance of Event-Driven Wireless Sensor Network by Using Static Sink and Mobile Sink

PubMed Central

Chen, Jiehui; Salim, Mariam B.; Matsumoto, Mitsuji

2010-01-01

Wireless Sensor Networks (WSNs) designed for mission-critical applications suffer from limited sensing capacities, particularly fast energy depletion. Regarding this, mobile sinks can be used to balance the energy consumption in WSNs, but the frequent location updates of the mobile sinks can lead to data collisions and rapid energy consumption for some specific sensors. This paper explores an optimal barrier coverage based sensor deployment for event driven WSNs where a dual-sink model was designed to evaluate the energy performance of not only static sensors, but Static Sink (SS) and Mobile Sinks (MSs) simultaneously, based on parameters such as sensor transmission range r and the velocity of the mobile sink v, etc. Moreover, a MS mobility model was developed to enable SS and MSs to effectively collaborate, while achieving spatiotemporal energy performance efficiency by using the knowledge of the cumulative density function (cdf), Poisson process and M/G/1 queue. The simulation results verified that the improved energy performance of the whole network was demonstrated clearly and our eDSA algorithm is more efficient than the static-sink model, reducing energy consumption approximately in half. Moreover, we demonstrate that our results are robust to realistic sensing models and also validate the correctness of our results through extensive simulations. PMID:22163503

Parenchymal texture analysis in digital mammography: robust texture feature identification and equivalence across devices.

PubMed

Keller, Brad M; Oustimov, Andrew; Wang, Yan; Chen, Jinbo; Acciavatti, Raymond J; Zheng, Yuanjie; Ray, Shonket; Gee, James C; Maidment, Andrew D A; Kontos, Despina

2015-04-01

An analytical framework is presented for evaluating the equivalence of parenchymal texture features across different full-field digital mammography (FFDM) systems using a physical breast phantom. Phantom images (FOR PROCESSING) are acquired from three FFDM systems using their automated exposure control setting. A panel of texture features, including gray-level histogram, co-occurrence, run length, and structural descriptors, are extracted. To identify features that are robust across imaging systems, a series of equivalence tests are performed on the feature distributions, in which the extent of their intersystem variation is compared to their intrasystem variation via the Hodges-Lehmann test statistic. Overall, histogram and structural features tend to be most robust across all systems, and certain features, such as edge enhancement, tend to be more robust to intergenerational differences between detectors of a single vendor than to intervendor differences. Texture features extracted from larger regions of interest (i.e., [Formula: see text]) and with a larger offset length (i.e., [Formula: see text]), when applicable, also appear to be more robust across imaging systems. This framework and observations from our experiments may benefit applications utilizing mammographic texture analysis on images acquired in multivendor settings, such as in multicenter studies of computer-aided detection and breast cancer risk assessment.

Source apportionment of soil heavy metals using robust absolute principal component scores-robust geographically weighted regression (RAPCS-RGWR) receptor model.

PubMed

Qu, Mingkai; Wang, Yan; Huang, Biao; Zhao, Yongcun

2018-06-01

The traditional source apportionment models, such as absolute principal component scores-multiple linear regression (APCS-MLR), are usually susceptible to outliers, which may be widely present in the regional geochemical dataset. Furthermore, the models are merely built on variable space instead of geographical space and thus cannot effectively capture the local spatial characteristics of each source contributions. To overcome the limitations, a new receptor model, robust absolute principal component scores-robust geographically weighted regression (RAPCS-RGWR), was proposed based on the traditional APCS-MLR model. Then, the new method was applied to the source apportionment of soil metal elements in a region of Wuhan City, China as a case study. Evaluations revealed that: (i) RAPCS-RGWR model had better performance than APCS-MLR model in the identification of the major sources of soil metal elements, and (ii) source contributions estimated by RAPCS-RGWR model were more close to the true soil metal concentrations than that estimated by APCS-MLR model. It is shown that the proposed RAPCS-RGWR model is a more effective source apportionment method than APCS-MLR (i.e., non-robust and global model) in dealing with the regional geochemical dataset. Copyright © 2018 Elsevier B.V. All rights reserved.

Identification of robust adaptation gene regulatory network parameters using an improved particle swarm optimization algorithm.

PubMed

Huang, X N; Ren, H P

2016-05-13

Robust adaptation is a critical ability of gene regulatory network (GRN) to survive in a fluctuating environment, which represents the system responding to an input stimulus rapidly and then returning to its pre-stimulus steady state timely. In this paper, the GRN is modeled using the Michaelis-Menten rate equations, which are highly nonlinear differential equations containing 12 undetermined parameters. The robust adaption is quantitatively described by two conflicting indices. To identify the parameter sets in order to confer the GRNs with robust adaptation is a multi-variable, multi-objective, and multi-peak optimization problem, which is difficult to acquire satisfactory solutions especially high-quality solutions. A new best-neighbor particle swarm optimization algorithm is proposed to implement this task. The proposed algorithm employs a Latin hypercube sampling method to generate the initial population. The particle crossover operation and elitist preservation strategy are also used in the proposed algorithm. The simulation results revealed that the proposed algorithm could identify multiple solutions in one time running. Moreover, it demonstrated a superior performance as compared to the previous methods in the sense of detecting more high-quality solutions within an acceptable time. The proposed methodology, owing to its universality and simplicity, is useful for providing the guidance to design GRN with superior robust adaptation.

Capillary-driven microfluidic paper-based analytical devices for lab on a chip screening of explosive residues in soil.

PubMed

Ueland, Maiken; Blanes, Lucas; Taudte, Regina V; Stuart, Barbara H; Cole, Nerida; Willis, Peter; Roux, Claude; Doble, Philip

2016-03-04

A novel microfluidic paper-based analytical device (μPAD) was designed to filter, extract, and pre-concentrate explosives from soil for direct analysis by a lab on a chip (LOC) device. The explosives were extracted via immersion of wax-printed μPADs directly into methanol soil suspensions for 10min, whereby dissolved explosives travelled upwards into the μPAD circular sampling reservoir. A chad was punched from the sampling reservoir and inserted into a LOC well containing the separation buffer for direct analysis, avoiding any further extraction step. Eight target explosives were separated and identified by fluorescence quenching. The minimum detectable amounts for all eight explosives were between 1.4 and 5.6ng with recoveries ranging from 53-82% from the paper chad, and 12-40% from soil. This method provides a robust and simple extraction method for rapid identification of explosives in complex soil samples. Copyright © 2016 Elsevier B.V. All rights reserved.

Identification of a small molecule inhibitor of Ebola virus genome replication and transcription using in silico screening.

PubMed

Easton, Victoria; McPhillie, Martin; Garcia-Dorival, Isabel; Barr, John N; Edwards, Thomas A; Foster, Richard; Fishwick, Colin; Harris, Mark

2018-06-02

Ebola virus (EBOV) causes a severe haemorrhagic fever in humans and has a mortality rate over 50%. With no licensed drug treatments available, EBOV poses a significant threat. Investigations into possible therapeutics have been severely hampered by the classification of EBOV as a BSL4 pathogen. Here, we describe a drug discovery pathway combining in silico screening of compounds predicted to bind to a hydrophobic pocket on the nucleoprotein (NP); with a robust and rapid EBOV minigenome assay for inhibitor validation at BSL2. One compound (MCCB4) was efficacious (EC 50 4.8 μM), exhibited low cytotoxicity (CC 50  > 100 μM) and was specific, with no effect on either a T7 RNA polymerase driven firefly luciferase or a Bunyamwera virus minigenome. Further investigations revealed that this small molecule inhibitor was able to outcompete established replication complexes, an essential aspect for a potential EBOV treatment. Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.

Pathways-Driven Sparse Regression Identifies Pathways and Genes Associated with High-Density Lipoprotein Cholesterol in Two Asian Cohorts

PubMed Central

Silver, Matt; Chen, Peng; Li, Ruoying; Cheng, Ching-Yu; Wong, Tien-Yin; Tai, E-Shyong; Teo, Yik-Ying; Montana, Giovanni

2013-01-01

Standard approaches to data analysis in genome-wide association studies (GWAS) ignore any potential functional relationships between gene variants. In contrast gene pathways analysis uses prior information on functional structure within the genome to identify pathways associated with a trait of interest. In a second step, important single nucleotide polymorphisms (SNPs) or genes may be identified within associated pathways. The pathways approach is motivated by the fact that genes do not act alone, but instead have effects that are likely to be mediated through their interaction in gene pathways. Where this is the case, pathways approaches may reveal aspects of a trait's genetic architecture that would otherwise be missed when considering SNPs in isolation. Most pathways methods begin by testing SNPs one at a time, and so fail to capitalise on the potential advantages inherent in a multi-SNP, joint modelling approach. Here, we describe a dual-level, sparse regression model for the simultaneous identification of pathways and genes associated with a quantitative trait. Our method takes account of various factors specific to the joint modelling of pathways with genome-wide data, including widespread correlation between genetic predictors, and the fact that variants may overlap multiple pathways. We use a resampling strategy that exploits finite sample variability to provide robust rankings for pathways and genes. We test our method through simulation, and use it to perform pathways-driven gene selection in a search for pathways and genes associated with variation in serum high-density lipoprotein cholesterol levels in two separate GWAS cohorts of Asian adults. By comparing results from both cohorts we identify a number of candidate pathways including those associated with cardiomyopathy, and T cell receptor and PPAR signalling. Highlighted genes include those associated with the L-type calcium channel, adenylate cyclase, integrin, laminin, MAPK signalling and immune function. PMID:24278029

Pathways-driven sparse regression identifies pathways and genes associated with high-density lipoprotein cholesterol in two Asian cohorts.

PubMed

Silver, Matt; Chen, Peng; Li, Ruoying; Cheng, Ching-Yu; Wong, Tien-Yin; Tai, E-Shyong; Teo, Yik-Ying; Montana, Giovanni

2013-11-01

Standard approaches to data analysis in genome-wide association studies (GWAS) ignore any potential functional relationships between gene variants. In contrast gene pathways analysis uses prior information on functional structure within the genome to identify pathways associated with a trait of interest. In a second step, important single nucleotide polymorphisms (SNPs) or genes may be identified within associated pathways. The pathways approach is motivated by the fact that genes do not act alone, but instead have effects that are likely to be mediated through their interaction in gene pathways. Where this is the case, pathways approaches may reveal aspects of a trait's genetic architecture that would otherwise be missed when considering SNPs in isolation. Most pathways methods begin by testing SNPs one at a time, and so fail to capitalise on the potential advantages inherent in a multi-SNP, joint modelling approach. Here, we describe a dual-level, sparse regression model for the simultaneous identification of pathways and genes associated with a quantitative trait. Our method takes account of various factors specific to the joint modelling of pathways with genome-wide data, including widespread correlation between genetic predictors, and the fact that variants may overlap multiple pathways. We use a resampling strategy that exploits finite sample variability to provide robust rankings for pathways and genes. We test our method through simulation, and use it to perform pathways-driven gene selection in a search for pathways and genes associated with variation in serum high-density lipoprotein cholesterol levels in two separate GWAS cohorts of Asian adults. By comparing results from both cohorts we identify a number of candidate pathways including those associated with cardiomyopathy, and T cell receptor and PPAR signalling. Highlighted genes include those associated with the L-type calcium channel, adenylate cyclase, integrin, laminin, MAPK signalling and immune function.

An information extraction framework for cohort identification using electronic health records.

PubMed

Liu, Hongfang; Bielinski, Suzette J; Sohn, Sunghwan; Murphy, Sean; Wagholikar, Kavishwar B; Jonnalagadda, Siddhartha R; Ravikumar, K E; Wu, Stephen T; Kullo, Iftikhar J; Chute, Christopher G

2013-01-01

Information extraction (IE), a natural language processing (NLP) task that automatically extracts structured or semi-structured information from free text, has become popular in the clinical domain for supporting automated systems at point-of-care and enabling secondary use of electronic health records (EHRs) for clinical and translational research. However, a high performance IE system can be very challenging to construct due to the complexity and dynamic nature of human language. In this paper, we report an IE framework for cohort identification using EHRs that is a knowledge-driven framework developed under the Unstructured Information Management Architecture (UIMA). A system to extract specific information can be developed by subject matter experts through expert knowledge engineering of the externalized knowledge resources used in the framework.

Processing tracking in jMRUI software for magnetic resonance spectra quantitation reproducibility assurance.

PubMed

Jabłoński, Michał; Starčuková, Jana; Starčuk, Zenon

2017-01-23

Proton magnetic resonance spectroscopy is a non-invasive measurement technique which provides information about concentrations of up to 20 metabolites participating in intracellular biochemical processes. In order to obtain any metabolic information from measured spectra a processing should be done in specialized software, like jMRUI. The processing is interactive and complex and often requires many trials before obtaining a correct result. This paper proposes a jMRUI enhancement for efficient and unambiguous history tracking and file identification. A database storing all processing steps, parameters and files used in processing was developed for jMRUI. The solution was developed in Java, authors used a SQL database for robust storage of parameters and SHA-256 hash code for unambiguous file identification. The developed system was integrated directly in jMRUI and it will be publically available. A graphical user interface was implemented in order to make the user experience more comfortable. The database operation is invisible from the point of view of the common user, all tracking operations are performed in the background. The implemented jMRUI database is a tool that can significantly help the user to track the processing history performed on data in jMRUI. The created tool is oriented to be user-friendly, robust and easy to use. The database GUI allows the user to browse the whole processing history of a selected file and learn e.g. what processing lead to the results, where the original data are stored, to obtain the list of all processing actions performed on spectra.

Set-Membership Identification for Robust Control Design

DTIC Science & Technology

1993-04-28

system G can be regarded as having no memory in (18) in terms of G and 0, we get of events prior to t = 1, the initial time. Roughly, this means all...algorithm in [1]. Also in our application, the size of the matrices involved is quite large and special attention should be paid to the memory ...management and algorithmic implementation; otherwise huge amounts of memory will be required to perform the optimization even for modest values of M and N

Robust ion current oscillations under a steady electric field: An ion channel analog.

PubMed

Yan, Yu; Wang, Yunshan; Senapati, Satyajyoti; Schiffbauer, Jarrod; Yossifon, Gilad; Chang, Hsueh-Chia

2016-08-01

We demonstrate a nonlinear, nonequilibrium field-driven ion flux phenomenon, which unlike Teorell's nonlinear multiple field theory, requires only the application of one field: robust autonomous current-mass flux oscillations across a porous monolith coupled to a capillary with a long air bubble, which mimics a hydrophobic protein in an ion channel. The oscillations are driven by the hysteretic wetting dynamics of the meniscus when electro-osmotic flow and pressure driven backflow, due to bubble expansion, compete to approach zero mass flux within the monolith. Delayed rupture of the film around the advancing bubble cuts off the electric field and switches the monolith mass flow from the former to the latter. The meniscus then recedes and repairs the rupture to sustain an oscillation for a range of applied fields. This generic mechanism shares many analogs with current oscillations in cell membrane ion channel. At sufficiently high voltage, the system undergoes a state transition characterized by appearance of the ubiquitous 1/f power spectrum.

Features of Cross-Correlation Analysis in a Data-Driven Approach for Structural Damage Assessment

PubMed Central

Camacho Navarro, Jhonatan; Ruiz, Magda; Villamizar, Rodolfo; Mujica, Luis

2018-01-01

This work discusses the advantage of using cross-correlation analysis in a data-driven approach based on principal component analysis (PCA) and piezodiagnostics to obtain successful diagnosis of events in structural health monitoring (SHM). In this sense, the identification of noisy data and outliers, as well as the management of data cleansing stages can be facilitated through the implementation of a preprocessing stage based on cross-correlation functions. Additionally, this work evidences an improvement in damage detection when the cross-correlation is included as part of the whole damage assessment approach. The proposed methodology is validated by processing data measurements from piezoelectric devices (PZT), which are used in a piezodiagnostics approach based on PCA and baseline modeling. Thus, the influence of cross-correlation analysis used in the preprocessing stage is evaluated for damage detection by means of statistical plots and self-organizing maps. Three laboratory specimens were used as test structures in order to demonstrate the validity of the methodology: (i) a carbon steel pipe section with leak and mass damage types, (ii) an aircraft wing specimen, and (iii) a blade of a commercial aircraft turbine, where damages are specified as mass-added. As the main concluding remark, the suitability of cross-correlation features combined with a PCA-based piezodiagnostic approach in order to achieve a more robust damage assessment algorithm is verified for SHM tasks. PMID:29762505

Features of Cross-Correlation Analysis in a Data-Driven Approach for Structural Damage Assessment.

PubMed

Camacho Navarro, Jhonatan; Ruiz, Magda; Villamizar, Rodolfo; Mujica, Luis; Quiroga, Jabid

2018-05-15

This work discusses the advantage of using cross-correlation analysis in a data-driven approach based on principal component analysis (PCA) and piezodiagnostics to obtain successful diagnosis of events in structural health monitoring (SHM). In this sense, the identification of noisy data and outliers, as well as the management of data cleansing stages can be facilitated through the implementation of a preprocessing stage based on cross-correlation functions. Additionally, this work evidences an improvement in damage detection when the cross-correlation is included as part of the whole damage assessment approach. The proposed methodology is validated by processing data measurements from piezoelectric devices (PZT), which are used in a piezodiagnostics approach based on PCA and baseline modeling. Thus, the influence of cross-correlation analysis used in the preprocessing stage is evaluated for damage detection by means of statistical plots and self-organizing maps. Three laboratory specimens were used as test structures in order to demonstrate the validity of the methodology: (i) a carbon steel pipe section with leak and mass damage types, (ii) an aircraft wing specimen, and (iii) a blade of a commercial aircraft turbine, where damages are specified as mass-added. As the main concluding remark, the suitability of cross-correlation features combined with a PCA-based piezodiagnostic approach in order to achieve a more robust damage assessment algorithm is verified for SHM tasks.

Wavefront-sensor-based electron density measurements for laser-plasma accelerators.

PubMed

Plateau, G R; Matlis, N H; Geddes, C G R; Gonsalves, A J; Shiraishi, S; Lin, C; van Mourik, R A; Leemans, W P

2010-03-01

Characterization of the electron density in laser produced plasmas is presented using direct wavefront analysis of a probe laser beam. The performance of a laser-driven plasma-wakefield accelerator depends on the plasma wavelength and hence on the electron density. Density measurements using a conventional folded-wave interferometer and using a commercial wavefront sensor are compared for different regimes of the laser-plasma accelerator. It is shown that direct wavefront measurements agree with interferometric measurements and, because of the robustness of the compact commercial device, offer greater phase sensitivity and straightforward analysis, improving shot-to-shot plasma density diagnostics.

Conductor requirements for high-temperature superconducting utility power transformers

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

Pleva, E. F.; Mehrotra, V.; Schwenterly, S W

High-temperature superconducting (HTS) coated conductors in utility power transformers must satisfy a set of operating requirements that are driven by two major considerations-HTS transformers must be economically competitive with conventional units, and the conductor must be robust enough to be used in a commercial manufacturing environment. The transformer design and manufacturing process will be described in order to highlight the various requirements that it imposes on the HTS conductor. Spreadsheet estimates of HTS transformer costs allow estimates of the conductor cost required for an HTS transformer to be competitive with a similarly performing conventional unit.

Pressure-driven laminar flow switching for rapid exchange of solution environment around surface adhered biological particles

PubMed Central

Allen, Peter B.; Milne, Graham; Doepker, Byron R.; Chiu, Daniel T.

2010-01-01

This paper describes a technique for rapidly exchanging the solution environment near a surface by displacing laminar flow fluid streams using sudden changes in applied pressure. The method employs off-chip solenoid valves to induce pressure changes, which is important in keeping the microfluidic design simple and the operation of the system robust. The performance of this technique is characterized using simulation and validated with experiments. This technique adds to the microfluidic tool box that is currently available for manipulating the solution environment around biological particles and molecules. PMID:20221560

Noise-Driven Manifestation of Learning in Mature Neural Networks

NASA Astrophysics Data System (ADS)

Monterola, Christopher; Saloma, Caesar

2002-10-01

We show that the generalization capability of a mature thresholding neural network to process above-threshold disturbances in a noise-free environment is extended to subthreshold disturbances by ambient noise without retraining. The ability to benefit from noise is intrinsic and does not have to be learned separately. Nonlinear dependence of sensitivity with noise strength is significantly narrower than in individual threshold systems. Noise has a minimal effect on network performance for above-threshold signals. We resolve two seemingly contradictory responses of trained networks to noise-their ability to benefit from its presence and their robustness against noisy strong disturbances.

Wavefront-sensor-based electron density measurements for laser-plasma accelerators

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

Plateau, Guillaume; Matlis, Nicholas; Geddes, Cameron

2010-02-20

Characterization of the electron density in laser produced plasmas is presented using direct wavefront analysis of a probe laser beam. The performance of a laser-driven plasma-wakefield accelerator depends on the plasma wavelength, hence on the electron density. Density measurements using a conventional folded-wave interferometer and using a commercial wavefront sensor are compared for different regimes of the laser-plasma accelerator. It is shown that direct wavefront measurements agree with interferometric measurements and, because of the robustness of the compact commercial device, have greater phase sensitivity, straightforward analysis, improving shot-to-shot plasma-density diagnostics.

Space Launch System Ascent Flight Control Design

NASA Technical Reports Server (NTRS)

VanZwieten, Tannen S.; Orr, Jeb S.; Wall, John H.; Hall, Charles E.

2014-01-01

A robust and flexible autopilot architecture for NASA's Space Launch System (SLS) family of launch vehicles is presented. As the SLS configurations represent a potentially significant increase in complexity and performance capability of the integrated flight vehicle, it was recognized early in the program that a new, generalized autopilot design should be formulated to fulfill the needs of this new space launch architecture. The present design concept is intended to leverage existing NASA and industry launch vehicle design experience and maintain the extensibility and modularity necessary to accommodate multiple vehicle configurations while relying on proven and flight-tested control design principles for large boost vehicles. The SLS flight control architecture combines a digital three-axis autopilot with traditional bending filters to support robust active or passive stabilization of the vehicle's bending and sloshing dynamics using optimally blended measurements from multiple rate gyros on the vehicle structure. The algorithm also relies on a pseudo-optimal control allocation scheme to maximize the performance capability of multiple vectored engines while accommodating throttling and engine failure contingencies in real time with negligible impact to stability characteristics. The architecture supports active in-flight load relief through the use of a nonlinear observer driven by acceleration measurements, and envelope expansion and robustness enhancement is obtained through the use of a multiplicative forward gain modulation law based upon a simple model reference adaptive control scheme.

Space Launch System Ascent Flight Control Design

NASA Technical Reports Server (NTRS)

Orr, Jeb S.; Wall, John H.; VanZwieten, Tannen S.; Hall, Charles E.

2014-01-01

A robust and flexible autopilot architecture for NASA's Space Launch System (SLS) family of launch vehicles is presented. The SLS configurations represent a potentially significant increase in complexity and performance capability when compared with other manned launch vehicles. It was recognized early in the program that a new, generalized autopilot design should be formulated to fulfill the needs of this new space launch architecture. The present design concept is intended to leverage existing NASA and industry launch vehicle design experience and maintain the extensibility and modularity necessary to accommodate multiple vehicle configurations while relying on proven and flight-tested control design principles for large boost vehicles. The SLS flight control architecture combines a digital three-axis autopilot with traditional bending filters to support robust active or passive stabilization of the vehicle's bending and sloshing dynamics using optimally blended measurements from multiple rate gyros on the vehicle structure. The algorithm also relies on a pseudo-optimal control allocation scheme to maximize the performance capability of multiple vectored engines while accommodating throttling and engine failure contingencies in real time with negligible impact to stability characteristics. The architecture supports active in-flight disturbance compensation through the use of nonlinear observers driven by acceleration measurements. Envelope expansion and robustness enhancement is obtained through the use of a multiplicative forward gain modulation law based upon a simple model reference adaptive control scheme.

Cross-species identification of genomic drivers of squamous cell carcinoma development across preneoplastic intermediates

PubMed Central

Chitsazzadeh, Vida; Coarfa, Cristian; Drummond, Jennifer A.; Nguyen, Tri; Joseph, Aaron; Chilukuri, Suneel; Charpiot, Elizabeth; Adelmann, Charles H.; Ching, Grace; Nguyen, Tran N.; Nicholas, Courtney; Thomas, Valencia D.; Migden, Michael; MacFarlane, Deborah; Thompson, Erika; Shen, Jianjun; Takata, Yoko; McNiece, Kayla; Polansky, Maxim A.; Abbas, Hussein A.; Rajapakshe, Kimal; Gower, Adam; Spira, Avrum; Covington, Kyle R.; Xiao, Weimin; Gunaratne, Preethi; Pickering, Curtis; Frederick, Mitchell; Myers, Jeffrey N.; Shen, Li; Yao, Hui; Su, Xiaoping; Rapini, Ronald P.; Wheeler, David A.; Hawk, Ernest T.; Flores, Elsa R.; Tsai, Kenneth Y.

2016-01-01

Cutaneous squamous cell carcinoma (cuSCC) comprises 15–20% of all skin cancers, accounting for over 700,000 cases in USA annually. Most cuSCC arise in association with a distinct precancerous lesion, the actinic keratosis (AK). To identify potential targets for molecularly targeted chemoprevention, here we perform integrated cross-species genomic analysis of cuSCC development through the preneoplastic AK stage using matched human samples and a solar ultraviolet radiation-driven Hairless mouse model. We identify the major transcriptional drivers of this progression sequence, showing that the key genomic changes in cuSCC development occur in the normal skin to AK transition. Our data validate the use of this ultraviolet radiation-driven mouse cuSCC model for cross-species analysis and demonstrate that cuSCC bears deep molecular similarities to multiple carcinogen-driven SCCs from diverse sites, suggesting that cuSCC may serve as an effective, accessible model for multiple SCC types and that common treatment and prevention strategies may be feasible. PMID:27574101

Multi-damage identification based on joint approximate diagonalisation and robust distance measure

NASA Astrophysics Data System (ADS)

Cao, S.; Ouyang, H.

2017-05-01

Mode shapes or operational deflection shapes are highly sensitive to damage and can be used for multi-damage identification. Nevertheless, one drawback of this kind of methods is that the extracted spatial shape features tend to be compromised by noise, which degrades their damage identification accuracy, especially for incipient damage. To overcome this, joint approximate diagonalisation (JAD) also known as simultaneous diagonalisation is investigated to estimate mode shapes (MS’s) statistically. The major advantage of JAD method is that it efficiently provides the common Eigen-structure of a set of power spectral density matrices. In this paper, a new criterion in terms of coefficient of variation (CV) is utilised to numerically demonstrate the better noise robustness and accuracy of JAD method over traditional frequency domain decomposition method (FDD). Another original contribution is that a new robust damage index (DI) is proposed, which is comprised of local MS distortions of several modes weighted by their associated vibration participation factors. The advantage of doing this is to include fair contributions from changes of all modes concerned. Moreover, the proposed DI provides a measure of damage-induced changes in ‘modal vibration energy’ in terms of the selected mode shapes. Finally, an experimental study is presented to verify the efficiency and noise robustness of JAD method and the proposed DI. The results show that the proposed DI is effective and robust under random vibration situations, which indicates that it has the potential to be applied to practical engineering structures with ambient excitations.

Effective identification of Lactobacillus casei group species: genome-based selection of the gene mutL as the target of a novel multiplex PCR assay.

PubMed

Bottari, Benedetta; Felis, Giovanna E; Salvetti, Elisa; Castioni, Anna; Campedelli, Ilenia; Torriani, Sandra; Bernini, Valentina; Gatti, Monica

2017-07-01

Lactobacillus casei,Lactobacillus paracasei and Lactobacillusrhamnosus form a closely related taxonomic group (the L. casei group) within the facultatively heterofermentative lactobacilli. Strains of these species have been used for a long time as probiotics in a wide range of products, and they represent the dominant species of nonstarter lactic acid bacteria in ripened cheeses, where they contribute to flavour development. The close genetic relationship among those species, as well as the similarity of biochemical properties of the strains, hinders the development of an adequate selective method to identify these bacteria. Despite this being a hot topic, as demonstrated by the large amount of literature about it, the results of different proposed identification methods are often ambiguous and unsatisfactory. The aim of this study was to develop a more robust species-specific identification assay for differentiating the species of the L. casei group. A taxonomy-driven comparative genomic analysis was carried out to select the potential target genes whose similarity could better reflect genome-wide diversity. The gene mutL appeared to be the most promising one and, therefore, a novel species-specific multiplex PCR assay was developed to rapidly and effectively distinguish L. casei, L. paracasei and L. rhamnosus strains. The analysis of a collection of 76 wild dairy isolates, previously identified as members of the L. casei group combining the results of multiple approaches, revealed that the novel designed primers, especially in combination with already existing ones, were able to improve the discrimination power at the species level and reveal previously undiscovered intraspecific biodiversity.

Robust Least-Squares Support Vector Machine With Minimization of Mean and Variance of Modeling Error.

PubMed

Lu, Xinjiang; Liu, Wenbo; Zhou, Chuang; Huang, Minghui

2017-06-13

The least-squares support vector machine (LS-SVM) is a popular data-driven modeling method and has been successfully applied to a wide range of applications. However, it has some disadvantages, including being ineffective at handling non-Gaussian noise as well as being sensitive to outliers. In this paper, a robust LS-SVM method is proposed and is shown to have more reliable performance when modeling a nonlinear system under conditions where Gaussian or non-Gaussian noise is present. The construction of a new objective function allows for a reduction of the mean of the modeling error as well as the minimization of its variance, and it does not constrain the mean of the modeling error to zero. This differs from the traditional LS-SVM, which uses a worst-case scenario approach in order to minimize the modeling error and constrains the mean of the modeling error to zero. In doing so, the proposed method takes the modeling error distribution information into consideration and is thus less conservative and more robust in regards to random noise. A solving method is then developed in order to determine the optimal parameters for the proposed robust LS-SVM. An additional analysis indicates that the proposed LS-SVM gives a smaller weight to a large-error training sample and a larger weight to a small-error training sample, and is thus more robust than the traditional LS-SVM. The effectiveness of the proposed robust LS-SVM is demonstrated using both artificial and real life cases.

Precise-spike-driven synaptic plasticity: learning hetero-association of spatiotemporal spike patterns.

PubMed

Yu, Qiang; Tang, Huajin; Tan, Kay Chen; Li, Haizhou

2013-01-01

A new learning rule (Precise-Spike-Driven (PSD) Synaptic Plasticity) is proposed for processing and memorizing spatiotemporal patterns. PSD is a supervised learning rule that is analytically derived from the traditional Widrow-Hoff rule and can be used to train neurons to associate an input spatiotemporal spike pattern with a desired spike train. Synaptic adaptation is driven by the error between the desired and the actual output spikes, with positive errors causing long-term potentiation and negative errors causing long-term depression. The amount of modification is proportional to an eligibility trace that is triggered by afferent spikes. The PSD rule is both computationally efficient and biologically plausible. The properties of this learning rule are investigated extensively through experimental simulations, including its learning performance, its generality to different neuron models, its robustness against noisy conditions, its memory capacity, and the effects of its learning parameters. Experimental results show that the PSD rule is capable of spatiotemporal pattern classification, and can even outperform a well studied benchmark algorithm with the proposed relative confidence criterion. The PSD rule is further validated on a practical example of an optical character recognition problem. The results again show that it can achieve a good recognition performance with a proper encoding. Finally, a detailed discussion is provided about the PSD rule and several related algorithms including tempotron, SPAN, Chronotron and ReSuMe.

Precise-Spike-Driven Synaptic Plasticity: Learning Hetero-Association of Spatiotemporal Spike Patterns

PubMed Central

Yu, Qiang; Tang, Huajin; Tan, Kay Chen; Li, Haizhou

2013-01-01

A new learning rule (Precise-Spike-Driven (PSD) Synaptic Plasticity) is proposed for processing and memorizing spatiotemporal patterns. PSD is a supervised learning rule that is analytically derived from the traditional Widrow-Hoff rule and can be used to train neurons to associate an input spatiotemporal spike pattern with a desired spike train. Synaptic adaptation is driven by the error between the desired and the actual output spikes, with positive errors causing long-term potentiation and negative errors causing long-term depression. The amount of modification is proportional to an eligibility trace that is triggered by afferent spikes. The PSD rule is both computationally efficient and biologically plausible. The properties of this learning rule are investigated extensively through experimental simulations, including its learning performance, its generality to different neuron models, its robustness against noisy conditions, its memory capacity, and the effects of its learning parameters. Experimental results show that the PSD rule is capable of spatiotemporal pattern classification, and can even outperform a well studied benchmark algorithm with the proposed relative confidence criterion. The PSD rule is further validated on a practical example of an optical character recognition problem. The results again show that it can achieve a good recognition performance with a proper encoding. Finally, a detailed discussion is provided about the PSD rule and several related algorithms including tempotron, SPAN, Chronotron and ReSuMe. PMID:24223789

Functional cliques in the amygdala and related brain networks driven by fear assessment acquired during movie viewing.

PubMed

Kinreich, Sivan; Intrator, Nathan; Hendler, Talma

2011-01-01

One of the greatest challenges involved in studying the brain mechanisms of fear is capturing the individual's unique instantaneous experience. Brain imaging studies to date commonly sacrifice valuable information regarding the individual real-time conscious experience, especially when focusing on elucidating the amygdala's activity. Here, we assumed that by using a minimally intrusive cue along with applying a robust clustering approach to probe the amygdala, it would be possible to rate fear in real time and to derive the related network of activation. During functional magnetic resonance imaging scanning, healthy volunteers viewed two excerpts from horror movies and were periodically auditory cued to rate their instantaneous experience of "I'm scared." Using graph theory and community mathematical concepts, data-driven clustering of the fear-related functional cliques in the amygdala was performed guided by the individually marked periods of heightened fear. Individually tailored functions derived from these amygdala activation cliques were subsequently applied as general linear model predictors to a whole-brain analysis to reveal the correlated networks. Our results suggest that by using a localized robust clustering approach, it is possible to probe activation in the right dorsal amygdala that is directly related to individual real-time emotional experience. Moreover, this fear-evoked amygdala revealed two opposing networks of co-activation and co-deactivation, which correspond to vigilance and rest-related circuits, respectively.

Learning partial differential equations via data discovery and sparse optimization

NASA Astrophysics Data System (ADS)

Schaeffer, Hayden

2017-01-01

We investigate the problem of learning an evolution equation directly from some given data. This work develops a learning algorithm to identify the terms in the underlying partial differential equations and to approximate the coefficients of the terms only using data. The algorithm uses sparse optimization in order to perform feature selection and parameter estimation. The features are data driven in the sense that they are constructed using nonlinear algebraic equations on the spatial derivatives of the data. Several numerical experiments show the proposed method's robustness to data noise and size, its ability to capture the true features of the data, and its capability of performing additional analytics. Examples include shock equations, pattern formation, fluid flow and turbulence, and oscillatory convection.

High-throughput measurements of biochemical responses using the plate::vision multimode 96 minilens array reader.

PubMed

Huang, Kuo-Sen; Mark, David; Gandenberger, Frank Ulrich

2006-01-01

The plate::vision is a high-throughput multimode reader capable of reading absorbance, fluorescence, fluorescence polarization, time-resolved fluorescence, and luminescence. Its performance has been shown to be quite comparable with other readers. When the reader is integrated into the plate::explorer, an ultrahigh-throughput screening system with event-driven software and parallel plate-handling devices, it becomes possible to run complicated assays with kinetic readouts in high-density microtiter plate formats for high-throughput screening. For the past 5 years, we have used the plate::vision and the plate::explorer to run screens and have generated more than 30 million data points. Their throughput, performance, and robustness have speeded up our drug discovery process greatly.

Learning partial differential equations via data discovery and sparse optimization.

PubMed

Schaeffer, Hayden

2017-01-01

We investigate the problem of learning an evolution equation directly from some given data. This work develops a learning algorithm to identify the terms in the underlying partial differential equations and to approximate the coefficients of the terms only using data. The algorithm uses sparse optimization in order to perform feature selection and parameter estimation. The features are data driven in the sense that they are constructed using nonlinear algebraic equations on the spatial derivatives of the data. Several numerical experiments show the proposed method's robustness to data noise and size, its ability to capture the true features of the data, and its capability of performing additional analytics. Examples include shock equations, pattern formation, fluid flow and turbulence, and oscillatory convection.

Learning partial differential equations via data discovery and sparse optimization

PubMed Central

2017-01-01

We investigate the problem of learning an evolution equation directly from some given data. This work develops a learning algorithm to identify the terms in the underlying partial differential equations and to approximate the coefficients of the terms only using data. The algorithm uses sparse optimization in order to perform feature selection and parameter estimation. The features are data driven in the sense that they are constructed using nonlinear algebraic equations on the spatial derivatives of the data. Several numerical experiments show the proposed method's robustness to data noise and size, its ability to capture the true features of the data, and its capability of performing additional analytics. Examples include shock equations, pattern formation, fluid flow and turbulence, and oscillatory convection. PMID:28265183

Outcome-Driven Service Provider Performance under Conditions of Complexity and Uncertainty, Defense Acquisition in Transition, Volume 2, 13-14 May 2009.

DTIC Science & Technology

2009-04-22

bandwidth and response times. Forrester Research uses the analogy of a consumer using an automated teller machine to explain how technical SLAs should...be crafted. “It’s not enough that you put your card and Personal Identification Number (PIN) [in the machine ] and request to withdraw cash...IRR) Net Present Value (NPV) Other Relevant Metrics Payback Period Cost/Benefit Ratio Cost, Economic, and/or Financial Analysis Yes Yes Yes

Dynamical density functional theory analysis of the laning instability in sheared soft matter.

PubMed

Scacchi, A; Archer, A J; Brader, J M

2017-12-01

Using dynamical density functional theory (DDFT) methods we investigate the laning instability of a sheared colloidal suspension. The nonequilibrium ordering at the laning transition is driven by nonaffine particle motion arising from interparticle interactions. Starting from a DDFT which incorporates the nonaffine motion, we perform a linear stability analysis that enables identification of the regions of parameter space where lanes form. We illustrate our general approach by applying it to a simple one-component fluid of soft penetrable particles.

Nonlinear data-driven identification of polymer electrolyte membrane fuel cells for diagnostic purposes: A Volterra series approach

NASA Astrophysics Data System (ADS)

Ritzberger, D.; Jakubek, S.

2017-09-01

In this work, a data-driven identification method, based on polynomial nonlinear autoregressive models with exogenous inputs (NARX) and the Volterra series, is proposed to describe the dynamic and nonlinear voltage and current characteristics of polymer electrolyte membrane fuel cells (PEMFCs). The structure selection and parameter estimation of the NARX model is performed on broad-band voltage/current data. By transforming the time-domain NARX model into a Volterra series representation using the harmonic probing algorithm, a frequency-domain description of the linear and nonlinear dynamics is obtained. With the Volterra kernels corresponding to different operating conditions, information from existing diagnostic tools in the frequency domain such as electrochemical impedance spectroscopy (EIS) and total harmonic distortion analysis (THDA) are effectively combined. Additionally, the time-domain NARX model can be utilized for fault detection by evaluating the difference between measured and simulated output. To increase the fault detectability, an optimization problem is introduced which maximizes this output residual to obtain proper excitation frequencies. As a possible extension it is shown, that by optimizing the periodic signal shape itself that the fault detectability is further increased.

A fluorescence-based polymerase chain reaction-linked single-strand conformation polymorphism (F-PCR-SSCP) assay for the identification of Fasciola spp.

PubMed

Alasaad, Samer; Soriguer, Ramón C; Abu-Madi, Marawan; El Behairy, Ahmed; Baños, Pablo Díez; Píriz, Ana; Fickel, Joerns; Zhu, Xing-Quan

2011-06-01

The present study aimed to establish a fluorescence-based polymerase chain reaction-linked single-strand conformation polymorphism (F-PCR-SSCP) assay for the identification of Fasciola spp. Based on the sequences of the second internal transcribed spacer (ITS-2) of the nuclear ribosomal DNA, we designed a set of genus-specific primers for the amplification of Fasciola ITS-2, with an estimated size of 140 bp. These primers were labelled by fluorescence dyes, and the PCR products were analyzed by capillary electrophoresis under non-denaturing conditions (F-PCR-SSCP). Capillary electrophoresis analysis of the fluorescence-labelled DNA fragments displayed three different peak profiles that allowed the accurate identification of Fasciola species: one single peak specific for either Fasciola hepatica or Fasciola gigantica and a doublet peak corresponding to the "intermediate" Fasciola. Validation of our novel method was performed using Fasciola specimens from different host animals from China, Spain, Nigeria, and Egypt. This F-PCR-SSCP assay provides a rapid, simple, and robust tool for the identification and differentiation between Fasciola spp.

Alphabetic letter identification: Effects of perceivability, similarity, and bias☆

PubMed Central

Mueller, Shane T.; Weidemann, Christoph T.

2012-01-01

The legibility of the letters in the Latin alphabet has been measured numerous times since the beginning of experimental psychology. To identify the theoretical mechanisms attributed to letter identification, we report a comprehensive review of literature, spanning more than a century. This review revealed that identification accuracy has frequently been attributed to a subset of three common sources: perceivability, bias, and similarity. However, simultaneous estimates of these values have rarely (if ever) been performed. We present the results of two new experiments which allow for the simultaneous estimation of these factors, and examine how the shape of a visual mask impacts each of them, as inferred through a new statistical model. Results showed that the shape and identity of the mask impacted the inferred perceivability, bias, and similarity space of a letter set, but that there were aspects of similarity that were robust to the choice of mask. The results illustrate how the psychological concepts of perceivability, bias, and similarity can be estimated simultaneously, and how each make powerful contributions to visual letter identification. PMID:22036587

De-identification of health records using Anonym: effectiveness and robustness across datasets.

PubMed

Zuccon, Guido; Kotzur, Daniel; Nguyen, Anthony; Bergheim, Anton

2014-07-01

Evaluate the effectiveness and robustness of Anonym, a tool for de-identifying free-text health records based on conditional random fields classifiers informed by linguistic and lexical features, as well as features extracted by pattern matching techniques. De-identification of personal health information in electronic health records is essential for the sharing and secondary usage of clinical data. De-identification tools that adapt to different sources of clinical data are attractive as they would require minimal intervention to guarantee high effectiveness. The effectiveness and robustness of Anonym are evaluated across multiple datasets, including the widely adopted Integrating Biology and the Bedside (i2b2) dataset, used for evaluation in a de-identification challenge. The datasets used here vary in type of health records, source of data, and their quality, with one of the datasets containing optical character recognition errors. Anonym identifies and removes up to 96.6% of personal health identifiers (recall) with a precision of up to 98.2% on the i2b2 dataset, outperforming the best system proposed in the i2b2 challenge. The effectiveness of Anonym across datasets is found to depend on the amount of information available for training. Findings show that Anonym compares to the best approach from the 2006 i2b2 shared task. It is easy to retrain Anonym with new datasets; if retrained, the system is robust to variations of training size, data type and quality in presence of sufficient training data. Crown Copyright © 2014. Published by Elsevier B.V. All rights reserved.

A numerical study of sensory-guided multiple views for improved object identification

NASA Astrophysics Data System (ADS)

Blakeslee, B. A.; Zelnio, E. G.; Koditschek, D. E.

2014-06-01

We explore the potential on-line adjustment of sensory controls for improved object identification and discrimination in the context of a simulated high resolution camera system carried onboard a maneuverable robotic platform that can actively choose its observational position and pose. Our early numerical studies suggest the significant efficacy and enhanced performance achieved by even very simple feedback-driven iteration of the view in contrast to identification from a fixed pose, uninformed by any active adaptation. Specifically, we contrast the discriminative performance of the same conventional classification system when informed by: a random glance at a vehicle; two random glances at a vehicle; or a random glance followed by a guided second look. After each glance, edge detection algorithms isolate the most salient features of the image and template matching is performed through the use of the Hausdor↵ distance, comparing the simulated sensed images with reference images of the vehicles. We present initial simulation statistics that overwhelmingly favor the third scenario. We conclude with a sketch of our near-future steps in this study that will entail: the incorporation of more sophisticated image processing and template matching algorithms; more complex discrimination tasks such as distinguishing between two similar vehicles or vehicles in motion; more realistic models of the observers mobility including platform dynamics and eventually environmental constraints; and expanding the sensing task beyond the identification of a specified object selected from a pre-defined library of alternatives.

Investigating speech perception in children with dyslexia: is there evidence of a consistent deficit in individuals?

PubMed Central

Messaoud-Galusi, Souhila; Hazan, Valerie; Rosen, Stuart

2012-01-01

Purpose The claim that speech perception abilities are impaired in dyslexia was investigated in a group of 62 dyslexic children and 51 average readers matched in age. Method To test whether there was robust evidence of speech perception deficits in children with dyslexia, speech perception in noise and quiet was measured using eight different tasks involving the identification and discrimination of a complex and highly natural synthetic ‘pea’-‘bee’ contrast (copy synthesised from natural models) and the perception of naturally-produced words. Results Children with dyslexia, on average, performed more poorly than average readers in the synthetic syllables identification task in quiet and in across-category discrimination (but not when tested using an adaptive procedure). They did not differ from average readers on two tasks of word recognition in noise or identification of synthetic syllables in noise. For all tasks, a majority of individual children with dyslexia performed within norms. Finally, speech perception generally did not correlate with pseudo-word reading or phonological processing, the core skills related to dyslexia. Conclusions On the tasks and speech stimuli we used, most children with dyslexia do not appear to show a consistent deficit in speech perception. PMID:21930615

Carrier-envelope phase-dependent effect of high-order sideband generation in ultrafast driven optomechanical system.

PubMed

Xiong, Hao; Si, Liu-Gang; Lü, Xin-You; Yang, Xiaoxue; Wu, Ying

2013-02-01

We analyze the features of the output field of a generic optomechanical system that is driven by a control field and a nanosecond driven pulse, and find a robust high-order sideband generation in optomechanical systems. The typical spectral structure, plateau and cutoff, confirms the nonperturbative nature of the effect, which is similar to high-order harmonic generation in atoms or molecules. Based on the phenomenon, we show that the carrier-envelope phase of laser pulses that contain huge numbers of cycles can cause profound effects.

Characterization of performance-emission indices of a diesel engine using ANFIS operating in dual-fuel mode with LPG

NASA Astrophysics Data System (ADS)

Chakraborty, Amitav; Roy, Sumit; Banerjee, Rahul

2018-03-01

This experimental work highlights the inherent capability of an adaptive-neuro fuzzy inference system (ANFIS) based model to act as a robust system identification tool (SIT) in prognosticating the performance and emission parameters of an existing diesel engine running of diesel-LPG dual fuel mode. The developed model proved its adeptness by successfully harnessing the effects of the input parameters of load, injection duration and LPG energy share on output parameters of BSFCEQ, BTE, NOX, SOOT, CO and HC. Successive evaluation of the ANFIS model, revealed high levels of resemblance with the already forecasted ANN results for the same input parameters and it was evident that similar to ANN, ANFIS also has the innate ability to act as a robust SIT. The ANFIS predicted data harmonized the experimental data with high overall accuracy. The correlation coefficient (R) values are stretched in between 0.99207 to 0.999988. The mean absolute percentage error (MAPE) tallies were recorded in the range of 0.02-0.173% with the root mean square errors (RMSE) in acceptable margins. Hence the developed model is capable of emulating the actual engine parameters with commendable ranges of accuracy, which in turn would act as a robust prediction platform in the future domains of optimization.

Performance Evaluation and Online Realization of Data-driven Normalization Methods Used in LC/MS based Untargeted Metabolomics Analysis.

PubMed

Li, Bo; Tang, Jing; Yang, Qingxia; Cui, Xuejiao; Li, Shuang; Chen, Sijie; Cao, Quanxing; Xue, Weiwei; Chen, Na; Zhu, Feng

2016-12-13

In untargeted metabolomics analysis, several factors (e.g., unwanted experimental &biological variations and technical errors) may hamper the identification of differential metabolic features, which requires the data-driven normalization approaches before feature selection. So far, ≥16 normalization methods have been widely applied for processing the LC/MS based metabolomics data. However, the performance and the sample size dependence of those methods have not yet been exhaustively compared and no online tool for comparatively and comprehensively evaluating the performance of all 16 normalization methods has been provided. In this study, a comprehensive comparison on these methods was conducted. As a result, 16 methods were categorized into three groups based on their normalization performances across various sample sizes. The VSN, the Log Transformation and the PQN were identified as methods of the best normalization performance, while the Contrast consistently underperformed across all sub-datasets of different benchmark data. Moreover, an interactive web tool comprehensively evaluating the performance of 16 methods specifically for normalizing LC/MS based metabolomics data was constructed and hosted at http://server.idrb.cqu.edu.cn/MetaPre/. In summary, this study could serve as a useful guidance to the selection of suitable normalization methods in analyzing the LC/MS based metabolomics data.

Performance Evaluation and Online Realization of Data-driven Normalization Methods Used in LC/MS based Untargeted Metabolomics Analysis

PubMed Central

Li, Bo; Tang, Jing; Yang, Qingxia; Cui, Xuejiao; Li, Shuang; Chen, Sijie; Cao, Quanxing; Xue, Weiwei; Chen, Na; Zhu, Feng

2016-01-01

In untargeted metabolomics analysis, several factors (e.g., unwanted experimental & biological variations and technical errors) may hamper the identification of differential metabolic features, which requires the data-driven normalization approaches before feature selection. So far, ≥16 normalization methods have been widely applied for processing the LC/MS based metabolomics data. However, the performance and the sample size dependence of those methods have not yet been exhaustively compared and no online tool for comparatively and comprehensively evaluating the performance of all 16 normalization methods has been provided. In this study, a comprehensive comparison on these methods was conducted. As a result, 16 methods were categorized into three groups based on their normalization performances across various sample sizes. The VSN, the Log Transformation and the PQN were identified as methods of the best normalization performance, while the Contrast consistently underperformed across all sub-datasets of different benchmark data. Moreover, an interactive web tool comprehensively evaluating the performance of 16 methods specifically for normalizing LC/MS based metabolomics data was constructed and hosted at http://server.idrb.cqu.edu.cn/MetaPre/. In summary, this study could serve as a useful guidance to the selection of suitable normalization methods in analyzing the LC/MS based metabolomics data. PMID:27958387

Adaptive control of large space structures using recursive lattice filters

NASA Technical Reports Server (NTRS)

Sundararajan, N.; Goglia, G. L.

1985-01-01

The use of recursive lattice filters for identification and adaptive control of large space structures is studied. Lattice filters were used to identify the structural dynamics model of the flexible structures. This identification model is then used for adaptive control. Before the identified model and control laws are integrated, the identified model is passed through a series of validation procedures and only when the model passes these validation procedures is control engaged. This type of validation scheme prevents instability when the overall loop is closed. Another important area of research, namely that of robust controller synthesis, was investigated using frequency domain multivariable controller synthesis methods. The method uses the Linear Quadratic Guassian/Loop Transfer Recovery (LQG/LTR) approach to ensure stability against unmodeled higher frequency modes and achieves the desired performance.

Robustness effect of gap junctions between Golgi cells on cerebellar cortex oscillations

PubMed Central

2011-01-01

Background Previous one-dimensional network modeling of the cerebellar granular layer has been successfully linked with a range of cerebellar cortex oscillations observed in vivo. However, the recent discovery of gap junctions between Golgi cells (GoCs), which may cause oscillations by themselves, has raised the question of how gap-junction coupling affects GoC and granular-layer oscillations. To investigate this question, we developed a novel two-dimensional computational model of the GoC-granule cell (GC) circuit with and without gap junctions between GoCs. Results Isolated GoCs coupled by gap junctions had a strong tendency to generate spontaneous oscillations without affecting their mean firing frequencies in response to distributed mossy fiber input. Conversely, when GoCs were synaptically connected in the granular layer, gap junctions increased the power of the oscillations, but the oscillations were primarily driven by the synaptic feedback loop between GoCs and GCs, and the gap junctions did not change oscillation frequency or the mean firing rate of either GoCs or GCs. Conclusion Our modeling results suggest that gap junctions between GoCs increase the robustness of cerebellar cortex oscillations that are primarily driven by the feedback loop between GoCs and GCs. The robustness effect of gap junctions on synaptically driven oscillations observed in our model may be a general mechanism, also present in other regions of the brain. PMID:22330240

High-performance scientific computing in the cloud

NASA Astrophysics Data System (ADS)

Jorissen, Kevin; Vila, Fernando; Rehr, John

2011-03-01

Cloud computing has the potential to open up high-performance computational science to a much broader class of researchers, owing to its ability to provide on-demand, virtualized computational resources. However, before such approaches can become commonplace, user-friendly tools must be developed that hide the unfamiliar cloud environment and streamline the management of cloud resources for many scientific applications. We have recently shown that high-performance cloud computing is feasible for parallelized x-ray spectroscopy calculations. We now present benchmark results for a wider selection of scientific applications focusing on electronic structure and spectroscopic simulation software in condensed matter physics. These applications are driven by an improved portable interface that can manage virtual clusters and run various applications in the cloud. We also describe a next generation of cluster tools, aimed at improved performance and a more robust cluster deployment. Supported by NSF grant OCI-1048052.

Identification of copy number variation-driven genes for liver cancer via bioinformatics analysis.

PubMed

Lu, Xiaojie; Ye, Kun; Zou, Kailin; Chen, Jinlian

2014-11-01

To screen out copy number variation (CNV)-driven differentially expressed genes (DEGs) in liver cancer and advance our understanding of the pathogenesis, an integrated analysis of liver cancer-related CNV data from The Cancer Genome Atlas (TCGA) and gene expression data from EBI Array Express database were performed. The DEGs were identified by package limma based on the cut-off of |log2 (fold-change)|>0.585 and adjusted p-value<0.05. Using hg19 annotation information provided by UCSC, liver cancer-related CNVs were then screened out. TF-target gene interactions were also predicted with information from UCSC using DAVID online tools. As a result, 25 CNV-driven genes were obtained, including tripartite motif containing 28 (TRIM28) and RanBP-type and C3HC4-type zinc finger containing 1 (RBCK1). In the transcriptional regulatory network, 8 known cancer-related transcription factors (TFs) interacted with 21 CNV-driven genes, suggesting that the other 8 TFs may be involved in liver cancer. These genes may be potential biomarkers for early detection and prevention of liver cancer. These findings may improve our knowledge of the pathogenesis of liver cancer. Nevertheless, further experiments are still needed to confirm our findings.

A New Quantum Watermarking Based on Quantum Wavelet Transforms

NASA Astrophysics Data System (ADS)

Heidari, Shahrokh; Naseri, Mosayeb; Gheibi, Reza; Baghfalaki, Masoud; Rasoul Pourarian, Mohammad; Farouk, Ahmed

2017-06-01

Quantum watermarking is a technique to embed specific information, usually the owner’s identification, into quantum cover data such for copyright protection purposes. In this paper, a new scheme for quantum watermarking based on quantum wavelet transforms is proposed which includes scrambling, embedding and extracting procedures. The invisibility and robustness performances of the proposed watermarking method is confirmed by simulation technique. The invisibility of the scheme is examined by the peak-signal-to-noise ratio (PSNR) and the histogram calculation. Furthermore the robustness of the scheme is analyzed by the Bit Error Rate (BER) and the Correlation Two-Dimensional (Corr 2-D) calculation. The simulation results indicate that the proposed watermarking scheme indicate not only acceptable visual quality but also a good resistance against different types of attack. Supported by Kermanshah Branch, Islamic Azad University, Kermanshah, Iran

Adaptive testing for multiple traits in a proportional odds model with applications to detect SNP-brain network associations.

PubMed

Kim, Junghi; Pan, Wei

2017-04-01

There has been increasing interest in developing more powerful and flexible statistical tests to detect genetic associations with multiple traits, as arising from neuroimaging genetic studies. Most of existing methods treat a single trait or multiple traits as response while treating an SNP as a predictor coded under an additive inheritance mode. In this paper, we follow an earlier approach in treating an SNP as an ordinal response while treating traits as predictors in a proportional odds model (POM). In this way, it is not only easier to handle mixed types of traits, e.g., some quantitative and some binary, but it is also potentially more robust to the commonly adopted additive inheritance mode. More importantly, we develop an adaptive test in a POM so that it can maintain high power across many possible situations. Compared to the existing methods treating multiple traits as responses, e.g., in a generalized estimating equation (GEE) approach, the proposed method can be applied to a high dimensional setting where the number of phenotypes (p) can be larger than the sample size (n), in addition to a usual small P setting. The promising performance of the proposed method was demonstrated with applications to the Alzheimer's Disease Neuroimaging Initiative (ADNI) data, in which either structural MRI driven phenotypes or resting-state functional MRI (rs-fMRI) derived brain functional connectivity measures were used as phenotypes. The applications led to the identification of several top SNPs of biological interest. Furthermore, simulation studies showed competitive performance of the new method, especially for p>n. © 2017 WILEY PERIODICALS, INC.

Flow-cytometric identification of vinegars using a multi-parameter analysis optical detection module

NASA Astrophysics Data System (ADS)

Verschooten, T.; Ottevaere, H.; Vervaeke, M.; Van Erps, J.; Callewaert, M.; De Malsche, W.; Thienpont, H.

2015-09-01

We show a proof-of-concept demonstration of a multi-parameter analysis low-cost optical detection system for the flowcytometric identification of vinegars. This multi-parameter analysis system can simultaneously measure laser induced fluorescence, absorption and scattering excited by two time-multiplexed lasers of different wavelengths. To our knowledge no other polymer optofluidic chip based system offers more simultaneous measurements. The design of the optofluidic channels is aimed at countering the effects that viscous fingering, air bubbles, and emulsion samples can have on the correct operation of such a detection system. Unpredictable variations in viscosity and refractive index of the channel content can be turned into a source of information. The sample is excited by two laser diodes that are driven by custom made low-cost laser drivers. The optofluidic chip is built to be robust and easy to handle and is reproducible using hot embossing. We show a custom optomechanical holder for the optofluidic chip that ensures correct alignment and automatic connection to the external fluidic system. We show an experiment in which 92 samples of vinegar are measured. We are able to identify 9 different kinds of vinegar with an accuracy of 94%. Thus we show an alternative approach to the classic optical spectroscopy solution at a lowered. Furthermore, we have shown the possibility of predicting the viscosity and turbidity of vinegars with a goodness-of-fit R2 over 0.947.

A General Identification of Instabilities in Solar Wind Plasma, and a Particular Application to the WIND Data Set.

NASA Astrophysics Data System (ADS)

Klein, Kristopher; Kasper, Justin; Korreck, Kelly; Alterman, Benjamin

2017-04-01

The role of free-energy driven instabilities in governing heating and acceleration processes in the heliosphere has been studied for over half a century, with significant recent advancements enabled by the statistical analysis of decades worth of observations from missions such as WIND. Typical studies focus on marginal stability boundaries in a reduced parameter space, such as the canonical plasma beta versus temperature anisotropy plane, due to a single source of free energy. We present a more general method of determining stability, accounting for all possible sources of free energy in the constituent plasma velocity distributions. Through this novel implementation, we can efficiently determine if the plasma is linearly unstable, and if so, how many normal modes are growing. Such identification will enabling us to better pinpoint the dominant heating or acceleration processes in solar wind plasma. The theory behind this approach is reviewed, followed by a discussion of our methods for a robust numerical implementation, and an initial application to portions of the WIND data set. Further application of this method to velocity distribution measurements from current missions, including WIND, upcoming missions, including Solar Probe Plus and Solar Orbiter, and missions currently in preliminary phases, such as ESA's THOR and NASA's IMAP, will help elucidate how instabilities shape the evolution of the heliosphere.

Validation of powder X-ray diffraction following EN ISO/IEC 17025.

PubMed

Eckardt, Regina; Krupicka, Erik; Hofmeister, Wolfgang

2012-05-01

Powder X-ray diffraction (PXRD) is used widely in forensic science laboratories with the main focus of qualitative phase identification. Little is found in literature referring to the topic of validation of PXRD in the field of forensic sciences. According to EN ISO/IEC 17025, the method has to be tested for several parameters. Trueness, specificity, and selectivity of PXRD were tested using certified reference materials or a combination thereof. All three tested parameters showed the secure performance of the method. Sample preparation errors were simulated to evaluate the robustness of the method. These errors were either easily detected by the operator or nonsignificant for phase identification. In case of the detection limit, a statistical evaluation of the signal-to-noise ratio showed that a peak criterion of three sigma is inadequate and recommendations for a more realistic peak criterion are given. Finally, the results of an international proficiency test showed the secure performance of PXRD. © 2012 American Academy of Forensic Sciences.

Fault-tolerant optimised tracking control for unknown discrete-time linear systems using a combined reinforcement learning and residual compensation methodology

NASA Astrophysics Data System (ADS)

Han, Ke-Zhen; Feng, Jian; Cui, Xiaohong

2017-10-01

This paper considers the fault-tolerant optimised tracking control (FTOTC) problem for unknown discrete-time linear system. A research scheme is proposed on the basis of data-based parity space identification, reinforcement learning and residual compensation techniques. The main characteristic of this research scheme lies in the parity-space-identification-based simultaneous tracking control and residual compensation. The specific technical line consists of four main contents: apply subspace aided method to design observer-based residual generator; use reinforcement Q-learning approach to solve optimised tracking control policy; rely on robust H∞ theory to achieve noise attenuation; adopt fault estimation triggered by residual generator to perform fault compensation. To clarify the design and implementation procedures, an integrated algorithm is further constructed to link up these four functional units. The detailed analysis and proof are subsequently given to explain the guaranteed FTOTC performance of the proposed conclusions. Finally, a case simulation is provided to verify its effectiveness.

Physiological-based modelling of marine fish early life stages provides process knowledge on climate impacts

NASA Astrophysics Data System (ADS)

Peck, M. A.

2016-02-01

Gaining a cause-and-effect understanding of climate-driven changes in marine fish populations at appropriate spatial scales is important for providing robust advice for ecosystem-based fisheries management. Coupling long-term, retrospective analyses and 3-d biophysical, individual-based models (IBMs) shows great potential to reveal mechanism underlying historical changes and to project future changes in marine fishes. IBMs created for marine fish early life stages integrate organismal-level physiological responses and climate-driven changes in marine habitats (from ocean physics to lower trophic level productivity) to test and reveal processes affecting marine fish recruitment. Case studies are provided for hindcasts and future (A1 and B2 projection) simulations performed on some of the most ecologically- and commercially-important pelagic and demersal fishes in the North Sea including European anchovy, Atlantic herring, European sprat and Atlantic cod. We discuss the utility of coupling biophysical IBMs to size-spectrum models to better project indirect (trophodynamic) pathways of climate influence on the early life stages of these and other fishes. Opportunities and challenges are discussed regarding the ability of these physiological-based tools to capture climate-driven changes in living marine resources and food web dynamics of shelf seas.

Novel HPGe Probe solution for Harsh Environments

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

Clauss, J.; Pirard, B.; Menaa, N.

2015-07-01

In situ measurement is a privileged way of monitoring radioactive contamination compared to analyzing samples in a distant, specialized laboratory. Scintillators based spectrometers offer small footprints and are easy to easy to use, however they do not provide an accurate nuclide identification capability and activities measurement because notably of their limited energy resolution, for instance when low minimum detectable activity (MDA) are required, or in complex mixture of sources. On the other hand, High Purity Germanium (HPGe) detectors provide unmatched nuclide identification capability with the lowest MDA but they are not always of practical use on the field because themore » crystal needs to be cooled down to liquid nitrogen temperature, increasing the overall weight, bulkiness and complexity of the measurement. This paper presents the configuration and performance of a novel turnkey and compact HPGe solution developed by Canberra for radionuclide identification under harsh environments. Radio-contaminations surveys now can be undertaken outdoor under various weather conditions, in contaminated areas, underground or underwater locations (including under sea water), with fast on site deployment. The spectrometer is also designed in a small diameter tubular shape to offer minimal footprint for an operation in narrow and confined spaces. Besides, this innovative design does not mitigate the performances nor the reliability experienced with standard laboratory-grade HPGe spectrometers. This achievement relies on advanced technologies such as the encapsulation of the crystal in ultra-high vacuum (UHV) which provides higher robustness and does not requires thermal cycles faced with regular HPGe equipment. It also relies on a low vibration, low consumption electrical cooler so that no liquid nitrogen is being used. The detector is connected to a state-of-the-art digital spectroscopy suite embedded in an autonomous acquisition station monitoring the cryo-cooler and including the spectroscopy software. This system presented is therefore a new solution to perform high resolution spectroscopy where no current products are compact or robust enough to be installed, for instance for soil and water decontamination measurement when anthropogenic radioactive sources must be identified from natural ones or when low contamination levels are expected, the MDA being 3 to 5 times higher than scintillator-based detectors of similar sizes. Environmental inspection such as well logging for geophysics and mining, water contamination monitoring, or decommissioning works can thus benefits from both ruggedness, mobility without compromise on the nuclide identification or activity measurement capability. (authors)« less

Rapid and Accurate Molecular Identification of the Emerging Multidrug-Resistant Pathogen Candida auris

PubMed Central

Zhao, Yanan; Lockhart, Shawn R.; Berrio, Indira

2017-01-01

ABSTRACT Candida auris is an emerging multidrug-resistant fungal pathogen causing nosocomial and invasive infections associated with high mortality. C. auris is commonly misidentified as several different yeast species by commercially available phenotypic identification platforms. Thus, there is an urgent need for a reliable diagnostic method. In this paper, we present fast, robust, easy-to-perform and interpret PCR and real-time PCR assays to identify C. auris and related species: Candida duobushaemulonii, Candida haemulonii, and Candida lusitaniae. Targeting rDNA region nucleotide sequences, primers specific for C. auris only or C. auris and related species were designed. A panel of 140 clinical fungal isolates was used in both PCR and real-time PCR assays followed by electrophoresis or melting temperature analysis, respectively. The identification results from the assays were 100% concordant with DNA sequencing results. These molecular assays overcome the deficiencies of existing phenotypic tests to identify C. auris and related species. PMID:28539346

Rapid and Accurate Molecular Identification of the Emerging Multidrug-Resistant Pathogen Candida auris.

PubMed

Kordalewska, Milena; Zhao, Yanan; Lockhart, Shawn R; Chowdhary, Anuradha; Berrio, Indira; Perlin, David S

2017-08-01

Candida auris is an emerging multidrug-resistant fungal pathogen causing nosocomial and invasive infections associated with high mortality. C. auris is commonly misidentified as several different yeast species by commercially available phenotypic identification platforms. Thus, there is an urgent need for a reliable diagnostic method. In this paper, we present fast, robust, easy-to-perform and interpret PCR and real-time PCR assays to identify C. auris and related species: Candida duobushaemulonii , Candida haemulonii , and Candida lusitaniae Targeting rDNA region nucleotide sequences, primers specific for C. auris only or C. auris and related species were designed. A panel of 140 clinical fungal isolates was used in both PCR and real-time PCR assays followed by electrophoresis or melting temperature analysis, respectively. The identification results from the assays were 100% concordant with DNA sequencing results. These molecular assays overcome the deficiencies of existing phenotypic tests to identify C. auris and related species. Copyright © 2017 Kordalewska et al.

A forward model-based validation of cardiovascular system identification

NASA Technical Reports Server (NTRS)

Mukkamala, R.; Cohen, R. J.

2001-01-01

We present a theoretical evaluation of a cardiovascular system identification method that we previously developed for the analysis of beat-to-beat fluctuations in noninvasively measured heart rate, arterial blood pressure, and instantaneous lung volume. The method provides a dynamical characterization of the important autonomic and mechanical mechanisms responsible for coupling the fluctuations (inverse modeling). To carry out the evaluation, we developed a computational model of the cardiovascular system capable of generating realistic beat-to-beat variability (forward modeling). We applied the method to data generated from the forward model and compared the resulting estimated dynamics with the actual dynamics of the forward model, which were either precisely known or easily determined. We found that the estimated dynamics corresponded to the actual dynamics and that this correspondence was robust to forward model uncertainty. We also demonstrated the sensitivity of the method in detecting small changes in parameters characterizing autonomic function in the forward model. These results provide confidence in the performance of the cardiovascular system identification method when applied to experimental data.

An Information Extraction Framework for Cohort Identification Using Electronic Health Records

PubMed Central

Liu, Hongfang; Bielinski, Suzette J.; Sohn, Sunghwan; Murphy, Sean; Wagholikar, Kavishwar B.; Jonnalagadda, Siddhartha R.; Ravikumar, K.E.; Wu, Stephen T.; Kullo, Iftikhar J.; Chute, Christopher G

Information extraction (IE), a natural language processing (NLP) task that automatically extracts structured or semi-structured information from free text, has become popular in the clinical domain for supporting automated systems at point-of-care and enabling secondary use of electronic health records (EHRs) for clinical and translational research. However, a high performance IE system can be very challenging to construct due to the complexity and dynamic nature of human language. In this paper, we report an IE framework for cohort identification using EHRs that is a knowledge-driven framework developed under the Unstructured Information Management Architecture (UIMA). A system to extract specific information can be developed by subject matter experts through expert knowledge engineering of the externalized knowledge resources used in the framework. PMID:24303255

The biopharmaceutics risk assessment roadmap for optimizing clinical drug product performance.

PubMed

Selen, Arzu; Dickinson, Paul A; Müllertz, Anette; Crison, John R; Mistry, Hitesh B; Cruañes, Maria T; Martinez, Marilyn N; Lennernäs, Hans; Wigal, Tim L; Swinney, David C; Polli, James E; Serajuddin, Abu T M; Cook, Jack A; Dressman, Jennifer B

2014-11-01

The biopharmaceutics risk assessment roadmap (BioRAM) optimizes drug product development and performance by using therapy-driven target drug delivery profiles as a framework to achieve the desired therapeutic outcome. Hence, clinical relevance is directly built into early formulation development. Biopharmaceutics tools are used to identify and address potential challenges to optimize the drug product for patient benefit. For illustration, BioRAM is applied to four relatively common therapy-driven drug delivery scenarios: rapid therapeutic onset, multiphasic delivery, delayed therapeutic onset, and maintenance of target exposure. BioRAM considers the therapeutic target with the drug substance characteristics and enables collection of critical knowledge for development of a dosage form that can perform consistently for meeting the patient's needs. Accordingly, the key factors are identified and in vitro, in vivo, and in silico modeling and simulation techniques are used to elucidate the optimal drug delivery rate and pattern. BioRAM enables (1) feasibility assessment for the dosage form, (2) development and conduct of appropriate "learning and confirming" studies, (3) transparency in decision-making, (4) assurance of drug product quality during lifecycle management, and (5) development of robust linkages between the desired clinical outcome and the necessary product quality attributes for inclusion in the quality target product profile. © 2014 Wiley Periodicals, Inc. and the American Pharmacists Association.

Optimization of a chemical identification algorithm

NASA Astrophysics Data System (ADS)

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

2010-04-01

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

Chaos and Hyperchaos in Coupled Antiphase Driven Toda Oscillators

NASA Astrophysics Data System (ADS)

Stankevich, Nataliya V.; Dvorak, Anton; Astakhov, Vladimir; Jaros, Patrycja; Kapitaniak, Marcin; Perlikowski, Przemysław; Kapitaniak, Tomasz

2018-01-01

The dynamics of two coupled antiphase driven Toda oscillators is studied. We demonstrate three different routes of transition to chaotic dynamics associated with different bifurcations of periodic and quasi-periodic regimes. As a result of these, two types of chaotic dynamics with one and two positive Lyapunov exponents are observed. We argue that the results obtained are robust as they can exist in a wide range of the system parameters.

Identification of significant features by the Global Mean Rank test.

PubMed

Klammer, Martin; Dybowski, J Nikolaj; Hoffmann, Daniel; Schaab, Christoph

2014-01-01

With the introduction of omics-technologies such as transcriptomics and proteomics, numerous methods for the reliable identification of significantly regulated features (genes, proteins, etc.) have been developed. Experimental practice requires these tests to successfully deal with conditions such as small numbers of replicates, missing values, non-normally distributed expression levels, and non-identical distributions of features. With the MeanRank test we aimed at developing a test that performs robustly under these conditions, while favorably scaling with the number of replicates. The test proposed here is a global one-sample location test, which is based on the mean ranks across replicates, and internally estimates and controls the false discovery rate. Furthermore, missing data is accounted for without the need of imputation. In extensive simulations comparing MeanRank to other frequently used methods, we found that it performs well with small and large numbers of replicates, feature dependent variance between replicates, and variable regulation across features on simulation data and a recent two-color microarray spike-in dataset. The tests were then used to identify significant changes in the phosphoproteomes of cancer cells induced by the kinase inhibitors erlotinib and 3-MB-PP1 in two independently published mass spectrometry-based studies. MeanRank outperformed the other global rank-based methods applied in this study. Compared to the popular Significance Analysis of Microarrays and Linear Models for Microarray methods, MeanRank performed similar or better. Furthermore, MeanRank exhibits more consistent behavior regarding the degree of regulation and is robust against the choice of preprocessing methods. MeanRank does not require any imputation of missing values, is easy to understand, and yields results that are easy to interpret. The software implementing the algorithm is freely available for academic and commercial use.

Robust gene selection methods using weighting schemes for microarray data analysis.

PubMed

Kang, Suyeon; Song, Jongwoo

2017-09-02

A common task in microarray data analysis is to identify informative genes that are differentially expressed between two different states. Owing to the high-dimensional nature of microarray data, identification of significant genes has been essential in analyzing the data. However, the performances of many gene selection techniques are highly dependent on the experimental conditions, such as the presence of measurement error or a limited number of sample replicates. We have proposed new filter-based gene selection techniques, by applying a simple modification to significance analysis of microarrays (SAM). To prove the effectiveness of the proposed method, we considered a series of synthetic datasets with different noise levels and sample sizes along with two real datasets. The following findings were made. First, our proposed methods outperform conventional methods for all simulation set-ups. In particular, our methods are much better when the given data are noisy and sample size is small. They showed relatively robust performance regardless of noise level and sample size, whereas the performance of SAM became significantly worse as the noise level became high or sample size decreased. When sufficient sample replicates were available, SAM and our methods showed similar performance. Finally, our proposed methods are competitive with traditional methods in classification tasks for microarrays. The results of simulation study and real data analysis have demonstrated that our proposed methods are effective for detecting significant genes and classification tasks, especially when the given data are noisy or have few sample replicates. By employing weighting schemes, we can obtain robust and reliable results for microarray data analysis.

A Comprehensive review of group level model performance in the presence of heteroscedasticity: Can a single model control Type I errors in the presence of outliers?

PubMed Central

Mumford, Jeanette A.

2017-01-01

Even after thorough preprocessing and a careful time series analysis of functional magnetic resonance imaging (fMRI) data, artifact and other issues can lead to violations of the assumption that the variance is constant across subjects in the group level model. This is especially concerning when modeling a continuous covariate at the group level, as the slope is easily biased by outliers. Various models have been proposed to deal with outliers including models that use the first level variance or that use the group level residual magnitude to differentially weight subjects. The most typically used robust regression, implementing a robust estimator of the regression slope, has been previously studied in the context of fMRI studies and was found to perform well in some scenarios, but a loss of Type I error control can occur for some outlier settings. A second type of robust regression using a heteroscedastic autocorrelation consistent (HAC) estimator, which produces robust slope and variance estimates has been shown to perform well, with better Type I error control, but with large sample sizes (500–1000 subjects). The Type I error control with smaller sample sizes has not been studied in this model and has not been compared to other modeling approaches that handle outliers such as FSL’s Flame 1 and FSL’s outlier de-weighting. Focusing on group level inference with a continuous covariate over a range of sample sizes and degree of heteroscedasticity, which can be driven either by the within- or between-subject variability, both styles of robust regression are compared to ordinary least squares (OLS), FSL’s Flame 1, Flame 1 with outlier de-weighting algorithm and Kendall’s Tau. Additionally, subject omission using the Cook’s Distance measure with OLS and nonparametric inference with the OLS statistic are studied. Pros and cons of these models as well as general strategies for detecting outliers in data and taking precaution to avoid inflated Type I error rates are discussed. PMID:28030782

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.

Oceanic eddy detection and lifetime forecast using machine learning methods

NASA Astrophysics Data System (ADS)

Ashkezari, Mohammad D.; Hill, Christopher N.; Follett, Christopher N.; Forget, Gaël.; Follows, Michael J.

2016-12-01

We report a novel altimetry-based machine learning approach for eddy identification and characterization. The machine learning models use daily maps of geostrophic velocity anomalies and are trained according to the phase angle between the zonal and meridional components at each grid point. The trained models are then used to identify the corresponding eddy phase patterns and to predict the lifetime of a detected eddy structure. The performance of the proposed method is examined at two dynamically different regions to demonstrate its robust behavior and region independency.

Tools for Understanding Space Weather Impacts to Satellites

NASA Astrophysics Data System (ADS)

Green, J. C.; Shprits, Y.; Likar, J. J.; Kellerman, A. C.; Quinn, R. A.; Whelan, P.; Reker, N.; Huston, S. L.

2017-12-01

Space weather causes dramatic changes in the near-Earth radiation environment. Intense particle fluxes can damage electronic components on satellites, causing temporary malfunctions, degraded performance, or a complete system/mission loss. Understanding whether space weather is the cause of such problems expedites investigations and guides successful design improvements resulting in a more robust satellite architecture. Here we discuss our progress in developing tools for satellite designers, manufacturers, and decision makers - tools that summarize space weather impacts to specific satellite assets and enable confident identification of the cause and right solution.

Experimental investigation of shaping disturbance observer design for motion control of precision mechatronic stages with resonances

NASA Astrophysics Data System (ADS)

Yang, Jin; Hu, Chuxiong; Zhu, Yu; Wang, Ze; Zhang, Ming

2017-08-01

In this paper, shaping disturbance observer (SDOB) is investigated for precision mechatronic stages with middle-frequency zero/pole type resonance to achieve good motion control performance in practical manufacturing situations. Compared with traditional standard disturbance observer (DOB), in SDOB a pole-zero cancellation based shaping filter is cascaded to the mechatronic stage plant to meet the challenge of motion control performance deterioration caused by actual resonance. Noting that pole-zero cancellation is inevitably imperfect and the controller may even consequently become unstable in practice, frequency domain stability analysis is conducted to find out how each parameter of the shaping filter affects the control stability. Moreover, the robust design criterion of the shaping filter, and the design procedure of SDOB, are both proposed to guide the actual design and facilitate practical implementation. The SDOB with the proposed design criterion is applied to a linear motor driven stage and a voice motor driven stage, respectively. Experimental results consistently validate the effectiveness nature of the proposed SDOB scheme in practical mechatronics motion applications. The proposed SDOB design actually could be an effective unit in the controller design for motion stages of mechanical manufacture equipments.

Commentary: The Materials Project: A materials genome approach to accelerating materials innovation

NASA Astrophysics Data System (ADS)

Jain, Anubhav; Ong, Shyue Ping; Hautier, Geoffroy; Chen, Wei; Richards, William Davidson; Dacek, Stephen; Cholia, Shreyas; Gunter, Dan; Skinner, David; Ceder, Gerbrand; Persson, Kristin A.

2013-07-01

Accelerating the discovery of advanced materials is essential for human welfare and sustainable, clean energy. In this paper, we introduce the Materials Project (www.materialsproject.org), a core program of the Materials Genome Initiative that uses high-throughput computing to uncover the properties of all known inorganic materials. This open dataset can be accessed through multiple channels for both interactive exploration and data mining. The Materials Project also seeks to create open-source platforms for developing robust, sophisticated materials analyses. Future efforts will enable users to perform ``rapid-prototyping'' of new materials in silico, and provide researchers with new avenues for cost-effective, data-driven materials design.

Robust, functional nanocrystal solids by infilling with atomic layer deposition.

PubMed

Liu, Yao; Gibbs, Markelle; Perkins, Craig L; Tolentino, Jason; Zarghami, Mohammad H; Bustamante, Jorge; Law, Matt

2011-12-14

Thin films of colloidal semiconductor nanocrystals (NCs) are inherently metatstable materials prone to oxidative and photothermal degradation driven by their large surface-to-volume ratios and high surface energies. (1) The fabrication of practical electronic devices based on NC solids hinges on preventing oxidation, surface diffusion, ripening, sintering, and other unwanted physicochemical changes that can plague these materials. Here we use low-temperature atomic layer deposition (ALD) to infill conductive PbSe NC solids with metal oxides to produce inorganic nanocomposites in which the NCs are locked in place and protected against oxidative and photothermal damage. Infilling NC field-effect transistors and solar cells with amorphous alumina yields devices that operate with enhanced and stable performance for at least months in air. Furthermore, ALD infilling with ZnO lowers the height of the inter-NC tunnel barrier for electron transport, yielding PbSe NC films with electron mobilities of 1 cm2 V(-1) s(-1). Our ALD technique is a versatile means to fabricate robust NC solids for optoelectronic devices.

The integrated effects of future climate and hydrologic uncertainty on sustainable flood risk management

NASA Astrophysics Data System (ADS)

Steinschneider, S.; Wi, S.; Brown, C. M.

2013-12-01

Flood risk management performance is investigated within the context of integrated climate and hydrologic modeling uncertainty to explore system robustness. The research question investigated is whether structural and hydrologic parameterization uncertainties are significant relative to other uncertainties such as climate change when considering water resources system performance. Two hydrologic models are considered, a conceptual, lumped parameter model that preserves the water balance and a physically-based model that preserves both water and energy balances. In the conceptual model, parameter and structural uncertainties are quantified and propagated through the analysis using a Bayesian modeling framework with an innovative error model. Mean climate changes and internal climate variability are explored using an ensemble of simulations from a stochastic weather generator. The approach presented can be used to quantify the sensitivity of flood protection adequacy to different sources of uncertainty in the climate and hydrologic system, enabling the identification of robust projects that maintain adequate performance despite the uncertainties. The method is demonstrated in a case study for the Coralville Reservoir on the Iowa River, where increased flooding over the past several decades has raised questions about potential impacts of climate change on flood protection adequacy.

Task-driven imaging in cone-beam computed tomography.

PubMed

Gang, G J; Stayman, J W; Ouadah, S; Ehtiati, T; Siewerdsen, J H

Conventional workflow in interventional imaging often ignores a wealth of prior information of the patient anatomy and the imaging task. This work introduces a task-driven imaging framework that utilizes such information to prospectively design acquisition and reconstruction techniques for cone-beam CT (CBCT) in a manner that maximizes task-based performance in subsequent imaging procedures. The framework is employed in jointly optimizing tube current modulation, orbital tilt, and reconstruction parameters in filtered backprojection reconstruction for interventional imaging. Theoretical predictors of noise and resolution relates acquisition and reconstruction parameters to task-based detectability. Given a patient-specific prior image and specification of the imaging task, an optimization algorithm prospectively identifies the combination of imaging parameters that maximizes task-based detectability. Initial investigations were performed for a variety of imaging tasks in an elliptical phantom and an anthropomorphic head phantom. Optimization of tube current modulation and view-dependent reconstruction kernel was shown to have greatest benefits for a directional task (e.g., identification of device or tissue orientation). The task-driven approach yielded techniques in which the dose and sharp kernels were concentrated in views contributing the most to the signal power associated with the imaging task. For example, detectability of a line pair detection task was improved by at least three fold compared to conventional approaches. For radially symmetric tasks, the task-driven strategy yielded results similar to a minimum variance strategy in the absence of kernel modulation. Optimization of the orbital tilt successfully avoided highly attenuating structures that can confound the imaging task by introducing noise correlations masquerading at spatial frequencies of interest. This work demonstrated the potential of a task-driven imaging framework to improve image quality and reduce dose beyond that achievable with conventional imaging approaches.

Robust rotation of rotor in a thermally driven nanomotor

PubMed Central

Cai, Kun; Yu, Jingzhou; Shi, Jiao; Qin, Qing-Hua

2017-01-01

In the fabrication of a thermally driven rotary nanomotor with the dimension of a few nanometers, fabrication and control precision may have great influence on rotor’s stability of rotational frequency (SRF). To investigate effects of uncertainty of some major factors including temperature, tube length, axial distance between tubes, diameter of tubes and the inward radial deviation (IRD) of atoms in stators on the frequency’s stability, theoretical analysis integrating with numerical experiments are carried out. From the results obtained via molecular dynamics simulation, some key points are illustrated for future fabrication of the thermal driven rotary nanomotor. PMID:28393898

Coevolution in management fashion: an agent-based model of consultant-driven innovation.

PubMed

Strang, David; David, Robert J; Akhlaghpour, Saeed

2014-07-01

The rise of management consultancy has been accompanied by increasingly marked faddish cycles in management techniques, but the mechanisms that underlie this relationship are not well understood. The authors develop a simple agent-based framework that models innovation adoption and abandonment on both the supply and demand sides. In opposition to conceptions of consultants as rhetorical wizards who engineer waves of management fashion, firms and consultants are treated as boundedly rational actors who chase the secrets of success by mimicking their highest-performing peers. Computational experiments demonstrate that consultant-driven versions of this dynamic in which the outcomes of firms are strongly conditioned by their choice of consultant are robustly faddish. The invasion of boom markets by low-quality consultants undercuts popular innovations while simultaneously restarting the fashion cycle by prompting the flight of high-quality consultants into less densely occupied niches. Computational experiments also indicate conditions involving consultant mobility, aspiration levels, mimic probabilities, and client-provider matching that attenuate faddishness.

Robust detection-isolation-accommodation for sensor failures

NASA Technical Reports Server (NTRS)

Weiss, J. L.; Pattipati, K. R.; Willsky, A. S.; Eterno, J. S.; Crawford, J. T.

1985-01-01

The results of a one year study to: (1) develop a theory for Robust Failure Detection and Identification (FDI) in the presence of model uncertainty, (2) develop a design methodology which utilizes the robust FDI ththeory, (3) apply the methodology to a sensor FDI problem for the F-100 jet engine, and (4) demonstrate the application of the theory to the evaluation of alternative FDI schemes are presented. Theoretical results in statistical discrimination are used to evaluate the robustness of residual signals (or parity relations) in terms of their usefulness for FDI. Furthermore, optimally robust parity relations are derived through the optimization of robustness metrics. The result is viewed as decentralization of the FDI process. A general structure for decentralized FDI is proposed and robustness metrics are used for determining various parameters of the algorithm.

Potential use of nitrate reductase as a biomarker for the identification of active and dormant inhibitors of Mycobacterium tuberculosis in a THP1 infection model.

PubMed

Sarkar, Sampa; Sarkar, Dhiman

2012-08-01

The development of a macrophage-based, antitubercular high-throughput screening system could expedite discovery programs for identifying novel inhibitors. In this study, the kinetics of nitrate reduction (NR) by Mycobacterium tuberculosis during growth in Thp1 macrophages was found to be almost parallel to viable bacilli count. NR in the culture medium containing 50 mM of nitrate was found to be optimum on the fifth day after infection with M. tuberculosis. The signal-to-noise (S/N) ratio and Z-factor obtained from this macrophage-based assay were 5.4 and 0.965, respectively, which confirms the robustness of the assay protocol. The protocol was further validated by using standard antitubercular inhibitors such as rifampicin, isoniazid, streptomycin, ethambutol, and pyrazinamide, added at their IC(90) value, on the day of infection. These inhibitors were not able to kill the bacilli when added to the culture on the fifth day after infection. Interestingly, pentachlorophenol and rifampicin killed the bacilli immediately after addition on the fifth day of infection. Altogether, this assay protocol using M. tuberculosis-infected Thp-1 macrophages provides a novel, cost-efficient, robust, and easy-to-perform screening platform for the identification of both active and hypoxic stage-specific inhibitors against tuberculosis.

MALDI-TOF Mass Spectrometry: A Powerful Tool for Clinical Microbiology at Hôpital Principal de Dakar, Senegal (West Africa)

PubMed Central

Lo, Cheikh I.; Fall, Bécaye; Sambe-Ba, Bissoume; Diawara, Silman; Gueye, Mamadou W.; Mediannikov, Oleg; Sokhna, Cheikh; Faye, Ngor; Diemé, Yaya; Wade, Boubacar; Raoult, Didier; Fenollar, Florence

2015-01-01

Our team in Europe has developed the routine clinical laboratory identification of microorganisms by matrix-assisted laser desorption ionization time-of-flight (MALDI-TOF) mass spectrometry (MS). To evaluate the utility of MALDI-TOF MS in tropical Africa in collaboration with local teams, we installed an apparatus in the Hôpital Principal de Dakar (Senegal), performed routine identification of isolates, and confirmed or completed their identification in France. In the case of discordance or a lack of identification, molecular biology was performed. Overall, 153/191 (80.1%) and 174/191 (91.1%) isolates yielded an accurate and concordant identification for the species and genus, respectively, with the 2 different MALDI-TOF MSs in Dakar and Marseille. The 10 most common bacteria, representing 94.2% of all bacteria routinely identified in the laboratory in Dakar (Escherichia coli, Klebsiella pneumoniae, Streptococcus agalactiae, Acinetobacter baumannii, Pseudomonas aeruginosa, Staphylococcus aureus, Staphylococcus haemolyticus, Enterobacter cloacae, Enterococcus faecalis, and Staphylococcus epidermidis) were accurately identified with the MALDI-TOF MS in Dakar. The most frequent misidentification in Dakar was at the species level for Achromobacter xylosoxidans, which was inaccurately identified as Achromobacter denitrificans, and the bacteria absent from the database, such as Exiguobacterium aurientacum or Kytococcus schroeteri, could not be identified. A few difficulties were observed with MALDI-TOF MS for Bacillus sp. or oral streptococci. 16S rRNA sequencing identified a novel bacterium, “Necropsobacter massiliensis.” The robust identification of microorganisms by MALDI-TOF MS in Dakar and Marseille demonstrates that MALDI-TOF MS can be used as a first-line tool in clinical microbiology laboratories in tropical countries. PMID:26716681

Evaluation of regional climate simulations for air quality modelling purposes

NASA Astrophysics Data System (ADS)

Menut, Laurent; Tripathi, Om P.; Colette, Augustin; Vautard, Robert; Flaounas, Emmanouil; Bessagnet, Bertrand

2013-05-01

In order to evaluate the future potential benefits of emission regulation on regional air quality, while taking into account the effects of climate change, off-line air quality projection simulations are driven using weather forcing taken from regional climate models. These regional models are themselves driven by simulations carried out using global climate models (GCM) and economical scenarios. Uncertainties and biases in climate models introduce an additional "climate modeling" source of uncertainty that is to be added to all other types of uncertainties in air quality modeling for policy evaluation. In this article we evaluate the changes in air quality-related weather variables induced by replacing reanalyses-forced by GCM-forced regional climate simulations. As an example we use GCM simulations carried out in the framework of the ERA-interim programme and of the CMIP5 project using the Institut Pierre-Simon Laplace climate model (IPSLcm), driving regional simulations performed in the framework of the EURO-CORDEX programme. In summer, we found compensating deficiencies acting on photochemistry: an overestimation by GCM-driven weather due to a positive bias in short-wave radiation, a negative bias in wind speed, too many stagnant episodes, and a negative temperature bias. In winter, air quality is mostly driven by dispersion, and we could not identify significant differences in either wind or planetary boundary layer height statistics between GCM-driven and reanalyses-driven regional simulations. However, precipitation appears largely overestimated in GCM-driven simulations, which could significantly affect the simulation of aerosol concentrations. The identification of these biases will help interpreting results of future air quality simulations using these data. Despite these, we conclude that the identified differences should not lead to major difficulties in using GCM-driven regional climate simulations for air quality projections.

Detection-enhanced steady state entanglement with ions.

PubMed

Bentley, C D B; Carvalho, A R R; Kielpinski, D; Hope, J J

2014-07-25

Driven dissipative steady state entanglement schemes take advantage of coupling to the environment to robustly prepare highly entangled states. We present a scheme for two trapped ions to generate a maximally entangled steady state with fidelity above 0.99, appropriate for use in quantum protocols. Furthermore, we extend the scheme by introducing detection of our dissipation process, significantly enhancing the fidelity. Our scheme is robust to anomalous heating and requires no sympathetic cooling.

Synthesis and operation of an FFT-decoupled fixed-order reversed-field pinch plasma control system based on identification data

NASA Astrophysics Data System (ADS)

Olofsson, K. Erik J.; Brunsell, Per R.; Witrant, Emmanuel; Drake, James R.

2010-10-01

Recent developments and applications of system identification methods for the reversed-field pinch (RFP) machine EXTRAP T2R have yielded plasma response parameters for decoupled dynamics. These data sets are fundamental for a real-time implementable fast Fourier transform (FFT) decoupled discrete-time fixed-order strongly stabilizing synthesis as described in this work. Robustness is assessed over the data set by bootstrap calculation of the sensitivity transfer function worst-case H_{\\infty} -gain distribution. Output tracking and magnetohydrodynamic mode m = 1 tracking are considered in the same framework simply as two distinct weighted traces of a performance channel output-covariance matrix as derived from the closed-loop discrete-time Lyapunov equation. The behaviour of the resulting multivariable controller is investigated with dedicated T2R experiments.

Data-Driven Learning of Q-Matrix

ERIC Educational Resources Information Center

Liu, Jingchen; Xu, Gongjun; Ying, Zhiliang

2012-01-01

The recent surge of interests in cognitive assessment has led to developments of novel statistical models for diagnostic classification. Central to many such models is the well-known "Q"-matrix, which specifies the item-attribute relationships. This article proposes a data-driven approach to identification of the "Q"-matrix and estimation of…

New robust statistical procedures for the polytomous logistic regression models.

PubMed

Castilla, Elena; Ghosh, Abhik; Martin, Nirian; Pardo, Leandro

2018-05-17

This article derives a new family of estimators, namely the minimum density power divergence estimators, as a robust generalization of the maximum likelihood estimator for the polytomous logistic regression model. Based on these estimators, a family of Wald-type test statistics for linear hypotheses is introduced. Robustness properties of both the proposed estimators and the test statistics are theoretically studied through the classical influence function analysis. Appropriate real life examples are presented to justify the requirement of suitable robust statistical procedures in place of the likelihood based inference for the polytomous logistic regression model. The validity of the theoretical results established in the article are further confirmed empirically through suitable simulation studies. Finally, an approach for the data-driven selection of the robustness tuning parameter is proposed with empirical justifications. © 2018, The International Biometric Society.

Speaker Recognition by Combining MFCC and Phase Information in Noisy Conditions

NASA Astrophysics Data System (ADS)

Wang, Longbiao; Minami, Kazue; Yamamoto, Kazumasa; Nakagawa, Seiichi

In this paper, we investigate the effectiveness of phase for speaker recognition in noisy conditions and combine the phase information with mel-frequency cepstral coefficients (MFCCs). To date, almost speaker recognition methods are based on MFCCs even in noisy conditions. For MFCCs which dominantly capture vocal tract information, only the magnitude of the Fourier Transform of time-domain speech frames is used and phase information has been ignored. High complement of the phase information and MFCCs is expected because the phase information includes rich voice source information. Furthermore, some researches have reported that phase based feature was robust to noise. In our previous study, a phase information extraction method that normalizes the change variation in the phase depending on the clipping position of the input speech was proposed, and the performance of the combination of the phase information and MFCCs was remarkably better than that of MFCCs. In this paper, we evaluate the robustness of the proposed phase information for speaker identification in noisy conditions. Spectral subtraction, a method skipping frames with low energy/Signal-to-Noise (SN) and noisy speech training models are used to analyze the effect of the phase information and MFCCs in noisy conditions. The NTT database and the JNAS (Japanese Newspaper Article Sentences) database added with stationary/non-stationary noise were used to evaluate our proposed method. MFCCs outperformed the phase information for clean speech. On the other hand, the degradation of the phase information was significantly smaller than that of MFCCs for noisy speech. The individual result of the phase information was even better than that of MFCCs in many cases by clean speech training models. By deleting unreliable frames (frames having low energy/SN), the speaker identification performance was improved significantly. By integrating the phase information with MFCCs, the speaker identification error reduction rate was about 30%-60% compared with the standard MFCC-based method.

Audio fingerprint extraction for content identification

NASA Astrophysics Data System (ADS)

Shiu, Yu; Yeh, Chia-Hung; Kuo, C. C. J.

2003-11-01

In this work, we present an audio content identification system that identifies some unknown audio material by comparing its fingerprint with those extracted off-line and saved in the music database. We will describe in detail the procedure to extract audio fingerprints and demonstrate that they are robust to noise and content-preserving manipulations. The main feature in the proposed system is the zero-crossing rate extracted with the octave-band filter bank. The zero-crossing rate can be used to describe the dominant frequency in each subband with a very low computational cost. The size of audio fingerprint is small and can be efficiently stored along with the compressed files in the database. It is also robust to many modifications such as tempo change and time-alignment distortion. Besides, the octave-band filter bank is used to enhance the robustness to distortion, especially those localized on some frequency regions.

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

Mehrez, Loujaine; Ghanem, Roger; Aitharaju, Venkat

Design of non-crimp fabric (NCF) composites entails major challenges pertaining to (1) the complex fine-scale morphology of the constituents, (2) the manufacturing-produced inconsistency of this morphology spatially, and thus (3) the ability to build reliable, robust, and efficient computational surrogate models to account for this complex nature. Traditional approaches to construct computational surrogate models have been to average over the fluctuations of the material properties at different scale lengths. This fails to account for the fine-scale features and fluctuations in morphology, material properties of the constituents, as well as fine-scale phenomena such as damage and cracks. In addition, it failsmore » to accurately predict the scatter in macroscopic properties, which is vital to the design process and behavior prediction. In this work, funded in part by the Department of Energy, we present an approach for addressing these challenges by relying on polynomial chaos representations of both input parameters and material properties at different scales. Moreover, we emphasize the efficiency and robustness of integrating the polynomial chaos expansion with multiscale tools to perform multiscale assimilation, characterization, propagation, and prediction, all of which are necessary to construct the data-driven surrogate models required to design under the uncertainty of composites. These data-driven constructions provide an accurate map from parameters (and their uncertainties) at all scales and the system-level behavior relevant for design. While this perspective is quite general and applicable to all multiscale systems, NCF composites present a particular hierarchy of scales that permits the efficient implementation of these concepts.« less

Monitoring volcanic thermal activity by Robust Satellite Techniques: achievements and perspectives

NASA Astrophysics Data System (ADS)

Tramutoli, V.; Marchese, F.; Mazzeo, G.; Pergola, N.

2009-12-01

Satellite data have been increasingly used in last decades to study active volcanoes and to monitor thermal activity variation in space-time domain. Several satellite techniques and original methods have been developed and tested, devoted to hotspot detection and thermal monitoring. Among them, a multi-temporal approach, named RST (Robust Satellite Techniques), has shown high performances in detecting hotspots, with a low false positive rate under different observational and atmospheric conditions, providing also a potential toward low-level thermal anomalies which may announce incoming eruptions. As the RST scheme is intrinsically exportable on different geographic areas and satellite sensors, it has been applied and tested on a number of volcanoes and in different environmental conditions. This work presents major results and outcomes of studies carried out on Etna and Stromboli (Italy), Merapi (Java Indonesia), Asamayama (Japan), Jebel Al Tair (Yemen) by using different satellite systems and sensors (e.g. NOAA-AVHRR, EOS-MODIS, MSG-SEVIRI). Performances on hotspot detection, early warning and real-time monitoring, together with capabilities in possible thermal precursor identification, will be presented and discussed.

Smart phones: platform enabling modular, chemical, biological, and explosives sensing

NASA Astrophysics Data System (ADS)

Finch, Amethist S.; Coppock, Matthew; Bickford, Justin R.; Conn, Marvin A.; Proctor, Thomas J.; Stratis-Cullum, Dimitra N.

2013-05-01

Reliable, robust, and portable technologies are needed for the rapid identification and detection of chemical, biological, and explosive (CBE) materials. A key to addressing the persistent threat to U.S. troops in the current war on terror is the rapid detection and identification of the precursor materials used in development of improvised explosive devices, homemade explosives, and bio-warfare agents. However, a universal methodology for detection and prevention of CBE materials in the use of these devices has proven difficult. Herein, we discuss our efforts towards the development of a modular, robust, inexpensive, pervasive, archival, and compact platform (android based smart phone) enabling the rapid detection of these materials.

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

Cost-sensitive learning for emotion robust speaker recognition.

PubMed

Li, Dongdong; Yang, Yingchun; Dai, Weihui

2014-01-01

In the field of information security, voice is one of the most important parts in biometrics. Especially, with the development of voice communication through the Internet or telephone system, huge voice data resources are accessed. In speaker recognition, voiceprint can be applied as the unique password for the user to prove his/her identity. However, speech with various emotions can cause an unacceptably high error rate and aggravate the performance of speaker recognition system. This paper deals with this problem by introducing a cost-sensitive learning technology to reweight the probability of test affective utterances in the pitch envelop level, which can enhance the robustness in emotion-dependent speaker recognition effectively. Based on that technology, a new architecture of recognition system as well as its components is proposed in this paper. The experiment conducted on the Mandarin Affective Speech Corpus shows that an improvement of 8% identification rate over the traditional speaker recognition is achieved.

Cost-Sensitive Learning for Emotion Robust Speaker Recognition

PubMed Central

Li, Dongdong; Yang, Yingchun

2014-01-01

In the field of information security, voice is one of the most important parts in biometrics. Especially, with the development of voice communication through the Internet or telephone system, huge voice data resources are accessed. In speaker recognition, voiceprint can be applied as the unique password for the user to prove his/her identity. However, speech with various emotions can cause an unacceptably high error rate and aggravate the performance of speaker recognition system. This paper deals with this problem by introducing a cost-sensitive learning technology to reweight the probability of test affective utterances in the pitch envelop level, which can enhance the robustness in emotion-dependent speaker recognition effectively. Based on that technology, a new architecture of recognition system as well as its components is proposed in this paper. The experiment conducted on the Mandarin Affective Speech Corpus shows that an improvement of 8% identification rate over the traditional speaker recognition is achieved. PMID:24999492

Optimal sensor placement for time-domain identification using a wavelet-based genetic algorithm

NASA Astrophysics Data System (ADS)

Mahdavi, Seyed Hossein; Razak, Hashim Abdul

2016-06-01

This paper presents a wavelet-based genetic algorithm strategy for optimal sensor placement (OSP) effective for time-domain structural identification. Initially, the GA-based fitness evaluation is significantly improved by using adaptive wavelet functions. Later, a multi-species decimal GA coding system is modified to be suitable for an efficient search around the local optima. In this regard, a local operation of mutation is introduced in addition with regeneration and reintroduction operators. It is concluded that different characteristics of applied force influence the features of structural responses, and therefore the accuracy of time-domain structural identification is directly affected. Thus, the reliable OSP strategy prior to the time-domain identification will be achieved by those methods dealing with minimizing the distance of simulated responses for the entire system and condensed system considering the force effects. The numerical and experimental verification on the effectiveness of the proposed strategy demonstrates the considerably high computational performance of the proposed OSP strategy, in terms of computational cost and the accuracy of identification. It is deduced that the robustness of the proposed OSP algorithm lies in the precise and fast fitness evaluation at larger sampling rates which result in the optimum evaluation of the GA-based exploration and exploitation phases towards the global optimum solution.

A minimum cost tolerance allocation method for rocket engines and robust rocket engine design

NASA Technical Reports Server (NTRS)

Gerth, Richard J.

1993-01-01

Rocket engine design follows three phases: systems design, parameter design, and tolerance design. Systems design and parameter design are most effectively conducted in a concurrent engineering (CE) environment that utilize methods such as Quality Function Deployment and Taguchi methods. However, tolerance allocation remains an art driven by experience, handbooks, and rules of thumb. It was desirable to develop and optimization approach to tolerancing. The case study engine was the STME gas generator cycle. The design of the major components had been completed and the functional relationship between the component tolerances and system performance had been computed using the Generic Power Balance model. The system performance nominals (thrust, MR, and Isp) and tolerances were already specified, as were an initial set of component tolerances. However, the question was whether there existed an optimal combination of tolerances that would result in the minimum cost without any degradation in system performance.

Computational methods in the development of a knowledge-based system for the prediction of solid catalyst performance.

PubMed

Procelewska, Joanna; Galilea, Javier Llamas; Clerc, Frederic; Farrusseng, David; Schüth, Ferdi

2007-01-01

The objective of this work is the construction of a correlation between characteristics of heterogeneous catalysts, encoded in a descriptor vector, and their experimentally measured performances in the propene oxidation reaction. In this paper the key issue in the modeling process, namely the selection of adequate input variables, is explored. Several data-driven feature selection strategies were applied in order to obtain an estimate of the differences in variance and information content of various attributes, furthermore to compare their relative importance. Quantitative property activity relationship techniques using probabilistic neural networks have been used for the creation of various semi-empirical models. Finally, a robust classification model, assigning selected attributes of solid compounds as input to an appropriate performance class in the model reaction was obtained. It has been evident that the mathematical support for the primary attributes set proposed by chemists can be highly desirable.

Gas chromatography - mass spectrometry data processing made easy.

PubMed

Johnsen, Lea G; Skou, Peter B; Khakimov, Bekzod; Bro, Rasmus

2017-06-23

Evaluation of GC-MS data may be challenging due to the high complexity of data including overlapped, embedded, retention time shifted and low S/N ratio peaks. In this work, we demonstrate a new approach, PARAFAC2 based Deconvolution and Identification System (PARADISe), for processing raw GC-MS data. PARADISe is a computer platform independent freely available software incorporating a number of newly developed algorithms in a coherent framework. It offers a solution for analysts dealing with complex chromatographic data. It allows extraction of chemical/metabolite information directly from the raw data. Using PARADISe requires only few inputs from the analyst to process GC-MS data and subsequently converts raw netCDF data files into a compiled peak table. Furthermore, the method is generally robust towards minor variations in the input parameters. The method automatically performs peak identification based on deconvoluted mass spectra using integrated NIST search engine and generates an identification report. In this paper, we compare PARADISe with AMDIS and ChromaTOF in terms of peak quantification and show that PARADISe is more robust to user-defined settings and that these are easier (and much fewer) to set. PARADISe is based on non-proprietary scientifically evaluated approaches and we here show that PARADISe can handle more overlapping signals, lower signal-to-noise peaks and do so in a manner that requires only about an hours worth of work regardless of the number of samples. We also show that there are no non-detects in PARADISe, meaning that all compounds are detected in all samples. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

Robust identification and localization of intramedullary nail holes for distal locking using CBCT: a simulation study.

PubMed

Kamarianakis, Z; Buliev, I; Pallikarakis, N

2011-05-01

Closed intramedullary nailing is a common technique for treatment of femur and tibia fractures. The most challenging step in this procedure is the precise placement of the lateral screws that stabilize the fragmented bone. The present work concerns the development and the evaluation of a method to accurately identify in the 3D space the axes of the nail hole canals. A limited number of projection images are acquired around the leg with the help of a C-arm. On two of them, the locking hole entries are interactively selected and a rough localization of the hole axes is performed. Perpendicularly to one of them, cone-beam computed tomography (CBCT) reconstructions are produced. The accurate identification and localization of the hole axes are done by an identification of the centers of the nail holes on the tomograms and a further 3D linear regression through principal component analysis (PCA). Various feature-based approaches (RANSAC, least-square fitting, Hough transform) have been compared for best matching the contours and the centers of the holes on the tomograms. The robustness of the suggested method was investigated using simulations. Programming is done in Matlab and C++. Results obtained on synthetic data confirm very good localization accuracy - mean translational error of 0.14 mm (std=0.08 mm) and mean angular error of 0.84° (std=0.35°) at no radiation excess. Successful localization can be further used to guide a surgeon or a robot for correct drilling the bone along the nail openings. Copyright © 2010 IPEM. Published by Elsevier Ltd. All rights reserved.

Analytical redundancy and the design of robust failure detection systems

NASA Technical Reports Server (NTRS)

Chow, E. Y.; Willsky, A. S.

1984-01-01

The Failure Detection and Identification (FDI) process is viewed as consisting of two stages: residual generation and decision making. It is argued that a robust FDI system can be achieved by designing a robust residual generation process. Analytical redundancy, the basis for residual generation, is characterized in terms of a parity space. Using the concept of parity relations, residuals can be generated in a number of ways and the design of a robust residual generation process can be formulated as a minimax optimization problem. An example is included to illustrate this design methodology. Previously announcedd in STAR as N83-20653

Pose invariant face recognition: 3D model from single photo

NASA Astrophysics Data System (ADS)

Napoléon, Thibault; Alfalou, Ayman

2017-02-01

Face recognition is widely studied in the literature for its possibilities in surveillance and security. In this paper, we report a novel algorithm for the identification task. This technique is based on an optimized 3D modeling allowing to reconstruct faces in different poses from a limited number of references (i.e. one image by class/person). Particularly, we propose to use an active shape model to detect a set of keypoints on the face necessary to deform our synthetic model with our optimized finite element method. Indeed, in order to improve our deformation, we propose a regularization by distances on graph. To perform the identification we use the VanderLugt correlator well know to effectively address this task. On the other hand we add a difference of Gaussian filtering step to highlight the edges and a description step based on the local binary patterns. The experiments are performed on the PHPID database enhanced with our 3D reconstructed faces of each person with an azimuth and an elevation ranging from -30° to +30°. The obtained results prove the robustness of our new method with 88.76% of good identification when the classic 2D approach (based on the VLC) obtains just 44.97%.

Research on Intelligent Interface in Double-front Work Machines

NASA Astrophysics Data System (ADS)

Kamezaki, Mitsuhiro; Iwata, Hiroyasu; Sugano, Shigeki

This paper proposes a work state identification method with full independent of work environmental conditions and operator skill levels for construction machinery. Advanced operated-work machines, which have been designed for complicated tasks, require intelligent systems that can provide the quantitative work analysis needed to determine effective work procedures and that can provide operational and cognitive support for operators. Construction work environments are extremely complicated, however, and this makes state identification, which is a key technology for an intelligent system, difficult. We therefore defined primitive static states (PSS) that are determined using on-off information for the lever inputs and manipulator loads for each part of the grapple and front and that are completely independent of the various environmental conditions and variation in operator skill level that can cause an incorrect work state identification. To confirm the usefulness of PSS, we performed experiments with a demolition task by using our virtual reality simulator. We confirmed that PSS could robustly and accurately identify the work states and that untrained skills could be easily inferred from the results of PSS-based work analysis. We also confirmed in skill-training experiments that advice information based on PSS-based skill analysis greatly improved operator's work performance. We thus confirmed that PSS can adequately identify work states and are useful for work analysis and skill improvement.

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.

A Hybrid One-Way ANOVA Approach for the Robust and Efficient Estimation of Differential Gene Expression with Multiple Patterns

PubMed Central

Mollah, Mohammad Manir Hossain; Jamal, Rahman; Mokhtar, Norfilza Mohd; Harun, Roslan; Mollah, Md. Nurul Haque

2015-01-01

Background Identifying genes that are differentially expressed (DE) between two or more conditions with multiple patterns of expression is one of the primary objectives of gene expression data analysis. Several statistical approaches, including one-way analysis of variance (ANOVA), are used to identify DE genes. However, most of these methods provide misleading results for two or more conditions with multiple patterns of expression in the presence of outlying genes. In this paper, an attempt is made to develop a hybrid one-way ANOVA approach that unifies the robustness and efficiency of estimation using the minimum β-divergence method to overcome some problems that arise in the existing robust methods for both small- and large-sample cases with multiple patterns of expression. Results The proposed method relies on a β-weight function, which produces values between 0 and 1. The β-weight function with β = 0.2 is used as a measure of outlier detection. It assigns smaller weights (≥ 0) to outlying expressions and larger weights (≤ 1) to typical expressions. The distribution of the β-weights is used to calculate the cut-off point, which is compared to the observed β-weight of an expression to determine whether that gene expression is an outlier. This weight function plays a key role in unifying the robustness and efficiency of estimation in one-way ANOVA. Conclusion Analyses of simulated gene expression profiles revealed that all eight methods (ANOVA, SAM, LIMMA, EBarrays, eLNN, KW, robust BetaEB and proposed) perform almost identically for m = 2 conditions in the absence of outliers. However, the robust BetaEB method and the proposed method exhibited considerably better performance than the other six methods in the presence of outliers. In this case, the BetaEB method exhibited slightly better performance than the proposed method for the small-sample cases, but the the proposed method exhibited much better performance than the BetaEB method for both the small- and large-sample cases in the presence of more than 50% outlying genes. The proposed method also exhibited better performance than the other methods for m > 2 conditions with multiple patterns of expression, where the BetaEB was not extended for this condition. Therefore, the proposed approach would be more suitable and reliable on average for the identification of DE genes between two or more conditions with multiple patterns of expression. PMID:26413858

Basin-scale heterogeneity in Antarctic precipitation and its impact on surface mass variability

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

Fyke, Jeremy; Lenaerts, Jan T. M.; Wang, Hailong

Annually averaged precipitation in the form of snow, the dominant term of the Antarctic Ice Sheet surface mass balance, displays large spatial and temporal variability. Here we present an analysis of spatial patterns of regional Antarctic precipitation variability and their impact on integrated Antarctic surface mass balance variability simulated as part of a preindustrial 1800-year global, fully coupled Community Earth System Model simulation. Correlation and composite analyses based on this output allow for a robust exploration of Antarctic precipitation variability. We identify statistically significant relationships between precipitation patterns across Antarctica that are corroborated by climate reanalyses, regional modeling and icemore » core records. These patterns are driven by variability in large-scale atmospheric moisture transport, which itself is characterized by decadal- to centennial-scale oscillations around the long-term mean. We suggest that this heterogeneity in Antarctic precipitation variability has a dampening effect on overall Antarctic surface mass balance variability, with implications for regulation of Antarctic-sourced sea level variability, detection of an emergent anthropogenic signal in Antarctic mass trends and identification of Antarctic mass loss accelerations.« less

Basin-scale heterogeneity in Antarctic precipitation and its impact on surface mass variability

DOE PAGES

Fyke, Jeremy; Lenaerts, Jan T. M.; Wang, Hailong

2017-11-15

Annually averaged precipitation in the form of snow, the dominant term of the Antarctic Ice Sheet surface mass balance, displays large spatial and temporal variability. Here we present an analysis of spatial patterns of regional Antarctic precipitation variability and their impact on integrated Antarctic surface mass balance variability simulated as part of a preindustrial 1800-year global, fully coupled Community Earth System Model simulation. Correlation and composite analyses based on this output allow for a robust exploration of Antarctic precipitation variability. We identify statistically significant relationships between precipitation patterns across Antarctica that are corroborated by climate reanalyses, regional modeling and icemore » core records. These patterns are driven by variability in large-scale atmospheric moisture transport, which itself is characterized by decadal- to centennial-scale oscillations around the long-term mean. We suggest that this heterogeneity in Antarctic precipitation variability has a dampening effect on overall Antarctic surface mass balance variability, with implications for regulation of Antarctic-sourced sea level variability, detection of an emergent anthropogenic signal in Antarctic mass trends and identification of Antarctic mass loss accelerations.« less

Species and hybrid identification of sturgeon caviar: a new molecular approach to detect illegal trade.

PubMed

Boscari, E; Barmintseva, A; Pujolar, J M; Doukakis, P; Mugue, N; Congiu, L

2014-05-01

Overexploitation of wild populations due to the high economic value of caviar has driven sturgeons to near extinction. The high prices commanded by caviar on world markets have made it a magnet for illegal and fraudulent caviar trade, often involving low-value farmed caviar being sold as top-quality caviar. We present a new molecular approach for the identification of pure sturgeon species and hybrids that are among the most commercialized species in Europe and North America. Our test is based on the discovery of species-specific single nucleotide polymorphisms (SNPs) in the ribosomal protein S7, supplemented with the Vimentin gene and the mitochondrial D-loop. Test validations performed in 702 specimens of target and nontarget sturgeon species demonstrated a 100% identification success for Acipenser naccarii, A. fulvescens, A. stellatus, A. sinensis and A. transmontanus. In addition to species identification, our approach allows the identification of Bester and AL hybrids, two of the most economically important hybrids in the world, with 80% and 100% success, respectively. Moreover, the approach has the potential to identify many other existing sturgeon hybrids. The development of a standardized sturgeon identification tool will directly benefit trade law enforcement, providing the tools to monitor and regulate the legal trade of caviar and protect sturgeon stocks from illicit producers and traders, hence contributing to safeguarding this group of heavily threatened species. © 2013 John Wiley & Sons Ltd.

Speed in Information Processing with a Computer Driven Visual Display in a Real-time Digital Simulation. M.S. Thesis - Virginia Polytechnic Inst.

NASA Technical Reports Server (NTRS)

Kyle, R. G.

1972-01-01

Information transfer between the operator and computer-generated display systems is an area where the human factors engineer discovers little useful design data relating human performance to system effectiveness. This study utilized a computer-driven, cathode-ray-tube graphic display to quantify human response speed in a sequential information processing task. The performance criteria was response time to sixteen cell elements of a square matrix display. A stimulus signal instruction specified selected cell locations by both row and column identification. An equal probable number code, from one to four, was assigned at random to the sixteen cells of the matrix and correspondingly required one of four, matched keyed-response alternatives. The display format corresponded to a sequence of diagnostic system maintenance events, that enable the operator to verify prime system status, engage backup redundancy for failed subsystem components, and exercise alternate decision-making judgements. The experimental task bypassed the skilled decision-making element and computer processing time, in order to determine a lower bound on the basic response speed for given stimulus/response hardware arrangement.

Modeling, simulation, and fabrication of a fully integrated, acid-stable, scalable solar-driven water-splitting system.

PubMed

Walczak, Karl; Chen, Yikai; Karp, Christoph; Beeman, Jeffrey W; Shaner, Matthew; Spurgeon, Joshua; Sharp, Ian D; Amashukeli, Xenia; West, William; Jin, Jian; Lewis, Nathan S; Xiang, Chengxiang

2015-02-01

A fully integrated solar-driven water-splitting system comprised of WO3 /FTO/p(+) n Si as the photoanode, Pt/TiO2 /Ti/n(+) p Si as the photocathode, and Nafion as the membrane separator, was simulated, assembled, operated in 1.0 M HClO4 , and evaluated for performance and safety characteristics under dual side illumination. A multi-physics model that accounted for the performance of the photoabsorbers and electrocatalysts, ion transport in the solution electrolyte, and gaseous product crossover was first used to define the optimal geometric design space for the system. The photoelectrodes and the membrane separators were then interconnected in a louvered design system configuration, for which the light-absorbing area and the solution-transport pathways were simultaneously optimized. The performance of the photocathode and the photoanode were separately evaluated in a traditional three-electrode photoelectrochemical cell configuration. The photocathode and photoanode were then assembled back-to-back in a tandem configuration to provide sufficient photovoltage to sustain solar-driven unassisted water-splitting. The current-voltage characteristics of the photoelectrodes showed that the low photocurrent density of the photoanode limited the overall solar-to-hydrogen (STH) conversion efficiency due to the large band gap of WO3 . A hydrogen-production rate of 0.17 mL hr(-1) and a STH conversion efficiency of 0.24 % was observed in a full cell configuration for >20 h with minimal product crossover in the fully operational, intrinsically safe, solar-driven water-splitting system. The solar-to-hydrogen conversion efficiency, ηSTH , calculated using the multiphysics numerical simulation was in excellent agreement with the experimental behavior of the system. The value of ηSTH was entirely limited by the performance of the photoelectrochemical assemblies employed in this study. The louvered design provides a robust platform for implementation of various types of photoelectrochemical assemblies, and can provide an approach to significantly higher solar conversion efficiencies as new and improved materials become available. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Reversed Priming Effects May Be Driven by Misperception Rather than Subliminal Processing.

PubMed

Sand, Anders

2016-01-01

A new paradigm for investigating whether a cognitive process is independent of perception was recently suggested. In the paradigm, primes are shown at an intermediate signal strength that leads to trial-to-trial and inter-individual variability in prime perception. Here, I used this paradigm and an objective measure of perception to assess the influence of prime identification responses on Stroop priming. I found that sensory states producing correct and incorrect prime identification responses were also associated with qualitatively different priming effects. Incorrect prime identification responses were associated with reversed priming effects but in contrast to previous studies, I interpret this to result from the (mis-)perception of primes rather than from a subliminal process. Furthermore, the intermediate signal strength also produced inter-individual variability in prime perception that strongly influenced priming effects: only participants who on average perceived the primes were Stroop primed. I discuss how this new paradigm, with a wide range of d' values, is more appropriate when regression analysis on inter-individual identification performance is used to investigate perception-dependent processing. The results of this study, in line with previous results, suggest that drawing conclusions about subliminal processes based on data averaged over individuals may be unwarranted.

Reversed Priming Effects May Be Driven by Misperception Rather than Subliminal Processing

PubMed Central

Sand, Anders

2016-01-01

A new paradigm for investigating whether a cognitive process is independent of perception was recently suggested. In the paradigm, primes are shown at an intermediate signal strength that leads to trial-to-trial and inter-individual variability in prime perception. Here, I used this paradigm and an objective measure of perception to assess the influence of prime identification responses on Stroop priming. I found that sensory states producing correct and incorrect prime identification responses were also associated with qualitatively different priming effects. Incorrect prime identification responses were associated with reversed priming effects but in contrast to previous studies, I interpret this to result from the (mis-)perception of primes rather than from a subliminal process. Furthermore, the intermediate signal strength also produced inter-individual variability in prime perception that strongly influenced priming effects: only participants who on average perceived the primes were Stroop primed. I discuss how this new paradigm, with a wide range of d′ values, is more appropriate when regression analysis on inter-individual identification performance is used to investigate perception-dependent processing. The results of this study, in line with previous results, suggest that drawing conclusions about subliminal processes based on data averaged over individuals may be unwarranted. PMID:26925016

A framework for the automated data-driven constitutive characterization of composites

Treesearch

J.G. Michopoulos; John Hermanson; T. Furukawa; A. Iliopoulos

2010-01-01

We present advances on the development of a mechatronically and algorithmically automated framework for the data-driven identification of constitutive material models based on energy density considerations. These models can capture both the linear and nonlinear constitutive response of multiaxially loaded composite materials in a manner that accounts for progressive...

KWICgrouper--Designing a Tool for Corpus-Driven Concordance Analysis

ERIC Educational Resources Information Center

O'Donnell, Matthew Brook

2008-01-01

The corpus-driven analysis of concordance data often results in the identification of groups of lines in which repeated patterns around the node item establish membership in a particular function meaning group (Mahlberg, 2005). This paper explains the KWICgrouper, a concept designed to support this kind of concordance analysis. Groups are defined…

A novel quantification-driven proteomic strategy identifies an endogenous peptide of pleiotrophin as a new biomarker of Alzheimer's disease.

PubMed

Skillbäck, Tobias; Mattsson, Niklas; Hansson, Karl; Mirgorodskaya, Ekaterina; Dahlén, Rahil; van der Flier, Wiesje; Scheltens, Philip; Duits, Floor; Hansson, Oskar; Teunissen, Charlotte; Blennow, Kaj; Zetterberg, Henrik; Gobom, Johan

2017-10-17

We present a new, quantification-driven proteomic approach to identifying biomarkers. In contrast to the identification-driven approach, limited in scope to peptides that are identified by database searching in the first step, all MS data are considered to select biomarker candidates. The endopeptidome of cerebrospinal fluid from 40 Alzheimer's disease (AD) patients, 40 subjects with mild cognitive impairment, and 40 controls with subjective cognitive decline was analyzed using multiplex isobaric labeling. Spectral clustering was used to match MS/MS spectra. The top biomarker candidate cluster (215% higher in AD compared to controls, area under ROC curve = 0.96) was identified as a fragment of pleiotrophin located near the protein's C-terminus. Analysis of another cohort (n = 60 over four clinical groups) verified that the biomarker was increased in AD patients while no change in controls, Parkinson's disease or progressive supranuclear palsy was observed. The identification of the novel biomarker pleiotrophin 151-166 demonstrates that our quantification-driven proteomic approach is a promising method for biomarker discovery, which may be universally applicable in clinical proteomics.

Synaptically Driven Phosphorylation of Ribosomal Protein S6 Is Differentially Regulated at Active Synapses versus Dendrites and Cell Bodies by MAPK and PI3K/mTOR Signaling Pathways

ERIC Educational Resources Information Center

Pirbhoy, Patricia Salgado; Farris, Shannon; Steward, Oswald

2017-01-01

High-frequency stimulation of the medial perforant path triggers robust phosphorylation of ribosomal protein S6 (rpS6) in activated dendritic domains and granule cell bodies. Here we dissect the signaling pathways responsible for synaptically driven rpS6 phosphorylation in the dentate gyrus using pharmacological agents to inhibit PI3-kinase/mTOR…

Robust Feedback Control of Flow Induced Structural Radiation of Sound

NASA Technical Reports Server (NTRS)

Heatwole, Craig M.; Bernhard, Robert J.; Franchek, Matthew A.

1997-01-01

A significant component of the interior noise of aircraft and automobiles is a result of turbulent boundary layer excitation of the vehicular structure. In this work, active robust feedback control of the noise due to this non-predictable excitation is investigated. Both an analytical model and experimental investigations are used to determine the characteristics of the flow induced structural sound radiation problem. The problem is shown to be broadband in nature with large system uncertainties associated with the various operating conditions. Furthermore the delay associated with sound propagation is shown to restrict the use of microphone feedback. The state of the art control methodologies, IL synthesis and adaptive feedback control, are evaluated and shown to have limited success for solving this problem. A robust frequency domain controller design methodology is developed for the problem of sound radiated from turbulent flow driven plates. The control design methodology uses frequency domain sequential loop shaping techniques. System uncertainty, sound pressure level reduction performance, and actuator constraints are included in the design process. Using this design method, phase lag was added using non-minimum phase zeros such that the beneficial plant dynamics could be used. This general control approach has application to lightly damped vibration and sound radiation problems where there are high bandwidth control objectives requiring a low controller DC gain and controller order.

Perceptron ensemble of graph-based positive-unlabeled learning for disease gene identification.

PubMed

Jowkar, Gholam-Hossein; Mansoori, Eghbal G

2016-10-01

Identification of disease genes, using computational methods, is an important issue in biomedical and bioinformatics research. According to observations that diseases with the same or similar phenotype have the same biological characteristics, researchers have tried to identify genes by using machine learning tools. In recent attempts, some semi-supervised learning methods, called positive-unlabeled learning, is used for disease gene identification. In this paper, we present a Perceptron ensemble of graph-based positive-unlabeled learning (PEGPUL) on three types of biological attributes: gene ontologies, protein domains and protein-protein interaction networks. In our method, a reliable set of positive and negative genes are extracted using co-training schema. Then, the similarity graph of genes is built using metric learning by concentrating on multi-rank-walk method to perform inference from labeled genes. At last, a Perceptron ensemble is learned from three weighted classifiers: multilevel support vector machine, k-nearest neighbor and decision tree. The main contributions of this paper are: (i) incorporating the statistical properties of gene data through choosing proper metrics, (ii) statistical evaluation of biological features, and (iii) noise robustness characteristic of PEGPUL via using multilevel schema. In order to assess PEGPUL, we have applied it on 12950 disease genes with 949 positive genes from six class of diseases and 12001 unlabeled genes. Compared with some popular disease gene identification methods, the experimental results show that PEGPUL has reasonable performance. Copyright © 2016 Elsevier Ltd. All rights reserved.

Performance Evaluation of the Q Exactive HF-X for Shotgun Proteomics.

PubMed

Kelstrup, Christian D; Bekker-Jensen, Dorte B; Arrey, Tabiwang N; Hogrebe, Alexander; Harder, Alexander; Olsen, Jesper V

2018-01-05

Progress in proteomics is mainly driven by advances in mass spectrometric (MS) technologies. Here we benchmarked the performance of the latest MS instrument in the benchtop Orbitrap series, the Q Exactive HF-X, against its predecessor for proteomics applications. A new peak-picking algorithm, a brighter ion source, and optimized ion transfers enable productive MS/MS acquisition above 40 Hz at 7500 resolution. The hardware and software improvements collectively resulted in improved peptide and protein identifications across all comparable conditions, with an increase of up to 50 percent at short LC-MS gradients, yielding identification rates of more than 1000 unique peptides per minute. Alternatively, the Q Exactive HF-X is capable of achieving the same proteome coverage as its predecessor in approximately half the gradient time or at 10-fold lower sample loads. The Q Exactive HF-X also enables rapid phosphoproteomics with routine analysis of more than 5000 phosphopeptides with short single-shot 15 min LC-MS/MS measurements, or 16 700 phosphopeptides quantified across ten conditions in six gradient hours using TMT10-plex and offline peptide fractionation. Finally, exciting perspectives for data-independent acquisition are highlighted with reproducible identification of 55 000 unique peptides covering 5900 proteins in half an hour of MS analysis.

30 CFR 18.14 - Identification of tested noncertified explosion-proof enclosures.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 30 Mineral Resources 1 2010-07-01 2010-07-01 false Identification of tested noncertified explosion-proof enclosures. 18.14 Section 18.14 Mineral Resources MINE SAFETY AND HEALTH ADMINISTRATION, DEPARTMENT OF LABOR TESTING, EVALUATION, AND APPROVAL OF MINING PRODUCTS ELECTRIC MOTOR-DRIVEN MINE EQUIPMENT...

Identification and characterization of earthquake clusters: a comparative analysis for selected sequences in Italy

NASA Astrophysics Data System (ADS)

Peresan, Antonella; Gentili, Stefania

2017-04-01

Identification and statistical characterization of seismic clusters may provide useful insights about the features of seismic energy release and their relation to physical properties of the crust within a given region. Moreover, a number of studies based on spatio-temporal analysis of main-shocks occurrence require preliminary declustering of the earthquake catalogs. Since various methods, relying on different physical/statistical assumptions, may lead to diverse classifications of earthquakes into main events and related events, we aim to investigate the classification differences among different declustering techniques. Accordingly, a formal selection and comparative analysis of earthquake clusters is carried out for the most relevant earthquakes in North-Eastern Italy, as reported in the local OGS-CRS bulletins, compiled at the National Institute of Oceanography and Experimental Geophysics since 1977. The comparison is then extended to selected earthquake sequences associated with a different seismotectonic setting, namely to events that occurred in the region struck by the recent Central Italy destructive earthquakes, making use of INGV data. Various techniques, ranging from classical space-time windows methods to ad hoc manual identification of aftershocks, are applied for detection of earthquake clusters. In particular, a statistical method based on nearest-neighbor distances of events in space-time-energy domain, is considered. Results from clusters identification by the nearest-neighbor method turn out quite robust with respect to the time span of the input catalogue, as well as to minimum magnitude cutoff. The identified clusters for the largest events reported in North-Eastern Italy since 1977 are well consistent with those reported in earlier studies, which were aimed at detailed manual aftershocks identification. The study shows that the data-driven approach, based on the nearest-neighbor distances, can be satisfactorily applied to decompose the seismic catalog into background seismicity and individual sequences of earthquake clusters, also in areas characterized by moderate seismic activity, where the standard declustering techniques may turn out rather gross approximations. With these results acquired, the main statistical features of seismic clusters are explored, including complex interdependence of related events, with the aim to characterize the space-time patterns of earthquakes occurrence in North-Eastern Italy and capture their basic differences with Central Italy sequences.

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.

A database de-identification framework to enable direct queries on medical data for secondary use.

PubMed

Erdal, B S; Liu, J; Ding, J; Chen, J; Marsh, C B; Kamal, J; Clymer, B D

2012-01-01

To qualify the use of patient clinical records as non-human-subject for research purpose, electronic medical record data must be de-identified so there is minimum risk to protected health information exposure. This study demonstrated a robust framework for structured data de-identification that can be applied to any relational data source that needs to be de-identified. Using a real world clinical data warehouse, a pilot implementation of limited subject areas were used to demonstrate and evaluate this new de-identification process. Query results and performances are compared between source and target system to validate data accuracy and usability. The combination of hashing, pseudonyms, and session dependent randomizer provides a rigorous de-identification framework to guard against 1) source identifier exposure; 2) internal data analyst manually linking to source identifiers; and 3) identifier cross-link among different researchers or multiple query sessions by the same researcher. In addition, a query rejection option is provided to refuse queries resulting in less than preset numbers of subjects and total records to prevent users from accidental subject identification due to low volume of data. This framework does not prevent subject re-identification based on prior knowledge and sequence of events. Also, it does not deal with medical free text de-identification, although text de-identification using natural language processing can be included due its modular design. We demonstrated a framework resulting in HIPAA Compliant databases that can be directly queried by researchers. This technique can be augmented to facilitate inter-institutional research data sharing through existing middleware such as caGrid.

ArcAtlas in the Classroom: Pattern Identification, Description, and Explanation

ERIC Educational Resources Information Center

DeMers, Michael N.; Vincent, Jeffrey S.

2007-01-01

The use of geographic information systems (GIS) in the classroom provides a robust and effective method of teaching the primary spatial skills of identification, description, and explanation of spatial pattern. A major handicap for the development of GIS-based learning experiences, especially for non-GIS specialist educators, is the availability…

Thermally induced light-driven microfluidics using a MOEMS-based laser scanner for particle manipulation

NASA Astrophysics Data System (ADS)

Kremer, Matthias P.; Tortschanoff, Andreas

2014-03-01

One key challenge in the field of microfluidics and lab-on-a-chip experiments for biological or chemical applications is the remote manipulation of fluids, droplets and particles. These can be volume elements of reactants, particles coated with markers, cells or many others. Light-driven microfluidics is one way of accomplishing this challenge. In our work, we manipulated micrometre sized polystyrene beads in a microfluidic environment by inducing thermal flows. Therefore, the beads were held statically in an unstructured microfluidic chamber, containing a dyed watery solution. Inside this chamber, the beads were moved along arbitrary trajectories on a micrometre scale. The experiments were performed, using a MOEMS (micro-opto-electro-mechanical-systems)-based laser scanner with a variable focal length. This scanner system is integrated in a compact device, which is flexibly applicable to various microscope setups. The device utilizes a novel approach for varying the focal length, using an electrically tunable lens. A quasi statically driven MOEMS mirror is used for beam steering. The combination of a tunable lens and a dual axis micromirror makes the device very compact and robust and is capable of positioning the laser focus at any arbitrary location within a three dimensional working space. Hence, the developed device constitutes a valuable extension to manually executed microfluidic lab-on-chip experiments.

Schema-driven facilitation of new hierarchy learning in the transitive inference paradigm

PubMed Central

Kumaran, Dharshan

2013-01-01

Prior knowledge, in the form of a mental schema or framework, is viewed to facilitate the learning of new information in a range of experimental and everyday scenarios. Despite rising interest in the cognitive and neural mechanisms underlying schema-driven facilitation of new learning, few paradigms have been developed to examine this issue in humans. Here we develop a multiphase experimental scenario aimed at characterizing schema-based effects in the context of a paradigm that has been very widely used across species, the transitive inference task. We show that an associative schema, comprised of prior knowledge of the rank positions of familiar items in the hierarchy, has a marked effect on transitivity performance and the development of relational knowledge of the hierarchy that cannot be accounted for by more general changes in task strategy. Further, we show that participants are capable of deploying prior knowledge to successful effect under surprising conditions (i.e., when corrective feedback is totally absent), but only when the associative schema is robust. Finally, our results provide insights into the cognitive mechanisms underlying such schema-driven effects, and suggest that new hierarchy learning in the transitive inference task can occur through a contextual transfer mechanism that exploits the structure of associative experiences. PMID:23782509

Multiscale-Driven approach to detecting change in Synthetic Aperture Radar (SAR) imagery

NASA Astrophysics Data System (ADS)

Gens, R.; Hogenson, K.; Ajadi, O. A.; Meyer, F. J.; Myers, A.; Logan, T. A.; Arnoult, K., Jr.

2017-12-01

Detecting changes between Synthetic Aperture Radar (SAR) images can be a useful but challenging exercise. SAR with its all-weather capabilities can be an important resource in identifying and estimating the expanse of events such as flooding, river ice breakup, earthquake damage, oil spills, and forest growth, as it can overcome shortcomings of optical methods related to cloud cover. However, detecting change in SAR imagery can be impeded by many factors including speckle, complex scattering responses, low temporal sampling, and difficulty delineating boundaries. In this presentation we use a change detection method based on a multiscale-driven approach. By using information at different resolution levels, we attempt to obtain more accurate change detection maps in both heterogeneous and homogeneous regions. Integrated within the processing flow are processes that 1) improve classification performance by combining Expectation-Maximization algorithms with mathematical morphology, 2) achieve high accuracy in preserving boundaries using measurement level fusion techniques, and 3) combine modern non-local filtering and 2D-discrete stationary wavelet transform to provide robustness against noise. This multiscale-driven approach to change detection has recently been incorporated into the Alaska Satellite Facility (ASF) Hybrid Pluggable Processing Pipeline (HyP3) using radiometrically terrain corrected SAR images. Examples primarily from natural hazards are presented to illustrate the capabilities and limitations of the change detection method.

Schema-driven facilitation of new hierarchy learning in the transitive inference paradigm.

PubMed

Kumaran, Dharshan

2013-06-19

Prior knowledge, in the form of a mental schema or framework, is viewed to facilitate the learning of new information in a range of experimental and everyday scenarios. Despite rising interest in the cognitive and neural mechanisms underlying schema-driven facilitation of new learning, few paradigms have been developed to examine this issue in humans. Here we develop a multiphase experimental scenario aimed at characterizing schema-based effects in the context of a paradigm that has been very widely used across species, the transitive inference task. We show that an associative schema, comprised of prior knowledge of the rank positions of familiar items in the hierarchy, has a marked effect on transitivity performance and the development of relational knowledge of the hierarchy that cannot be accounted for by more general changes in task strategy. Further, we show that participants are capable of deploying prior knowledge to successful effect under surprising conditions (i.e., when corrective feedback is totally absent), but only when the associative schema is robust. Finally, our results provide insights into the cognitive mechanisms underlying such schema-driven effects, and suggest that new hierarchy learning in the transitive inference task can occur through a contextual transfer mechanism that exploits the structure of associative experiences.

Reframing measurement for structural health monitoring: a full-field strategy for structural identification

NASA Astrophysics Data System (ADS)

Dizaji, Mehrdad S.; Harris, Devin K.; Alipour, Mohamad; Ozbulut, Osman E.

2018-03-01

Structural health monitoring (SHM) describes a decision-making framework that is fundamentally guided by state change detection of structural systems. This framework typically relies on the use of continuous or semi-continuous monitoring of measured response to quantify this state change in structural system behavior, which is often related to the initiation of some form of damage. Measurement approaches used for traditional SHM are numerous, but most are limited to either describing localized or global phenomena, making it challenging to characterize operational structural systems which exhibit both. In addition to these limitations in sensing, SHM has also suffered from the inherent robustness inherent to most full-scale structural systems, making it challenging to identify local damage. These challenges highlight the opportunity for alternative strategies for SHM, strategies that are able to provide data suitable to translate into rich information. This paper describes preliminary results from a refined structural identification (St-ID) approach using fullfield measurements derived from high-speed 3D Digital Image Correlation (HSDIC) to characterize uncertain parameters (i.e. boundary and constitutive properties) of a laboratory scale structural component. The St-ID approach builds from prior work by supplementing full-field deflection and strain response with vibration response derived from HSDIC. Inclusion of the modal characteristics within a hybrid-genetic algorithm optimization scheme allowed for simultaneous integration of mechanical and modal response, thus enabling a more robust St-ID strategy than could be achieved with traditional sensing techniques. The use of full-field data is shown to provide a more comprehensive representation of the global and local behavior, which in turn increases the robustness of the St-Id framework. This work serves as the foundation for a new paradigm in SHM that emphasizes characterizing structural performance using a smaller number, but richer set of measurements.

Anabolic steroid use and body image psychopathology in men: Delineating between appearance- versus performance-driven motivations.

PubMed

Murray, Stuart B; Griffiths, Scott; Mond, Jonathan M; Kean, Joseph; Blashill, Aaron J

2016-08-01

Anabolic androgenic steroid (AAS) use has been robustly associated with negative body image, and eating- and muscularity-oriented psychopathology. However, with AAS being increasingly utilized for both appearance and athletic performance-related purposes, we investigated whether comorbid body image psychopathology varies as a function of motivation for usage. Self-reported motivation for current and initial AAS use was recorded amongst 122 AAS using males, alongside measures of current disordered eating and muscle dysmorphia psychopathology. Those reporting AAS for appearance purposes reported greater overall eating disorder psychopathology, F(2, 118)=7.45, p=0.001, ηp(2)=0.11, and muscle dysmorphia psychopathology, F(2, 118)=7.22, p<0.001, ηp(2)=0.11, than those using AAS primarily for performance purposes. Additionally, greater dietary restraint, F(2, 116)=3.61, p=0.030, ηp(2)=0.06, functional impairment, F(2, 118)=3.26, p=0.042, ηp(2)=0.05, and drive for size, F(2, 118)=10.76, p<0.001, ηp(2)=0.15, was demonstrated in those using ASS for appearance purposes. Motivation for AAS use may be important in accounting for differential profiles of body image psychopathology amongst users. Men whose AAS use is driven primarily by appearance-related concerns may be a particularly dysfunctional subgroup. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Echo State Networks for data-driven downhole pressure estimation in gas-lift oil wells.

PubMed

Antonelo, Eric A; Camponogara, Eduardo; Foss, Bjarne

2017-01-01

Process measurements are of vital importance for monitoring and control of industrial plants. When we consider offshore oil production platforms, wells that require gas-lift technology to yield oil production from low pressure oil reservoirs can become unstable under some conditions. This undesirable phenomenon is usually called slugging flow, and can be identified by an oscillatory behavior of the downhole pressure measurement. Given the importance of this measurement and the unreliability of the related sensor, this work aims at designing data-driven soft-sensors for downhole pressure estimation in two contexts: one for speeding up first-principle model simulation of a vertical riser model; and another for estimating the downhole pressure using real-world data from an oil well from Petrobras based only on topside platform measurements. Both tasks are tackled by employing Echo State Networks (ESN) as an efficient technique for training Recurrent Neural Networks. We show that a single ESN is capable of robustly modeling both the slugging flow behavior and a steady state based only on a square wave input signal representing the production choke opening in the vertical riser. Besides, we compare the performance of a standard network to the performance of a multiple timescale hierarchical architecture in the second task and show that the latter architecture performs better in modeling both large irregular transients and more commonly occurring small oscillations. Copyright © 2016 Elsevier Ltd. All rights reserved.

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.

Precision pointing and control of flexible spacecraft

NASA Technical Reports Server (NTRS)

Bantell, M. H., Jr.

1987-01-01

The problem and long term objectives for the precision pointing and control of flexible spacecraft are given. The four basic objectives are stated in terms of two principle tasks. Under Task 1, robust low order controllers, improved structural modeling methods for control applications and identification methods for structural dynamics are being developed. Under Task 2, a lab test experiment for verification of control laws and system identification algorithms is being developed. For Task 1, work has focused on robust low order controller design and some initial considerations for structural modeling in control applications. For Task 2, work has focused on experiment design and fabrication, along with sensor selection and initial digital controller implementation. Conclusions are given.

A Method for the Direct Identification of Differentiating Muscle Cells by a Fluorescent Mitochondrial Dye

PubMed Central

Miyake, Tetsuaki; McDermott, John C.; Gramolini, Anthony O.

2011-01-01

Identification of differentiating muscle cells generally requires fixation, antibodies directed against muscle specific proteins, and lengthy staining processes or, alternatively, transfection of muscle specific reporter genes driving GFP expression. In this study, we examined the possibility of using the robust mitochondrial network seen in maturing muscle cells as a marker of cellular differentiation. The mitochondrial fluorescent tracking dye, MitoTracker, which is a cell-permeable, low toxicity, fluorescent dye, allowed us to distinguish and track living differentiating muscle cells visually by epi-fluorescence microscopy. MitoTracker staining provides a robust and simple detection strategy for living differentiating cells in culture without the need for fixation or biochemical processing. PMID:22174849

DETECTION AND IDENTIFICATION OF SPEECH SOUNDS USING CORTICAL ACTIVITY PATTERNS

PubMed Central

Centanni, T.M.; Sloan, A.M.; Reed, A.C.; Engineer, C.T.; Rennaker, R.; Kilgard, M.P.

2014-01-01

We have developed a classifier capable of locating and identifying speech sounds using activity from rat auditory cortex with an accuracy equivalent to behavioral performance without the need to specify the onset time of the speech sounds. This classifier can identify speech sounds from a large speech set within 40 ms of stimulus presentation. To compare the temporal limits of the classifier to behavior, we developed a novel task that requires rats to identify individual consonant sounds from a stream of distracter consonants. The classifier successfully predicted the ability of rats to accurately identify speech sounds for syllable presentation rates up to 10 syllables per second (up to 17.9 ± 1.5 bits/sec), which is comparable to human performance. Our results demonstrate that the spatiotemporal patterns generated in primary auditory cortex can be used to quickly and accurately identify consonant sounds from a continuous speech stream without prior knowledge of the stimulus onset times. Improved understanding of the neural mechanisms that support robust speech processing in difficult listening conditions could improve the identification and treatment of a variety of speech processing disorders. PMID:24286757

Φ-score: A cell-to-cell phenotypic scoring method for sensitive and selective hit discovery in cell-based assays.

PubMed

Guyon, Laurent; Lajaunie, Christian; Fer, Frédéric; Bhajun, Ricky; Sulpice, Eric; Pinna, Guillaume; Campalans, Anna; Radicella, J Pablo; Rouillier, Philippe; Mary, Mélissa; Combe, Stéphanie; Obeid, Patricia; Vert, Jean-Philippe; Gidrol, Xavier

2015-09-18

Phenotypic screening monitors phenotypic changes induced by perturbations, including those generated by drugs or RNA interference. Currently-used methods for scoring screen hits have proven to be problematic, particularly when applied to physiologically relevant conditions such as low cell numbers or inefficient transfection. Here, we describe the Φ-score, which is a novel scoring method for the identification of phenotypic modifiers or hits in cell-based screens. Φ-score performance was assessed with simulations, a validation experiment and its application to gene identification in a large-scale RNAi screen. Using robust statistics and a variance model, we demonstrated that the Φ-score showed better sensitivity, selectivity and reproducibility compared to classical approaches. The improved performance of the Φ-score paves the way for cell-based screening of primary cells, which are often difficult to obtain from patients in sufficient numbers. We also describe a dedicated merging procedure to pool scores from small interfering RNAs targeting the same gene so as to provide improved visualization and hit selection.

Φ-score: A cell-to-cell phenotypic scoring method for sensitive and selective hit discovery in cell-based assays

PubMed Central

Guyon, Laurent; Lajaunie, Christian; fer, Frédéric; bhajun, Ricky; sulpice, Eric; pinna, Guillaume; campalans, Anna; radicella, J. Pablo; rouillier, Philippe; mary, Mélissa; combe, Stéphanie; obeid, Patricia; vert, Jean-Philippe; gidrol, Xavier

2015-01-01

Phenotypic screening monitors phenotypic changes induced by perturbations, including those generated by drugs or RNA interference. Currently-used methods for scoring screen hits have proven to be problematic, particularly when applied to physiologically relevant conditions such as low cell numbers or inefficient transfection. Here, we describe the Φ-score, which is a novel scoring method for the identification of phenotypic modifiers or hits in cell-based screens. Φ-score performance was assessed with simulations, a validation experiment and its application to gene identification in a large-scale RNAi screen. Using robust statistics and a variance model, we demonstrated that the Φ-score showed better sensitivity, selectivity and reproducibility compared to classical approaches. The improved performance of the Φ-score paves the way for cell-based screening of primary cells, which are often difficult to obtain from patients in sufficient numbers. We also describe a dedicated merging procedure to pool scores from small interfering RNAs targeting the same gene so as to provide improved visualization and hit selection. PMID:26382112

"iSS-Hyb-mRMR": Identification of splicing sites using hybrid space of pseudo trinucleotide and pseudo tetranucleotide composition.

PubMed

Iqbal, Muhammad; Hayat, Maqsood

2016-05-01

Gene splicing is a vital source of protein diversity. Perfectly eradication of introns and joining exons is the prominent task in eukaryotic gene expression, as exons are usually interrupted by introns. Identification of splicing sites through experimental techniques is complicated and time-consuming task. With the avalanche of genome sequences generated in the post genomic age, it remains a complicated and challenging task to develop an automatic, robust and reliable computational method for fast and effective identification of splicing sites. In this study, a hybrid model "iSS-Hyb-mRMR" is proposed for quickly and accurately identification of splicing sites. Two sample representation methods namely; pseudo trinucleotide composition (PseTNC) and pseudo tetranucleotide composition (PseTetraNC) were used to extract numerical descriptors from DNA sequences. Hybrid model was developed by concatenating PseTNC and PseTetraNC. In order to select high discriminative features, minimum redundancy maximum relevance algorithm was applied on the hybrid feature space. The performance of these feature representation methods was tested using various classification algorithms including K-nearest neighbor, probabilistic neural network, general regression neural network, and fitting network. Jackknife test was used for evaluation of its performance on two benchmark datasets S1 and S2, respectively. The predictor, proposed in the current study achieved an accuracy of 93.26%, sensitivity of 88.77%, and specificity of 97.78% for S1, and the accuracy of 94.12%, sensitivity of 87.14%, and specificity of 98.64% for S2, respectively. It is observed, that the performance of proposed model is higher than the existing methods in the literature so for; and will be fruitful in the mechanism of RNA splicing, and other research academia. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Dynamic and fluid-structure interaction simulations of bioprosthetic heart valves using parametric design with T-splines and Fung-type material models

NASA Astrophysics Data System (ADS)

Hsu, Ming-Chen; Kamensky, David; Xu, Fei; Kiendl, Josef; Wang, Chenglong; Wu, Michael C. H.; Mineroff, Joshua; Reali, Alessandro; Bazilevs, Yuri; Sacks, Michael S.

2015-06-01

This paper builds on a recently developed immersogeometric fluid-structure interaction (FSI) methodology for bioprosthetic heart valve (BHV) modeling and simulation. It enhances the proposed framework in the areas of geometry design and constitutive modeling. With these enhancements, BHV FSI simulations may be performed with greater levels of automation, robustness and physical realism. In addition, the paper presents a comparison between FSI analysis and standalone structural dynamics simulation driven by prescribed transvalvular pressure, the latter being a more common modeling choice for this class of problems. The FSI computation achieved better physiological realism in predicting the valve leaflet deformation than its standalone structural dynamics counterpart.

Explicet: graphical user interface software for metadata-driven management, analysis and visualization of microbiome data.

PubMed

Robertson, Charles E; Harris, J Kirk; Wagner, Brandie D; Granger, David; Browne, Kathy; Tatem, Beth; Feazel, Leah M; Park, Kristin; Pace, Norman R; Frank, Daniel N

2013-12-01

Studies of the human microbiome, and microbial community ecology in general, have blossomed of late and are now a burgeoning source of exciting research findings. Along with the advent of next-generation sequencing platforms, which have dramatically increased the scope of microbiome-related projects, several high-performance sequence analysis pipelines (e.g. QIIME, MOTHUR, VAMPS) are now available to investigators for microbiome analysis. The subject of our manuscript, the graphical user interface-based Explicet software package, fills a previously unmet need for a robust, yet intuitive means of integrating the outputs of the software pipelines with user-specified metadata and then visualizing the combined data.

A Data-Driven Solution for Performance Improvement

NASA Technical Reports Server (NTRS)

2002-01-01

Marketed as the "Software of the Future," Optimal Engineering Systems P.I. EXPERT(TM) technology offers statistical process control and optimization techniques that are critical to businesses looking to restructure or accelerate operations in order to gain a competitive edge. Kennedy Space Center granted Optimal Engineering Systems the funding and aid necessary to develop a prototype of the process monitoring and improvement software. Completion of this prototype demonstrated that it was possible to integrate traditional statistical quality assurance tools with robust optimization techniques in a user- friendly format that is visually compelling. Using an expert system knowledge base, the software allows the user to determine objectives, capture constraints and out-of-control processes, predict results, and compute optimal process settings.

Container weld identification using portable laser scanners

NASA Astrophysics Data System (ADS)

Taddei, Pierluigi; Boström, Gunnar; Puig, David; Kravtchenko, Victor; Sequeira, Vítor

2015-03-01

Identification and integrity verification of sealed containers for security applications can be obtained by employing noninvasive portable optical systems. We present a portable laser range imaging system capable of identifying welds, a byproduct of a container's physical sealing, with micrometer accuracy. It is based on the assumption that each weld has a unique three-dimensional (3-D) structure which cannot be copied or forged. We process the 3-D surface to generate a normalized depth map which is invariant to mechanical alignment errors and that is used to build compact signatures representing the weld. A weld is identified by performing cross correlations of its signature against a set of known signatures. The system has been tested on realistic datasets, containing hundreds of welds, yielding no false positives or false negatives and thus showing the robustness of the system and the validity of the chosen signature.

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.

Photonics: From target recognition to lesion detection

NASA Technical Reports Server (NTRS)

Henry, E. Michael

1994-01-01

Since 1989, Martin Marietta has invested in the development of an innovative concept for robust real-time pattern recognition for any two-dimensioanal sensor. This concept has been tested in simulation, and in laboratory and field hardware, for a number of DOD and commercial uses from automatic target recognition to manufacturing inspection. We have now joined Rose Health Care Systems in developing its use for medical diagnostics. The concept is based on determining regions of interest by using optical Fourier bandpassing as a scene segmentation technique, enhancing those regions using wavelet filters, passing the enhanced regions to a neural network for analysis and initial pattern identification, and following this initial identification with confirmation by optical correlation. The optical scene segmentation and pattern confirmation are performed by the same optical module. The neural network is a recursive error minimization network with a small number of connections and nodes that rapidly converges to a global minimum.

A measurement fusion method for nonlinear system identification using a cooperative learning algorithm.

PubMed

Xia, Youshen; Kamel, Mohamed S

2007-06-01

Identification of a general nonlinear noisy system viewed as an estimation of a predictor function is studied in this article. A measurement fusion method for the predictor function estimate is proposed. In the proposed scheme, observed data are first fused by using an optimal fusion technique, and then the optimal fused data are incorporated in a nonlinear function estimator based on a robust least squares support vector machine (LS-SVM). A cooperative learning algorithm is proposed to implement the proposed measurement fusion method. Compared with related identification methods, the proposed method can minimize both the approximation error and the noise error. The performance analysis shows that the proposed optimal measurement fusion function estimate has a smaller mean square error than the LS-SVM function estimate. Moreover, the proposed cooperative learning algorithm can converge globally to the optimal measurement fusion function estimate. Finally, the proposed measurement fusion method is applied to ARMA signal and spatial temporal signal modeling. Experimental results show that the proposed measurement fusion method can provide a more accurate model.

Health condition identification of multi-stage planetary gearboxes using a mRVM-based method

NASA Astrophysics Data System (ADS)

Lei, Yaguo; Liu, Zongyao; Wu, Xionghui; Li, Naipeng; Chen, Wu; Lin, Jing

2015-08-01

Multi-stage planetary gearboxes are widely applied in aerospace, automotive and heavy industries. Their key components, such as gears and bearings, can easily suffer from damage due to tough working environment. Health condition identification of planetary gearboxes aims to prevent accidents and save costs. This paper proposes a method based on multiclass relevance vector machine (mRVM) to identify health condition of multi-stage planetary gearboxes. In this method, a mRVM algorithm is adopted as a classifier, and two features, i.e. accumulative amplitudes of carrier orders (AACO) and energy ratio based on difference spectra (ERDS), are used as the input of the classifier to classify different health conditions of multi-stage planetary gearboxes. To test the proposed method, seven health conditions of a two-stage planetary gearbox are considered and vibration data is acquired from the planetary gearbox under different motor speeds and loading conditions. The results of three tests based on different data show that the proposed method obtains an improved identification performance and robustness compared with the existing method.

An empirical assessment of high-performing medical groups: results from a national study.

PubMed

Shortell, Stephen M; Schmittdiel, Julie; Wang, Margaret C; Li, Rui; Gillies, Robin R; Casalino, Lawrence P; Bodenheimer, Thomas; Rundall, Thomas G

2005-08-01

The performance of medical groups is receiving increased attention. Relatively little conceptual or empirical work exists that examines the various dimensions of medical group performance. Using a national database of 693 medical groups, this article develops a scorecard approach to assessing group performance and presents a theory-driven framework for differentiating between high-performing versus low-performing medical groups. The clinical quality of care, financial performance, and organizational learning capability of medical groups are assessed in relation to environmental forces, resource acquisition and resource deployment factors, and a quality-centered culture. Findings support the utility of the performance scorecard approach and identification of a number of key factors differentiating high-performing from low-performing groups including, in particular, the importance of a quality-centered culture and the requirement of outside reporting from third party organizations. The findings hold a number of important implications for policy and practice, and the framework presented provides a foundation for future research.

Determining the feasibility of objective adherence measurement with blister packaging smart technology.

PubMed

van Onzenoort, Hein A; Neef, Cees; Verberk, Willem W; van Iperen, H Peter; de Leeuw, Peter W; van der Kuy, Paul-Hugo M

2012-05-15

The results of a feasibility study of blister-pack smart technology for monitoring medication adherence are reported. Research in the area of objective therapy compliance measurement has led to the development of microprocessor-driven systems that record the time a unit dose is removed from blister packaging. One device under development is the Smart Blister-a label imprinted with event-detection circuitry that can be affixed to standard commercial blister cards. In the first trial of the device in actual clinical practice, 115 community-dwelling Dutch patients receiving valsartan maintenance therapy (160 mg once daily) were given 14-day blister packages equipped with the Smart Blister. On the return of empty blister cards to the 20 participating community pharmacies, the stored information was scanned and downloaded for data analysis and patient counseling purposes. A total of 245 Smart Blister-equipped packages were used by valsartan recipients during the eight-month study. The device was largely effective in recording patient and blister-card identification data and other desired information. However, in 17% of cases, the Smart Blister system registered multiple tablet-removal events at the same time, presumably indicating unintentional breakage of nearby conductive circuits and the need for design refinements. The Smart Blister-equipped medication cards were generally well received by patients and pharmacies. An evaluation of the functionality and robustness of the Smart Blister in a real-world clinical practice situation yielded some promising results, but the findings also indicated a need for design refinements and additional performance testing of the device.

Temporal variability of spectro-temporal receptive fields in the anesthetized auditory cortex.

PubMed

Meyer, Arne F; Diepenbrock, Jan-Philipp; Ohl, Frank W; Anemüller, Jörn

2014-01-01

Temporal variability of neuronal response characteristics during sensory stimulation is a ubiquitous phenomenon that may reflect processes such as stimulus-driven adaptation, top-down modulation or spontaneous fluctuations. It poses a challenge to functional characterization methods such as the receptive field, since these often assume stationarity. We propose a novel method for estimation of sensory neurons' receptive fields that extends the classic static linear receptive field model to the time-varying case. Here, the long-term estimate of the static receptive field serves as the mean of a probabilistic prior distribution from which the short-term temporally localized receptive field may deviate stochastically with time-varying standard deviation. The derived corresponding generalized linear model permits robust characterization of temporal variability in receptive field structure also for highly non-Gaussian stimulus ensembles. We computed and analyzed short-term auditory spectro-temporal receptive field (STRF) estimates with characteristic temporal resolution 5-30 s based on model simulations and responses from in total 60 single-unit recordings in anesthetized Mongolian gerbil auditory midbrain and cortex. Stimulation was performed with short (100 ms) overlapping frequency-modulated tones. Results demonstrate identification of time-varying STRFs, with obtained predictive model likelihoods exceeding those from baseline static STRF estimation. Quantitative characterization of STRF variability reveals a higher degree thereof in auditory cortex compared to midbrain. Cluster analysis indicates that significant deviations from the long-term static STRF are brief, but reliably estimated. We hypothesize that the observed variability more likely reflects spontaneous or state-dependent internal fluctuations that interact with stimulus-induced processing, rather than experimental or stimulus design.

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.

Inducer analysis/pump model development

NASA Astrophysics Data System (ADS)

Cheng, Gary C.

1994-03-01

Current design of high performance turbopumps for rocket engines requires effective and robust analytical tools to provide design information in a productive manner. The main goal of this study was to develop a robust and effective computational fluid dynamics (CFD) pump model for general turbopump design and analysis applications. A finite difference Navier-Stokes flow solver, FDNS, which includes an extended k-epsilon turbulence model and appropriate moving zonal interface boundary conditions, was developed to analyze turbulent flows in turbomachinery devices. In the present study, three key components of the turbopump, the inducer, impeller, and diffuser, were investigated by the proposed pump model, and the numerical results were benchmarked by the experimental data provided by Rocketdyne. For the numerical calculation of inducer flows with tip clearance, the turbulence model and grid spacing are very important. Meanwhile, the development of the cross-stream secondary flow, generated by curved blade passage and the flow through tip leakage, has a strong effect on the inducer flow. Hence, the prediction of the inducer performance critically depends on whether the numerical scheme of the pump model can simulate the secondary flow pattern accurately or not. The impeller and diffuser, however, are dominated by pressure-driven flows such that the effects of turbulence model and grid spacing (except near leading and trailing edges of blades) are less sensitive. The present CFD pump model has been proved to be an efficient and robust analytical tool for pump design due to its very compact numerical structure (requiring small memory), fast turnaround computing time, and versatility for different geometries.

Inducer analysis/pump model development

NASA Technical Reports Server (NTRS)

Cheng, Gary C.

1994-01-01

Current design of high performance turbopumps for rocket engines requires effective and robust analytical tools to provide design information in a productive manner. The main goal of this study was to develop a robust and effective computational fluid dynamics (CFD) pump model for general turbopump design and analysis applications. A finite difference Navier-Stokes flow solver, FDNS, which includes an extended k-epsilon turbulence model and appropriate moving zonal interface boundary conditions, was developed to analyze turbulent flows in turbomachinery devices. In the present study, three key components of the turbopump, the inducer, impeller, and diffuser, were investigated by the proposed pump model, and the numerical results were benchmarked by the experimental data provided by Rocketdyne. For the numerical calculation of inducer flows with tip clearance, the turbulence model and grid spacing are very important. Meanwhile, the development of the cross-stream secondary flow, generated by curved blade passage and the flow through tip leakage, has a strong effect on the inducer flow. Hence, the prediction of the inducer performance critically depends on whether the numerical scheme of the pump model can simulate the secondary flow pattern accurately or not. The impeller and diffuser, however, are dominated by pressure-driven flows such that the effects of turbulence model and grid spacing (except near leading and trailing edges of blades) are less sensitive. The present CFD pump model has been proved to be an efficient and robust analytical tool for pump design due to its very compact numerical structure (requiring small memory), fast turnaround computing time, and versatility for different geometries.

Fractal dimension based damage identification incorporating multi-task sparse Bayesian learning

NASA Astrophysics Data System (ADS)

Huang, Yong; Li, Hui; Wu, Stephen; Yang, Yongchao

2018-07-01

Sensitivity to damage and robustness to noise are critical requirements for the effectiveness of structural damage detection. In this study, a two-stage damage identification method based on the fractal dimension analysis and multi-task Bayesian learning is presented. The Higuchi’s fractal dimension (HFD) based damage index is first proposed, directly examining the time-frequency characteristic of local free vibration data of structures based on the irregularity sensitivity and noise robustness analysis of HFD. Katz’s fractal dimension is then presented to analyze the abrupt irregularity change of the spatial curve of the displacement mode shape along the structure. At the second stage, the multi-task sparse Bayesian learning technique is employed to infer the final damage localization vector, which borrow the dependent strength of the two fractal dimension based damage indication information and also incorporate the prior knowledge that structural damage occurs at a limited number of locations in a structure in the absence of its collapse. To validate the capability of the proposed method, a steel beam and a bridge, named Yonghe Bridge, are analyzed as illustrative examples. The damage identification results demonstrate that the proposed method is capable of localizing single and multiple damages regardless of its severity, and show superior robustness under heavy noise as well.

Robust image features: concentric contrasting circles and their image extraction

NASA Astrophysics Data System (ADS)

Gatrell, Lance B.; Hoff, William A.; Sklair, Cheryl W.

1992-03-01

Many computer vision tasks can be simplified if special image features are placed on the objects to be recognized. A review of special image features that have been used in the past is given and then a new image feature, the concentric contrasting circle, is presented. The concentric contrasting circle image feature has the advantages of being easily manufactured, easily extracted from the image, robust extraction (true targets are found, while few false targets are found), it is a passive feature, and its centroid is completely invariant to the three translational and one rotational degrees of freedom and nearly invariant to the remaining two rotational degrees of freedom. There are several examples of existing parallel implementations which perform most of the extraction work. Extraction robustness was measured by recording the probability of correct detection and the false alarm rate in a set of images of scenes containing mockups of satellites, fluid couplings, and electrical components. A typical application of concentric contrasting circle features is to place them on modeled objects for monocular pose estimation or object identification. This feature is demonstrated on a visually challenging background of a specular but wrinkled surface similar to a multilayered insulation spacecraft thermal blanket.

Robust hashing for 3D models

NASA Astrophysics Data System (ADS)

Berchtold, Waldemar; Schäfer, Marcel; Rettig, Michael; Steinebach, Martin

2014-02-01

3D models and applications are of utmost interest in both science and industry. With the increment of their usage, their number and thereby the challenge to correctly identify them increases. Content identification is commonly done by cryptographic hashes. However, they fail as a solution in application scenarios such as computer aided design (CAD), scientific visualization or video games, because even the smallest alteration of the 3D model, e.g. conversion or compression operations, massively changes the cryptographic hash as well. Therefore, this work presents a robust hashing algorithm for 3D mesh data. The algorithm applies several different bit extraction methods. They are built to resist desired alterations of the model as well as malicious attacks intending to prevent correct allocation. The different bit extraction methods are tested against each other and, as far as possible, the hashing algorithm is compared to the state of the art. The parameters tested are robustness, security and runtime performance as well as False Acceptance Rate (FAR) and False Rejection Rate (FRR), also the probability calculation of hash collision is included. The introduced hashing algorithm is kept adaptive e.g. in hash length, to serve as a proper tool for all applications in practice.

Robust and fast characterization of OCT-based optical attenuation using a novel frequency-domain algorithm for brain cancer detection

NASA Astrophysics Data System (ADS)

Yuan, Wu; Kut, Carmen; Liang, Wenxuan; Li, Xingde

2017-03-01

Cancer is known to alter the local optical properties of tissues. The detection of OCT-based optical attenuation provides a quantitative method to efficiently differentiate cancer from non-cancer tissues. In particular, the intraoperative use of quantitative OCT is able to provide a direct visual guidance in real time for accurate identification of cancer tissues, especially these without any obvious structural layers, such as brain cancer. However, current methods are suboptimal in providing high-speed and accurate OCT attenuation mapping for intraoperative brain cancer detection. In this paper, we report a novel frequency-domain (FD) algorithm to enable robust and fast characterization of optical attenuation as derived from OCT intensity images. The performance of this FD algorithm was compared with traditional fitting methods by analyzing datasets containing images from freshly resected human brain cancer and from a silica phantom acquired by a 1310 nm swept-source OCT (SS-OCT) system. With graphics processing unit (GPU)-based CUDA C/C++ implementation, this new attenuation mapping algorithm can offer robust and accurate quantitative interpretation of OCT images in real time during brain surgery.

Resonance-assisted decay of nondispersive wave packets.

PubMed

Wimberger, Sandro; Schlagheck, Peter; Eltschka, Christopher; Buchleitner, Andreas

2006-07-28

We present a quantitative semiclassical theory for the decay of nondispersive electronic wave packets in driven, ionizing Rydberg systems. Statistically robust quantities are extracted combining resonance-assisted tunneling with subsequent transport across chaotic phase space and a final ionization step.

Force control compensation method with variable load stiffness and damping of the hydraulic drive unit force control system

NASA Astrophysics Data System (ADS)

Kong, Xiangdong; Ba, Kaixian; Yu, Bin; Cao, Yuan; Zhu, Qixin; Zhao, Hualong

2016-05-01

Each joint of hydraulic drive quadruped robot is driven by the hydraulic drive unit (HDU), and the contacting between the robot foot end and the ground is complex and variable, which increases the difficulty of force control inevitably. In the recent years, although many scholars researched some control methods such as disturbance rejection control, parameter self-adaptive control, impedance control and so on, to improve the force control performance of HDU, the robustness of the force control still needs improving. Therefore, how to simulate the complex and variable load characteristics of the environment structure and how to ensure HDU having excellent force control performance with the complex and variable load characteristics are key issues to be solved in this paper. The force control system mathematic model of HDU is established by the mechanism modeling method, and the theoretical models of a novel force control compensation method and a load characteristics simulation method under different environment structures are derived, considering the dynamic characteristics of the load stiffness and the load damping under different environment structures. Then, simulation effects of the variable load stiffness and load damping under the step and sinusoidal load force are analyzed experimentally on the HDU force control performance test platform, which provides the foundation for the force control compensation experiment research. In addition, the optimized PID control parameters are designed to make the HDU have better force control performance with suitable load stiffness and load damping, under which the force control compensation method is introduced, and the robustness of the force control system with several constant load characteristics and the variable load characteristics respectively are comparatively analyzed by experiment. The research results indicate that if the load characteristics are known, the force control compensation method presented in this paper has positive compensation effects on the load characteristics variation, i.e., this method decreases the effects of the load characteristics variation on the force control performance and enhances the force control system robustness with the constant PID parameters, thereby, the online PID parameters tuning control method which is complex needs not be adopted. All the above research provides theoretical and experimental foundation for the force control method of the quadruped robot joints with high robustness.

Local structure preserving sparse coding for infrared target recognition

PubMed Central

Han, Jing; Yue, Jiang; Zhang, Yi; Bai, Lianfa

2017-01-01

Sparse coding performs well in image classification. However, robust target recognition requires a lot of comprehensive template images and the sparse learning process is complex. We incorporate sparsity into a template matching concept to construct a local sparse structure matching (LSSM) model for general infrared target recognition. A local structure preserving sparse coding (LSPSc) formulation is proposed to simultaneously preserve the local sparse and structural information of objects. By adding a spatial local structure constraint into the classical sparse coding algorithm, LSPSc can improve the stability of sparse representation for targets and inhibit background interference in infrared images. Furthermore, a kernel LSPSc (K-LSPSc) formulation is proposed, which extends LSPSc to the kernel space to weaken the influence of the linear structure constraint in nonlinear natural data. Because of the anti-interference and fault-tolerant capabilities, both LSPSc- and K-LSPSc-based LSSM can implement target identification based on a simple template set, which just needs several images containing enough local sparse structures to learn a sufficient sparse structure dictionary of a target class. Specifically, this LSSM approach has stable performance in the target detection with scene, shape and occlusions variations. High performance is demonstrated on several datasets, indicating robust infrared target recognition in diverse environments and imaging conditions. PMID:28323824

Robust regression and posterior predictive simulation increase power to detect early bursts of trait evolution.

PubMed

Slater, Graham J; Pennell, Matthew W

2014-05-01

A central prediction of much theory on adaptive radiations is that traits should evolve rapidly during the early stages of a clade's history and subsequently slowdown in rate as niches become saturated--a so-called "Early Burst." Although a common pattern in the fossil record, evidence for early bursts of trait evolution in phylogenetic comparative data has been equivocal at best. We show here that this may not necessarily be due to the absence of this pattern in nature. Rather, commonly used methods to infer its presence perform poorly when when the strength of the burst--the rate at which phenotypic evolution declines--is small, and when some morphological convergence is present within the clade. We present two modifications to existing comparative methods that allow greater power to detect early bursts in simulated datasets. First, we develop posterior predictive simulation approaches and show that they outperform maximum likelihood approaches at identifying early bursts at moderate strength. Second, we use a robust regression procedure that allows for the identification and down-weighting of convergent taxa, leading to moderate increases in method performance. We demonstrate the utility and power of these approach by investigating the evolution of body size in cetaceans. Model fitting using maximum likelihood is equivocal with regards the mode of cetacean body size evolution. However, posterior predictive simulation combined with a robust node height test return low support for Brownian motion or rate shift models, but not the early burst model. While the jury is still out on whether early bursts are actually common in nature, our approach will hopefully facilitate more robust testing of this hypothesis. We advocate the adoption of similar posterior predictive approaches to improve the fit and to assess the adequacy of macroevolutionary models in general.

Towards a Video Passive Content Fingerprinting Method for Partial-Copy Detection Robust against Non-Simulated Attacks

PubMed Central

2016-01-01

Passive content fingerprinting is widely used for video content identification and monitoring. However, many challenges remain unsolved especially for partial-copies detection. The main challenge is to find the right balance between the computational cost of fingerprint extraction and fingerprint dimension, without compromising detection performance against various attacks (robustness). Fast video detection performance is desirable in several modern applications, for instance, in those where video detection involves the use of large video databases or in applications requiring real-time video detection of partial copies, a process whose difficulty increases when videos suffer severe transformations. In this context, conventional fingerprinting methods are not fully suitable to cope with the attacks and transformations mentioned before, either because the robustness of these methods is not enough or because their execution time is very high, where the time bottleneck is commonly found in the fingerprint extraction and matching operations. Motivated by these issues, in this work we propose a content fingerprinting method based on the extraction of a set of independent binary global and local fingerprints. Although these features are robust against common video transformations, their combination is more discriminant against severe video transformations such as signal processing attacks, geometric transformations and temporal and spatial desynchronization. Additionally, we use an efficient multilevel filtering system accelerating the processes of fingerprint extraction and matching. This multilevel filtering system helps to rapidly identify potential similar video copies upon which the fingerprint process is carried out only, thus saving computational time. We tested with datasets of real copied videos, and the results show how our method outperforms state-of-the-art methods regarding detection scores. Furthermore, the granularity of our method makes it suitable for partial-copy detection; that is, by processing only short segments of 1 second length. PMID:27861492

Three-dimensional reduced graphene oxide/polyaniline nanocomposite film prepared by diffusion driven layer-by-layer assembly for high-performance supercapacitors

NASA Astrophysics Data System (ADS)

Hong, Xiaodong; Zhang, Binbin; Murphy, Elizabeth; Zou, Jianli; Kim, Franklin

2017-03-01

As a simple and versatile method, diffusion driven Layer-by-Layer assembly (dd-LbL) is developed to assemble graphene oxide (GO) into three-dimensional (3D) structure. The assembled GO macrostructure can be reduced through a hydrothermal treatment and used as a high volumetric capacitance electrode in supercapacitors. In this report we use rGO framework created from dd-LbL as a scaffold for in situ polymerization of aniline within the pores of the framework to form rGO/polyaniline (rGO/PANI) composite. The rGO/PANI composite affords a robust and porous structure, which facilitates electrolyte diffusion and exhibits excellent electrochemical performance as binder-free electrodes in a sandwich-configuration supercapacitor. Combining electric double layer capacitance and pseudo-capacitance, rGO/PANI electrodes exhibit a specific capacitance of 438.8 F g-1 at discharge rate of 5 mA (mass of electrodes were 10.0 mg, 0.5 A g-1) in 1 mol L-1 H2SO4 electrolyte; furthermore, the generated PANI nanoparticles in rGO template achieve a higher capacitance of 763 F g-1. The rGO/PANI composite electrodes also show an improved recyclability, 76.5% of capacitance retains after recycled 2000 times.

Hourly runoff forecasting for flood risk management: Application of various computational intelligence models

NASA Astrophysics Data System (ADS)

Badrzadeh, Honey; Sarukkalige, Ranjan; Jayawardena, A. W.

2015-10-01

Reliable river flow forecasts play a key role in flood risk mitigation. Among different approaches of river flow forecasting, data driven approaches have become increasingly popular in recent years due to their minimum information requirements and ability to simulate nonlinear and non-stationary characteristics of hydrological processes. In this study, attempts are made to apply four different types of data driven approaches, namely traditional artificial neural networks (ANN), adaptive neuro-fuzzy inference systems (ANFIS), wavelet neural networks (WNN), and, hybrid ANFIS with multi resolution analysis using wavelets (WNF). Developed models applied for real time flood forecasting at Casino station on Richmond River, Australia which is highly prone to flooding. Hourly rainfall and runoff data were used to drive the models which have been used for forecasting with 1, 6, 12, 24, 36 and 48 h lead-time. The performance of models further improved by adding an upstream river flow data (Wiangaree station), as another effective input. All models perform satisfactorily up to 12 h lead-time. However, the hybrid wavelet-based models significantly outperforming the ANFIS and ANN models in the longer lead-time forecasting. The results confirm the robustness of the proposed structure of the hybrid models for real time runoff forecasting in the study area.

A Real-Time Robust Method to Detect BeiDou GEO/IGSO Orbital Maneuvers

PubMed Central

Huang, Guanwen; Qin, Zhiwei; Zhang, Qin; Wang, Le; Yan, Xingyuan; Fan, Lihong; Wang, Xiaolei

2017-01-01

The frequent maneuvering of BeiDou Geostationary Orbit (GEO) and Inclined Geosynchronous Orbit (IGSO) satellites affects the availability of real-time orbit, and decreases the accuracy and performance of positioning, navigation and time (PNT) services. BeiDou satellite maneuver information cannot be obtained by common users. BeiDou broadcast ephemeris is the only indicator of the health status of satellites, which are broadcast on an hourly basis, easily leading to ineffective observations. Sometimes, identification errors of satellite abnormity also appear in the broadcast ephemeris. This study presents a real-time robust detection method for a satellite orbital maneuver with high frequency and high reliability. By using the broadcast ephemeris and pseudo-range observations, the time discrimination factor and the satellite identification factor were defined and used for the real-time detection of start time and the pseudo-random noise code (PRN) of satellites was used for orbital maneuvers. Data from a Multi-GNSS Experiment (MGEX) was collected and analyzed. The results show that the start time and the PRN of the satellite orbital maneuver could be detected accurately in real time. In addition, abnormal start times and satellite abnormities caused by non-maneuver factors also could be detected using the proposed method. The new method not only improves the utilization of observations for users with the data effective for about 92 min, but also promotes the reliability of real-time PNT services. PMID:29186058

A Real-Time Robust Method to Detect BeiDou GEO/IGSO Orbital Maneuvers.

PubMed

Huang, Guanwen; Qin, Zhiwei; Zhang, Qin; Wang, Le; Yan, Xingyuan; Fan, Lihong; Wang, Xiaolei

2017-11-29

The frequent maneuvering of BeiDou Geostationary Orbit (GEO) and Inclined Geosynchronous Orbit (IGSO) satellites affects the availability of real-time orbit, and decreases the accuracy and performance of positioning, navigation and time (PNT) services. BeiDou satellite maneuver information cannot be obtained by common users. BeiDou broadcast ephemeris is the only indicator of the health status of satellites, which are broadcast on an hourly basis, easily leading to ineffective observations. Sometimes, identification errors of satellite abnormity also appear in the broadcast ephemeris. This study presents a real-time robust detection method for a satellite orbital maneuver with high frequency and high reliability. By using the broadcast ephemeris and pseudo-range observations, the time discrimination factor and the satellite identification factor were defined and used for the real-time detection of start time and the pseudo-random noise code (PRN) of satellites was used for orbital maneuvers. Data from a Multi-GNSS Experiment (MGEX) was collected and analyzed. The results show that the start time and the PRN of the satellite orbital maneuver could be detected accurately in real time. In addition, abnormal start times and satellite abnormities caused by non-maneuver factors also could be detected using the proposed method. The new method not only improves the utilization of observations for users with the data effective for about 92 min, but also promotes the reliability of real-time PNT services.

Effects of temporal and spatial resolution of calibration data on integrated hydrologic water quality model identification

NASA Astrophysics Data System (ADS)

Jiang, Sanyuan; Jomaa, Seifeddine; Büttner, Olaf; Rode, Michael

2014-05-01

Hydrological water quality modeling is increasingly used for investigating runoff and nutrient transport processes as well as watershed management but it is mostly unclear how data availablity determins model identification. In this study, the HYPE (HYdrological Predictions for the Environment) model, which is a process-based, semi-distributed hydrological water quality model, was applied in two different mesoscale catchments (Selke (463 km2) and Weida (99 km2)) located in central Germany to simulate discharge and inorganic nitrogen (IN) transport. PEST and DREAM(ZS) were combined with the HYPE model to conduct parameter calibration and uncertainty analysis. Split-sample test was used for model calibration (1994-1999) and validation (1999-2004). IN concentration and daily IN load were found to be highly correlated with discharge, indicating that IN leaching is mainly controlled by runoff. Both dynamics and balances of water and IN load were well captured with NSE greater than 0.83 during validation period. Multi-objective calibration (calibrating hydrological and water quality parameters simultaneously) was found to outperform step-wise calibration in terms of model robustness. Multi-site calibration was able to improve model performance at internal sites, decrease parameter posterior uncertainty and prediction uncertainty. Nitrogen-process parameters calibrated using continuous daily averages of nitrate-N concentration observations produced better and more robust simulations of IN concentration and load, lower posterior parameter uncertainty and IN concentration prediction uncertainty compared to the calibration against uncontinuous biweekly nitrate-N concentration measurements. Both PEST and DREAM(ZS) are efficient in parameter calibration. However, DREAM(ZS) is more sound in terms of parameter identification and uncertainty analysis than PEST because of its capability to evolve parameter posterior distributions and estimate prediction uncertainty based on global search and Bayesian inference schemes.

The Activation of Embedded Words in Spoken Word Identification Is Robust but Constrained: Evidence from the Picture-Word Interference Paradigm

ERIC Educational Resources Information Center

Bowers, Jeffrey S.; Davis, Colin J.; Mattys, Sven L.; Damian, Markus F.; Hanley, Derek

2009-01-01

Three picture-word interference (PWI) experiments assessed the extent to which embedded subset words are activated during the identification of spoken superset words (e.g., "bone" in "trombone"). Participants named aloud pictures (e.g., "brain") while spoken distractors were presented. In the critical condition,…

Identification of Chinese Herbal Medicines with Electronic Nose Technology: Applications and Challenges

PubMed Central

Zhou, Huaying; Luo, Dehan; GholamHosseini, Hamid; Li, Zhong; He, Jiafeng

2017-01-01

This paper provides a review of the most recent works in machine olfaction as applied to the identification of Chinese Herbal Medicines (CHMs). Due to the wide variety of CHMs, the complexity of growing sources and the diverse specifications of herb components, the quality control of CHMs is a challenging issue. Much research has demonstrated that an electronic nose (E-nose) as an advanced machine olfaction system, can overcome this challenge through identification of the complex odors of CHMs. E-nose technology, with better usability, high sensitivity, real-time detection and non-destructive features has shown better performance in comparison with other analytical techniques such as gas chromatography-mass spectrometry (GC-MS). Although there has been immense development of E-nose techniques in other applications, there are limited reports on the application of E-noses for the quality control of CHMs. The aim of current study is to review practical implementation and advantages of E-noses for robust and effective odor identification of CHMs. It covers the use of E-nose technology to study the effects of growing regions, identification methods, production procedures and storage time on CHMs. Moreover, the challenges and applications of E-nose for CHM identification are investigated. Based on the advancement in E-nose technology, odor may become a new quantitative index for quality control of CHMs and drug discovery. It was also found that more research could be done in the area of odor standardization and odor reproduction for remote sensing. PMID:28486407

Identification of Chinese Herbal Medicines with Electronic Nose Technology: Applications and Challenges.

PubMed

Zhou, Huaying; Luo, Dehan; GholamHosseini, Hamid; Li, Zhong; He, Jiafeng

2017-05-09

This paper provides a review of the most recent works in machine olfaction as applied to the identification of Chinese Herbal Medicines (CHMs). Due to the wide variety of CHMs, the complexity of growing sources and the diverse specifications of herb components, the quality control of CHMs is a challenging issue. Much research has demonstrated that an electronic nose (E-nose) as an advanced machine olfaction system, can overcome this challenge through identification of the complex odors of CHMs. E-nose technology, with better usability, high sensitivity, real-time detection and non-destructive features has shown better performance in comparison with other analytical techniques such as gas chromatography-mass spectrometry (GC-MS). Although there has been immense development of E-nose techniques in other applications, there are limited reports on the application of E-noses for the quality control of CHMs. The aim of current study is to review practical implementation and advantages of E-noses for robust and effective odor identification of CHMs. It covers the use of E-nose technology to study the effects of growing regions, identification methods, production procedures and storage time on CHMs. Moreover, the challenges and applications of E-nose for CHM identification are investigated. Based on the advancement in E-nose technology, odor may become a new quantitative index for quality control of CHMs and drug discovery. It was also found that more research could be done in the area of odor standardization and odor reproduction for remote sensing.

Robust Selection Algorithm (RSA) for Multi-Omic Biomarker Discovery; Integration with Functional Network Analysis to Identify miRNA Regulated Pathways in Multiple Cancers.

PubMed

Sehgal, Vasudha; Seviour, Elena G; Moss, Tyler J; Mills, Gordon B; Azencott, Robert; Ram, Prahlad T

2015-01-01

MicroRNAs (miRNAs) play a crucial role in the maintenance of cellular homeostasis by regulating the expression of their target genes. As such, the dysregulation of miRNA expression has been frequently linked to cancer. With rapidly accumulating molecular data linked to patient outcome, the need for identification of robust multi-omic molecular markers is critical in order to provide clinical impact. While previous bioinformatic tools have been developed to identify potential biomarkers in cancer, these methods do not allow for rapid classification of oncogenes versus tumor suppressors taking into account robust differential expression, cutoffs, p-values and non-normality of the data. Here, we propose a methodology, Robust Selection Algorithm (RSA) that addresses these important problems in big data omics analysis. The robustness of the survival analysis is ensured by identification of optimal cutoff values of omics expression, strengthened by p-value computed through intensive random resampling taking into account any non-normality in the data and integration into multi-omic functional networks. Here we have analyzed pan-cancer miRNA patient data to identify functional pathways involved in cancer progression that are associated with selected miRNA identified by RSA. Our approach demonstrates the way in which existing survival analysis techniques can be integrated with a functional network analysis framework to efficiently identify promising biomarkers and novel therapeutic candidates across diseases.

r-Process nucleosynthesis from three-dimensional jet-driven core-collapse supernovae with magnetic misalignments

NASA Astrophysics Data System (ADS)

Halevi, Goni; Mösta, Philipp

2018-06-01

We investigate r-process nucleosynthesis in three-dimensional general relativistic magnetohydrodynamic simulations of jet-driven supernovae resulting from rapidly rotating, strongly magnetized core-collapse. We explore the effect of misaligning the pre-collapse magnetic field with respect to the rotation axis by performing four simulations: one aligned model and models with 15°, 30°, and 45° misalignments. The simulations we present employ a microphysical finite-temperature equation of state and a leakage scheme that captures the overall energetics and lepton number exchange due to post-bounce neutrino emission and absorption. We track the thermodynamic properties of the ejected material with Lagrangian tracer particles and analyse its composition with the nuclear reaction network SKYNET. By using different neutrino luminosities in post-processing the tracer data with SKYNET, we constrain the impact of uncertainties in neutrino luminosities. We find that, for the aligned model considered here, the use of an approximate leakage scheme results in neutrino luminosity uncertainties corresponding to a factor of 100-1000 uncertainty in the abundance of third peak r-process elements. Our results show that for misalignments of 30° or less, r-process elements are robustly produced as long as neutrino luminosities are reasonably low (≲ 5 × 1052 erg s-1). For a more extreme misalignment of 45°, we find the production of r-process elements beyond the second peak significantly reduced. We conclude that robust r-process nucleosynthesis in magnetorotational supernovae requires a progenitor stellar core with a large poloidal magnetic field component that is at least moderately (within ˜30°) aligned with the rotation axis.

Biomimetic photo-actuation: progress and challenges

NASA Astrophysics Data System (ADS)

Dicker, Michael P. M.; Weaver, Paul M.; Rossiter, Jonathan M.; Bond, Ian P.; Faul, Charl F. J.

2016-04-01

Photo-actuation, such as that observed in the reversible sun-tracking movements of heliotropic plants, is produced by a complex, yet elegant series of processes. In the heliotropic leaf movements of the Cornish Mallow, photo-actuation involves the generation, transport and manipulation of chemical signals from a distributed network of sensors in the leaf veins to a specialized osmosis driven actuation region in the leaf stem. It is theorized that such an arrangement is both efficient in terms of materials use and operational energy conversion, as well as being highly robust. We concern ourselves with understanding and mimicking these light driven, chemically controlled actuating systems with the aim of generating intelligent structures which share the properties of efficiency and robustness that are so important to survival in Nature. In this work we present recent progress in mimicking these photo-actuating systems through remote light exposure of a metastable state photoacid and the resulting signal and energy transfer through solution to a pH-responsive hydrogel actuator. Reversible actuation strains of 20% were achieved from this arrangement, with modelling then employed to reveal the critical influence hydrogel pKa has on this result. Although the strong actuation achieved highlights the progress that has been made in replicating the principles of biomimetic photo-actuation, challenges such as photoacid degradation were also revealed. It is anticipated that current work can directly lead to the development of high-performance and low-cost solartrackers for increased photovoltaic energy capture and to the creation of new types of intelligent structures employing chemical control systems.

An evaluation of the effectiveness of a risk-based monitoring approach implemented with clinical trials involving implantable cardiac medical devices.

PubMed

Diani, Christopher A; Rock, Angie; Moll, Phil

2017-12-01

Background Risk-based monitoring is a concept endorsed by the Food and Drug Administration to improve clinical trial data quality by focusing monitoring efforts on critical data elements and higher risk investigator sites. BIOTRONIK approached this by implementing a comprehensive strategy that assesses risk and data quality through a combination of operational controls and data surveillance. This publication demonstrates the effectiveness of a data-driven risk assessment methodology when used in conjunction with a tailored monitoring plan. Methods We developed a data-driven risk assessment system to rank 133 investigator sites comprising 3442 subjects and identify those sites that pose a potential risk to the integrity of data collected in implantable cardiac device clinical trials. This included identification of specific risk factors and a weighted scoring mechanism. We conducted trend analyses for risk assessment data collected over 1 year to assess the overall impact of our data surveillance process combined with other operational monitoring efforts. Results Trending analyses of key risk factors revealed an improvement in the quality of data collected during the observation period. The three risk factors follow-up compliance rate, unavailability of critical data, and noncompliance rate correspond closely with Food and Drug Administration's risk-based monitoring guidance document. Among these three risk factors, 100% (12/12) of quantiles analyzed showed an increase in data quality. Of these, 67% (8/12) of the improving trends in worst performing quantiles had p-values less than 0.05, and 17% (2/12) had p-values between 0.05 and 0.06. Among the poorest performing site quantiles, there was a statistically significant decrease in subject follow-up noncompliance rates, protocol noncompliance rates, and incidence of missing critical data. Conclusion One year after implementation of a comprehensive strategy for risk-based monitoring, including a data-driven risk assessment methodology to target on-site monitoring visits, statistically significant improvement was seen in a majority of measurable risk factors at the worst performing site quantiles. For the three risk factors which are most critical to the overall compliance of cardiac rhythm management medical device studies: follow-up compliance rate, unavailability of critical data, and noncompliance rate, we measured significant improvement in data quality. Although the worst performing site quantiles improved but not significantly in some risk factors such as subject attrition, the data-driven risk assessment highlighted key areas on which to continue focusing both on-site and centralized monitoring efforts. Data-driven surveillance of clinical trial performance provides actionable observations that can improve site performance. Clinical trials utilizing risk-based monitoring by leveraging a data-driven quality assessment combined with specific operational procedures may lead to an improvement in data quality and resource efficiencies.

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.

Knowledge discovery by accuracy maximization

PubMed Central

Cacciatore, Stefano; Luchinat, Claudio; Tenori, Leonardo

2014-01-01

Here we describe KODAMA (knowledge discovery by accuracy maximization), an unsupervised and semisupervised learning algorithm that performs feature extraction from noisy and high-dimensional data. Unlike other data mining methods, the peculiarity of KODAMA is that it is driven by an integrated procedure of cross-validation of the results. The discovery of a local manifold’s topology is led by a classifier through a Monte Carlo procedure of maximization of cross-validated predictive accuracy. Briefly, our approach differs from previous methods in that it has an integrated procedure of validation of the results. In this way, the method ensures the highest robustness of the obtained solution. This robustness is demonstrated on experimental datasets of gene expression and metabolomics, where KODAMA compares favorably with other existing feature extraction methods. KODAMA is then applied to an astronomical dataset, revealing unexpected features. Interesting and not easily predictable features are also found in the analysis of the State of the Union speeches by American presidents: KODAMA reveals an abrupt linguistic transition sharply separating all post-Reagan from all pre-Reagan speeches. The transition occurs during Reagan’s presidency and not from its beginning. PMID:24706821

Adaptive output feedback control of flexible-joint robots using neural networks: dynamic surface design approach.

PubMed

Yoo, Sung Jin; Park, Jin Bae; Choi, Yoon Ho

2008-10-01

In this paper, we propose a new robust output feedback control approach for flexible-joint electrically driven (FJED) robots via the observer dynamic surface design technique. The proposed method only requires position measurements of the FJED robots. To estimate the link and actuator velocity information of the FJED robots with model uncertainties, we develop an adaptive observer using self-recurrent wavelet neural networks (SRWNNs). The SRWNNs are used to approximate model uncertainties in both robot (link) dynamics and actuator dynamics, and all their weights are trained online. Based on the designed observer, the link position tracking controller using the estimated states is induced from the dynamic surface design procedure. Therefore, the proposed controller can be designed more simply than the observer backstepping controller. From the Lyapunov stability analysis, it is shown that all signals in a closed-loop adaptive system are uniformly ultimately bounded. Finally, the simulation results on a three-link FJED robot are presented to validate the good position tracking performance and robustness of the proposed control system against payload uncertainties and external disturbances.

Robust, functional nanocrystal solids by infilling with atomic layer deposition

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

Liu, Yao; Gibbs, Markelle; Perkins, Craig L.

2011-12-14

Thin films of colloidal semiconductor nanocrystals (NCs) are inherently metatstable materials prone to oxidative and photothermal degradation driven by their large surface-to-volume ratios and high surface energies. The fabrication of practical electronic devices based on NC solids hinges on preventing oxidation, surface diffusion, ripening, sintering, and other unwanted physicochemical changes that can plague these materials. Here we use low-temperature atomic layer deposition (ALD) to infill conductive PbSe NC solids with metal oxides to produce inorganic nanocomposites in which the NCs are locked in place and protected against oxidative and photothermal damage. Infilling NC field-effect transistors and solar cells with amorphousmore » alumina yields devices that operate with enhanced and stable performance for at least months in air. Furthermore, ALD infilling with ZnO lowers the height of the inter-NC tunnel barrier for electron transport, yielding PbSe NC films with electron mobilities of 1 cm² V -1 s -1. Our ALD technique is a versatile means to fabricate robust NC solids for optoelectronic devices.« less

Smart Collision Avoidance and Hazard Routing Mechanism for Intelligent Transport Network

NASA Astrophysics Data System (ADS)

Singh, Gurpreet; Gupta, Pooja; Wahab, Mohd Helmy Abd

2017-08-01

The smart vehicular ad-hoc network is the network that consists of vehicles for smooth movement and better management of the vehicular connectivity across the given network. This research paper aims to propose a set of solution for the VANETs consisting of the automatic driven vehicles, also called as the autonomous car. Such vehicular networks are always prone to collision due to the natural or un-natural reasons which must be solved before the large-scale deployment of the autonomous transport systems. The newly designed intelligent transport movement control mechanism is based upon the intelligent data propagation along with the vehicle collision and traffic jam prevention schema [8], which may help the future designs of smart cities to become more robust and less error-prone. In the proposed model, the focus is on designing a new dynamic and robust hazard routing protocol for intelligent vehicular networks for improvement of the overall performance in various aspects. It is expected to improve the overall transmission delay as well as the number of collisions or adversaries across the vehicular network zone.

A 1985-2015 data-driven global reconstruction of GRACE total water storage

NASA Astrophysics Data System (ADS)

Humphrey, Vincent; Gudmundsson, Lukas; Isabelle Seneviratne, Sonia

2016-04-01

After thirteen years of measurements, the Gravity Recovery and Climate Experiment (GRACE) mission has enabled for an unprecedented view on total water storage (TWS) variability. However, the relatively short record length, irregular time steps and multiple data gaps since 2011 still represent important limitations to a wider use of this dataset within the hydrological and climatological community especially for applications such as model evaluation or assimilation of GRACE in land surface models. To address this issue, we make use of the available GRACE record (2002-2015) to infer local statistical relationships between detrended monthly TWS anomalies and the main controlling atmospheric drivers (e.g. daily precipitation and temperature) at 1 degree resolution (Humphrey et al., in revision). Long-term and homogeneous monthly time series of detrended anomalies in total water storage are then reconstructed for the period 1985-2015. The quality of this reconstruction is evaluated in two different ways. First we perform a cross-validation experiment to assess the performance and robustness of the statistical model. Second we compare with independent basin-scale estimates of TWS anomalies derived by means of combined atmospheric and terrestrial water-balance using atmospheric water vapor flux convergence and change in atmospheric water vapor content (Mueller et al. 2011). The reconstructed time series are shown to provide robust data-driven estimates of global variations in water storage over large regions of the world. Example applications are provided for illustration, including an analysis of some selected major drought events which occurred before the GRACE era. References Humphrey V, Gudmundsson L, Seneviratne SI (in revision) Assessing global water storage variability from GRACE: trends, seasonal cycle, sub-seasonal anomalies and extremes. Surv Geophys Mueller B, Hirschi M, Seneviratne SI (2011) New diagnostic estimates of variations in terrestrial water storage based on ERA-Interim data. Hydrol Process 25:996-1008

Routine OGTT: a robust model including incretin effect for precise identification of insulin sensitivity and secretion in a single individual.

PubMed

De Gaetano, Andrea; Panunzi, Simona; Matone, Alice; Samson, Adeline; Vrbikova, Jana; Bendlova, Bela; Pacini, Giovanni

2013-01-01

In order to provide a method for precise identification of insulin sensitivity from clinical Oral Glucose Tolerance Test (OGTT) observations, a relatively simple mathematical model (Simple Interdependent glucose/insulin MOdel SIMO) for the OGTT, which coherently incorporates commonly accepted physiological assumptions (incretin effect and saturating glucose-driven insulin secretion) has been developed. OGTT data from 78 patients in five different glucose tolerance groups were analyzed: normal glucose tolerance (NGT), impaired glucose tolerance (IGT), impaired fasting glucose (IFG), IFG+IGT, and Type 2 Diabetes Mellitus (T2DM). A comparison with the 2011 Salinari (COntinuos GI tract MOdel, COMO) and the 2002 Dalla Man (Dalla Man MOdel, DMMO) models was made with particular attention to insulin sensitivity indices ISCOMO, ISDMMO and kxgi (the insulin sensitivity index for SIMO). ANOVA on kxgi values across groups resulted significant overall (P<0.001), and post-hoc comparisons highlighted the presence of three different groups: NGT (8.62×10(-5)±9.36×10(-5) min(-1)pM(-1)), IFG (5.30×10(-5)±5.18×10(-5)) and combined IGT, IFG+IGT and T2DM (2.09×10(-5)±1.95×10(-5), 2.38×10(-5)±2.28×10(-5) and 2.38×10(-5)±2.09×10(-5) respectively). No significance was obtained when comparing ISCOMO or ISDMMO across groups. Moreover, kxgi presented the lowest sample average coefficient of variation over the five groups (25.43%), with average CVs for ISCOMO and ISDMMO of 70.32% and 57.75% respectively; kxgi also presented the strongest correlations with all considered empirical measures of insulin sensitivity. While COMO and DMMO appear over-parameterized for fitting single-subject clinical OGTT data, SIMO provides a robust, precise, physiologically plausible estimate of insulin sensitivity, with which habitual empirical insulin sensitivity indices correlate well. The kxgi index, reflecting insulin secretion dependency on glycemia, also significantly differentiates clinically diverse subject groups. The SIMO model may therefore be of value for the quantification of glucose homeostasis from clinical OGTT data.

A cable-driven wrist robotic rehabilitator using a novel torque-field controller for human motion training.

PubMed

Chen, Weihai; Cui, Xiang; Zhang, Jianbin; Wang, Jianhua

2015-06-01

Rehabilitation technologies have great potentials in assisted motion training for stroke patients. Considering that wrist motion plays an important role in arm dexterous manipulation of activities of daily living, this paper focuses on developing a cable-driven wrist robotic rehabilitator (CDWRR) for motion training or assistance to subjects with motor disabilities. The CDWRR utilizes the wrist skeletal joints and arm segments as the supporting structure and takes advantage of cable-driven parallel design to build the system, which brings the properties of flexibility, low-cost, and low-weight. The controller of the CDWRR is designed typically based on a virtual torque-field, which is to plan "assist-as-needed" torques for the spherical motion of wrist responding to the orientation deviation in wrist motion training. The torque-field controller can be customized to different levels of rehabilitation training requirements by tuning the field parameters. Additionally, a rapidly convergent parameter self-identification algorithm is developed to obtain the uncertain parameters automatically for the floating wearable structure of the CDWRR. Finally, experiments on a healthy subject are carried out to demonstrate the performance of the controller and the feasibility of the CDWRR on wrist motion training or assistance.

A cable-driven wrist robotic rehabilitator using a novel torque-field controller for human motion training

NASA Astrophysics Data System (ADS)

Chen, Weihai; Cui, Xiang; Zhang, Jianbin; Wang, Jianhua

2015-06-01

Rehabilitation technologies have great potentials in assisted motion training for stroke patients. Considering that wrist motion plays an important role in arm dexterous manipulation of activities of daily living, this paper focuses on developing a cable-driven wrist robotic rehabilitator (CDWRR) for motion training or assistance to subjects with motor disabilities. The CDWRR utilizes the wrist skeletal joints and arm segments as the supporting structure and takes advantage of cable-driven parallel design to build the system, which brings the properties of flexibility, low-cost, and low-weight. The controller of the CDWRR is designed typically based on a virtual torque-field, which is to plan "assist-as-needed" torques for the spherical motion of wrist responding to the orientation deviation in wrist motion training. The torque-field controller can be customized to different levels of rehabilitation training requirements by tuning the field parameters. Additionally, a rapidly convergent parameter self-identification algorithm is developed to obtain the uncertain parameters automatically for the floating wearable structure of the CDWRR. Finally, experiments on a healthy subject are carried out to demonstrate the performance of the controller and the feasibility of the CDWRR on wrist motion training or assistance.

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.

Bayesian-Driven First-Principles Calculations for Accelerating Exploration of Fast Ion Conductors for Rechargeable Battery Application.

PubMed

Jalem, Randy; Kanamori, Kenta; Takeuchi, Ichiro; Nakayama, Masanobu; Yamasaki, Hisatsugu; Saito, Toshiya

2018-04-11

Safe and robust batteries are urgently requested today for power sources of electric vehicles. Thus, a growing interest has been noted for fabricating those with solid electrolytes. Materials search by density functional theory (DFT) methods offers great promise for finding new solid electrolytes but the evaluation is known to be computationally expensive, particularly on ion migration property. In this work, we proposed a Bayesian-optimization-driven DFT-based approach to efficiently screen for compounds with low ion migration energies ([Formula: see text]. We demonstrated this on 318 tavorite-type Li- and Na-containing compounds. We found that the scheme only requires ~30% of the total DFT-[Formula: see text] evaluations on the average to recover the optimal compound ~90% of the time. Its recovery performance for desired compounds in the tavorite search space is ~2× more than random search (i.e., for [Formula: see text] < 0.3 eV). Our approach offers a promising way for addressing computational bottlenecks in large-scale material screening for fast ionic conductors.

Digital control of magnetic bearings in a cryogenic cooler

NASA Technical Reports Server (NTRS)

Feeley, J.; Law, A.; Lind, F.

1990-01-01

This paper describes the design of a digital control system for control of magnetic bearings used in a spaceborne cryogenic cooler. The cooler was developed by Philips Laboratories for the NASA Goddard Space Flight Center. Six magnetic bearing assemblies are used to levitate the piston, displacer, and counter-balance of the cooler. The piston and displacer are driven by linear motors in accordance with Stirling cycle thermodynamic principles to produce the desired cooling effect. The counter-balance is driven by a third linear motor to cancel motion induced forces that would otherwise be transmitted to the spacecraft. An analog control system is currently used for bearing control. The purpose of this project is to investigate the possibilities for improved performance using digital control. Areas for potential improvement include transient and steady state control characteristics, robustness, reliability, adaptability, alternate control modes, size, weight, and cost. The present control system is targeted for the Intel 80196 microcontroller family. The eventual introduction of application specific integrated circuit (ASIC) technology to this problem may produce a unique and elegant solution both here and in related industrial problems.

Straightforward assay for quantification of social avoidance in Drosophila melanogaster.

PubMed

Fernandez, Robert W; Nurilov, Marat; Feliciano, Omar; McDonald, Ian S; Simon, Anne F

2014-12-13

Drosophila melanogaster is an emerging model to study different aspects of social interactions. For example, flies avoid areas previously occupied by stressed conspecifics due to an odorant released during stress known as the Drosophila stress odorant (dSO). Through the use of the T-maze apparatus, one can quantify the avoidance of the dSO by responder flies in a very affordable and robust assay. Conditions necessary to obtain a strong performance are presented here. A stressful experience is necessary for the flies to emit dSO, as well as enough emitter flies to cause a robust avoidance response to the presence of dSO. Genetic background, but not their group size, strongly altered the avoidance of the dSO by the responder flies. Canton-S and Elwood display a higher performance in avoiding the dSO than Oregon and Samarkand strains. This behavioral assay will allow identification of mechanisms underlying this social behavior, and the assessment of the influence of genes and environmental conditions on both emission and avoidance of the dSO. Such an assay can be included in batteries of simple diagnostic tests used to identify social deficiencies of mutants or environmental conditions of interest.

Telescopic multi-resolution augmented reality

NASA Astrophysics Data System (ADS)

Jenkins, Jeffrey; Frenchi, Christopher; Szu, Harold

2014-05-01

To ensure a self-consistent scaling approximation, the underlying microscopic fluctuation components can naturally influence macroscopic means, which may give rise to emergent observable phenomena. In this paper, we describe a consistent macroscopic (cm-scale), mesoscopic (micron-scale), and microscopic (nano-scale) approach to introduce Telescopic Multi-Resolution (TMR) into current Augmented Reality (AR) visualization technology. We propose to couple TMR-AR by introducing an energy-matter interaction engine framework that is based on known Physics, Biology, Chemistry principles. An immediate payoff of TMR-AR is a self-consistent approximation of the interaction between microscopic observables and their direct effect on the macroscopic system that is driven by real-world measurements. Such an interdisciplinary approach enables us to achieve more than multiple scale, telescopic visualization of real and virtual information but also conducting thought experiments through AR. As a result of the consistency, this framework allows us to explore a large dimensionality parameter space of measured and unmeasured regions. Towards this direction, we explore how to build learnable libraries of biological, physical, and chemical mechanisms. Fusing analytical sensors with TMR-AR libraries provides a robust framework to optimize testing and evaluation through data-driven or virtual synthetic simulations. Visualizing mechanisms of interactions requires identification of observable image features that can indicate the presence of information in multiple spatial and temporal scales of analog data. The AR methodology was originally developed to enhance pilot-training as well as `make believe' entertainment industries in a user-friendly digital environment We believe TMR-AR can someday help us conduct thought experiments scientifically, to be pedagogically visualized in a zoom-in-and-out, consistent, multi-scale approximations.

Bridge damage detection using spatiotemporal patterns extracted from dense sensor network

NASA Astrophysics Data System (ADS)

Liu, Chao; Gong, Yongqiang; Laflamme, Simon; Phares, Brent; Sarkar, Soumik

2017-01-01

The alarmingly degrading state of transportation infrastructures combined with their key societal and economic importance calls for automatic condition assessment methods to facilitate smart management of maintenance and repairs. With the advent of ubiquitous sensing and communication capabilities, scalable data-driven approaches is of great interest, as it can utilize large volume of streaming data without requiring detailed physical models that can be inaccurate and computationally expensive to run. Properly designed, a data-driven methodology could enable fast and automatic evaluation of infrastructures, discovery of causal dependencies among various sub-system dynamic responses, and decision making with uncertainties and lack of labeled data. In this work, a spatiotemporal pattern network (STPN) strategy built on symbolic dynamic filtering (SDF) is proposed to explore spatiotemporal behaviors in a bridge network. Data from strain gauges installed on two bridges are generated using finite element simulation for three types of sensor networks from a density perspective (dense, nominal, sparse). Causal relationships among spatially distributed strain data streams are extracted and analyzed for vehicle identification and detection, and for localization of structural degradation in bridges. Multiple case studies show significant capabilities of the proposed approach in: (i) capturing spatiotemporal features to discover causality between bridges (geographically close), (ii) robustness to noise in data for feature extraction, (iii) detecting and localizing damage via comparison of bridge responses to similar vehicle loads, and (iv) implementing real-time health monitoring and decision making work flow for bridge networks. Also, the results demonstrate increased sensitivity in detecting damages and higher reliability in quantifying the damage level with increase in sensor network density.

Automated statistical experimental design approach for rapid separation of coenzyme Q10 and identification of its biotechnological process related impurities using UHPLC and UHPLC-APCI-MS.

PubMed

Talluri, Murali V N Kumar; Kalariya, Pradipbhai D; Dharavath, Shireesha; Shaikh, Naeem; Garg, Prabha; Ramisetti, Nageswara Rao; Ragampeta, Srinivas

2016-09-01

A novel ultra high performance liquid chromatography method development strategy was ameliorated by applying quality by design approach. The developed systematic approach was divided into five steps (i) Analytical Target Profile, (ii) Critical Quality Attributes, (iii) Risk Assessments of Critical parameters using design of experiments (screening and optimization phases), (iv) Generation of design space, and (v) Process Capability Analysis (Cp) for robustness study using Monte Carlo simulation. The complete quality-by-design-based method development was made automated and expedited by employing sub-2 μm particles column with an ultra high performance liquid chromatography system. Successful chromatographic separation of the Coenzyme Q10 from its biotechnological process related impurities was achieved on a Waters Acquity phenyl hexyl (100 mm × 2.1 mm, 1.7 μm) column with gradient elution of 10 mM ammonium acetate buffer (pH 4.0) and a mixture of acetonitrile/2-propanol (1:1) as the mobile phase. Through this study, fast and organized method development workflow was developed and robustness of the method was also demonstrated. The method was validated for specificity, linearity, accuracy, precision, and robustness in compliance to the International Conference on Harmonization, Q2 (R1) guidelines. The impurities were identified by atmospheric pressure chemical ionization-mass spectrometry technique. Further, the in silico toxicity of impurities was analyzed using TOPKAT and DEREK software. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Control of CA3 output by feedforward inhibition despite developmental changes in the excitation-inhibition balance.

PubMed

Torborg, Christine L; Nakashiba, Toshiaki; Tonegawa, Susumu; McBain, Chris J

2010-11-17

In somatosensory cortex, the relative balance of excitation and inhibition determines how effectively feedforward inhibition enforces the temporal fidelity of action potentials. Within the CA3 region of the hippocampus, glutamatergic mossy fiber (MF) synapses onto CA3 pyramidal cells (PCs) provide strong monosynaptic excitation that exhibit prominent facilitation during repetitive activity. We demonstrate in the juvenile CA3 that MF-driven polysynaptic IPSCs facilitate to maintain a fixed EPSC-IPSC ratio during short-term plasticity. In contrast, in young adult mice this MF-driven polysynaptic inhibitory input can facilitate or depress in response to short trains of activity. Transgenic mice lacking the feedback inhibitory loop continue to exhibit both facilitating and depressing polysynaptic IPSCs, indicating that this robust inhibition is not caused by the secondary engagement of feedback inhibition. Surprisingly, eliminating MF-driven inhibition onto CA3 pyramidal cells by blockade of GABA(A) receptors did not lead to a loss of temporal precision of the first action potential observed after a stimulus but triggered in many cases a long excitatory plateau potential capable of triggering repetitive action potential firing. These observations indicate that, unlike other regions of the brain, the temporal precision of single MF-driven action potentials is dictated primarily by the kinetics of MF EPSPs, not feedforward inhibition. Instead, feedforward inhibition provides a robust regulation of CA3 PC excitability across development to prevent excessive depolarization by the monosynaptic EPSP and multiple action potential firings.

A robust set of black walnut microsatellites for parentage and clonal identification

Treesearch

Rodney L. Robichaud; Jeffrey C. Glaubitz; Olin E. Rhodes; Keith Woeste

2006-01-01

We describe the development of a robust and powerful suite of 12 microsatellite marker loci for use in genetic investigations of black walnut and related species. These 12 loci were chosen from a set of 17 candidate loci used to genotype 222 trees sampled from a 38-year-old black walnut progeny test. The 222 genotypes represent a sampling from the broad geographic...

Overcoming Species Boundaries in Peptide Identification with Bayesian Information Criterion-driven Error-tolerant Peptide Search (BICEPS)*

PubMed Central

Renard, Bernhard Y.; Xu, Buote; Kirchner, Marc; Zickmann, Franziska; Winter, Dominic; Korten, Simone; Brattig, Norbert W.; Tzur, Amit; Hamprecht, Fred A.; Steen, Hanno

2012-01-01

Currently, the reliable identification of peptides and proteins is only feasible when thoroughly annotated sequence databases are available. Although sequencing capacities continue to grow, many organisms remain without reliable, fully annotated reference genomes required for proteomic analyses. Standard database search algorithms fail to identify peptides that are not exactly contained in a protein database. De novo searches are generally hindered by their restricted reliability, and current error-tolerant search strategies are limited by global, heuristic tradeoffs between database and spectral information. We propose a Bayesian information criterion-driven error-tolerant peptide search (BICEPS) and offer an open source implementation based on this statistical criterion to automatically balance the information of each single spectrum and the database, while limiting the run time. We show that BICEPS performs as well as current database search algorithms when such algorithms are applied to sequenced organisms, whereas BICEPS only uses a remotely related organism database. For instance, we use a chicken instead of a human database corresponding to an evolutionary distance of more than 300 million years (International Chicken Genome Sequencing Consortium (2004) Sequence and comparative analysis of the chicken genome provide unique perspectives on vertebrate evolution. Nature 432, 695–716). We demonstrate the successful application to cross-species proteomics with a 33% increase in the number of identified proteins for a filarial nematode sample of Litomosoides sigmodontis. PMID:22493179

LFQProfiler and RNP(xl): Open-Source Tools for Label-Free Quantification and Protein-RNA Cross-Linking Integrated into Proteome Discoverer.

PubMed

Veit, Johannes; Sachsenberg, Timo; Chernev, Aleksandar; Aicheler, Fabian; Urlaub, Henning; Kohlbacher, Oliver

2016-09-02

Modern mass spectrometry setups used in today's proteomics studies generate vast amounts of raw data, calling for highly efficient data processing and analysis tools. Software for analyzing these data is either monolithic (easy to use, but sometimes too rigid) or workflow-driven (easy to customize, but sometimes complex). Thermo Proteome Discoverer (PD) is a powerful software for workflow-driven data analysis in proteomics which, in our eyes, achieves a good trade-off between flexibility and usability. Here, we present two open-source plugins for PD providing additional functionality: LFQProfiler for label-free quantification of peptides and proteins, and RNP(xl) for UV-induced peptide-RNA cross-linking data analysis. LFQProfiler interacts with existing PD nodes for peptide identification and validation and takes care of the entire quantitative part of the workflow. We show that it performs at least on par with other state-of-the-art software solutions for label-free quantification in a recently published benchmark ( Ramus, C.; J. Proteomics 2016 , 132 , 51 - 62 ). The second workflow, RNP(xl), represents the first software solution to date for identification of peptide-RNA cross-links including automatic localization of the cross-links at amino acid resolution and localization scoring. It comes with a customized integrated cross-link fragment spectrum viewer for convenient manual inspection and validation of the results.

Wavelet Filtering to Reduce Conservatism in Aeroservoelastic Robust Stability Margins

NASA Technical Reports Server (NTRS)

Brenner, Marty; Lind, Rick

1998-01-01

Wavelet analysis for filtering and system identification was used to improve the estimation of aeroservoelastic stability margins. The conservatism of the robust stability margins was reduced with parametric and nonparametric time-frequency analysis of flight data in the model validation process. Nonparametric wavelet processing of data was used to reduce the effects of external desirableness and unmodeled dynamics. Parametric estimates of modal stability were also extracted using the wavelet transform. Computation of robust stability margins for stability boundary prediction depends on uncertainty descriptions derived from the data for model validation. F-18 high Alpha Research Vehicle aeroservoelastic flight test data demonstrated improved robust stability prediction by extension of the stability boundary beyond the flight regime.

Low-cost real-time automatic wheel classification system

NASA Astrophysics Data System (ADS)

Shabestari, Behrouz N.; Miller, John W. V.; Wedding, Victoria

1992-11-01

This paper describes the design and implementation of a low-cost machine vision system for identifying various types of automotive wheels which are manufactured in several styles and sizes. In this application, a variety of wheels travel on a conveyor in random order through a number of processing steps. One of these processes requires the identification of the wheel type which was performed manually by an operator. A vision system was designed to provide the required identification. The system consisted of an annular illumination source, a CCD TV camera, frame grabber, and 386-compatible computer. Statistical pattern recognition techniques were used to provide robust classification as well as a simple means for adding new wheel designs to the system. Maintenance of the system can be performed by plant personnel with minimal training. The basic steps for identification include image acquisition, segmentation of the regions of interest, extraction of selected features, and classification. The vision system has been installed in a plant and has proven to be extremely effective. The system properly identifies the wheels correctly up to 30 wheels per minute regardless of rotational orientation in the camera's field of view. Correct classification can even be achieved if a portion of the wheel is blocked off from the camera. Significant cost savings have been achieved by a reduction in scrap associated with incorrect manual classification as well as a reduction of labor in a tedious task.

Accounting for sequential trial effects in the flanker task: conflict adaptation or associative priming?

PubMed

Nieuwenhuis, Sander; Stins, John F; Posthuma, Danielle; Polderman, Tinca J C; Boomsma, Dorret I; de Geus, Eco J

2006-09-01

The conflict-control loop theory proposes that the detection of conflict in information processing triggers an increase in cognitive control, resulting in improved performance on the subsequent trial. This theory seems consistent with the robust finding that conflict susceptibility is reduced following correct trials associated with high conflict: the conflict adaptation effect. However, despite providing favorable conditions for eliciting and detecting conflict-triggered performance adjustments, none of the five experiments reported here provide unequivocal evidence of such adjustments. Instead, the results corroborate and extend earlier findings by demonstrating that the conflict adaptation effect, at least in the flanker task, is only present for a specific subset of trial sequences that is characterized by a response repetition. This pattern of results provides strong evidence that the conflict adaptation effect reflects associative stimulus-response priming instead of conflict-driven adaptations in cognitive control.

Stabilization of a Quadrotor With Uncertain Suspended Load Using Sliding Mode Control

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

Zhou, Xu; Liu, Rui; Zhang, Jiucai

2016-08-21

The stability and trajectory control of a quadrotor carrying a suspended load with a fixed known mass has been extensively studied in recent years. However, the load mass is not always known beforehand in practical applications. This mass uncertainty brings uncertain disturbances to the quadrotor system, causing existing controllers to have a worse performance or to be collapsed. To improve the quadrotor's stability in this situation, we investigate the impacts of the uncertain load mass on the quadrotor. By comparing the simulation results of two controllers -- the proportional-derivative (PD) controller and the sliding mode controller (SMC) driven by amore » sliding mode disturbance of observer (SMDO), the quadrotor's performance is verified to be worse as the uncertainty increases. The simulation results also show a controller with stronger robustness against disturbances is better for practical applications.« less

Identification of Differential Item Functioning in Multiple-Group Settings: A Multivariate Outlier Detection Approach

ERIC Educational Resources Information Center

Magis, David; De Boeck, Paul

2011-01-01

We focus on the identification of differential item functioning (DIF) when more than two groups of examinees are considered. We propose to consider items as elements of a multivariate space, where DIF items are outlying elements. Following this approach, the situation of multiple groups is a quite natural case. A robust statistics technique is…

Inverse algorithms for 2D shallow water equations in presence of wet dry fronts: Application to flood plain dynamics

NASA Astrophysics Data System (ADS)

Monnier, J.; Couderc, F.; Dartus, D.; Larnier, K.; Madec, R.; Vila, J.-P.

2016-11-01

The 2D shallow water equations adequately model some geophysical flows with wet-dry fronts (e.g. flood plain or tidal flows); nevertheless deriving accurate, robust and conservative numerical schemes for dynamic wet-dry fronts over complex topographies remains a challenge. Furthermore for these flows, data are generally complex, multi-scale and uncertain. Robust variational inverse algorithms, providing sensitivity maps and data assimilation processes may contribute to breakthrough shallow wet-dry front dynamics modelling. The present study aims at deriving an accurate, positive and stable finite volume scheme in presence of dynamic wet-dry fronts, and some corresponding inverse computational algorithms (variational approach). The schemes and algorithms are assessed on classical and original benchmarks plus a real flood plain test case (Lèze river, France). Original sensitivity maps with respect to the (friction, topography) pair are performed and discussed. The identification of inflow discharges (time series) or friction coefficients (spatially distributed parameters) demonstrate the algorithms efficiency.

A more robust model of the biodiesel reaction, allowing identification of process conditions for significantly enhanced rate and water tolerance.

PubMed

Eze, Valentine C; Phan, Anh N; Harvey, Adam P

2014-03-01

A more robust kinetic model of base-catalysed transesterification than the conventional reaction scheme has been developed. All the relevant reactions in the base-catalysed transesterification of rapeseed oil (RSO) to fatty acid methyl ester (FAME) were investigated experimentally, and validated numerically in a model implemented using MATLAB. It was found that including the saponification of RSO and FAME side reactions and hydroxide-methoxide equilibrium data explained various effects that are not captured by simpler conventional models. Both the experiment and modelling showed that the "biodiesel reaction" can reach the desired level of conversion (>95%) in less than 2min. Given the right set of conditions, the transesterification can reach over 95% conversion, before the saponification losses become significant. This means that the reaction must be performed in a reactor exhibiting good mixing and good control of residence time, and the reaction mixture must be quenched rapidly as it leaves the reactor. Copyright © 2014 Elsevier Ltd. All rights reserved.

Evaluation of two outlier-detection-based methods for detecting tissue-selective genes from microarray data.

PubMed

Kadota, Koji; Konishi, Tomokazu; Shimizu, Kentaro

2007-05-01

Large-scale expression profiling using DNA microarrays enables identification of tissue-selective genes for which expression is considerably higher and/or lower in some tissues than in others. Among numerous possible methods, only two outlier-detection-based methods (an AIC-based method and Sprent's non-parametric method) can treat equally various types of selective patterns, but they produce substantially different results. We investigated the performance of these two methods for different parameter settings and for a reduced number of samples. We focused on their ability to detect selective expression patterns robustly. We applied them to public microarray data collected from 36 normal human tissue samples and analyzed the effects of both changing the parameter settings and reducing the number of samples. The AIC-based method was more robust in both cases. The findings confirm that the use of the AIC-based method in the recently proposed ROKU method for detecting tissue-selective expression patterns is correct and that Sprent's method is not suitable for ROKU.

A Robust Symmetry-Based Approach to Exploit Terra-SAR-X Dual-Pol Data for Targets at Sea Observation

NASA Astrophysics Data System (ADS)

Velotto, D.; Nunziata, F.; Migliaccio, M.; Lehner, S.

2013-08-01

In this study a simple physical property, known as reflection symmetry, is exploited to differentiate the objects in marine scenes, i.e. sea surface and metallic targets. First the reflection property is verified and demonstrated against actual SAR images, by measuring the magnitude of the correlation between cross- polarized channels (i.e. HH/HV or VH/VV). Then, a sensitivity study is performed to show the potential of the proposed method in observing man-made metallic target at sea. The robustness of the proposed technique is demonstrated using coherent dual-polarimetric X- band SAR data acquired by the satellite TerraSAR-X in both cross-polarization combinations, with different incidence angle and weather conditions. Co-located ground truth information provided by Automatic Identification System (AIS) reports and harbor charts are used to locate ships, navigation aids and buoys. The proposed method outperforms standard single polarization SAR targets at sea observation independently of the radar geometry and oceanic phenomena.

A framework for sensitivity analysis of decision trees.

PubMed

Kamiński, Bogumił; Jakubczyk, Michał; Szufel, Przemysław

2018-01-01

In the paper, we consider sequential decision problems with uncertainty, represented as decision trees. Sensitivity analysis is always a crucial element of decision making and in decision trees it often focuses on probabilities. In the stochastic model considered, the user often has only limited information about the true values of probabilities. We develop a framework for performing sensitivity analysis of optimal strategies accounting for this distributional uncertainty. We design this robust optimization approach in an intuitive and not overly technical way, to make it simple to apply in daily managerial practice. The proposed framework allows for (1) analysis of the stability of the expected-value-maximizing strategy and (2) identification of strategies which are robust with respect to pessimistic/optimistic/mode-favoring perturbations of probabilities. We verify the properties of our approach in two cases: (a) probabilities in a tree are the primitives of the model and can be modified independently; (b) probabilities in a tree reflect some underlying, structural probabilities, and are interrelated. We provide a free software tool implementing the methods described.

Anomaly Detection of Electromyographic Signals.

PubMed

Ijaz, Ahsan; Choi, Jongeun

2018-04-01

In this paper, we provide a robust framework to detect anomalous electromyographic (EMG) signals and identify contamination types. As a first step for feature selection, optimally selected Lawton wavelets transform is applied. Robust principal component analysis (rPCA) is then performed on these wavelet coefficients to obtain features in a lower dimension. The rPCA based features are used for constructing a self-organizing map (SOM). Finally, hierarchical clustering is applied on the SOM that separates anomalous signals residing in the smaller clusters and breaks them into logical units for contamination identification. The proposed methodology is tested using synthetic and real world EMG signals. The synthetic EMG signals are generated using a heteroscedastic process mimicking desired experimental setups. A sub-part of these synthetic signals is introduced with anomalies. These results are followed with real EMG signals introduced with synthetic anomalies. Finally, a heterogeneous real world data set is used with known quality issues under an unsupervised setting. The framework provides recall of 90% (± 3.3) and precision of 99%(±0.4).

Noise suppression methods for robust speech processing

NASA Astrophysics Data System (ADS)

Boll, S. F.; Ravindra, H.; Randall, G.; Armantrout, R.; Power, R.

1980-05-01

Robust speech processing in practical operating environments requires effective environmental and processor noise suppression. This report describes the technical findings and accomplishments during this reporting period for the research program funded to develop real time, compressed speech analysis synthesis algorithms whose performance in invariant under signal contamination. Fulfillment of this requirement is necessary to insure reliable secure compressed speech transmission within realistic military command and control environments. Overall contributions resulting from this research program include the understanding of how environmental noise degrades narrow band, coded speech, development of appropriate real time noise suppression algorithms, and development of speech parameter identification methods that consider signal contamination as a fundamental element in the estimation process. This report describes the current research and results in the areas of noise suppression using the dual input adaptive noise cancellation using the short time Fourier transform algorithms, articulation rate change techniques, and a description of an experiment which demonstrated that the spectral subtraction noise suppression algorithm can improve the intelligibility of 2400 bps, LPC 10 coded, helicopter speech by 10.6 point.

Study of environmental sound source identification based on hidden Markov model for robust speech recognition

NASA Astrophysics Data System (ADS)

Nishiura, Takanobu; Nakamura, Satoshi

2003-10-01

Humans communicate with each other through speech by focusing on the target speech among environmental sounds in real acoustic environments. We can easily identify the target sound from other environmental sounds. For hands-free speech recognition, the identification of the target speech from environmental sounds is imperative. This mechanism may also be important for a self-moving robot to sense the acoustic environments and communicate with humans. Therefore, this paper first proposes hidden Markov model (HMM)-based environmental sound source identification. Environmental sounds are modeled by three states of HMMs and evaluated using 92 kinds of environmental sounds. The identification accuracy was 95.4%. This paper also proposes a new HMM composition method that composes speech HMMs and an HMM of categorized environmental sounds for robust environmental sound-added speech recognition. As a result of the evaluation experiments, we confirmed that the proposed HMM composition outperforms the conventional HMM composition with speech HMMs and a noise (environmental sound) HMM trained using noise periods prior to the target speech in a captured signal. [Work supported by Ministry of Public Management, Home Affairs, Posts and Telecommunications of Japan.

Soft and Robust Identification of Body Fluid Using Fourier Transform Infrared Spectroscopy and Chemometric Strategies for Forensic Analysis.

PubMed

Takamura, Ayari; Watanabe, Ken; Akutsu, Tomoko; Ozawa, Takeaki

2018-05-31

Body fluid (BF) identification is a critical part of a criminal investigation because of its ability to suggest how the crime was committed and to provide reliable origins of DNA. In contrast to current methods using serological and biochemical techniques, vibrational spectroscopic approaches provide alternative advantages for forensic BF identification, such as non-destructivity and versatility for various BF types and analytical interests. However, unexplored issues remain for its practical application to forensics; for example, a specific BF needs to be discriminated from all other suspicious materials as well as other BFs, and the method should be applicable even to aged BF samples. Herein, we describe an innovative modeling method for discriminating the ATR FT-IR spectra of various BFs, including peripheral blood, saliva, semen, urine and sweat, to meet the practical demands described above. Spectra from unexpected non-BF samples were efficiently excluded as outliers by adopting the Q-statistics technique. The robustness of the models against aged BFs was significantly improved by using the discrimination scheme of a dichotomous classification tree with hierarchical clustering. The present study advances the use of vibrational spectroscopy and a chemometric strategy for forensic BF identification.

Aeroservoelastic Model Validation and Test Data Analysis of the F/A-18 Active Aeroelastic Wing

NASA Technical Reports Server (NTRS)

Brenner, Martin J.; Prazenica, Richard J.

2003-01-01

Model validation and flight test data analysis require careful consideration of the effects of uncertainty, noise, and nonlinearity. Uncertainty prevails in the data analysis techniques and results in a composite model uncertainty from unmodeled dynamics, assumptions and mechanics of the estimation procedures, noise, and nonlinearity. A fundamental requirement for reliable and robust model development is an attempt to account for each of these sources of error, in particular, for model validation, robust stability prediction, and flight control system development. This paper is concerned with data processing procedures for uncertainty reduction in model validation for stability estimation and nonlinear identification. F/A-18 Active Aeroelastic Wing (AAW) aircraft data is used to demonstrate signal representation effects on uncertain model development, stability estimation, and nonlinear identification. Data is decomposed using adaptive orthonormal best-basis and wavelet-basis signal decompositions for signal denoising into linear and nonlinear identification algorithms. Nonlinear identification from a wavelet-based Volterra kernel procedure is used to extract nonlinear dynamics from aeroelastic responses, and to assist model development and uncertainty reduction for model validation and stability prediction by removing a class of nonlinearity from the uncertainty.

Resolving Recent Plant Radiations: Power and Robustness of Genotyping-by-Sequencing.

PubMed

Fernández-Mazuecos, Mario; Mellers, Greg; Vigalondo, Beatriz; Sáez, Llorenç; Vargas, Pablo; Glover, Beverley J

2018-03-01

Disentangling species boundaries and phylogenetic relationships within recent evolutionary radiations is a challenge due to the poor morphological differentiation and low genetic divergence between species, frequently accompanied by phenotypic convergence, interspecific gene flow and incomplete lineage sorting. Here we employed a genotyping-by-sequencing (GBS) approach, in combination with morphometric analyses, to investigate a small western Mediterranean clade in the flowering plant genus Linaria that radiated in the Quaternary. After confirming the morphological and genetic distinctness of eight species, we evaluated the relative performances of concatenation and coalescent methods to resolve phylogenetic relationships. Specifically, we focused on assessing the robustness of both approaches to variations in the parameter used to estimate sequence homology (clustering threshold). Concatenation analyses suffered from strong systematic bias, as revealed by the high statistical support for multiple alternative topologies depending on clustering threshold values. By contrast, topologies produced by two coalescent-based methods (NJ$_{\\mathrm{st}}$, SVDquartets) were robust to variations in the clustering threshold. Reticulate evolution may partly explain incongruences between NJ$_{\\mathrm{st}}$, SVDquartets and concatenated trees. Integration of morphometric and coalescent-based phylogenetic results revealed (i) extensive morphological divergence associated with recent splits between geographically close or sympatric sister species and (ii) morphological convergence in geographically disjunct species. These patterns are particularly true for floral traits related to pollinator specialization, including nectar spur length, tube width and corolla color, suggesting pollinator-driven diversification. Given its relatively simple and inexpensive implementation, GBS is a promising technique for the phylogenetic and systematic study of recent radiations, but care must be taken to evaluate the robustness of results to variation of data assembly parameters.

Resolving anthropogenic aerosol pollution types - deconvolution and exploratory classification of pollution events

NASA Astrophysics Data System (ADS)

Äijälä, Mikko; Heikkinen, Liine; Fröhlich, Roman; Canonaco, Francesco; Prévôt, André S. H.; Junninen, Heikki; Petäjä, Tuukka; Kulmala, Markku; Worsnop, Douglas; Ehn, Mikael

2017-03-01

Mass spectrometric measurements commonly yield data on hundreds of variables over thousands of points in time. Refining and synthesizing this raw data into chemical information necessitates the use of advanced, statistics-based data analytical techniques. In the field of analytical aerosol chemistry, statistical, dimensionality reductive methods have become widespread in the last decade, yet comparable advanced chemometric techniques for data classification and identification remain marginal. Here we present an example of combining data dimensionality reduction (factorization) with exploratory classification (clustering), and show that the results cannot only reproduce and corroborate earlier findings, but also complement and broaden our current perspectives on aerosol chemical classification. We find that applying positive matrix factorization to extract spectral characteristics of the organic component of air pollution plumes, together with an unsupervised clustering algorithm, k-means+ + , for classification, reproduces classical organic aerosol speciation schemes. Applying appropriately chosen metrics for spectral dissimilarity along with optimized data weighting, the source-specific pollution characteristics can be statistically resolved even for spectrally very similar aerosol types, such as different combustion-related anthropogenic aerosol species and atmospheric aerosols with similar degree of oxidation. In addition to the typical oxidation level and source-driven aerosol classification, we were also able to classify and characterize outlier groups that would likely be disregarded in a more conventional analysis. Evaluating solution quality for the classification also provides means to assess the performance of mass spectral similarity metrics and optimize weighting for mass spectral variables. This facilitates algorithm-based evaluation of aerosol spectra, which may prove invaluable for future development of automatic methods for spectra identification and classification. Robust, statistics-based results and data visualizations also provide important clues to a human analyst on the existence and chemical interpretation of data structures. Applying these methods to a test set of data, aerosol mass spectrometric data of organic aerosol from a boreal forest site, yielded five to seven different recurring pollution types from various sources, including traffic, cooking, biomass burning and nearby sawmills. Additionally, three distinct, minor pollution types were discovered and identified as amine-dominated aerosols.

Machine learning for the automatic detection of anomalous events

NASA Astrophysics Data System (ADS)

Fisher, Wendy D.

In this dissertation, we describe our research contributions for a novel approach to the application of machine learning for the automatic detection of anomalous events. We work in two different domains to ensure a robust data-driven workflow that could be generalized for monitoring other systems. Specifically, in our first domain, we begin with the identification of internal erosion events in earth dams and levees (EDLs) using geophysical data collected from sensors located on the surface of the levee. As EDLs across the globe reach the end of their design lives, effectively monitoring their structural integrity is of critical importance. The second domain of interest is related to mobile telecommunications, where we investigate a system for automatically detecting non-commercial base station routers (BSRs) operating in protected frequency space. The presence of non-commercial BSRs can disrupt the connectivity of end users, cause service issues for the commercial providers, and introduce significant security concerns. We provide our motivation, experimentation, and results from investigating a generalized novel data-driven workflow using several machine learning techniques. In Chapter 2, we present results from our performance study that uses popular unsupervised clustering algorithms to gain insights to our real-world problems, and evaluate our results using internal and external validation techniques. Using EDL passive seismic data from an experimental laboratory earth embankment, results consistently show a clear separation of events from non-events in four of the five clustering algorithms applied. Chapter 3 uses a multivariate Gaussian machine learning model to identify anomalies in our experimental data sets. For the EDL work, we used experimental data from two different laboratory earth embankments. Additionally, we explore five wavelet transform methods for signal denoising. The best performance is achieved with the Haar wavelets. We achieve up to 97.3% overall accuracy and less than 1.4% false negatives in anomaly detection. In Chapter 4, we research using two-class and one-class support vector machines (SVMs) for an effective anomaly detection system. We again use the two different EDL data sets from experimental laboratory earth embankments (each having approximately 80% normal and 20% anomalies) to ensure our workflow is robust enough to work with multiple data sets and different types of anomalous events (e.g., cracks and piping). We apply Haar wavelet-denoising techniques and extract nine spectral features from decomposed segments of the time series data. The two-class SVM with 10-fold cross validation achieved over 94% overall accuracy and 96% F1-score. Our approach provides a means for automatically identifying anomalous events using various machine learning techniques. Detecting internal erosion events in aging EDLs, earlier than is currently possible, can allow more time to prevent or mitigate catastrophic failures. Results show that we can successfully separate normal from anomalous data observations in passive seismic data, and provide a step towards techniques for continuous real-time monitoring of EDL health. Our lightweight non-commercial BSR detection system also has promise in separating commercial from non-commercial BSR scans without the need for prior geographic location information, extensive time-lapse surveys, or a database of known commercial carriers. (Abstract shortened by ProQuest.).

Trends in PET Scan Usage for Imaging of Patients Diagnosed With Nonmetastatic Urologic Cancer.

PubMed

Adejoro, Oluwakayode; Alishahi, Amin; Soubra, Ayman; Konety, Badrinath

2016-02-01

The precise utility of positron emission tomography (PET) scanning for urologic cancers is not well defined. We examined the trends of usage in a population-based data set. PET scans were performed in 3.60% of patients with bladder cancer, 1.09% of those with prostate cancer, and 5.32% of those with renal cell carcinoma. This selective usage might be driven by reimbursement constraints or identification of appropriate medical indications. Positron emission tomography (PET) scanning is increasingly being used for imaging a variety of cancers, including urologic cancers. The precise utility of PET scanning for bladder cancer, prostate cancer, and renal cell carcinoma (RCC) is not yet well known. We examined the trends in PET scan usage for 3 cancers using a large population-based data set. We analyzed all individuals identified with a diagnosis of nonmetastatic bladder cancer, prostate cancer, and RCC from the Surveillance, Epidemiology, and End Results-Medicare data set for 2004 to 2009 with follow-up data available to 2010. Logistic regression analysis and χ(2) and trend tests were performed to determine the predictors of performing PET scanning. Separate models were run for each of the cancer diagnoses. All analyses were performed using SAS, version 9.3, and P < .05 was considered significant. We identified 20,865, 70,414, and 7007 patients with a diagnosis of bladder cancer, prostate cancer, and RCC, respectively, from 2004 to 2009. PET scans had been performed for 3.60% of patients with bladder cancer, 1.09% of those with prostate cancer, and 5.32% of those with RCC. On regression analysis, a more recent year of diagnosis, younger age, and high stage or grade were predictors of PET scan usage for patients with bladder cancer and RCC. A higher Gleason score and higher D'Amico risk group predicted imaging with prostate cancer. The usage of PET scanning for bladder cancer, prostate cancer, and RCC is increasing but still very selective. The selective use might be driven by a combination of reimbursement constraints and careful identification of the appropriate medical indication. Copyright © 2016 Elsevier Inc. All rights reserved.

Influence of a high vacuum on the precise positioning using an ultrasonic linear motor.

PubMed

Kim, Wan-Soo; Lee, Dong-Jin; Lee, Sun-Kyu

2011-01-01

This paper presents an investigation of the ultrasonic linear motor stage for use in a high vacuum environment. The slider table is driven by the hybrid bolt-clamped Langevin-type ultrasonic linear motor, which is excited with its different modes of natural frequencies in both lateral and longitudinal directions. In general, the friction behavior in a vacuum environment becomes different from that in an environment of atmospheric pressure and this difference significantly affects the performance of the ultrasonic linear motor. In this paper, to consistently provide stable and high power of output in a high vacuum, frequency matching was conducted. Moreover, to achieve the fine control performance in the vacuum environment, a modified nominal characteristic trajectory following control method was adopted. Finally, the stage was operated under high vacuum condition, and the operating performances were investigated compared with that of a conventional PI compensator. As a result, robustness of positioning was accomplished in a high vacuum condition with nanometer-level accuracy.

Stochastic Simulation Tool for Aerospace Structural Analysis

NASA Technical Reports Server (NTRS)

Knight, Norman F.; Moore, David F.

2006-01-01

Stochastic simulation refers to incorporating the effects of design tolerances and uncertainties into the design analysis model and then determining their influence on the design. A high-level evaluation of one such stochastic simulation tool, the MSC.Robust Design tool by MSC.Software Corporation, has been conducted. This stochastic simulation tool provides structural analysts with a tool to interrogate their structural design based on their mathematical description of the design problem using finite element analysis methods. This tool leverages the analyst's prior investment in finite element model development of a particular design. The original finite element model is treated as the baseline structural analysis model for the stochastic simulations that are to be performed. A Monte Carlo approach is used by MSC.Robust Design to determine the effects of scatter in design input variables on response output parameters. The tool was not designed to provide a probabilistic assessment, but to assist engineers in understanding cause and effect. It is driven by a graphical-user interface and retains the engineer-in-the-loop strategy for design evaluation and improvement. The application problem for the evaluation is chosen to be a two-dimensional shell finite element model of a Space Shuttle wing leading-edge panel under re-entry aerodynamic loading. MSC.Robust Design adds value to the analysis effort by rapidly being able to identify design input variables whose variability causes the most influence in response output parameters.

Robust PBPK/PD-Based Model Predictive Control of Blood Glucose.

PubMed

Schaller, Stephan; Lippert, Jorg; Schaupp, Lukas; Pieber, Thomas R; Schuppert, Andreas; Eissing, Thomas

2016-07-01

Automated glucose control (AGC) has not yet reached the point where it can be applied clinically [3]. Challenges are accuracy of subcutaneous (SC) glucose sensors, physiological lag times, and both inter- and intraindividual variability. To address above issues, we developed a novel scheme for MPC that can be applied to AGC. An individualizable generic whole-body physiology-based pharmacokinetic and dynamics (PBPK/PD) model of the glucose, insulin, and glucagon metabolism has been used as the predictive kernel. The high level of mechanistic detail represented by the model takes full advantage of the potential of MPC and may make long-term prediction possible as it captures at least some relevant sources of variability [4]. Robustness against uncertainties was increased by a control cascade relying on proportional-integrative derivative-based offset control. The performance of this AGC scheme was evaluated in silico and retrospectively using data from clinical trials. This analysis revealed that our approach handles sensor noise with a MARD of 10%-14%, and model uncertainties and disturbances. The results suggest that PBPK/PD models are well suited for MPC in a glucose control setting, and that their predictive power in combination with the integrated database-driven (a priori individualizable) model framework will help overcome current challenges in the development of AGC systems. This study provides a new, generic, and robust mechanistic approach to AGC using a PBPK platform with extensive a priori (database) knowledge for individualization.

A comparison of Data Driven models of solving the task of gender identification of author in Russian language texts for cases without and with the gender deception

NASA Astrophysics Data System (ADS)

Sboev, A.; Moloshnikov, I.; Gudovskikh, D.; Rybka, R.

2017-12-01

In this work we compare several data-driven approaches to the task of author’s gender identification for texts with or without gender imitation. The data corpus has been specially gathered with crowdsourcing for this task. The best models are convolutional neural network with input of morphological data (fl-measure: 88%±3) for texts without imitation, and gradient boosting model with vector of character n-grams frequencies as input data (f1-measure: 64% ± 3) for texts with gender imitation. The method to filter the crowdsourced corpus using limited reference sample of texts to increase the accuracy of result is discussed.

Implementation of statistical process control for proteomic experiments via LC MS/MS.

PubMed

Bereman, Michael S; Johnson, Richard; Bollinger, James; Boss, Yuval; Shulman, Nick; MacLean, Brendan; Hoofnagle, Andrew N; MacCoss, Michael J

2014-04-01

Statistical process control (SPC) is a robust set of tools that aids in the visualization, detection, and identification of assignable causes of variation in any process that creates products, services, or information. A tool has been developed termed Statistical Process Control in Proteomics (SProCoP) which implements aspects of SPC (e.g., control charts and Pareto analysis) into the Skyline proteomics software. It monitors five quality control metrics in a shotgun or targeted proteomic workflow. None of these metrics require peptide identification. The source code, written in the R statistical language, runs directly from the Skyline interface, which supports the use of raw data files from several of the mass spectrometry vendors. It provides real time evaluation of the chromatographic performance (e.g., retention time reproducibility, peak asymmetry, and resolution), and mass spectrometric performance (targeted peptide ion intensity and mass measurement accuracy for high resolving power instruments) via control charts. Thresholds are experiment- and instrument-specific and are determined empirically from user-defined quality control standards that enable the separation of random noise and systematic error. Finally, Pareto analysis provides a summary of performance metrics and guides the user to metrics with high variance. The utility of these charts to evaluate proteomic experiments is illustrated in two case studies.

Testing genuine tripartite quantum nonlocality with three two-level atoms in a driven cavity

NASA Astrophysics Data System (ADS)

Yuan, H.; Wei, L. F.

2013-10-01

It is known that the violation of Svetlichny's inequality (SI), rather than the usual Mermin's inequality (MI), is a robust criterion to confirm the existence of genuine multipartite quantum nonlocality. In this paper, we propose a feasible approach to test SI with three two-level atoms (TLAs) dispersively coupled to a driven cavity. The proposal is based on the joint measurements of the states of three TLAs by probing the steady-state transmission spectra of the driven cavity: each peak marks one of the computational basis states and its relative height corresponds to the probability superposed in the detected three-TLA state. With these kinds of joint measurements, the correlation functions in SI can be directly calculated, and thus the SI can be efficiently tested for typical tripartite entanglement, i.e., genuine tripartite entanglement [e.g., Greenberger-Horne-Zeilinger (GHZ) and W states] and biseparable three-qubit entangled states (e.g., |χ>12|ξ>3). Our numerical experiments show that the SI is violated only by three-qubit GHZ and W states, not by biseparable three-qubit entangled state |χ>12|ξ>3, while the MI can still be violated by biseparable three-qubit entangled states. Thus the violation of SI can be regarded as a robust criterion for the existence of genuine tripartite entanglement.

Validating Inertial Confinement Fusion (ICF) predictive capability using perturbed capsules

NASA Astrophysics Data System (ADS)

Schmitt, Mark; Magelssen, Glenn; Tregillis, Ian; Hsu, Scott; Bradley, Paul; Dodd, Evan; Cobble, James; Flippo, Kirk; Offerman, Dustin; Obrey, Kimberly; Wang, Yi-Ming; Watt, Robert; Wilke, Mark; Wysocki, Frederick; Batha, Steven

2009-11-01

Achieving ignition on NIF is a monumental step on the path toward utilizing fusion as a controlled energy source. Obtaining robust ignition requires accurate ICF models to predict the degradation of ignition caused by heterogeneities in capsule construction and irradiation. LANL has embarked on a project to induce controlled defects in capsules to validate our ability to predict their effects on fusion burn. These efforts include the validation of feature-driven hydrodynamics and mix in a convergent geometry. This capability is needed to determine the performance of capsules imploded under less-than-optimum conditions on future IFE facilities. LANL's recently initiated Defect Implosion Experiments (DIME) conducted at Rochester's Omega facility are providing input for these efforts. Recent simulation and experimental results will be shown.

Dynamic and fluid–structure interaction simulations of bioprosthetic heart valves using parametric design with T-splines and Fung-type material models

PubMed Central

Kamensky, David; Xu, Fei; Kiendl, Josef; Wang, Chenglong; Wu, Michael C. H.; Mineroff, Joshua; Reali, Alessandro; Bazilevs, Yuri; Sacks, Michael S.

2015-01-01

This paper builds on a recently developed immersogeometric fluid–structure interaction (FSI) methodology for bioprosthetic heart valve (BHV) modeling and simulation. It enhances the proposed framework in the areas of geometry design and constitutive modeling. With these enhancements, BHV FSI simulations may be performed with greater levels of automation, robustness and physical realism. In addition, the paper presents a comparison between FSI analysis and standalone structural dynamics simulation driven by prescribed transvalvular pressure, the latter being a more common modeling choice for this class of problems. The FSI computation achieved better physiological realism in predicting the valve leaflet deformation than its standalone structural dynamics counterpart. PMID:26392645

A defect-driven diagnostic method for machine tool spindles

PubMed Central

Vogl, Gregory W.; Donmez, M. Alkan

2016-01-01

Simple vibration-based metrics are, in many cases, insufficient to diagnose machine tool spindle condition. These metrics couple defect-based motion with spindle dynamics; diagnostics should be defect-driven. A new method and spindle condition estimation device (SCED) were developed to acquire data and to separate system dynamics from defect geometry. Based on this method, a spindle condition metric relying only on defect geometry is proposed. Application of the SCED on various milling and turning spindles shows that the new approach is robust for diagnosing the machine tool spindle condition. PMID:28065985

Spin Mode Switching at the Edge of a Quantum Hall System.

PubMed

Khanna, Udit; Murthy, Ganpathy; Rao, Sumathi; Gefen, Yuval

2017-11-03

Quantum Hall states can be characterized by their chiral edge modes. Upon softening the edge potential, the edge has long been known to undergo spontaneous reconstruction driven by charging effects. In this Letter we demonstrate a qualitatively distinct phenomenon driven by exchange effects, in which the ordering of the edge modes at ν=3 switches abruptly as the edge potential is made softer, while the ordering in the bulk remains intact. We demonstrate that this phenomenon is robust, and has many verifiable experimental signatures in transport.

Pulsed dynamical decoupling for fast and robust two-qubit gates on trapped ions

NASA Astrophysics Data System (ADS)

Arrazola, I.; Casanova, J.; Pedernales, J. S.; Wang, Z.-Y.; Solano, E.; Plenio, M. B.

2018-05-01

We propose a pulsed dynamical decoupling protocol as the generator of tunable, fast, and robust quantum phase gates between two microwave-driven trapped-ion hyperfine qubits. The protocol consists of sequences of π pulses acting on ions that are oriented along an externally applied magnetic-field gradient. In contrast to existing approaches, in our design the two vibrational modes of the ion chain cooperate under the influence of the external microwave driving to achieve significantly increased gate speeds. Our scheme is robust against the dominant noise sources, which are errors on the magnetic-field and microwave pulse intensities, as well as motional heating, predicting two-qubit gates with fidelities above 99.9% in tens of microseconds.

Nonlinear stability and control study of highly maneuverable high performance aircraft, phase 2

NASA Technical Reports Server (NTRS)

Mohler, R. R.

1992-01-01

Research leading to the development of new nonlinear methodologies for the adaptive control and stability analysis of high angle of attack aircraft such as the F-18 is discussed. The emphasis has been on nonlinear adaptive control, but associated model development, system identification, stability analysis, and simulation were studied in some detail as well. Studies indicated that nonlinear adaptive control can outperform linear adaptive control for rapid maneuvers with large changes in angle of attack. Included here are studies on nonlinear model algorithmic controller design and an analysis of nonlinear system stability using robust stability analysis for linear systems.

Chemistry, manufacturing and controls in passive transdermal drug delivery systems.

PubMed

Goswami, Tarun; Audett, Jay

2015-01-01

Transdermal drug delivery systems (TDDS) are used for the delivery of the drugs through the skin into the systemic circulation by applying them to the intact skin. The development of TDDS is a complex and multidisciplinary affair which involves identification of suitable drug, excipients and various other components. There have been numerous problems reported with respect to TDDS quality and performance. These problems can be reduced by appropriately addressing chemistry, manufacturing and controls requirements, which would thereby result in development of robust TDDS product and processes. This article provides recommendations on the chemistry, manufacturing and controls focusing on the unique technical aspects of TDDS.

Convenient, Sensitive and High-Throughput Method for Screening Botanic Origin

NASA Astrophysics Data System (ADS)

Yuan, Yuan; Jiang, Chao; Liu, Libing; Yu, Shulin; Cui, Zhanhu; Chen, Min; Lin, Shufang; Wang, Shu; Huang, Luqi

2014-06-01

In this work, a rapid (within 4-5 h), sensitive and visible new method for assessing botanic origin is developed by combining loop-mediated isothermal amplification with cationic conjugated polymers. The two Chinese medicinal materials (Jin-Yin-Hua and Shan-Yin-Hua) with similar morphology and chemical composition were clearly distinguished by gene SNP genotyping assays. The identification of plant species in Patented Chinese drugs containing Lonicera buds is successfully performed using this detection system. The method is also robust enough to be used in high-throughput screening. This new method is very helpful to identify herbal materials, and is beneficial for detecting safety and quality of botanic products.

Handling Real-World Context Awareness, Uncertainty and Vagueness in Real-Time Human Activity Tracking and Recognition with a Fuzzy Ontology-Based Hybrid Method

PubMed Central

Díaz-Rodríguez, Natalia; Cadahía, Olmo León; Cuéllar, Manuel Pegalajar; Lilius, Johan; Calvo-Flores, Miguel Delgado

2014-01-01

Human activity recognition is a key task in ambient intelligence applications to achieve proper ambient assisted living. There has been remarkable progress in this domain, but some challenges still remain to obtain robust methods. Our goal in this work is to provide a system that allows the modeling and recognition of a set of complex activities in real life scenarios involving interaction with the environment. The proposed framework is a hybrid model that comprises two main modules: a low level sub-activity recognizer, based on data-driven methods, and a high-level activity recognizer, implemented with a fuzzy ontology to include the semantic interpretation of actions performed by users. The fuzzy ontology is fed by the sub-activities recognized by the low level data-driven component and provides fuzzy ontological reasoning to recognize both the activities and their influence in the environment with semantics. An additional benefit of the approach is the ability to handle vagueness and uncertainty in the knowledge-based module, which substantially outperforms the treatment of incomplete and/or imprecise data with respect to classic crisp ontologies. We validate these advantages with the public CAD-120 dataset (Cornell Activity Dataset), achieving an accuracy of 90.1% and 91.07% for low-level and high-level activities, respectively. This entails an improvement over fully data-driven or ontology-based approaches. PMID:25268914

78 FR 38848 - Re-establishing the Sanctuary Nomination Process

Federal Register 2010, 2011, 2012, 2013, 2014

2013-06-28

... process for local communities and other interested parties to provide NOAA with robust, criteria-driven... coastal communities around the country have requested NOAA, the Department of Commerce, and the President... benefits that coastal communities realize from an adjacent national marine sanctuary, including, but not...

Robust Synchronization Models for Presentation System Using SMIL-Driven Approach

ERIC Educational Resources Information Center

Asnawi, Rustam; Ahmad, Wan Fatimah Wan; Rambli, Dayang Rohaya Awang

2013-01-01

Current common Presentation System (PS) models are slide based oriented and lack synchronization analysis either with temporal or spatial constraints. Such models, in fact, tend to lead to synchronization problems, particularly on parallel synchronization with spatial constraints between multimedia element presentations. However, parallel…

Comparing the new generation accelerator driven subcritical reactor system (ADS) to traditional critical reactors

NASA Astrophysics Data System (ADS)

Kemah, Elif; Akkaya, Recep; Tokgöz, Seyit Rıza

2017-02-01

In recent years, the accelerator driven subcritical reactors have taken great interest worldwide. The Accelerator Driven System (ADS) has been used to produce neutron in subcritical state by the external proton beam source. These reactors, which are hybrid systems, are important in production of clean and safe energy and conversion of radioactive waste. The ADS with the selection of reliability and robust target materials have been the new generation of fission reactors. In addition, in the ADS Reactors the problems of long-lived radioactive fission products and waste actinides encountered in the fission process of the reactor during incineration can be solved, and ADS has come to the forefront of thorium as fuel for the reactors.

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.

Identification and MS-assisted interpretation of genetically influenced NMR signals in human plasma

PubMed Central

2013-01-01

Nuclear magnetic resonance spectroscopy (NMR) provides robust readouts of many metabolic parameters in one experiment. However, identification of clinically relevant markers in 1H NMR spectra is a major challenge. Association of NMR-derived quantities with genetic variants can uncover biologically relevant metabolic traits. Using NMR data of plasma samples from 1,757 individuals from the KORA study together with 655,658 genetic variants, we show that ratios between NMR intensities at two chemical shift positions can provide informative and robust biomarkers. We report seven loci of genetic association with NMR-derived traits (APOA1, CETP, CPS1, GCKR, FADS1, LIPC, PYROXD2) and characterize these traits biochemically using mass spectrometry. These ratios may now be used in clinical studies. PMID:23414815

Robust tracking control of a magnetically suspended rigid body

NASA Technical Reports Server (NTRS)

Lim, Kyong B.; Cox, David E.

1994-01-01

This study is an application of H-infinity and micro-synthesis for designing robust tracking controllers for the Large Angle Magnetic Suspension Test Facility. The modeling, design, analysis, simulation, and testing of a control law that guarantees tracking performance under external disturbances and model uncertainties is investigated. The type of uncertainties considered and the tracking performance metric used is discussed. This study demonstrates the tradeoff between tracking performance at low frequencies and robustness at high frequencies. Two sets of controllers were designed and tested. The first set emphasized performance over robustness, while the second set traded off performance for robustness. Comparisons of simulation and test results are also included. Current simulation and experimental results indicate that reasonably good robust tracking performance can be attained for this system using multivariable robust control approach.

Uniform heating of materials into the warm dense matter regime with laser-driven quasimonoenergetic ion beams

DOE PAGES

Bang, W.; Albright, B. J.; Bradley, P. A.; ...

2015-12-01

In a recent experiment at the Trident laser facility, a laser-driven beam of quasimonoenergetic aluminum ions was used to heat solid gold and diamond foils isochorically to 5.5 and 1.7 eV, respectively. Here theoretical calculations are presented that suggest the gold and diamond were heated uniformly by these laser-driven ion beams. According to calculations and SESAME equation-of-state tables, laser-driven aluminum ion beams achievable at Trident, with a finite energy spread of ΔE/E~20%, are expected to heat the targets more uniformly than a beam of 140-MeV aluminum ions with zero energy spread. As a result, the robustness of the expected heatingmore » uniformity relative to the changes in the incident ion energy spectra is evaluated, and expected plasma temperatures of various target materials achievable with the current experimental platform are presented.« less

Uniform heating of materials into the warm dense matter regime with laser-driven quasimonoenergetic ion beams

NASA Astrophysics Data System (ADS)

Bang, W.; Albright, B. J.; Bradley, P. A.; Vold, E. L.; Boettger, J. C.; Fernández, J. C.

2015-12-01

In a recent experiment at the Trident laser facility, a laser-driven beam of quasimonoenergetic aluminum ions was used to heat solid gold and diamond foils isochorically to 5.5 and 1.7 eV, respectively. Here theoretical calculations are presented that suggest the gold and diamond were heated uniformly by these laser-driven ion beams. According to calculations and SESAME equation-of-state tables, laser-driven aluminum ion beams achievable at Trident, with a finite energy spread of ΔE /E ˜20 %, are expected to heat the targets more uniformly than a beam of 140-MeV aluminum ions with zero energy spread. The robustness of the expected heating uniformity relative to the changes in the incident ion energy spectra is evaluated, and expected plasma temperatures of various target materials achievable with the current experimental platform are presented.

Automated selected reaction monitoring data analysis workflow for large-scale targeted proteomic studies.

PubMed

Surinova, Silvia; Hüttenhain, Ruth; Chang, Ching-Yun; Espona, Lucia; Vitek, Olga; Aebersold, Ruedi

2013-08-01

Targeted proteomics based on selected reaction monitoring (SRM) mass spectrometry is commonly used for accurate and reproducible quantification of protein analytes in complex biological mixtures. Strictly hypothesis-driven, SRM assays quantify each targeted protein by collecting measurements on its peptide fragment ions, called transitions. To achieve sensitive and accurate quantitative results, experimental design and data analysis must consistently account for the variability of the quantified transitions. This consistency is especially important in large experiments, which increasingly require profiling up to hundreds of proteins over hundreds of samples. Here we describe a robust and automated workflow for the analysis of large quantitative SRM data sets that integrates data processing, statistical protein identification and quantification, and dissemination of the results. The integrated workflow combines three software tools: mProphet for peptide identification via probabilistic scoring; SRMstats for protein significance analysis with linear mixed-effect models; and PASSEL, a public repository for storage, retrieval and query of SRM data. The input requirements for the protocol are files with SRM traces in mzXML format, and a file with a list of transitions in a text tab-separated format. The protocol is especially suited for data with heavy isotope-labeled peptide internal standards. We demonstrate the protocol on a clinical data set in which the abundances of 35 biomarker candidates were profiled in 83 blood plasma samples of subjects with ovarian cancer or benign ovarian tumors. The time frame to realize the protocol is 1-2 weeks, depending on the number of replicates used in the experiment.

Improving Leishmania Species Identification in Different Types of Samples from Cutaneous Lesions

PubMed Central

Cruz-Barrera, Mónica L.; Ovalle-Bracho, Clemencia; Ortegon-Vergara, Viviana; Pérez-Franco, Jairo E.

2015-01-01

The discrimination of Leishmania species from patient samples has epidemiological and clinical relevance. In this study, different gene target PCR-restriction fragment length polymorphism (RFLP) protocols were evaluated for their robustness as Leishmania species discriminators in 61 patients with cutaneous leishmaniasis. We modified the hsp70-PCR-RFLP protocol and found it to be the most reliable protocol for species identification. PMID:25609727

Substructure System Identification for Finite Element Model Updating

NASA Technical Reports Server (NTRS)

Craig, Roy R., Jr.; Blades, Eric L.

1997-01-01

This report summarizes research conducted under a NASA grant on the topic 'Substructure System Identification for Finite Element Model Updating.' The research concerns ongoing development of the Substructure System Identification Algorithm (SSID Algorithm), a system identification algorithm that can be used to obtain mathematical models of substructures, like Space Shuttle payloads. In the present study, particular attention was given to the following topics: making the algorithm robust to noisy test data, extending the algorithm to accept experimental FRF data that covers a broad frequency bandwidth, and developing a test analytical model (TAM) for use in relating test data to reduced-order finite element models.

FireFly: reconfigurable optical wireless networking data centers

NASA Astrophysics Data System (ADS)

Kavehrad, Mohsen; Deng, Peng; Gupta, H.; Longtin, J.; Das, S. R.; Sekar, V.

2017-01-01

We explore a novel, free-space optics based approach for building data center interconnects. Data centers (DCs) are a critical piece of today's networked applications in both private and public sectors. The key factors that have driven this trend are economies of scale, reduced management costs, better utilization of hardware via statistical multiplexing, and the ability to elastically scale applications in response to changing workload patterns. A robust DC network fabric is fundamental to the success of DCs and to ensure that the network does not become a bottleneck for high-performance applications. In this context, DC network design must satisfy several goals: high performance (e.g., high throughput and low latency), low equipment and management cost, robustness to dynamic traffic patterns, incremental expandability to add new servers or racks, and other practical concerns such as cabling complexity, and power and cooling costs. Current DC network architectures do not seem to provide a satisfactory solution, with respect to the above requirements. In particular, traditional static (wired) networks are either overprovisioned or oversubscribed. Recent works have tried to overcome the above limitations by augmenting a static (wired) "core" with some flexible links (RF-wireless or optical). These augmented architectures show promise, but offer only incremental improvement in performance. Specifically, RFwireless based augmented solutions also offer only limited performance improvement, due to inherent interference and range constraints of RF links. This paper explores an alternative design point—a fully flexible and all-wireless DC interrack network based on free-space optical (FSO) links. We call this FireFly as in; Free-space optical Inter-Rack nEtwork with high FLexibilitY. We will present our designs and tests using various configurations that can help the performance and reliability of the FSO links.

A novel method for accurate needle-tip identification in trans-rectal ultrasound-based high-dose-rate prostate brachytherapy.

PubMed

Zheng, Dandan; Todor, Dorin A

2011-01-01

In real-time trans-rectal ultrasound (TRUS)-based high-dose-rate prostate brachytherapy, the accurate identification of needle-tip position is critical for treatment planning and delivery. Currently, needle-tip identification on ultrasound images can be subject to large uncertainty and errors because of ultrasound image quality and imaging artifacts. To address this problem, we developed a method based on physical measurements with simple and practical implementation to improve the accuracy and robustness of needle-tip identification. Our method uses measurements of the residual needle length and an off-line pre-established coordinate transformation factor, to calculate the needle-tip position on the TRUS images. The transformation factor was established through a one-time systematic set of measurements of the probe and template holder positions, applicable to all patients. To compare the accuracy and robustness of the proposed method and the conventional method (ultrasound detection), based on the gold-standard X-ray fluoroscopy, extensive measurements were conducted in water and gel phantoms. In water phantom, our method showed an average tip-detection accuracy of 0.7 mm compared with 1.6 mm of the conventional method. In gel phantom (more realistic and tissue-like), our method maintained its level of accuracy while the uncertainty of the conventional method was 3.4mm on average with maximum values of over 10mm because of imaging artifacts. A novel method based on simple physical measurements was developed to accurately detect the needle-tip position for TRUS-based high-dose-rate prostate brachytherapy. The method demonstrated much improved accuracy and robustness over the conventional method. Copyright © 2011 American Brachytherapy Society. Published by Elsevier Inc. All rights reserved.

A novel composite adaptive flap controller design by a high-efficient modified differential evolution identification approach.

PubMed

Li, Nailu; Mu, Anle; Yang, Xiyun; Magar, Kaman T; Liu, Chao

2018-05-01

The optimal tuning of adaptive flap controller can improve adaptive flap control performance on uncertain operating environments, but the optimization process is usually time-consuming and it is difficult to design proper optimal tuning strategy for the flap control system (FCS). To solve this problem, a novel adaptive flap controller is designed based on a high-efficient differential evolution (DE) identification technique and composite adaptive internal model control (CAIMC) strategy. The optimal tuning can be easily obtained by DE identified inverse of the FCS via CAIMC structure. To achieve fast tuning, a high-efficient modified adaptive DE algorithm is proposed with new mutant operator and varying range adaptive mechanism for the FCS identification. A tradeoff between optimized adaptive flap control and low computation cost is successfully achieved by proposed controller. Simulation results show the robustness of proposed method and its superiority to conventional adaptive IMC (AIMC) flap controller and the CAIMC flap controllers using other DE algorithms on various uncertain operating conditions. The high computation efficiency of proposed controller is also verified based on the computation time on those operating cases. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

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

NASA Technical Reports Server (NTRS)

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

2003-01-01

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

Tools for Protecting the Privacy of Specific Individuals in Video

NASA Astrophysics Data System (ADS)

Chen, Datong; Chang, Yi; Yan, Rong; Yang, Jie

2007-12-01

This paper presents a system for protecting the privacy of specific individuals in video recordings. We address the following two problems: automatic people identification with limited labeled data, and human body obscuring with preserved structure and motion information. In order to address the first problem, we propose a new discriminative learning algorithm to improve people identification accuracy using limited training data labeled from the original video and imperfect pairwise constraints labeled from face obscured video data. We employ a robust face detection and tracking algorithm to obscure human faces in the video. Our experiments in a nursing home environment show that the system can obtain a high accuracy of people identification using limited labeled data and noisy pairwise constraints. The study result indicates that human subjects can perform reasonably well in labeling pairwise constraints with the face masked data. For the second problem, we propose a novel method of body obscuring, which removes the appearance information of the people while preserving rich structure and motion information. The proposed approach provides a way to minimize the risk of exposing the identities of the protected people while maximizing the use of the captured data for activity/behavior analysis.

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.

Modeling, Control, and Estimation of Flexible, Aerodynamic Structures

NASA Astrophysics Data System (ADS)

Ray, Cody W.

Engineers have long been inspired by nature’s flyers. Such animals navigate complex environments gracefully and efficiently by using a variety of evolutionary adaptations for high-performance flight. Biologists have discovered a variety of sensory adaptations that provide flow state feedback and allow flying animals to feel their way through flight. A specialized skeletal wing structure and plethora of robust, adaptable sensory systems together allow nature’s flyers to adapt to myriad flight conditions and regimes. In this work, motivated by biology and the successes of bio-inspired, engineered aerial vehicles, linear quadratic control of a flexible, morphing wing design is investigated, helping to pave the way for truly autonomous, mission-adaptive craft. The proposed control algorithm is demonstrated to morph a wing into desired positions. Furthermore, motivated specifically by the sensory adaptations organisms possess, this work transitions to an investigation of aircraft wing load identification using structural response as measured by distributed sensors. A novel, recursive estimation algorithm is utilized to recursively solve the inverse problem of load identification, providing both wing structural and aerodynamic states for use in a feedback control, mission-adaptive framework. The recursive load identification algorithm is demonstrated to provide accurate load estimate in both simulation and experiment.

A randomised approach for NARX model identification based on a multivariate Bernoulli distribution

NASA Astrophysics Data System (ADS)

Bianchi, F.; Falsone, A.; Prandini, M.; Piroddi, L.

2017-04-01

The identification of polynomial NARX models is typically performed by incremental model building techniques. These methods assess the importance of each regressor based on the evaluation of partial individual models, which may ultimately lead to erroneous model selections. A more robust assessment of the significance of a specific model term can be obtained by considering ensembles of models, as done by the RaMSS algorithm. In that context, the identification task is formulated in a probabilistic fashion and a Bernoulli distribution is employed to represent the probability that a regressor belongs to the target model. Then, samples of the model distribution are collected to gather reliable information to update it, until convergence to a specific model. The basic RaMSS algorithm employs multiple independent univariate Bernoulli distributions associated to the different candidate model terms, thus overlooking the correlations between different terms, which are typically important in the selection process. Here, a multivariate Bernoulli distribution is employed, in which the sampling of a given term is conditioned by the sampling of the others. The added complexity inherent in considering the regressor correlation properties is more than compensated by the achievable improvements in terms of accuracy of the model selection process.

In silico analysis of cacao (Theobroma cacao L.) genes that involved in pathogen and disease responses

NASA Astrophysics Data System (ADS)

Agung, Muhammad Budi; Budiarsa, I. Made; Suwastika, I. Nengah

2017-02-01

Cocoa bean is one of the main commodities from Indonesia for the world, which still have problem regarding yield degradation due to pathogens and disease attack. Developing robust cacao plant that genetically resistant to pathogen and disease attack is an ideal solution in over taking on this problem. The aim of this study was to identify Theobroma cacao genes on database of cacao genome that homolog to response genes of pathogen and disease attack in other plant, through in silico analysis. Basic information survey and gene identification were performed in GenBank and The Arabidopsis Information Resource database. The In silico analysis contains protein BLAST, homology test of each gene's protein candidates, and identification of homologue gene in Cacao Genome Database using data source "Theobroma cacao cv. Matina 1-6 v1.1" genome. Identification found that Thecc1EG011959t1 (EDS1), Thecc1EG006803t1 (EDS5), Thecc1EG013842t1 (ICS1), and Thecc1EG015614t1 (BG_PPAP) gene of Cacao Genome Database were Theobroma cacao genes that homolog to plant's resistance genes which highly possible to have similar functions of each gene's homologue gene.

Which Kind of Provider’s Operation Volumes Matters? Associations between CABG Surgical Site Infection Risk and Hospital and Surgeon Operation Volumes among Medical Centers in Taiwan

PubMed Central

Yu, Tsung-Hsien; Tung, Yu-Chi; Chung, Kuo-Piao

2015-01-01

Background Volume-infection relationships have been examined for high-risk surgical procedures, but the conclusions remain controversial. The inconsistency might be due to inaccurate identification of cases of infection and different methods of categorizing service volumes. This study takes coronary artery bypass graft (CABG) surgical site infections (SSIs) as an example to examine whether a relationship exists between operation volumes and SSIs, when different SSIs case identification, definitions and categorization methods of operation volumes were implemented. Methods A population-based cross-sectional multilevel study was conducted. A total of 7,007 patients who received CABG surgery between 2006 and 2008 from19 medical centers in Taiwan were recruited. SSIs associated with CABG surgery were identified using International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9 CM) codes and a Classification and Regression Trees (CART) model. Two definitions of surgeon and hospital operation volumes were used: (1) the cumulative CABG operation volumes within the study period; and (2) the cumulative CABG operation volumes in the previous one year before each CABG surgery. Operation volumes were further treated in three different ways: (1) a continuous variable; (2) a categorical variable based on the quartile; and (3) a data-driven categorical variable based on k-means clustering algorithm. Furthermore, subgroup analysis for comorbidities was also conducted. Results This study showed that hospital volumes were not significantly associated with SSIs, no matter which definitions or categorization methods of operation volume, or SSIs case identification approaches were used. On the contrary, the relationships between surgeon’s volumes varied. Most of the models demonstrated that the low-volume surgeons had higher risk than high-volume surgeons. Conclusion Surgeon volumes were more important than hospital volumes in exploring the relationship between CABG operation volumes and SSIs in Taiwan. However, the relationships were not robust. Definitions and categorization methods of operation volume and correct identification of SSIs are important issues for future research. PMID:26053035

Optical tomographic imaging for breast cancer detection

NASA Astrophysics Data System (ADS)

Cong, Wenxiang; Intes, Xavier; Wang, Ge

2017-09-01

Diffuse optical breast imaging utilizes near-infrared (NIR) light propagation through tissues to assess the optical properties of tissues for the identification of abnormal tissue. This optical imaging approach is sensitive, cost-effective, and does not involve any ionizing radiation. However, the image reconstruction of diffuse optical tomography (DOT) is a nonlinear inverse problem and suffers from severe illposedness due to data noise, NIR light scattering, and measurement incompleteness. An image reconstruction method is proposed for the detection of breast cancer. This method splits the image reconstruction problem into the localization of abnormal tissues and quantification of absorption variations. The localization of abnormal tissues is performed based on a well-posed optimization model, which can be solved via a differential evolution optimization method to achieve a stable reconstruction. The quantification of abnormal absorption is then determined in localized regions of relatively small extents, in which a potential tumor might be. Consequently, the number of unknown absorption variables can be greatly reduced to overcome the underdetermined nature of DOT. Numerical simulation experiments are performed to verify merits of the proposed method, and the results show that the image reconstruction method is stable and accurate for the identification of abnormal tissues, and robust against the measurement noise of data.

GTA: a game theoretic approach to identifying cancer subnetwork markers.

PubMed

Farahmand, S; Goliaei, S; Ansari-Pour, N; Razaghi-Moghadam, Z

2016-03-01

The identification of genetic markers (e.g. genes, pathways and subnetworks) for cancer has been one of the most challenging research areas in recent years. A subset of these studies attempt to analyze genome-wide expression profiles to identify markers with high reliability and reusability across independent whole-transcriptome microarray datasets. Therefore, the functional relationships of genes are integrated with their expression data. However, for a more accurate representation of the functional relationships among genes, utilization of the protein-protein interaction network (PPIN) seems to be necessary. Herein, a novel game theoretic approach (GTA) is proposed for the identification of cancer subnetwork markers by integrating genome-wide expression profiles and PPIN. The GTA method was applied to three distinct whole-transcriptome breast cancer datasets to identify the subnetwork markers associated with metastasis. To evaluate the performance of our approach, the identified subnetwork markers were compared with gene-based, pathway-based and network-based markers. We show that GTA is not only capable of identifying robust metastatic markers, it also provides a higher classification performance. In addition, based on these GTA-based subnetworks, we identified a new bonafide candidate gene for breast cancer susceptibility.

Development and application of a robust N-glycan profiling method for heightened characterization of monoclonal antibodies and related glycoproteins.

PubMed

Shang, Tanya Q; Saati, Andrew; Toler, Kelly N; Mo, Jianming; Li, Heyi; Matlosz, Tonya; Lin, Xi; Schenk, Jennifer; Ng, Chee-Keng; Duffy, Toni; Porter, Thomas J; Rouse, Jason C

2014-07-01

A highly robust hydrophilic interaction liquid chromatography (HILIC) method that involves both fluorescence and mass spectrometric detection was developed for profiling and characterizing enzymatically released and 2-aminobenzamide (2-AB)-derivatized mAb N-glycans. Online HILIC/mass spectrometry (MS) with a quadrupole time-of-flight mass spectrometer provides accurate mass identifications of the separated, 2-AB-labeled N-glycans. The method features a high-resolution, low-shedding HILIC column with acetonitrile and water-based mobile phases containing trifluoroacetic acid (TFA) as a modifier. This column and solvent system ensures the combination of robust chromatographic performance and full compatibility and sensitivity with online MS in addition to the baseline separation of all typical mAb N-glycans. The use of TFA provided distinct advantages over conventional ammonium formate as a mobile phase additive, such as, optimal elution order for sialylated N-glycans, reproducible chromatographic profiles, and matching total ion current chromatograms, as well as minimal signal splitting, analyte adduction, and fragmentation during HILIC/MS, maximizing sensitivity for trace-level species. The robustness and selectivity of HILIC for N-glycan analyses allowed for method qualification. The method is suitable for bioprocess development activities, heightened characterization, and clinical drug substance release. Application of this HILIC/MS method to the detailed characterization of a marketed therapeutic mAb, Rituxan(®), is described. © 2014 Wiley Periodicals, Inc. and the American Pharmacists Association.

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%.

Development of a rapid and simplified protocol for direct bacterial identification from positive blood cultures by using matrix assisted laser desorption ionization time-of- flight mass spectrometry.

PubMed

Jakovljev, Aleksandra; Bergh, Kåre

2015-11-06

Bloodstream infections represent serious conditions carrying a high mortality and morbidity rate. Rapid identification of microorganisms and prompt institution of adequate antimicrobial therapy is of utmost importance for a successful outcome. Aiming at the development of a rapid, simplified and efficient protocol, we developed and compared two in-house preparatory methods for the direct identification of bacteria from positive blood culture flasks (BD BACTEC FX system) by using matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI TOF MS). Both methods employed saponin and distilled water for erythrocyte lysis. In method A the cellular pellet was overlaid with formic acid on the MALDI TOF target plate for protein extraction, whereas in method B the pellet was exposed to formic acid followed by acetonitrile prior to placing on the target plate. Best results were obtained by method A. Direct identification was achieved for 81.9 % and 65.8 % (50.3 % and 26.2 % with scores >2.0) of organisms by method A and method B, respectively. Overall concordance with final identification was 100 % to genus and 97.9 % to species level. By applying a lower cut-off score value, the levels of identification obtained by method A and method B increased to 89.3 % and 77.8 % of organisms (81.9 % and 65.8 % identified with scores >1.7), respectively. Using the lowered score criteria, concordance with final results was obtained for 99.3 % of genus and 96.6 % of species identifications. The reliability of results, rapid performance (approximately 25 min) and applicability of in-house method A have contributed to implementation of this robust and cost-effective method in our laboratory.

Optimal Multi-Type Sensor Placement for Structural Identification by Static-Load Testing

PubMed Central

Papadopoulou, Maria; Vernay, Didier; Smith, Ian F. C.

2017-01-01

Assessing ageing infrastructure is a critical challenge for civil engineers due to the difficulty in the estimation and integration of uncertainties in structural models. Field measurements are increasingly used to improve knowledge of the real behavior of a structure; this activity is called structural identification. Error-domain model falsification (EDMF) is an easy-to-use model-based structural-identification methodology which robustly accommodates systematic uncertainties originating from sources such as boundary conditions, numerical modelling and model fidelity, as well as aleatory uncertainties from sources such as measurement error and material parameter-value estimations. In most practical applications of structural identification, sensors are placed using engineering judgment and experience. However, since sensor placement is fundamental to the success of structural identification, a more rational and systematic method is justified. This study presents a measurement system design methodology to identify the best sensor locations and sensor types using information from static-load tests. More specifically, three static-load tests were studied for the sensor system design using three types of sensors for a performance evaluation of a full-scale bridge in Singapore. Several sensor placement strategies are compared using joint entropy as an information-gain metric. A modified version of the hierarchical algorithm for sensor placement is proposed to take into account mutual information between load tests. It is shown that a carefully-configured measurement strategy that includes multiple sensor types and several load tests maximizes information gain. PMID:29240684

Vehicle active steering control research based on two-DOF robust internal model control

NASA Astrophysics Data System (ADS)

Wu, Jian; Liu, Yahui; Wang, Fengbo; Bao, Chunjiang; Sun, Qun; Zhao, Youqun

2016-07-01

Because of vehicle's external disturbances and model uncertainties, robust control algorithms have obtained popularity in vehicle stability control. The robust control usually gives up performance in order to guarantee the robustness of the control algorithm, therefore an improved robust internal model control(IMC) algorithm blending model tracking and internal model control is put forward for active steering system in order to reach high performance of yaw rate tracking with certain robustness. The proposed algorithm inherits the good model tracking ability of the IMC control and guarantees robustness to model uncertainties. In order to separate the design process of model tracking from the robustness design process, the improved 2 degree of freedom(DOF) robust internal model controller structure is given from the standard Youla parameterization. Simulations of double lane change maneuver and those of crosswind disturbances are conducted for evaluating the robust control algorithm, on the basis of a nonlinear vehicle simulation model with a magic tyre model. Results show that the established 2-DOF robust IMC method has better model tracking ability and a guaranteed level of robustness and robust performance, which can enhance the vehicle stability and handling, regardless of variations of the vehicle model parameters and the external crosswind interferences. Contradiction between performance and robustness of active steering control algorithm is solved and higher control performance with certain robustness to model uncertainties is obtained.

Complex Constructivism: A Theoretical Model of Complexity and Cognition

ERIC Educational Resources Information Center

Doolittle, Peter E.

2014-01-01

Education has long been driven by its metaphors for teaching and learning. These metaphors have influenced both educational research and educational practice. Complexity and constructivism are two theories that provide functional and robust metaphors. Complexity provides a metaphor for the structure of myriad phenomena, while constructivism…

Synaptic vesicle distribution by conveyor belt.

PubMed

Moughamian, Armen J; Holzbaur, Erika L F

2012-03-02

The equal distribution of synaptic vesicles among synapses along the axon is critical for robust neurotransmission. Wong et al. show that the continuous circulation of synaptic vesicles throughout the axon driven by molecular motors ultimately yields this even distribution. Copyright © 2012 Elsevier Inc. All rights reserved.

Implicit Value Updating Explains Transitive Inference Performance: The Betasort Model

PubMed Central

Jensen, Greg; Muñoz, Fabian; Alkan, Yelda; Ferrera, Vincent P.; Terrace, Herbert S.

2015-01-01

Transitive inference (the ability to infer that B > D given that B > C and C > D) is a widespread characteristic of serial learning, observed in dozens of species. Despite these robust behavioral effects, reinforcement learning models reliant on reward prediction error or associative strength routinely fail to perform these inferences. We propose an algorithm called betasort, inspired by cognitive processes, which performs transitive inference at low computational cost. This is accomplished by (1) representing stimulus positions along a unit span using beta distributions, (2) treating positive and negative feedback asymmetrically, and (3) updating the position of every stimulus during every trial, whether that stimulus was visible or not. Performance was compared for rhesus macaques, humans, and the betasort algorithm, as well as Q-learning, an established reward-prediction error (RPE) model. Of these, only Q-learning failed to respond above chance during critical test trials. Betasort’s success (when compared to RPE models) and its computational efficiency (when compared to full Markov decision process implementations) suggests that the study of reinforcement learning in organisms will be best served by a feature-driven approach to comparing formal models. PMID:26407227

Implicit Value Updating Explains Transitive Inference Performance: The Betasort Model.

PubMed

Jensen, Greg; Muñoz, Fabian; Alkan, Yelda; Ferrera, Vincent P; Terrace, Herbert S

2015-01-01

Transitive inference (the ability to infer that B > D given that B > C and C > D) is a widespread characteristic of serial learning, observed in dozens of species. Despite these robust behavioral effects, reinforcement learning models reliant on reward prediction error or associative strength routinely fail to perform these inferences. We propose an algorithm called betasort, inspired by cognitive processes, which performs transitive inference at low computational cost. This is accomplished by (1) representing stimulus positions along a unit span using beta distributions, (2) treating positive and negative feedback asymmetrically, and (3) updating the position of every stimulus during every trial, whether that stimulus was visible or not. Performance was compared for rhesus macaques, humans, and the betasort algorithm, as well as Q-learning, an established reward-prediction error (RPE) model. Of these, only Q-learning failed to respond above chance during critical test trials. Betasort's success (when compared to RPE models) and its computational efficiency (when compared to full Markov decision process implementations) suggests that the study of reinforcement learning in organisms will be best served by a feature-driven approach to comparing formal models.

Immunity-based detection, identification, and evaluation of aircraft sub-system failures

NASA Astrophysics Data System (ADS)

Moncayo, Hever Y.

This thesis describes the design, development, and flight-simulation testing of an integrated Artificial Immune System (AIS) for detection, identification, and evaluation of a wide variety of sensor, actuator, propulsion, and structural failures/damages including the prediction of the achievable states and other limitations on performance and handling qualities. The AIS scheme achieves high detection rate and low number of false alarms for all the failure categories considered. Data collected using a motion-based flight simulator are used to define the self for an extended sub-region of the flight envelope. The NASA IFCS F-15 research aircraft model is used and represents a supersonic fighter which include model following adaptive control laws based on non-linear dynamic inversion and artificial neural network augmentation. The flight simulation tests are designed to analyze and demonstrate the performance of the immunity-based aircraft failure detection, identification and evaluation (FDIE) scheme. A general robustness analysis is also presented by determining the achievable limits for a desired performance in the presence of atmospheric perturbations. For the purpose of this work, the integrated AIS scheme is implemented based on three main components. The first component performs the detection when one of the considered failures is present in the system. The second component consists in the identification of the failure category and the classification according to the failed element. During the third phase a general evaluation of the failure is performed with the estimation of the magnitude/severity of the failure and the prediction of its effect on reducing the flight envelope of the aircraft system. Solutions and alternatives to specific design issues of the AIS scheme, such as data clustering and empty space optimization, data fusion and duplication removal, definition of features, dimensionality reduction, and selection of cluster/detector shape are also analyzed in this thesis. They showed to have an important effect on detection performance and are a critical aspect when designing the configuration of the AIS. The results presented in this thesis show that the AIS paradigm addresses directly the complexity and multi-dimensionality associated with a damaged aircraft dynamic response and provides the tools necessary for a comprehensive/integrated solution to the FDIE problem. Excellent detection, identification, and evaluation performance has been recorded for all types of failures considered. The implementation of the proposed AIS-based scheme can potentially have a significant impact on the safety of aircraft operation. The output information obtained from the scheme will be useful to increase pilot situational awareness and determine automated compensation.

Computer-aided detection of human cone photoreceptor inner segments using multi-scale circular voting

NASA Astrophysics Data System (ADS)

Liu, Jianfei; Dubra, Alfredo; Tam, Johnny

2016-03-01

Cone photoreceptors are highly specialized cells responsible for the origin of vision in the human eye. Their inner segments can be noninvasively visualized using adaptive optics scanning light ophthalmoscopes (AOSLOs) with nonconfocal split detection capabilities. Monitoring the number of cones can lead to more precise metrics for real-time diagnosis and assessment of disease progression. Cell identification in split detection AOSLO images is hindered by cell regions with heterogeneous intensity arising from shadowing effects and low contrast boundaries due to overlying blood vessels. Here, we present a multi-scale circular voting approach to overcome these challenges through the novel combination of: 1) iterative circular voting to identify candidate cells based on their circular structures, 2) a multi-scale strategy to identify the optimal circular voting response, and 3) clustering to improve robustness while removing false positives. We acquired images from three healthy subjects at various locations on the retina and manually labeled cell locations to create ground-truth for evaluating the detection accuracy. The images span a large range of cell densities. The overall recall, precision, and F1 score were 91±4%, 84±10%, and 87±7% (Mean±SD). Results showed that our method for the identification of cone photoreceptor inner segments performs well even with low contrast cell boundaries and vessel obscuration. These encouraging results demonstrate that the proposed approach can robustly and accurately identify cells in split detection AOSLO images.

Mining manufacturing data for discovery of high productivity process characteristics.

PubMed

Charaniya, Salim; Le, Huong; Rangwala, Huzefa; Mills, Keri; Johnson, Kevin; Karypis, George; Hu, Wei-Shou

2010-06-01

Modern manufacturing facilities for bioproducts are highly automated with advanced process monitoring and data archiving systems. The time dynamics of hundreds of process parameters and outcome variables over a large number of production runs are archived in the data warehouse. This vast amount of data is a vital resource to comprehend the complex characteristics of bioprocesses and enhance production robustness. Cell culture process data from 108 'trains' comprising production as well as inoculum bioreactors from Genentech's manufacturing facility were investigated. Each run constitutes over one-hundred on-line and off-line temporal parameters. A kernel-based approach combined with a maximum margin-based support vector regression algorithm was used to integrate all the process parameters and develop predictive models for a key cell culture performance parameter. The model was also used to identify and rank process parameters according to their relevance in predicting process outcome. Evaluation of cell culture stage-specific models indicates that production performance can be reliably predicted days prior to harvest. Strong associations between several temporal parameters at various manufacturing stages and final process outcome were uncovered. This model-based data mining represents an important step forward in establishing a process data-driven knowledge discovery in bioprocesses. Implementation of this methodology on the manufacturing floor can facilitate a real-time decision making process and thereby improve the robustness of large scale bioprocesses. 2010 Elsevier B.V. All rights reserved.

Design of laser-driven SiO2-YAG:Ce composite thick film: Facile synthesis, robust thermal performance, and application in solid-state laser lighting

NASA Astrophysics Data System (ADS)

Xu, Jian; Liu, Bingguo; Liu, Zhiwen; Gong, Yuxuan; Hu, Baofu; Wang, Jian; Li, Hui; Wang, Xinliang; Du, Baoli

2018-01-01

In recent times, there have been rapid advances in the solid-state laser lighting technology. Due to the large amounts of heat accumulated from the high flux laser radiation, color conversion materials used in solid-state laser lighting devices should possess high durability, high thermal conductivity, and low thermal quenching. The aim of this study is to develop a thermally robust SiO2-YAG:Ce composite thick film (CTF) for high-power solid-state laser lighting applications. Commercial colloidal silica which was used as the source of SiO2, played the roles of an adhesive, a filler, and a protecting agent. Compared to the YAG:Ce powder, the CTF exhibits remarkable thermal stability (11.3% intensity drop at 200 °C) and durability (4.5% intensity drop after 1000 h, at 85 °C and 85% humidity). Furthermore, the effects of the substrate material and the thickness of the CTF on the laser lighting performance were investigated in terms of their thermal quenching and luminescence saturation behaviors, respectively. The CTF with a thickness of 50 μm on a sapphire substrate does not show luminescence saturation, despite a high-power density of incident radiation i.e. 20 W/mm2. These results demonstrate the potential applicability of the CTF in solid-state laser lighting devices.

Stochastic Synapses Enable Efficient Brain-Inspired Learning Machines.

PubMed

Neftci, Emre O; Pedroni, Bruno U; Joshi, Siddharth; Al-Shedivat, Maruan; Cauwenberghs, Gert

2016-01-01

Recent studies have shown that synaptic unreliability is a robust and sufficient mechanism for inducing the stochasticity observed in cortex. Here, we introduce Synaptic Sampling Machines (S2Ms), a class of neural network models that uses synaptic stochasticity as a means to Monte Carlo sampling and unsupervised learning. Similar to the original formulation of Boltzmann machines, these models can be viewed as a stochastic counterpart of Hopfield networks, but where stochasticity is induced by a random mask over the connections. Synaptic stochasticity plays the dual role of an efficient mechanism for sampling, and a regularizer during learning akin to DropConnect. A local synaptic plasticity rule implementing an event-driven form of contrastive divergence enables the learning of generative models in an on-line fashion. S2Ms perform equally well using discrete-timed artificial units (as in Hopfield networks) or continuous-timed leaky integrate and fire neurons. The learned representations are remarkably sparse and robust to reductions in bit precision and synapse pruning: removal of more than 75% of the weakest connections followed by cursory re-learning causes a negligible performance loss on benchmark classification tasks. The spiking neuron-based S2Ms outperform existing spike-based unsupervised learners, while potentially offering substantial advantages in terms of power and complexity, and are thus promising models for on-line learning in brain-inspired hardware.

Stochastic Synapses Enable Efficient Brain-Inspired Learning Machines

PubMed Central

Neftci, Emre O.; Pedroni, Bruno U.; Joshi, Siddharth; Al-Shedivat, Maruan; Cauwenberghs, Gert

2016-01-01

Recent studies have shown that synaptic unreliability is a robust and sufficient mechanism for inducing the stochasticity observed in cortex. Here, we introduce Synaptic Sampling Machines (S2Ms), a class of neural network models that uses synaptic stochasticity as a means to Monte Carlo sampling and unsupervised learning. Similar to the original formulation of Boltzmann machines, these models can be viewed as a stochastic counterpart of Hopfield networks, but where stochasticity is induced by a random mask over the connections. Synaptic stochasticity plays the dual role of an efficient mechanism for sampling, and a regularizer during learning akin to DropConnect. A local synaptic plasticity rule implementing an event-driven form of contrastive divergence enables the learning of generative models in an on-line fashion. S2Ms perform equally well using discrete-timed artificial units (as in Hopfield networks) or continuous-timed leaky integrate and fire neurons. The learned representations are remarkably sparse and robust to reductions in bit precision and synapse pruning: removal of more than 75% of the weakest connections followed by cursory re-learning causes a negligible performance loss on benchmark classification tasks. The spiking neuron-based S2Ms outperform existing spike-based unsupervised learners, while potentially offering substantial advantages in terms of power and complexity, and are thus promising models for on-line learning in brain-inspired hardware. PMID:27445650

Fatty Acid Synthase Activity as a Target for c-Met Driven Prostate Cancer

DTIC Science & Technology

2013-07-01

to aid future studies. Identification is a highly significant finding with regard to the potential for future therapeutic development targeted at...Met trafficking, stability, and ultimately oncogenic potential . Palmitoylation defective mutants will be used in animal models of c-Met driven tumor...growth (Aim 2). In addition, future work toward identifying the enzyme responsible for palmitoylation of c- Met will provide a new specific target

Active FOXO1 is a Key Determinant of Isoform-Specific Progesterone Receptor Transactivation and Senescence Programming

PubMed Central

Diep, Caroline H.; Knutson, Todd P.; Lange, Carol A.

2015-01-01

Progesterone promotes differentiation coupled to proliferation and pro-survival in the breast, but inhibits estrogen-driven growth in the reproductive tract and ovaries. Herein, it is demonstrated, using progesterone receptor (PR) isoform-specific ovarian cancer model systems, that PR-A and PR-B promote distinct gene expression profiles that differ from PR-driven genes in breast cancer cells. In ovarian cancer models, PR-A primarily regulates genes independently of progestin, while PR-B is the dominant ligand-dependent isoform. Notably, FOXO1 and the PR/FOXO1 target-gene p21 (CDKN1A) are repressed by PR-A, but induced by PR-B. In the presence of progestin, PR-B, but not PR-A, robustly induced cellular senescence via FOXO1-dependent induction of p21 and p15 (CDKN2B). Chromatin immunoprecipitation (ChIP) assays performed on PR-isoform specific cells demonstrated that while each isoform is recruited to the same PRE-containing region of the p21 promoter in response to progestin, only PR-B elicits active chromatin marks. Overexpression of constitutively active FOXO1 in PR-A-expressing cells conferred robust ligand-dependent upregulation of the PR-B target genes GZMA, IGFBP1, and p21, and induced cellular senescence. In the presence of endogenous active FOXO1, PR-A was phosphorylated on Ser294 and transactivated PR-B at PR-B target genes; these events were blocked by the FOXO1 inhibitor (AS1842856). PR isoform-specific regulation of the FOXO1/p21 axis recapitulated in human primary ovarian tumor explants treated with progestin; loss of progestin sensitivity correlated with high AKT activity. PMID:26577046

Girardia dorotocephala transcriptome sequence, assembly, and validation through characterization of piwi homologs and stem cell progeny markers

PubMed Central

Almazan, Eugene Matthew P.; Lesko, Sydney L.; Markey, Michael P.; Rouhana, Labib

2017-01-01

Planarian flatworms are popular models for the study of regeneration and stem cell biology in vivo. Technical advances and increased availability of genetic information have fueled the discovery of molecules responsible for stem cell pluripotency and regeneration in flatworms. Unfortunately, most of the planarian research performed worldwide utilizes species that are not natural habitants of North America, which limits their availability to newcomer laboratories and impedes their distribution for educational activities. In order to circumvent these limitations and increase the genetic information available for comparative studies, we sequenced the transcriptome of Girardia dorotocephala, a planarian species pandemic and commercially available in North America. A total of 254,802,670 paired sequence reads were obtained from RNA extracted from intact individuals, regenerating fragments, as well as freshly excised auricles of a clonal line of G. dorotocephala (MA-C2), and used for de novo assembly of its transcriptome. The resulting transcriptome draft was validated through functional analysis of genetic markers of stem cells and their progeny in G. dorotocephala. Akin to orthologs in other planarian species, G. dorotocephala Piwi1 (GdPiwi1) was found to be a robust marker of the planarian stem cell population and GdPiwi2 an essential component for stem cell-driven regeneration. Identification of G. dorotocephala homologs of the early stem cell descendent marker PROG-1 revealed a family of lysine-rich proteins expressed during epithelial cell differentiation. Sequences from the MA-C2 transcriptome were found to be 98–99% identical to nucleotide sequences from G. dorotocephala populations with different chromosomal number, demonstrating strong conservation regardless of karyotype evolution. Altogether, this work establishes G. dorotocephala as a viable and accessible option for analysis of gene function in North America. PMID:28774726

The ALICE-HMPID Detector Control System: Its evolution towards an expert and adaptive system

NASA Astrophysics Data System (ADS)

De Cataldo, G.; Franco, A.; Pastore, C.; Sgura, I.; Volpe, G.

2011-05-01

The High Momentum Particle IDentification (HMPID) detector is a proximity focusing Ring Imaging Cherenkov (RICH) for charged hadron identification. The HMPID is based on liquid C 6F 14 as the radiator medium and on a 10 m 2 CsI coated, pad segmented photocathode of MWPCs for UV Cherenkov photon detection. To ensure full remote control, the HMPID is equipped with a detector control system (DCS) responding to industrial standards for robustness and reliability. It has been implemented using PVSS as Slow Control And Data Acquisition (SCADA) environment, Programmable Logic Controller as control devices and Finite State Machines for modular and automatic command execution. In the perspective of reducing human presence at the experiment site, this paper focuses on DCS evolution towards an expert and adaptive control system, providing, respectively, automatic error recovery and stable detector performance. HAL9000, the first prototype of the HMPID expert system, is then presented. Finally an analysis of the possible application of the adaptive features is provided.

A Bayesian Approach for Sensor Optimisation in Impact Identification

PubMed Central

Mallardo, Vincenzo; Sharif Khodaei, Zahra; Aliabadi, Ferri M. H.

2016-01-01

This paper presents a Bayesian approach for optimizing the position of sensors aimed at impact identification in composite structures under operational conditions. The uncertainty in the sensor data has been represented by statistical distributions of the recorded signals. An optimisation strategy based on the genetic algorithm is proposed to find the best sensor combination aimed at locating impacts on composite structures. A Bayesian-based objective function is adopted in the optimisation procedure as an indicator of the performance of meta-models developed for different sensor combinations to locate various impact events. To represent a real structure under operational load and to increase the reliability of the Structural Health Monitoring (SHM) system, the probability of malfunctioning sensors is included in the optimisation. The reliability and the robustness of the procedure is tested with experimental and numerical examples. Finally, the proposed optimisation algorithm is applied to a composite stiffened panel for both the uniform and non-uniform probability of impact occurrence. PMID:28774064

Is Perceptual Priming Affected by Culture? A Study With German Middle-Class and Cameroonian Nso Farmer Children.

PubMed

Vöhringer, Isabel Aline; Poloczek, Sonja; Graf, Frauke; Lamm, Bettina; Teiser, Johanna; Fassbender, Ina; Freitag, Claudia; Suhrke, Janina; Teubert, Manuel; Keller, Heidi; Lohaus, Arnold; Schwarzer, Gudrun; Knopf, Monika

2015-01-01

The authors explored priming in children from different cultural environments with the aim to provide further evidence for the robustness of the priming effect. Perceptual priming was assessed by a picture fragment completion task in 3-year-old German middle-class and Cameroonian Nso farmer children. As expected, 3-year-olds from both highly diverging cultural contexts under study showed a priming effect, and, moreover, the effect was of comparable size in both cultural contexts. Hence, the children profited similarly from priming, which was supported by the nonsignificant interaction between cultural background and identification performance as well as the analysis of absolute difference scores. However, a culture-specific difference regarding the level of picture identification was found in that German middle-class children identified target as well as control pictures with less perceptual information than children in the Nso sample. Explanations for the cross-cultural demonstration of the priming effect as well as for the culturally diverging levels on which priming occurs are discussed.

Convenient Detection of the Citrus Greening (Huanglongbing) Bacterium ‘Candidatus Liberibacter asiaticus’ by Direct PCR from the Midrib Extract

PubMed Central

Fujikawa, Takashi; Miyata, Shin-Ichi; Iwanami, Toru

2013-01-01

A phloem-limited bacterium, ‘Candidatus Liberibacter asiaticus’ (Las) is a major pathogen of citrus greening (huanglongbing), one of the most destructive citrus diseases worldwide. The rapid identification and culling of infected trees and budwoods in quarantine are the most important control measures. DNA amplification including conventional polymerase chain reaction (PCR) has commonly been used for rapid detection and identification. However, long and laborious procedures for DNA extraction have greatly reduced the applicability of this method. In this study, we found that the Las bacterial cells in the midribs of infected leaves were extracted rapidly and easily by pulverization and centrifugation with mini homogenization tubes. We also found that the Las bacterial cells in the midrib extract were suitable for highly sensitive direct PCR. The performance of direct PCR using this extraction method was not inferior to that of conventional PCR. Thus, the direct PCR method described herein is characterized by its simplicity, sensitivity, and robustness, and is applicable to quarantine testing. PMID:23437295

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.

Temporal dynamics and the identification of musical key.

PubMed

Farbood, Morwaread Mary; Marcus, Gary; Poeppel, David

2013-08-01

A central process in music cognition involves the identification of key; however, little is known about how listeners accomplish this task in real time. This study derives from work that suggests overlap between the neural and cognitive resources underlying the analyses of both music and speech and is the first, to our knowledge, to explore the timescales at which the brain infers musical key. We investigated the temporal psychophysics of key-finding over a wide range of tempi using melodic sequences with strong structural cues, where statistical information about overall key profile was ambiguous. Listeners were able to provide robust judgments within specific limits, at rates as high as 400 beats per minute (bpm; ∼7 Hz) and as low as 30 bpm (0.5 Hz), but not outside those bounds. These boundaries on reliable performance show that the process of key-finding is restricted to timescales that are closely aligned with beat induction and speech processing. 2013 APA, all rights reserved

Automatic load forecasting. Final report

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

Nelson, D.J.; Vemuri, S.

A method which lends itself to on-line forecasting of hourly electric loads is presented and the results of its use are compared to models developed using the Box-Jenkins method. The method consists of processing the historical hourly loads with a sequential least-squares estimator to identify a finite order autoregressive model which in turn is used to obtain a parsimonious autoregressive-moving average model. A procedure is also defined for incorporating temperature as a variable to improve forecasts where loads are temperature dependent. The method presented has several advantages in comparison to the Box-Jenkins method including much less human intervention and improvedmore » model identification. The method has been tested using three-hourly data from the Lincoln Electric System, Lincoln, Nebraska. In the exhaustive analyses performed on this data base this method produced significantly better results than the Box-Jenkins method. The method also proved to be more robust in that greater confidence could be placed in the accuracy of models based upon the various measures available at the identification stage.« less

Characterizing wood-plastic composites via data-driven methodologies

Treesearch

John G. Michopoulos; John C. Hermanson; Robert Badaliance

2007-01-01

The recent increase of wood-plastic composite materials in various application areas has underlined the need for an efficient and robust methodology to characterize their nonlinear anisotropic constitutive behavior. In addition, the multiplicity of various loading conditions in structures utilizing these materials further increases the need for a characterization...

On Teacher Quality in Independent Schools

ERIC Educational Resources Information Center

Balossi, Matt; Hernandez, Natalia R.

2016-01-01

Independent schools pride themselves on providing a unique educational experience for students, one that is robust and mission-driven and capitalizes on lower student-to-teacher ratios that allow for more personalized learning and high-quality teachers. Numerous studies measure teacher effectiveness in public schools, yet there is little research…

The Gini Coefficient as a Tool for Image Family Idenitification in Strong Lensing Systems with Multiple Images

NASA Astrophysics Data System (ADS)

Florian, Michael K.; Gladders, Michael D.; Li, Nan; Sharon, Keren

2016-01-01

The sample of cosmological strong lensing systems has been steadily growing in recent years and with the advent of the next generation of space-based survey telescopes, the sample will reach into the thousands. The accuracy of strong lens models relies on robust identification of multiple image families of lensed galaxies. For the most massive lenses, often more than one background galaxy is magnified and multiply imaged, and even in the cases of only a single lensed source, identification of counter images is not always robust. Recently, we have shown that the Gini coefficient in space-telescope-quality imaging is a measurement of galaxy morphology that is relatively well-preserved by strong gravitational lensing. Here, we investigate its usefulness as a diagnostic for the purposes of image family identification and show that it can remove some of the degeneracies encountered when using color as the sole diagnostic, and can do so without the need for additional observations since whenever a color is available, two Gini coefficients are as well.

Visual Search in the Real World: Color Vision Deficiency Affects Peripheral Guidance, but Leaves Foveal Verification Largely Unaffected.

PubMed

Kugler, Günter; 't Hart, Bernard M; Kohlbecher, Stefan; Bartl, Klaus; Schumann, Frank; Einhäuser, Wolfgang; Schneider, Erich

2015-01-01

People with color vision deficiencies report numerous limitations in daily life, restricting, for example, their access to some professions. However, they use basic color terms systematically and in a similar manner as people with normal color vision. We hypothesize that a possible explanation for this discrepancy between color perception and behavioral consequences might be found in the gaze behavior of people with color vision deficiency. A group of participants with color vision deficiencies and a control group performed several search tasks in a naturalistic setting on a lawn. All participants wore a mobile eye-tracking-driven camera with a high foveal image resolution (EyeSeeCam). Search performance as well as fixations of objects of different colors were examined. Search performance was similar in both groups in a color-unrelated search task as well as in a search for yellow targets. While searching for red targets, participants with color vision deficiencies exhibited a strongly degraded performance. This was closely matched by the number of fixations on red objects shown by the two groups. Importantly, once they fixated a target, participants with color vision deficiencies exhibited only few identification errors. In contrast to controls, participants with color vision deficiencies are not able to enhance their search for red targets on a (green) lawn by an efficient guiding mechanism. The data indicate that the impaired guiding is the main influence on search performance, while foveal identification (verification) is largely unaffected by the color vision deficiency.