Estimation and identification study for flexible vehicles

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

Low thrust interplanetary trajectory open loop error analysis, volume 1 Final report

Computer program for open-loop error analysis of low thrust interplanetary trajectorie

Nonlinear and adaptive estimation techniques in reentry

The development and testing of nonlinear and adaptive estimators for reentry (e.g. space shuttle) navigation and model parameter estimation or identification are reported. Of particular interest is the identifcation of vehicle lift and drag characteristics in real time. Several nonlinear filters were developed and simulated. Adaptive filters for the real time identification of vehicle lift and drag characteristics, and unmodelable acceleration, were also developed and tested by simulation. The simulations feature an uncertain system environment with rather arbitrary model errors, thus providing a definitive test of estimator performance. It was found that nonlinear effects are indeed significant in reentry trajectory estimation and a nonlinear filter is demonstrated which successfully tracks through nonlinearities without degrading the information content of the data. Under the same conditions the usual extended Kalman filter diverges and is useless. The J-adaptive filter is shown to successfully track errors in the modeled vehicle lift and drag characteristics. The same filter concept is also shown to track successfully through rather arbitrary model errors, including lift and drag errors, vehicle mass errors, atmospheric density errors, and wind gust errors

Urban air quality estimation study, phase 1

Possibilities are explored for applying estimation theory to the analysis, interpretation, and use of air quality measurements in conjunction with simulation models to provide a cost effective method of obtaining reliable air quality estimates for wide urban areas. The physical phenomenology of real atmospheric plumes from elevated localized sources is discussed. A fluctuating plume dispersion model is derived. Individual plume parameter formulations are developed along with associated a priori information. Individual measurement models are developed

The Yeast Retrograde Response as a Model of Intracellular Signaling of Mitochondrial Dysfunction

Mitochondrial dysfunction activates intracellular signaling pathways that impact yeast longevity, and the best known of these pathways is the retrograde response. More recently, similar responses have been discerned in other systems, from invertebrates to human cells. However, the identity of the signal transducers is either unknown or apparently diverse, contrasting with the well-established signaling module of the yeast retrograde response. On the other hand, it has become equally clear that several other pathways and processes interact with the retrograde response, embedding it in a network responsive to a variety of cellular states. An examination of this network supports the notion that the master regulator NFκB aggregated a variety of mitochondria-related cellular responses at some point in evolution and has become the retrograde transcription factor. This has significant consequences for how we view some of the deficits associated with aging, such as inflammation. The support for NFκB as the retrograde response transcription factor is not only based on functional analyses. It is bolstered by the fact that NFκB can regulate Myc–Max, which is activated in human cells with dysfunctional mitochondria and impacts cellular metabolism. Myc–Max is homologous to the yeast retrograde response transcription factor Rtg1–Rtg3. Further research will be needed to disentangle the pro-aging from the anti-aging effects of NFκB. Interestingly, this is also a challenge for the complete understanding of the yeast retrograde response

Suboptimal filtering. Part 2 - Compensation for modeling errors in orbit determination problems Final report

Compensation for dynamic and measurement model errors in real time orbit determination system

Body composition, IGF1 status, and physical functionality in nonagenarians: implications for osteosarcopenia

OBJECTIVES: Body composition alterations occur during aging. The purpose of the present analysis was to explore the functional consequences of the overlap of sarcopenia and osteoporosis, and the potential role of insulin-like growth factor 1 (IGF1) in their development in the oldest old. SETTING AND PARTICIPANTS: Eighty-seven nonagenarians from the Louisiana Healthy Aging Study were included. MEASURES: The definition of sarcopenia was based on appendicular lean mass (ALM). Osteoporosis was diagnosed based on bone mineral density (BMD) T score. Four phenotypes were compared: (1) healthy body composition, that is, nonosteoporotic nonsarcopenic (CO, control group), (2) osteoporotic (O, low BMD T score), (3) sarcopenic (S, low ALM), and (4) osteosarcopenic (OS, low BMD T score and low ALM). Sex- and age-specific IGF1-Standard Deviation Scores (SDS) were calculated. The Continuous Scale-Physical Functional Performance (CS-PFP) test was performed. RESULTS: In OS men, IGF1-SDS values (-0.61 ±0.37 vs -0.04 ± 0.52, P = .02) were lower than those in CO males (control group), whereas IGF1-SDS were similar in the 4 body composition phenotypes in women. In men only, ALM was positively associated with IGF1-SDS values (P = .01) independent of age and C-reactive protein concentration. Regarding bone health, we found no association between IGF1-SDS values and BMD. IGF1-SDS was not associated with functional performance (CS-PFP) in men and women. CONCLUSIONS/IMPLICATIONS: IGF1 sensitivity in skeletal muscle and bone may differ by sex in the oldest old. IGF1 status did not appear to affect physical functionality. Determinants and clinical and functional characteristics of osteosarcopenia need to be further investigated in order to define conclusive diagnostic criteria

Examination of the Dimensions of Biological Age

The concept of biological age has been used more and more frequently in aging research in attempts to measure the progress of the biological aging process as opposed to the simple passage of time. Several approaches to quantify biological age have been utilized, including the use of biomarkers in the form of serum analytes, epigenetic markers, and deficit or frailty indices. Among these methods, the deficit index possesses a theoretical basis grounded in systems biology by incorporating networks, with their emergent properties, to describe the complex aging system. Application of the deficit index in human aging studies points to the increased energetic demands posed by an aging system that is losing integration. Different aspects of mitochondrial function appear to be responsible in males and females. The gut microbiome loses complexity in tandem with the host, as biological age increases, with likely impact on host metabolism and immunity. Specific DNA methylation changes are associated with biological age. They suggest declining connectivity within the aging network, at the cellular level. The deficit/frailty index may account for at least part of the departure at older ages of the observed mortality in the population from the exponential increase modeled by the Gompertz equation

Suboptimal filtering. Part 4 - Test-bed computer program Final report

Computer program plan for simulating real time observation schedules and combined effects of dynamic model errors in three-dimensional satellite motio

Time-varying Learning and Content Analytics via Sparse Factor Analysis

We propose SPARFA-Trace, a new machine learning-based framework for time-varying learning and content analytics for education applications. We develop a novel message passing-based, blind, approximate Kalman filter for sparse factor analysis (SPARFA), that jointly (i) traces learner concept knowledge over time, (ii) analyzes learner concept knowledge state transitions (induced by interacting with learning resources, such as textbook sections, lecture videos, etc, or the forgetting effect), and (iii) estimates the content organization and intrinsic difficulty of the assessment questions. These quantities are estimated solely from binary-valued (correct/incorrect) graded learner response data and a summary of the specific actions each learner performs (e.g., answering a question or studying a learning resource) at each time instance. Experimental results on two online course datasets demonstrate that SPARFA-Trace is capable of tracing each learner's concept knowledge evolution over time, as well as analyzing the quality and content organization of learning resources, the question-concept associations, and the question intrinsic difficulties. Moreover, we show that SPARFA-Trace achieves comparable or better performance in predicting unobserved learner responses than existing collaborative filtering and knowledge tracing approaches for personalized education