Constructing Parsimonious Analytic Models for Dynamic Systems via Symbolic Regression

Developing mathematical models of dynamic systems is central to many disciplines of engineering and science. Models facilitate simulations, analysis of the system's behavior, decision making and design of automatic control algorithms. Even inherently model-free control techniques such as reinforcement learning (RL) have been shown to benefit from the use of models, typically learned online. Any model construction method must address the tradeoff between the accuracy of the model and its complexity, which is difficult to strike. In this paper, we propose to employ symbolic regression (SR) to construct parsimonious process models described by analytic equations. We have equipped our method with two different state-of-the-art SR algorithms which automatically search for equations that fit the measured data: Single Node Genetic Programming (SNGP) and Multi-Gene Genetic Programming (MGGP). In addition to the standard problem formulation in the state-space domain, we show how the method can also be applied to input-output models of the NARX (nonlinear autoregressive with exogenous input) type. We present the approach on three simulated examples with up to 14-dimensional state space: an inverted pendulum, a mobile robot, and a bipedal walking robot. A comparison with deep neural networks and local linear regression shows that SR in most cases outperforms these commonly used alternative methods. We demonstrate on a real pendulum system that the analytic model found enables a RL controller to successfully perform the swing-up task, based on a model constructed from only 100 data samples

Avaliação da produtividade da microalga Haematococcus pluvialis em diferentes meios de cultura

TCC (graduação) - Universidade Federal de Santa Catarina. Centro de Ciências Agrárias. Curso de Engenharia de Aquicultura.Diversos estudos têm sido realizados com a microalga Haematococcus pluvialis devido ao seu potencial de produção do pigmento astaxantina, podendo acumular até 5% deste composto em termos de biomassa seca. Estudos relatam que este pigmento natural apresenta uma atividade biológica mais potente em comparação com outros carotenoides, alavancando o interesse da indústria farmacêutica, nutracêutica, cosmética e alimentícia. Por conta destas características, muitos estudos têm sido desenvolvidos justificando a produção comercial desta microalga. Para o cultivo de microalgas os nutrientes apresentem papel muito importante no crescimento e produção de biomassa, sendo este componente um item fundamental para a viabilidade econômica dos cultivos comerciais. O presente estudo teve como objetivo determinar o efeito de três diferentes meios de cultura (OHM, BBM e COMBO) no crescimento da microalga Haematococcus pluvialis. Os parâmetros de crescimento avaliados foram: densidade celular, biomassa máxima alcançada e produtividade. O meio de cultura que gerou a maior biomassa foi o BBM (0,57 g/l), seguido do OHM e COMBO com (0,47 g/l) e (0,36 g/l), respectivamente. Porém, com relação à produtividade, o emprego dos meios COMBO e BBM apresentou os melhores resultados, com 0,046 (g/l/d) e 0,044 (g/l/d) respectivamente, não havendo diferença estatística entre eles

Review and critical analysis: Rolling-element bearings for system life and reliability

A ball and cylindrical roller bearing technical specification which incorporates the latest state-of-the-art advancements was prepared for the purpose of improving bearing reliability in U.S. Army aircraft. The current U.S. Army aviation bearing designs and applications, including life analyses, were analyzed. A bearing restoration and refurbishment specification was prepared to improve bearing availability

Impacts of Climate Change Drivers on C4 Grassland Productivity: Scaling Driver Effects Through the Plant Community

Climate change drivers affect plant community productivity via three pathways: (i) direct effects of drivers on plants; (ii) the response of species abundances to drivers (community response); and (iii) the feedback effect of community change on productivity (community effect). The contribution of each pathway to driver–productivity relationships depends on functional traits of dominant species. We used data from three experiments in Texas, USA, to assess the role of community dynamics in the aboveground net primary productivity (ANPP) response of C4 grasslands to two climate drivers applied singly: atmospheric CO2 enrichment and augmented summer precipitation. The ANPPdriver response differed among experiments because community responses and effects differed. ANPP increased by 80–120 g m–2 per 100 μl l–1 rise in CO2 in separate experiments with pasture and tallgrass prairie assemblages. Augmenting ambient precipitation by 128 mm during one summer month each year increased ANPP more in native than in exotic communities in a third experiment. The community effect accounted for 21–38% of the ANPP CO2 response in the prairie experiment but little of the response in the pasture experiment. The community response to CO2 was linked to species traits associated with greater soil water from reduced transpiration (e.g. greater height). Community effects on the ANPP CO2 response and the greater ANPP response of native than exotic communities to augmented precipitation depended on species differences in transpiration efficiency. These results indicate that feedbacks from community change influenced ANPP-driver responses. However, the species traits that regulated community effects on ANPP differed from the traits that determined how communities responded to drivers

GIS Frameworks in the National Weather Service

Eugene Derner is a Senior Hydrologist for NOAA/National Weather Service at the Missouri Basin River Forecast Center. This presentation was given as part of the GIS Day@KU symposium on November 18, 2015. For more information about GIS Day@KU activities, please see http://www.gis.ku.edu/gisday/2015/.Platinum Sponsors: KU Department of Geography and Atmospheric Science; KU School of Business. Gold Sponsors: Bartlett & West; Kansas Biological Survey; KU Environmental Studies Program; KU Institute for Policy & Social Research; KU Libraries. Silver Sponsors: State of Kansas Data Access and Support Center (DASC). Bronze Sponsors: KU Center for Remote Sensing of Ice Sheets (CReSIS); TREKK Design Group, LLC; Wilson & Company, Engineers and Architects

Pattern Recognition in Intracranial EEG Signals and Prediction of Memory

This thesis presents a method for pattern recognition in neurocognitive data, in particular intracranial electroencephalographic (iEEG) data. The approach aims to reveal mechanisms underlying cognitive processes. This means that the algorithm has not only been designed to achieve above chance prediction results, but also to offer a better understanding and new insights into the functionality of the brain. A support vector machine algorithm has been developed which deals with the complex data structure, the high number of potential datapoints and features, as well as the typically small and unbalanced sample sizes. In particular, the periodicity of phase measures is taken into account for statistics and training of the model. Background information about cognitive processes provides an informative basis for feature selection. Time series analysis is used for feature extraction and circular statistics cope with the periodic characteristics of the data entering feature preselection. Then the algorithm was applied to iEEG data recorded in presurgical epilepsy patients during a continuous word recognition task. In two studies, memory formation was successfully predicted based on iEEG measures from rhinal cortex and hippocampus, two memory-related brain regions. Different iEEG measures (i.e. absolute phases, phase shifts and power values) were compared for their predictive capabilities. The results obtained by training the algorithm with iEEG data reveal the superiority of absolute phases compared to the other measures and their importance for memory processes. Hence, the presented method is able to provide valuable insights into basic mechanisms of brain functions. Finally, a further application comprising memory enhancement methods is presented. Here, the results of the previous application are turned into assumptions for further research. In particular, 5 Hz auditory beat stimulation was applied during an associative memory task. It was shown that phase locking was increased and that memory performance was altered depending on absolute phase values. These findings confirmed the importance of oscillatory phases for memory formation and the informative value of the previous outcomes. Taken together, the presented algorithm is able to expose key information patterns derived from neurocognitive data and might be used in memory enhancement applications

Data-efficient methods for model learning and control in robotics

Cílem této disertační práce je navrhnout řešení aktuálních problém ů v oblasti učení modelů z dat v robotice. Práce představuje několik variant a rozšíření symbolické regrese. Tato technika, založená na genetickém programování, je vhodná pro automatické vytváření kompaktních a přesných modelů v podobě analytických rovnic i z malých souborů dat. Jedním z problémů v robotice je velké množství dat, které jsou roboty během provozu shromažd’ovány, což vyžaduje výběr podmnožiny trénovacích vzorků. Tato práce představuje novou metodu výběru vzorku založenou na predikční chybě modelu a porovnává ji se čtyřmi alternativními metodami. Experimentální vyhodnocení na mobilním robotu ukazuje, že model naučený jen z několika desítek vzorků vybraných navrženou metodou může být využit pro úspěšné vykonání úlohy založené na řízení metodou posilovaného učení.Constructing mathematical models of dynamic systems is central to many engineering and science disciplines. Models facilitate simulations, analysis of the system’s behavior, decision making, and design of automatic control algorithms. Even inherently model-free control tech niques such as reinforcement learning have been shown to benefit from the use of models. However, applying model learning methods to robotics is not straightforward. Obtaining in formative data for constructing dynamic models can be difficult, especially when the models are to be learned during task execution. Despite their increasing popularity, commonly used model learning methods such as deep neural networks come with drawbacks. They are data hungry and require a lot of computational power to learn a large number of parameters in their complex structure. Their black-box nature does not offer any insight into or interpretation of the model. Also, configuring these methods to achieve good results is often a difficult task. The objective of this thesis is to address the present challenges in data-driven model learn ing in robotics. Several variants and extensions of symbolic regression are introduced. This technique, based on genetic programming, is suitable to automatically build compact and ac curate models in the form of analytic equations even from small data sets. One of the chal lenges is posed by the large amount of data the robots collect during their operation, demand ing techniques to select a smaller subset of training samples. To that end, this thesis presents a novel sample-selection method based on model prediction error and compares it to four al ternative approaches. A real-world experimental evaluation on a mobile robot shows that a model learned from only a few tens of samples selected by the proposed method can be used to accomplish a motion control task within a reinforcement learning scheme. Standard data-driven model learning techniques in many cases yield models that violate the physical constraints of the robot. However, a partial theoretical or empirical model of the robot is often known. It is shown in this work how symbolic regression can be naturally ex tended to include the prior information into the model construction process. An experimental evaluation on two real-world robotic platforms demonstrates that symbolic regression is able to automatically build models that are both accurate and physically valid and compensate for theoretical or empirical model deficiencies. Efficient methods are needed not only to learn robot models but also to learn models of the robot’s environment. The thesis is concluded by presenting a novel method for reliable robot localization in dynamic environments. The proposed approach introduces an environ ment representation based on weighted local visual features and a change detection algorithm that updates the weights as the robot moves around the environment. The core idea of the method consists in using the weights to distinguish the useful information in stable regions of the scene from the unreliable information in the regions that are changing. An extensive eval uation and comparison to state-of-the-art alternatives show that using the proposed change detection algorithm improves the localization accurac

Comparison of Perceived Stress in First-Year Pre-Med Students and First-Year Medical Students at USD

Stress can be caused by many factors, including money, relationships, promotions, grades, and responsibilities. In college, these stressors can be exacerbated. This research dives into stress in both undergraduate students as well as medical students. Previous literature has indicated that high levels of stress are present in students, and it is likely to have negative effects on the students, whether that is their mental or physical well-being. Schools have put interventions in place to help combat the stress levels present in their students. At the University of South Dakota (USD) and USD Sanford School of Medicine (USD SSOM), perceived stress levels are relatively high. Perceived stress surveys are sent out to detect the levels of stress in both pre-med undergraduate freshmen and first-year medical students. After analyzing the results, the paper investigates the current interventions both USD undergraduate and USD SSOM have in place to encourage the well-being of their students