913 resultados para supplementary control input


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A growing number of studies in humans demonstrate the involvement of vestibular information in tasks that are seemingly remote from well-known functions such as space constancy or postural control. In this review article we point out three emerging streams of research highlighting the importance of vestibular input: (1) Spatial Cognition: Modulation of vestibular signals can induce specific changes in spatial cognitive tasks like mental imagery and the processing of numbers. This has been shown in studies manipulating body orientation (changing the input from the otoliths), body rotation (changing the input from the semicircular canals), in clinical findings with vestibular patients, and in studies carried out in microgravity. There is also an effect in the reverse direction; top-down processes can affect perception of vestibular stimuli. (2) Body Representation: Numerous studies demonstrate that vestibular stimulation changes the representation of body parts, and sensitivity to tactile input or pain. Thus, the vestibular system plays an integral role in multisensory coordination of body representation. (3) Affective Processes and Disorders: Studies in psychiatric patients and patients with a vestibular disorder report a high comorbidity of vestibular dysfunctions and psychiatric symptoms. Recent studies investigated the beneficial effect of vestibular stimulation on psychiatric disorders, and how vestibular input can change mood and affect. These three emerging streams of research in vestibular science are—at least in part—associated with different neuronal core mechanisms. Spatial transformations draw on parietal areas, body representation is associated with somatosensory areas, and affective processes involve insular and cingulate cortices, all of which receive vestibular input. Even though a wide range of different vestibular cortical projection areas has been ascertained, their functionality still is scarcely understood.

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Foot-and-mouth disease (FMD) is a highly contagious disease that caused several large outbreaks in Europe in the last century. The last important outbreak in Switzerland took place in 1965/66 and affected more than 900 premises and more than 50,000 animals were slaughtered. Large-scale emergency vaccination of the cattle and pig population has been applied to control the epidemic. In recent years, many studies have used infectious disease models to assess the impact of different disease control measures, including models developed for diseases exotic for the specific region of interest. Often, the absence of real outbreak data makes a validation of such models impossible. This study aimed to evaluate whether a spatial, stochastic simulation model (the Davis Animal Disease Simulation model) can predict the course of a Swiss FMD epidemic based on the available historic input data on population structure, contact rates, epidemiology of the virus, and quality of the vaccine. In addition, the potential outcome of the 1965/66 FMD epidemic without application of vaccination was investigated. Comparing the model outcomes to reality, only the largest 10% of the simulated outbreaks approximated the number of animals being culled. However, the simulation model highly overestimated the number of culled premises. While the outbreak duration could not be well reproduced by the model compared to the 1965/66 epidemic, it was able to accurately estimate the size of the area infected. Without application of vaccination, the model predicted a much higher mean number of culled animals than with vaccination, demonstrating that vaccination was likely crucial in disease control for the Swiss FMD outbreak in 1965/66. The study demonstrated the feasibility to analyze historical outbreak data with modern analytical tools. However, it also confirmed that predicted epidemics from a most carefully parameterized model cannot integrate all eventualities of a real epidemic. Therefore, decision makers need to be aware that infectious disease models are useful tools to support the decision-making process but their results are not equal valuable as real observations and should always be interpreted with caution.

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Random Forests™ is reported to be one of the most accurate classification algorithms in complex data analysis. It shows excellent performance even when most predictors are noisy and the number of variables is much larger than the number of observations. In this thesis Random Forests was applied to a large-scale lung cancer case-control study. A novel way of automatically selecting prognostic factors was proposed. Also, synthetic positive control was used to validate Random Forests method. Throughout this study we showed that Random Forests can deal with large number of weak input variables without overfitting. It can account for non-additive interactions between these input variables. Random Forests can also be used for variable selection without being adversely affected by collinearities. ^ Random Forests can deal with the large-scale data sets without rigorous data preprocessing. It has robust variable importance ranking measure. Proposed is a novel variable selection method in context of Random Forests that uses the data noise level as the cut-off value to determine the subset of the important predictors. This new approach enhanced the ability of the Random Forests algorithm to automatically identify important predictors for complex data. The cut-off value can also be adjusted based on the results of the synthetic positive control experiments. ^ When the data set had high variables to observations ratio, Random Forests complemented the established logistic regression. This study suggested that Random Forests is recommended for such high dimensionality data. One can use Random Forests to select the important variables and then use logistic regression or Random Forests itself to estimate the effect size of the predictors and to classify new observations. ^ We also found that the mean decrease of accuracy is a more reliable variable ranking measurement than mean decrease of Gini. ^

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Hypercapnia and elevated temperatures resulting from climate change may have adverse consequences for many marine organisms. While diverse physiological and ecological effects have been identified, changes in those molecular mechanisms, which shape the physiological phenotype of a species and limit its capacity to compensate, remain poorly understood. Here, we use global gene expression profiling through RNA-Sequencing to study the transcriptional responses to ocean acidification and warming in gills of the boreal spider crab Hyas araneus exposed medium-term (10 weeks) to intermediate (1,120 µatm) and high (1,960 µatm) PCO2 at different temperatures (5°C and 10°C). The analyses reveal shifts in steady state gene expression from control to intermediate and from intermediate to high CO2 exposures. At 5°C acid-base, energy metabolism and stress response related genes were upregulated at intermediate PCO2, whereas high PCO2 induced a relative reduction in expression to levels closer to controls. A similar pattern was found at elevated temperature (10°C). There was a strong coordination between acid-base, metabolic and stress-related processes. Hemolymph parameters at intermediate PCO2 indicate enhanced capacity in acid-base compensation potentially supported by upregulation of a V-ATPase. The likely enhanced energy demand might be met by the upregulation of the electron transport system (ETS), but may lead to increased oxidative stress reflected in upregulated antioxidant defense transcripts. These mechanisms were attenuated by high PCO2, possibly as a result of limited acid-base compensation and metabolic down-regulation. Our findings indicate a PCO2 dependent threshold beyond which compensation by acclimation fails progressively. They also indicate a limited ability of this stenoecious crustacean to compensate for the effects of ocean acidification with and without concomitant warming.

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The goal of the work described in this paper is to develop a visual line guided system for being used on-board an Autonomous Guided Vehicle (AGV) commercial car, controlling the steering and using just the visual information of a line painted below the car. In order to implement the control of the vehicle, a Fuzzy Logic controller has been implemented, that has to be robust against curvature changes and velocity changes. The only input information for the controller is the visual distance from the image center captured by a camera pointing downwards to the guiding line on the road, at a commercial frequency of 30Hz. The good performance of the controller has successfully been demonstrated in a real environment at urban velocities. The presented results demonstrate the capability of the Fuzzy controller to follow a circuit in urban environments without previous information about the path or any other information from additional sensors

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The main objective of this work is the design and implementation of the digital control stage of a 280W AC/DC industrial power supply in a single low-cost microcontroller to replace the analog control stage. The switch-mode power supply (SMPS) consists of a PFC boost converter with fixed frequency operation and a variable frequency LLC series resonant DC/DC converter. Input voltage range is 85VRMS-550VRMS and the output voltage range is 24V-28V. A digital controller is especially suitable for this kind of SMPS to implement its multiple functionalities and to keep the efficiency and the performance high over the wide range of input voltages. Additional advantages of the digital control are reliability and size. The optimized design and implementation of the digital control stage it is presented. Experimental results show the stable operation of the controlled system and an estimation of the cost reduction achieved with the digital control stage.

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An electrically tunable system for the control of optical pulse sequences is proposed and demonstrated. It is based on the use of an electrooptic modulator for periodic phase modulation followed by a dispersive device to obtain the temporal Talbot effect. The proposed configuration allows for repetition rate multiplication with different multiplication factors and with the simultaneous control of the pulse train envelope by simply changing the electrical signal driving the modulator. Simulated and experimental results for an input optical pulse train of 10 GHz are shown for different multiplication factors and envelope shapes.

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Civil engineering structures such as floor systems with open-plan layout or lightweight footbridges are susceptible to excessive level of vibrations caused by human loading. Active vibration control (AVC) via inertial mass actuators has been shown to be a viable technique to mitigate vibrations, allowing structures to satisfy vibration serviceability limits. Most of the AVC applications involve the use of SISO (single input single-output) strategies based on collocated control. However, in the case of floor structures, in which mostof the vibration modes are locally spatially distributed, SISO or multi-SISO strategies are quite inefficient. In this paper, a MIMO (multi-inputs multi-outputs) control in decentralised and centralised configuration is designed. The design process simultaneously finds the placement of multiple actuators and sensors and the output feedback gains. Additionally, actuator dynamics, actuator nonlinearities and frequency and time weightings are considered into the design process. Results with SISO and decentralised and centralised MIMO control (for a given number of actuators and sensors) are compared, showing the advantages of MIMO control for floor vibration control.