962 resultados para Bilinear Predictive Control


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Over the last few years, the Data Center market has increased exponentially and this tendency continues today. As a direct consequence of this trend, the industry is pushing the development and implementation of different new technologies that would improve the energy consumption efficiency of data centers. An adaptive dashboard would allow the user to monitor the most important parameters of a data center in real time. For that reason, monitoring companies work with IoT big data filtering tools and cloud computing systems to handle the amounts of data obtained from the sensors placed in a data center.Analyzing the market trends in this field we can affirm that the study of predictive algorithms has become an essential area for competitive IT companies. Complex algorithms are used to forecast risk situations based on historical data and warn the user in case of danger. Considering that several different users will interact with this dashboard from IT experts or maintenance staff to accounting managers, it is vital to personalize it automatically. Following that line of though, the dashboard should only show relevant metrics to the user in different formats like overlapped maps or representative graphs among others. These maps will show all the information needed in a visual and easy-to-evaluate way. To sum up, this dashboard will allow the user to visualize and control a wide range of variables. Monitoring essential factors such as average temperature, gradients or hotspots as well as energy and power consumption and savings by rack or building would allow the client to understand how his equipment is behaving, helping him to optimize the energy consumption and efficiency of the racks. It also would help him to prevent possible damages in the equipment with predictive high-tech algorithms.

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A cross-sectional survey was made in 56 exceptionally healthy males, ranging in age from 20 to 84 years. Measurements were made of selected steroidal components and peptidic hormones in blood serum, and cognitive and physical tests were performed. Of those blood serum variables that gave highly significant negative correlations with age (r > −0.6), bioavailable testosterone (BT), dehydroepiandrosterone sulfate (DHEAS), and the ratio of insulin-like growth factor 1 (IGF-1) to growth hormone (GH) showed a stepwise pattern of age-related changes most closely resembling those of the age steps themselves. Of these, BT correlated best with significantly age-correlated cognitive and physical measures. Because DHEAS correlated well with BT and considerably less well than BT with the cognitive and physical measures, it seems likely that BT and/or substances to which BT gives rise in tissues play a more direct role in whatever processes are rate-limiting in the functions measured and that DHEAS relates more indirectly to these functions. The high correlation of IGF-1/GH with age, its relatively low correlation with BT, and the patterns of correlations of IGF-1/GH and BT with significantly age-correlated cognitive and physical measures suggest that the GH–IGF-1 axis and BT play independent roles in affecting these functions. Serial determinations made after oral ingestion of pregnenolone and data from the literature suggest there is interdependence of steroid metabolic systems with those operational in control of interrelations in the GH–IGF-1 axis. Longitudinal concurrent measurements of serum levels of BT, DHEAS, and IGF-1/GH together with detailed studies of their correlations with age-correlated functional measures may be useful in detecting early age-related dysregulations and may be helpful in devising ameliorative approaches.

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Escape of cancer cells from the circulation (extravasation) is thought to be a major rate-limiting step in metastasis, with few cells being able to extravasate. Furthermore, highly metastatic cells are believed to extravasate more readily than poorly metastatic cells. We assessed in vivo the extravasation ability of highly metastatic ras-transformed NIH 3T3 cells (PAP2) versus control nontumorigenic nontransformed NIH 3T3 cells and primary mouse embryo fibroblasts. Fluorescently labeled cells were injected intravenously into chicken embryo chorioallantoic membrane and analyzed by intravital videomicroscopy. The chorioallantoic membrane is an appropriate model for studying extravasation, since, at the embryonic stage used, the microvasculature exhibits a continuous basement membrane and adult permeability properties. The kinetics of extravasation were assessed by determining whether individual cells (n = 1481) were intravascular, extravascular, or in the process of extravasation, at 3, 6, and 24 h after injection. Contrary to expectations, our results showed that all three cell types extravasated with the same kinetics. By 24 h after injection > 89% of observed cells had completed extravasation from the capillary plexus. After extravasation, individual fibroblasts of all cell types demonstrated preferential migration within the mesenchymal layer toward arterioles, not to venules or lymphatics. Thus in this model and for these cells, extravasation is independent of metastatic ability. This suggests that the ability to extravasate in vivo is not necessarily predictive of subsequent metastasis formation, and that postextravasation events may be key determinants in metastasis.

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Although the study of factors affecting career success has shown connections between biographical and other aspects related to ability, knowledge and personality, few studies have examined the relationship be-tween emotional intelligence and professional success at the initial career stage. When these studies were carried out, the results showed significant relationships between the dimensions of emotional intelligence (emotional self-awareness, self-regulation, social awareness or social skills) and the level of professional competence. In this paper, we analyze the relationship between perceived emotional intelligence, measured by the Trait Meta-Mood Scale (TMMS-24) questionnaire, general intelligence assessed by the Cattell factor "g" test, scale 3, and extrinsic indicators of career success, in a sample of 130 graduates at the beginning of their careers. Results from hierarchical regression analysis indicate that emotional intelligence makes a specific contribution to the prediction of salary, after controlling the general intelligence effect. The perceived emotional intelligence dimensions of TMMS repair, TMMS attention and sex show a higher correlation and make a greater contribution to professional success than general intelligence. The implications of these results for the development of socio-emotional skills among University graduates are discussed.

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We investigated whether children’s inhibitory control is associated with their ability to produce irregular verb forms as well as learn from corrective feedback following their use of an over-regularized form. Forty-eight 3.5 to 4.5 year old children were tested on the irregular past tense and provided with adult corrective input via models of correct use or recasts of errors following ungrammatical responses. Inhibitory control was assessed with a three-item battery of tasks that required suppressing a prepotent response in favor of a non-canonical one. Results showed that inhibitory control was predictive of children’s initial production of irregular forms and not associated with their post-feedback production of irregulars. These findings show that children’s executive functioning skills may be a rate-limiting factor on their ability to produce correct forms, but might not interact with their ability to learn from input in this domain. Findings are discussed in terms of current theories of past-tense acquisition and learning from input more broadly.

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1Recent studies demonstrated the sensitivity of northern forest ecosystems to changes in the amount and duration of snow cover at annual to decadal time scales. However, the consequences of snowfall variability remain uncertain for ecological variables operating at longer time scales, especially the distributions of forest communities. 2The Great Lakes region of North America offers a unique setting to examine the long-term effects of variable snowfall on forest communities. Lake-effect snow produces a three-fold gradient in annual snowfall over tens of kilometres, and dramatic edaphic variations occur among landform types resulting from Quaternary glaciations. We tested the hypothesis that these factors interact to control the distributions of mesic (dominated by Acer saccharum, Tsuga canadensis and Fagus grandifolia) and xeric forests (dominated by Pinus and Quercus spp.) in northern Lower Michigan. 3We compiled pre-European-settlement vegetation data and overlaid these data with records of climate, water balance and soil, onto Landtype Association polygons in a geographical information system. We then used multivariate adaptive regression splines to model the abundance of mesic vegetation in relation to environmental controls. 4Snowfall is the most predictive among five variables retained by our model, and it affects model performance 29% more than soil texture, the second most important variable. The abundance of mesic trees is high on fine-textured soils regardless of snowfall, but it increases with snowfall on coarse-textured substrates. Lake-effect snowfall also determines the species composition within mesic forests. The weighted importance of A. saccharum is significantly greater than of T. canadensis or F. grandifolia within the lake-effect snowbelt, whereas T. canadensis is more plentiful outside the snowbelt. These patterns are probably driven by the influence of snowfall on soil moisture, nutrient availability and fire return intervals. 5Our results imply that a key factor dictating the spatio-temporal patterns of forest communities in the vast region around the Great Lakes is how the lake-effect snowfall regime responds to global change. Snowfall reductions will probably cause a major decrease in the abundance of ecologically and economically important species, such as A. saccharum.

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Background: Intravenous (IV) fluid administration is an integral component of clinical care. Errors in administration can cause detrimental patient outcomes and increase healthcare costs, although little is known about medication administration errors associated with continuous IV infusions. Objectives: ( 1) To ascertain the prevalence of medication administration errors for continuous IV infusions and identify the variables that caused them. ( 2) To quantify the probability of errors by fitting a logistic regression model to the data. Methods: A prospective study was conducted on three surgical wards at a teaching hospital in Australia. All study participants received continuous infusions of IV fluids. Parenteral nutrition and non-electrolyte containing intermittent drug infusions ( such as antibiotics) were excluded. Medication administration errors and contributing variables were documented using a direct observational approach. Results: Six hundred and eighty seven observations were made, with 124 (18.0%) having at least one medication administration error. The most common error observed was wrong administration rate. The median deviation from the prescribed rate was 247 ml/h (interquartile range 275 to + 33.8 ml/ h). Errors were more likely to occur if an IV infusion control device was not used and as the duration of the infusion increased. Conclusions: Administration errors involving continuous IV infusions occur frequently. They could be reduced by more common use of IV infusion control devices and regular checking of administration rates.

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The GuRm is a 1.2m tall, 23 degree of freedom humanoid consuucted at the University of Queensland for research into humanoid robotics. The key challenge being addressed by the GuRw projcct is the development of appropriate learning strategies for control and coodinadon of the robot’s many joints. The development of learning strategies is Seen as a way to sidestep the inherent intricacy of modeling a multi-DOP biped robot. This paper outlines the approach taken to generate an appmpria*e control scheme for the joinis of the GuRoo. The paper demonsrrates the determination of local feedback control parameters using a genetic algorithm. The feedback loop is then augmented by a predictive modulator that learns a form of feed-fonward control to overcome the irregular loads experienced at each joint during the gait cycle. The predictive modulator is based on thc CMAC architecture. Results from tats on the GuRoo platform show that both systems provide improvements in stability and tracking of joint control.

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Physiological and neuroimaging studies provide evidence to suggest that attentional mechanisms operating within the fronto-parietal network may exert top–down control on early visual areas, priming them for forthcoming sensory events. The believed consequence of such priming is enhanced task performance. Using the technique of magnetoencephalography (MEG), we investigated this possibility by examining whether attention-driven changes in cortical activity are correlated with performance on a line-orientation judgment task. We observed that, approximately 200 ms after a covert attentional shift towards the impending visual stimulus, the level of phase-resetting (transient neural coherence) within the calcarine significantly increased for 2–10 Hz activity. This was followed by a suppression of alpha activity (near 10 Hz) which persisted until the onset of the stimulus. The levels of phase-resetting, alpha suppression and subsequent behavioral performance varied between subjects in a systematic fashion. The magnitudes of phase-resetting and alpha-band power were negatively correlated, with high levels of coherence associated with high levels of performance. We propose that top–down attentional control mechanisms exert their initial effects within the calcarine through a phase-resetting within the 2–10 Hz band, which in turn triggers a suppression of alpha activity, priming early visual areas for incoming information and enhancing behavioral performance.

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The aim of this research was to improve the quantitative support to project planning and control principally through the use of more accurate forecasting for which new techniques were developed. This study arose from the observation that in most cases construction project forecasts were based on a methodology (c.1980) which relied on the DHSS cumulative cubic cost model and network based risk analysis (PERT). The former of these, in particular, imposes severe limitations which this study overcomes. Three areas of study were identified, namely growth curve forecasting, risk analysis and the interface of these quantitative techniques with project management. These fields have been used as a basis for the research programme. In order to give a sound basis for the research, industrial support was sought. This resulted in both the acquisition of cost profiles for a large number of projects and the opportunity to validate practical implementation. The outcome of this research project was deemed successful both in theory and practice. The new forecasting theory was shown to give major reductions in projection errors. The integration of the new predictive and risk analysis technologies with management principles, allowed the development of a viable software management aid which fills an acknowledged gap in current technology.

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Despite the availability of various control techniques and project control software many construction projects still do not achieve their cost and time objectives. Research in this area so far has mainly been devoted to identifying causes of cost and time overruns. There is limited research geared towards studying factors inhibiting the ability of practitioners to effectively control their projects. To fill this gap, a survey was conducted on 250 construction project organizations in the UK, which was followed by face-to-face interviews with experienced practitioners from 15 of these organizations. The common factors that inhibit both time and cost control during construction projects were first identified. Subsequently 90 mitigating measures have been developed for the top five leading inhibiting factors—design changes, risks/uncertainties, inaccurate evaluation of project time/duration, complexities and non-performance of subcontractors were recommended. These mitigating measures were classified as: preventive, predictive, corrective and organizational measures. They can be used as a checklist of good practice and help project managers to improve the effectiveness of control of their projects.

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Everyday human behaviour relies on our ability to predict outcomes on the basis of moment by moment information. Long-range neural phase synchronization has been hypothesized as a mechanism by which ‘predictions’ can exert an effect on the processing of incoming sensory events. Using magnetoencephalography (MEG) we have studied the relationship between the modulation of phase synchronization in a cerebral network of areas involved in visual target processing and the predictability of target occurrence. Our results reveal a striking increase in the modulation of phase synchronization associated with an increased probability of target occurrence. These observations are consistent with the hypothesis that long-range phase synchronization plays a critical functional role in humans' ability to effectively employ predictive heuristics.

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AMS subject classification: 90C31, 90A09, 49K15, 49L20.

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Automation of managed pressure drilling (MPD) enhances the safety and increases efficiency of drilling and that drives the development of controllers and observers for MPD. The objective is to maintain the bottom hole pressure (BHP) within the pressure window formed by the reservoir pressure and fracture pressure and also to reject kicks. Practical MPD automation solutions must address the nonlinearities and uncertainties caused by the variations in mud flow rate, choke opening, friction factor, mud density, etc. It is also desired that if pressure constraints are violated the controller must take appropriate actions to reject the ensuing kick. The objectives are addressed by developing two controllers: a gain switching robust controller and a nonlinear model predictive controller (NMPC). The robust gain switching controller is designed using H1 loop shaping technique, which was implemented using high gain bumpless transfer and 2D look up table. Six candidate controllers were designed in such a way they preserve robustness and performance for different choke openings and flow rates. It is demonstrated that uniform performance is maintained under different operating conditions and the controllers are able to reject kicks using pressure control and maintain BHP during drill pipe extension. The NMPC was designed to regulate the BHP and contain the outlet flow rate within certain tunable threshold. The important feature of that controller is that it can reject kicks without requiring any switching and thus there is no scope for shattering due to switching between pressure and flow control. That is achieved by exploiting the constraint handling capability of NMPC. Active set method was used for computing control inputs. It is demonstrated that NMPC is able to contain kicks and maintain BHP during drill pipe extension.

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Previously developed models for predicting absolute risk of invasive epithelial ovarian cancer have included a limited number of risk factors and have had low discriminatory power (area under the receiver operating characteristic curve (AUC) < 0.60). Because of this, we developed and internally validated a relative risk prediction model that incorporates 17 established epidemiologic risk factors and 17 genome-wide significant single nucleotide polymorphisms (SNPs) using data from 11 case-control studies in the United States (5,793 cases; 9,512 controls) from the Ovarian Cancer Association Consortium (data accrued from 1992 to 2010). We developed a hierarchical logistic regression model for predicting case-control status that included imputation of missing data. We randomly divided the data into an 80% training sample and used the remaining 20% for model evaluation. The AUC for the full model was 0.664. A reduced model without SNPs performed similarly (AUC = 0.649). Both models performed better than a baseline model that included age and study site only (AUC = 0.563). The best predictive power was obtained in the full model among women younger than 50 years of age (AUC = 0.714); however, the addition of SNPs increased the AUC the most for women older than 50 years of age (AUC = 0.638 vs. 0.616). Adapting this improved model to estimate absolute risk and evaluating it in prospective data sets is warranted.