Search results for "PREDICT"
showing 10 items of 2174 documents
Application of Model Predictive Control in Discrete Displacement Cylinders to Drive a Knuckle Boom Crane
2018
In this paper, two Discrete Displacement Cylinders (DDCs) are used to drive the boom of a knuckle boom crane. DDCs operate by connecting one of several available pressure levels to each chamber in order to produce different forces. A trade-off exists with such systems, between the accuracy of tracking and energy dissipation due to switching. A popular way to approach this problem is a Force Shifting Algorithm (FSA). However, in this paper, the trade-off is managed by use of a Model Predictive Control (MPC) algorithm. The tracking accuracy and energy efficiency of the MPC and FSA strategies for DDCs are compared to a PID strategy for standard cylinders. The comparison is obtained by use of a…
Measurement, Prediction, and Control of Individual Heart Rate Responses to Exercise-Basics and Options for Wearable Devices.
2018
The use of wearable devices or "wearables" in the physical activity domain has been increasing in the last years. These devices are used as training tools providing the user with detailed information about individual physiological responses and feedback to the physical training process. Advantages in sensor technology, miniaturization, energy consumption and processing power increased the usability of these wearables. Furthermore, available sensor technologies must be reliable, valid, and usable. Considering the variety of the existing sensors not all of them are suitable to be integrated in wearables. The application and development of wearables has to consider the characteristics of the p…
Daily streamlow prediction with uncertainty in ephemeral catchments using the GLUE methodology
2009
Abstract The Generalised Likelihood Uncertainty Estimation (GLUE) approach is presented here as a tool for estimating the predictive uncertainty of a rainfall–runoff model. The GLUE methodology allows to recognise the possible equifinality of different parameter sets and assesses the likelihood of a parameters set being acceptable simulator when model predictions are compared to observed field data. The results of the GLUE methodology depend greatly on the choice of the likelihood measure and on the choice of the threshold which determines if a parameters set is behavioural or not. Moreover the sampling size has a strong influence on the uncertainty assessment of the response of a rainfall–…
A kernel support vector machine based technique for Crohnâs disease classification in human patients
2017
In this paper a new technique for classification of patients affected by Crohnâs disease (CD) is proposed. The proposed technique is based on a Kernel Support Vector Machine (KSVM) and it adopts a Stratified K-Fold Cross-Validation strategy to enhance the KSVM classifier reliability. Traditional manual classification methods require radiological expertise and they usually are very time-consuming. Accordingly to three expert radiologists, a dataset composed of 300 patients has been selected for KSVM training and validation. Each patient was codified by 22 extracted qualitative features and classified as Positive or Negative as the related histological specimen result showed the CD. The eff…
Predictive models for energy saving in Wireless Sensor Networks
2011
ICT devices nowadays cannot disregard optimizations toward energy sustainability. Wireless Sensor Networks, in particular, are a representative class of a technology where special care must be given to energy saving, due to the typical scarcity and non-renewability of their energy sources, in order to enhance network lifetime. In our work we propose a novel approach that aims to adaptively control the sampling rate of wireless sensor nodes using prediction models, so that environmental phenomena can be consistently modeled while reducing the required amount of transmissions; the approach is tested on data available from a public dataset.
Gaussian Process Regression (GPR) Representation in Predictive Model Markup Language (PMML)
2017
International audience; This paper describes Gaussian process regression (GPR) models presented in predictive model markup language (PMML). PMML is an extensible-markup-language (XML) -based standard language used to represent data-mining and predictive analytic models, as well as pre- and post-processed data. The previous PMML version, PMML 4.2, did not provide capabilities for representing probabilistic (stochastic) machine-learning algorithms that are widely used for constructing predictive models taking the associated uncertainties into consideration. The newly released PMML version 4.3, which includes the GPR model, provides new features: confidence bounds and distribution for the pred…
What should I do next? Using shared representations to solve interaction problems
2011
Studies on how “the social mind” works reveal that cognitive agents engaged in joint actions actively estimate and influence another’s cognitive variables and form shared representations with them. (How) do shared representations enhance coordination? In this paper, we provide a probabilistic model of joint action that emphasizes how shared representations help solving interaction problems. We focus on two aspects of the model. First, we discuss how shared representations permit to coordinate at the level of cognitive variables (beliefs, intentions, and actions) and determine a coherent unfolding of action execution and predictive processes in the brains of two agents. Second, we discuss th…
Student Performance Prediction Based on Blended Learning
2021
Contribution: This article explored blended learning by implementing a student-centered teaching method based on the flipped classroom and small private online course (SPOC). The impact of general online learning behavior on student performance was analyzed. This work is practical and provides enlightenment for learning analysis and individualized teaching in blended learning. Background: Providing individualized teaching in a large class is an effective way to improve teaching quality, but the traditional teaching method makes it difficult for teachers to learn about each student’s learning situation. Blended learning offers the possibility of individualized teaching for teachers. The comb…
Information-based detection of nonlinear Granger causality in multivariate processes via a nonuniform embedding technique
2010
We present an approach, framed in information theory, to assess nonlinear causality between the subsystems of a whole stochastic or deterministic dynamical system. The approach follows a sequential procedure for nonuniform embedding of multivariate time series, whereby embedding vectors are built progressively on the basis of a minimization criterion applied to the entropy of the present state of the system conditioned to its past states. A corrected conditional entropy estimator compensating for the biasing effect of single points in the quantized hyperspace is used to guarantee the existence of a minimum entropy rate at which to terminate the procedure. The causal coupling is detected acc…
Estimation of recombinant protein production in Pichia pastoris base don a constraint-based model
2012
[EN] A previously validated constraint based model and possibilistic MFA have been used to design a simple estimator of protein production rate in Pichia pastoris cultures. A structured model of the yeast P. pastoris metabolism is used to predict the balance of key energetic equivalents such as ATP from available measurements, mainly substrate consumption, gases exchange rates and biomass specific growth. It has been shown that ATP flux can be related to biomass growth and protein productivity specific rates by linear regression. Cross-validation has been applied for robust parameter fitting on the basis of chemostat, steady-state experimental conditions. In this way, protein estimation can…