Stability for Receding-horizon Stochastic Model Predictive Control

A stochastic model predictive control (SMPC) approach is presented for discrete-time linear systems with arbitrary time-invariant probabilistic uncertainties and additive Gaussian process noise. Closed-loop stability of the SMPC approach is established by appropriate selection of the cost function. Polynomial chaos is used for uncertainty propagation through system dynamics. The performance of the SMPC approach is demonstrated using the Van de Vusse reactions.Comment: American Control Conference (ACC) 201

Fuzzy rule-based alertness state classification based on the optimization of EEG rhythm/channel combinations

This paper presents a method for automatically selecting the optimal EEG rhythm/channel combination capable of classifying the different human alertness states. We considered four alertness states, namely 'engaged', 'calm', 'drowsy', and 'asleep'. Energies associated with the conventional EEG rhythms, δ, θ, α, ß and γ, extracted from overlapping segments of the different EEG channels were used as features. The proposed method is a two-stage process. In the first stage, the optimal brain regions, represented by a set of EEG channels, are identified. In the second stage, a fuzzy rule-based alertness classification system (FRBACS) is developed to select the optimal EEG rhythms extracted from the previously selected EEG channels. The IF-THEN rules used in FRBACS are constructed using a novel bi-level differential evolution (DE) based search algorithm. Unlike most of the existing classification methods, the proposed classification approach reveals easy to interpret rules that describe each of the alertness states

Design of bang-bang controller based on a fuzzy-neuro approach

A fuzzy-neuro approach for the design of bang-bang controller is presented in this paper. The approach has been used with success for the time optimal bang-bang control of a heating system. The improved bang-bang controller suppresses the oscillations often observed at the output of an on-off controller. A fuzzy system is used for the implementation of the on-off control. An extension of the fuzzy control is provided by an equivalent neural network of the fuzzy system. A test application, that of a house heating with a two-state furnace, is developed and evaluated with standard hysteresis switching, fuzzy control, and fuzzy-neuro control.published_or_final_versio

Adiponectin could be a comprehensive marker of metabolic syndrome in obese children

Objectives: The objectives were to investigate the relationship between the serum adiponectin level and the metabolic syndrome (MS) phenotype in children, and to examine the independent association between the serum adiponectin level and the individual components of MS.Design: A cross-sectional design was used.Subjects: Fifty-six obese children with a body mass index ≥ 95th percentile for age and sex, and 50 normal-weight children matched for age and sex with the obese children, were used as controls.Outcome measures: The main outcome measure was the serum adiponectin level.Results: The serum adiponectin level was significantly lower in obese children, than in the normal-weight controls (7.35 ± 3.1 μg/dl vs. 10.64 ± 3.04 μg/dl). Obese children with MS have a significantly lower serum adiponectin level compared to obese children without MS (5.92 ± 1.9μg/dl vs. 8.57 ± 2.1 μg/dl). There was a significant negative correlation between the serum adiponectin level and waist circumference, triglyceride levels, systolic blood pressure, diastolic blood pressure, and fasting blood glucose. The serum adiponectin level correlated positively with the level of high-density lipoprotein cholesterol. After controlling for the confounding effect of age, sex and visceral fat, the adiponectin level remained a significant predictor of the MS [odds ratio (OR): 0.76, 95% CI: 0.63-0.91].Conclusion: Adiponectin demonstrated a consistent relationship to each MS component. Adiponectin may be a comprehensive marker of the MS condition

Motor imagery task classification using a signal-dependent orthogonal transform based feature extraction

© Springer International Publishing Switzerland 2015. In this paper, we present the results of classifying electroencephalographic (EEG) signals into four motor imagery tasks using a new method for feature extraction. This method is based on a signal-dependent orthogonal transform, referred to as LP-SVD, defined as the left singular vectors of the LPC filter impulse response matrix. Using a logistic tree based model classifier, the extracted features are mapped into one of four motor imagery movements, namely left hand, right hand, foot, and tongue. The proposed technique-based classification performance was benchmarked against those based on two widely used linear transform for feature extraction methods, namely discrete cosine transform (DCT) and adaptive autoregressive (AAR). By achieving an accuracy of 67.35 %, the LP-SVD based method outperformed the other two by large margins (+25 % compared to DCT and +6 % compared to AAR-based methods)

Stochastic Physics-Informed Neural Ordinary Differential Equations

Stochastic differential equations (SDEs) are used to describe a wide variety of complex stochastic dynamical systems. Learning the hidden physics within SDEs is crucial for unraveling fundamental understanding of these systems' stochastic and nonlinear behavior. We propose a flexible and scalable framework for training artificial neural networks to learn constitutive equations that represent hidden physics within SDEs. The proposed stochastic physics-informed neural ordinary differential equation framework (SPINODE) propagates stochasticity through the known structure of the SDE (i.e., the known physics) to yield a set of deterministic ODEs that describe the time evolution of statistical moments of the stochastic states. SPINODE then uses ODE solvers to predict moment trajectories. SPINODE learns neural network representations of the hidden physics by matching the predicted moments to those estimated from data. Recent advances in automatic differentiation and mini-batch gradient descent with adjoint sensitivity are leveraged to establish the unknown parameters of the neural networks. We demonstrate SPINODE on three benchmark in-silico case studies and analyze the framework's numerical robustness and stability. SPINODE provides a promising new direction for systematically unraveling the hidden physics of multivariate stochastic dynamical systems with multiplicative noise

The effects of brown algae Sargassum angustifolium extract on growth performance, survival and Vibriosis resistance in shrimp Litopenaeus vannamei

In this study, the effect of ethanolic extracts of Sargassum angustifolium on growth and survival of shrimp Litopenaeus vannamei juvenile was investigated under challenge with shrimp pathogen bacteria Vibrio harveyi. Powder form of the extract was bioencapsulated in Artemia and fed to L. vannamei juvenile reared as 5 groups inclouding C- (unenriched Artemia, without bacteria), C+ (unenriched Artemia, with bacteria), T1 (enriched Artemia with 200 mg l-1 SA extract, with bacteria), T2 (enriched Artemia with 400 mg l-1 SA extract, with bacteria), T3 (enriched Artemia with 600 mg l-1 SA extract, with bacteria). One week after culture all groups except C- were inoculated with V. harveyi at the rate of 1.5 × 108 CFU ml-1 for 15 minutes then after every water exchange 10 ml of V. harveyi at the rate of 1.5 × 107 CFU ml-1 was added to aquaria. Shrimps at group C- showed maximum survival (86.6%), specific growth rate (SGR, 11.33%) and less bacterial load (0.5 ± 0.03× 102 CFU g-1 tissue). While (C2) exhibited lowest survival (33.3%), SGR (9.90%) and more bacterial load (3.4 ± 0.05× 105 CFU g-1 tissue) and the difference was significant (p<0.05). In treatment groups survival and SGR were significantly (p< 0.05) more than C+ and less than C-, also bacterial load were less than C+ and more than C-. Among treatment groups T2 that fed with enriched artemia with 400 mg l-1 SA extract gave better results than the other treatments