Selection of optimal denoising-based regularization hyper-parameters for performance improvement in a sensor validation model

Multilayered auto-associative neural architectures have widely been used in empirical sensor modeling. Typically, such empirical sensor models are used in sensor calibration and fault monitoring systems. However, simultaneous optimization of related performance metrics, i.e., auto-sensitivity, cross-sensitivity, and fault-detectability, is not a trivial task. Learning procedures for parametric and other relevant non-parametric empirical models are sensitive to optimization and regularization methods. Therefore, there is a need for active learning strategies that can better exploit the underlying statistical structure among input sensors and are simple to regularize and fine-tune. To this end, we investigated the greedy layer-wise learning strategy and denoising-based regularization procedure for sensor model optimization. We further explored the effects of denoising-based regularization hyper-parameters such as noise-type and noise-level on sensor model performance and suggested optimal settings through rigorous experimentation. A visualization procedure was introduced to obtain insight into the internal semantics of the learned model. These visualizations allowed us to suggest an implicit noise-generating process for efficient regularization in higher-order layers. We found that the greedy-learning procedure improved the overall robustness of the sensor model. To keep experimentation unbiased and immune to noise-related artifacts in real sensors, the sensor data were sampled from simulators of a nuclear steam supply system of a pressurized water reactor and a Tennessee Eastman chemical process. Finally, we compared the performance of an optimally regularized sensor model with auto-associative neural network, auto-associative kernel regression, and fuzzy similarity-based sensor models.     

Green synthesis and characterization of RGO/Cu nanocomposites as photocatalytic degradation of organic pollutants in waste-water

Recently, nanocomposite photocatalysts based on semiconductors have attracted much attention due to their suitable bandgap. Combination of tow of several semiconductors can slow down the electron-hole recombination. In this regard, we have depicted an eco-friendly and green fabrication technique to synthesize RGO/Cu nanocomposite by the reduction of graphene oxide and Cu²⁺ ion utilizing spearmint extract as a reductant and capping agent. The sample was identified by FTIR, XRD, FESEM, EDS, HRTEM, and CV. The results of photocatalytic performance revealed that RGO/Cu is an efficient catalyst for degrading organic pollutants. This compound can eliminate Rhodamine B (RhB) and Methylene blue (MB) 91.0% and 72.0%, respectively.     

Exploration of connectivity with SEM: An fMRI study of resting state

In this era, the neuroscientists get attention to find a network of brain which is activated in resting state of mind. It is challenging for any analytical technique to find out the connectivity among Brodmann areas of brain in resting state. To find network, a study was conducted in which brains are scan by functional magnetic resonance imaging of four healthy right handed individuals. Exploratory factor analysis was used to explore that which Brodmann areas are related to each other and then structural equation model has applied on explored factors (lobes) to find a network of activated brain in resting state. © 2016 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 26, 264–269, 2016     

The release rate and antimicrobial activity of calcium-alginate films containing self-microemulsifying Thymus vulgaris essential oil against Escherichia coli and Staphylococcus aureus

The aim of this study was to fabricate antimicrobial calcium-alginate-based films containing the self-microemulsifying thyme essential oil (TEO) formulations using Tween 80 as the surfactant, and acetic (AA) or propionic (PA) acids as the cosurfactants. A Ca-alginate film containing nano-emulsified TEO as well as a neat Ca-alginate film were considered as the controls. The scanning electron microscopy micrographs showed a highly porous texture for SME films, which resulted in an increase in water vapor permeability and water absorption capacity of these films. The SME films released the TEO completely within 155 min and inhibited the growth of S. aureus and E. coli in in vitro antimicrobial tests. The population of S. aureus and E. coli reduced significantly in ground beef covered with SME films. The results of this study showed that self-microemulsifying TEO films could effectively increase the shelf life of ground beef by controlling its microbial population.     

Hydrogen production by silica membrane reactor during dehydrogenation of methylcyclohexane: CFD analysis

A comprehensive computational fluid dynamic model has been developed using COMSOL Multiphysics 5.4 software to predict the behavior of a membrane reactor in dehydrogenation of methylcyclohexane for hydrogen production. A reliable reaction kinetic of dehydrogenation reaction and a permeation mechanism of hydrogen through silica membrane have been used in computational fluid dynamic modeling. For performance comparison, an equivalent traditional fixed bed reactor without hydrogen removal has been also modeled. After model validation, it has been used to evaluate the operating parameters effect on the performance of both the silica membrane reactor and the equivalent traditional reactor as well. The operating temperature ranged between 473 and 553 K, pressure between 1 and 2.5 bar, sweep factor from ?6.22 to 25 and feed flow rate from 1 to 5 × 10^?6 mol/s. The membrane reactor performed better than the equivalent traditional reactor, achieving as best result complete methylcyclohexane conversion and 96% hydrogen recovery.     

Low Temperature Hall Effect Investigation of Conducting Polymer-Carbon Nanotubes Composite Network

Polypyrrole (PPy) and polypyrrole-carboxylic functionalized multi wall carbon nanotube composites (PPy/f-MWCNT) were synthesized by in situ chemical oxidative polymerization of pyrrole on the carbon nanotubes (CNTs). The structure of the resulting complex nanotubes was characterized by transmission electron microscopy (TEM) and X-ray diffraction (XRD). The effects of f-MWCNT concentration on the electrical properties of the resulting composites were studied at temperatures between 100 K and 300 K. The Hall mobility and Hall coefficient of PPy and PPy/f-MWCNT composite samples with different concentrations of f-MWCNT were measured using the van der Pauw technique. The mobility decreased slightly with increasing temperature, while the conductivity was dominated by the gradually increasing carrier density.     

A music symbols recognition method using pattern matching along with integrated projection and morphological operation techniques

Multimedia Tools and Applications - Optical Music Recognition (OMR) can be divided into three main phases: (i) staff line detection and removal. The goal of this phase is to detect and to remove...