A novel adaptive sampling based methodology for feasible region identification of compute intensive models using artificial neural network

Identification of feasible region of operations in multivariate processes is a problem of interest in several fields. This is particularly challenging when the process model is black-box in nature and/or is computationally expensive, as analytical solutions are not available and the number of possible model evaluations is limited. An efficient methodology is required to identify samples where the model is evaluated for developing a computationally efficient surrogate model. In this work, an artificial neural network based surrogate model is proposed which is integrated with a statistical-based approach (Jack-knifing) to estimate the variance of the surrogate model prediction. This allows implementation of an adaptive sampling approach where new samples are identified close to the feasible region boundary or in regions of high prediction uncertainty. The proposed approach performs better than a previously published kriging based method for different dimensionality case studies.     

Online embedded integrated scanning system for identifying failed fuel sub assemblies in fast reactor

The physical integrity of the fuel in fast reactor is of utmost concern for the healthiness of the reactor and operating people. Hence details of the failed fuel location in the core shall be determined at the earliest, to minimize reactor down time and radiation exposure. In the present reactor under construction, i.e., 500 MWe Prototype Fast Breeder Reactor (PFBR), a system for failed fuel identification, was proposed. The system follows a novel scheme to locate the failed fuel using failed fuel location module along with necessary instrumentation and control. This paper details out the scheme followed.     

Preparation of silica xerogel with high silanol content from sodium silicate and its application as CO2 adsorbent

In this study, silica xerogels with high silanol content were synthesized by using sodium silicate as low-cost silica source in the presence of hydrochloric acid and acetic acid via sol–gel process for CO₂ adsorption purpose. The effect of amount of acetic acid on the surface and structural properties of silica xerogel was investigated. Fourier transform infrared spectroscopy (FTIR) and thermal gravimetric analysis (TGA) revealed that the silanol groups existed on the surface of silica xerogel products and their concentration increased with increasing the amount of acetic acid. The BET surface area and total pore volume of the silica xerogel prepared using 6 mL of acetic acid (MMS-6) were found to be 1021 m²/g and 0.72 cm³/g, respectively. The pore structure of silica xerogel products consisted of the interparticle voids between the nanoparticles aggregates, and the interconnected wormhole microporous structure. The latter pore structure was more uniform on increasing the amount of acetic acid. CO₂ adsorption/desorption measurements were carried out using TGA unit with high purity CO₂ (99.999%). The highest CO₂ sorption capacity (83.6 mg_CO2/g_sorbent) was obtained with MMS-6 product. Thermal swing adsorption studies showed that the silica xerogel products exhibited strongly reversible CO₂ adsorption capacity and stable during 5-repeated cycle of adsorption/desorption experiment at 35 °C.     

Tactile Sensing—From Humans to Humanoids

Starting from human “sense of touch,” this paper reviews the state of tactile sensing in robotics. The physiology, coding, and transferring tactile data and perceptual importance of the “sense of touch” in humans are discussed. Following this, a number of design hints derived for robotic tactile sensing are presented. Various technologies and transduction methods used to improve the touch sense capability of robots are presented. Tactile sensing, focused to fingertips and hands until past decade or so, has now been extended to whole body, even though many issues remain open. Trend and methods to develop tactile sensing arrays for various body sites are presented. Finally, various system issues that keep tactile sensing away from widespread utility are discussed.      

A modified sorting task to investigate consumer perceptions of extra virgin olive oils

A two-stage sorting task was used to probe Californian consumers’ opinions of twenty-five extra virgin olive oils based on visual assessments of the bottles. The modification of the simple sorting task aimed to encourage consumers to further discriminate products through sub-groupings of products within the groups they had already made. Sorting data were analyzed using a 3-way extension of classical multidimensional scaling called DISTATIS which explores the level of consumer agreement as well as the structure of the extra virgin olive oil products. Consumer agreements were explained by 46% of the variances found in the ways consumers sorted the bottles. Decreased amount of explained variance from the first stage to the second stage of the sorting task was observed in the olive oil product structures. Consumers were asked to describe the characteristics defining each group formed. Consumer comments were analyzed qualitatively prior to statistical analysis and were later used to understand the sorting results. Despite background differences in the usage of olive oil products, the majority of the consumers perceived the product set similarly. The two-stage sorting task allowed subjects to provide more criteria or multidimensional views of their perception of the products.     

A Survey of Artificial Cognitive Systems: Implications for the Autonomous Development of Mental Capabilities in Computational Agents

This survey presents an overview of the autonomous development of mental capabilities in computational agents. It does so based on a characterization of cognitive systems as systems which exhibit adaptive, anticipatory, and purposive goal-directed behavior. We present a broad survey of the various paradigms of cognition, addressing cognitivist (physical symbol systems) approaches, emergent systems approaches, encompassing connectionist, dynamical, and enactive systems, and also efforts to combine the two in hybrid systems. We then review several cognitive architectures drawn from these paradigms. In each of these areas, we highlight the implications and attendant problems of adopting a developmental approach, both from phylogenetic and ontogenetic points of view. We conclude with a summary of the key architectural features that systems capable of autonomous development of mental capabilities should exhibit     

Hydrogen and carbon allotrope production through methane cracking over Ni/bimodal porous silica catalyst: Effect of nickel precursor

Nickel-based catalyst is highly active for hydrogen production through methane cracking reaction at moderate reaction temperature. However, Ni catalyst is easily deactivated by carbon encapsulation. In order to solve this problem, this research studies the effect of nickel precursors—nickel acetate (NA), nickel carbonate (NC) and nickel nitrate (NN)—on the activity and stability of nickel/bimodal porous silica (Ni/BPS) catalyst in methane cracking reaction. It was found that these nickel precursor solutions had different pH values, resulting in different interactions between surface silanol groups of BPS supports and Ni. Among these catalysts, Ni(NC)/BPS catalyst exhibited high nickel dispersion and weak interaction between Ni and BPS support; it then gave the highest CH₄ conversion and better stability compared to the other catalysts. In addition, H₂ yield of Ni(NC)/BPS catalyst was 2.90 and 1.40 times higher than those of Ni(NA)/BPS and Ni(NN)/BPS catalysts, respectively. Moreover, carbon nanofibers were grown in Ni(NC)/BPS and Ni(NN)/BPS catalysts, whereas carbon nanotubes were formed on Ni(NA)/BPS catalyst, due to the different nickel particle sizes, dispersions, and Ni—BPS support interactions.     

PH sensitive structural uniformity of rice husk ash‐derived MCM‐41 silica

In this study, highly uniform MCM‐41 mesoporous silica was synthesised from rice husk ash‐derived sodium silicate using cetyltrimethyl ammonium chloride (CTAC) as a template. The chemical composition of the gel mixture was based on a SiO₂:CTAC:H₂O molar ratio of 1:0.5:75. When the pH value was initially controlled at ranges of 5.0–10.5 for 5 h and then immediately adjusted to 11.25 for the next 1 h, the structural uniformity of MCM‐41 was abruptly increased by 3.3–7.5 times of the single pH value at 11.25, respectively. Moreover, thermal and hydrothermal stabilities at 900 and 250°C were clearly observed. © 2012 Canadian Society for Chemical Engineering     

Interaction of chitosan with tetraethyl orthosilicate on the formation of silica nanoparticles: Effect of pH and chitosan concentration

In this paper, we report the interaction of chitosan with tetraethyl orthosilicate on the formation of silica nanoparticles under different conditions of pH (5–9) and chitosan concentration (0–0.001379 mM). The silica nanoparticles were characterized by means of N₂-sorption, scanning electron microscopy and transmission electron microscopy. It was found that chitosan played two different roles on the formation of silica nanoparticles, depending on the pH values. At a low chitosan concentration, chitosan promoted the formation of larger silica nanoparticles in the pH range of 5–6, while it reduced the size of silica nanoparticles when the pH value was increased to the range of 6.5–8.5. However, based on the results of N₂-sorption analysis, two different sizes of silica nanoparticles caused by the growth of silica at different phases were observed. At high chitosan concentration, the size of silica nanoparticles continuously increased in the pH range of 5–6, while only smaller size of silica nanoparticles was obtained in the pH range of 6.5–8.5. At pH 9, chitosan molecules were found to have no significant effect on the formation of silica nanoparticles even though a large amount of chitosan was employed.