A hierarchical fuzzy system for modeling driver’s behavior

The study of human behavior during driving is of primary importance for improving the driver??s security. In this study, we propose a hierarchical driver_vehicle_environment fuzzy system to analyze driver??s behavior under stress conditions on a road. We include climate, road and car conditions in fuzzy modeling. For obtaining fuzzy rules, experts?? opinions are benefited by means of questionnaires on effects of parameters such as climate, road and car conditions on driving capabilities. The number of fuzzy rules is optimized by Particle Swarm Optimization (PSO) algorithm. Also the frequency of pressing on brake and gas pedals and the number of car??s direction changes are used to determine the driver??s behavior under different conditions. Three different positions are considered for driving and decision making; one position in driving lane and two positions in opposite lane. A fuzzy model called Model 1 is presented for modeling the change of steering angle and speed control by considering time distances with existing cars in these three positions, the information about the speed and direction of car, and the steering angle of car. The behaviors of different drivers under two stress conditions are investigated. Also we obtained two other models based on fuzzy rules called Model 2 and Model 3 by using Sugeno fuzzy inference. Model 2 has two linguistic terms and Model 3 has four linguistic terms for estimating the time distances with other cars. The results of three models are compared. The comparative studies have shown that simulation results are in good agreement with the real world situations.     

A comparative study of two approaches for supporting optimal network location queries

Given a set S of sites and a set O of weighted objects, an optimal location query finds the location(s) where introducing a new site maximizes the total weight of the objects that are closer to the new site than to any other site. With such a query, for instance, a franchise corporation (e.g., McDonald’s) can find a location to open a new store such that the number of potential store customers (i.e., people living close to the store) is maximized. Optimal location queries are computationally complex to compute and require efficient solutions that scale with large datasets. Previously, two specific approaches have been proposed for efficient computation of optimal location queries. However, they both assume p-norm distance (namely, L₁ and L₂/Euclidean); hence, they are not applicable where sites and objects are located on spatial networks. In this article, we focus on optimal network location (ONL) queries, i.e., optimal location queries in which objects and sites reside on a spatial network. We introduce two complementary approaches, namely EONL (short for Expansion-based ONL) and BONL (short for Bound-based ONL), which enable efficient computation of ONL queries with datasets of uniform and skewed distributions, respectively. Moreover, with an extensive experimental study we verify and compare the efficiency of our proposed approaches with real world datasets, and we demonstrate the importance of considering network distance (rather than p-norm distance) with ONL queries.     

Physics-guided machine learning from simulated data with different physical parameters

Knowledge and Information Systems - Physics-based models are widely used to study dynamical systems in a variety of scientific and engineering problems. However, these models are necessarily...     

Investigation of Hydrodynamics of High‐Temperature Fluidized Beds by Pressure Fluctuations

Hydrodynamics of a gas‐solid fluidized bed at elevated temperatures was investigated by analyzing pressure fluctuations in time and frequency domains. Sand particles were fluidized with air at various bed temperatures. At a constant gas velocity, the standard deviation, power spectrum density function, and wide‐band energy of pressure fluctuations reach a maximum at 300 °C. Increasing the temperature to this value causes larger bubble sizes and after the bubbles reach their maximum size, they break into smaller bubbles. The Archimedes number decreases with higher temperature and the type of fluidization becomes closer to that of Geldart A boundary at this maximum temperature. Based on estimation of the drag force acting on the emulsion phase, it was concluded that 300 °C was a transition temperature at which the drag force reaches a minimum due to a significant change of interparticle and hydrodynamic forces.     

A benzoate coprecipitation route for synthesizing nanocrystalline GDC powder with lowered sintering temperature

Electrolyte powders with low sintering temperature and high-ionic conductivity can considerably facilitate the fabrication and performance of solid oxide fuel cells (SOFCs). Gadolinia-doped ceria (GDC) is a promising electrolyte for developing intermediate- and low-temperature (IT and LT) SOFCs. However, the conventional sintering temperature for GDC is usually above 1200 °C unless additives are used. In this work, a nanocrystalline powder of GDC, (10 mol% Gd dopant, Gd_0.1Ce_0.9O_1.95) with low-sintering temperature has been synthesized using ammonium benzoate as a novel, environmentally friendly and cost-effective precursor/precipitant. The synthesized benzoate powders (termed washed- and non-washed samples) were calcined at a relatively low temperature of 500 °C for 6 h. Physicochemical characteristics were determined using thermal analysis (TG/DTA), Raman spectroscopy, FT-IR, SEM/EDX, XRD, nitrogen absorptiometry, and dilatometry. Dilatometry showed that the newly synthesized GDC samples (washed and non-washed routes) start to shrink at temperatures of 500 and 600 °C (respectively), reaching their maximum sintering rate at 650 and 750 °C. Sintering of pelletized electrolyte substrates at the sintering onset temperature for commercial GDC powder (950 °C) for 6 h, showed densification of washed- and non-washed samples, obtaining 97.48 and 98.43% respectively, relative to theoretical density. The electrochemical impedance spectroscopy (EIS) analysis for the electrolyte pellets sintered at 950 °C showed a total electrical conductivity of 3.83 × 10^?2 and 5.90 × 10^?2 S cm^?1 (under air atmosphere at 750 °C) for washed- and non-washed samples, respectively. This is the first report of a GDC synthesis, where a considerable improvement in sinterability and electrical conductivity of the product GDC is observed at 950 °C without additives addition.     

Effect of fuel type on structural and physicochemical properties of solution combustion synthesized CoCr2O4 ceramic pigment nanoparticles

Due to importance and wide applications, CoCr₂O₄ ceramic pigment nanoparticles were synthesized via low-temperature solution combustion route by different fuels including ethylenediamine/oxalic acid, ethylenediamine/citric acid, oxalic acid/citric acid and ethylenediamine/oxalic acid/citric acid. Physicochemical properties of the synthesized samples were determined by different techniques such as fourier transform infrared spectroscopy (FT-IR), X-ray diffraction (XRD), field emission scanning electron microscopy (FE-SEM) and energy dispersive X-ray spectroscopy (EDX) and color/optical properties were evaluated based on CIELAB system by spectrophotometer. Moreover, thermodynamic considerations of combustion reactions for CoCr₂O₄ nanopigments formation in terms of calculated adiabatic flame temperature and enthalpy of combustion reaction were studied. The Comparison of results and data showed that cobalt chromite pigment nanoparticles synthesized by using ethylenediamine/citric acid and ethylenediamine/oxalic acid/citric acid fuels exhibited higher purity, smaller crystallite size and lower degree agglomeration.     

Laccase‐catalyzed synthesis of polypyrrole‐multiwalled carbon nanotube composites as energy storage materials for capacitors

Multiwalled carbon nanotubes (MWNTs) were coated with polypyrrole (PPy) using in situ enzymatic polymerization of pyrrole catalyzed by a laccase (benzenediol:oxygen oxidoreductase, EC 1.10.3.2) from Trametes versicolor. Transmission electron microscopy revealed that the MWNTs were uniformly coated with very thin layers of PPy without any indication of globular polymer aggregate formations. The enzymatic synthesis of the MWNTs/PPy composites was quite simple being performed in a one‐pot aqueous solution (pH 4.0) under mild reaction conditions. The potential of the composites with respect to the development of energy storage devices was demonstrated by fabricating a two‐electrode coin cell capacitor (diameter 20 mm, thickness 1.6 mm) utilizing the composites as electrode materials. The capacitance of the cell was 28.0 F g^?1 for the electrode material as measured by a galvanostatic charge–discharge method. The energy density and power density were 2.55 and 805 W kg^?1, respectively, which were close to those of the capacitors classified as ultracapacitors. © 2015 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2016 , 133, 43307.