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 semantic image classifier based on hierarchical fuzzy association rule mining

One of the major challenges in the content-based information retrieval and machine learning techniques is to-build-the-so-called “semantic classifier” which is able to effectively and efficiently classify semantic concepts in a large database. This paper dealt with semantic image classification based on hierarchical Fuzzy Association Rules (FARs) mining in the image database. Intuitively, an association rule is a unique and significant combination of image features and a semantic concept, which determines the degree of correlation between features and concept. The main idea behind this approach is that any image visual concept has some associated features, so that, there are strong correlations between the concepts and their corresponding features. Regardless of the semantic gap, an image concept appears when the corresponding features emerge in an image and vice versa. Specially, this paper’s contribution was to propose a novel Fuzzy Association Rule for improving traditional association rules. Moreover, it was concerned with establishing a hierarchical fuzzy rule base in the training phase and setup corresponding fuzzy inference engine in order to classify images in the testing phase. The presented approach was independent from image segmentation and can be applied on multi-label images. Experimental results on a database of 6000 general-purpose images demonstrated the superiority of the proposed algorithm.     

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.     

Hamshahri: A standard Persian text collection

The Persian language is one of the dominant languages in the Middle East, so there are significant amount of Persian documents available on the Web. Due to the different nature of the Persian language compared to the other languages such as English, the design of information retrieval systems in Persian requires special considerations. However, there are relatively few studies on retrieval of Persian documents in the literature and one of the main reasons is the lack of a standard test collection. In this paper, we introduce a standard Persian text collection, named Hamshahri, which is built from a large number of newspaper articles according to TREC specifications. Furthermore, statistical information about documents, queries and their relevance judgments are presented in this paper. We believe that this collection is the largest Persian text collection, so far.     

Measurement of the amount and rate of methane dissolution in pure water and aqueous solution of SDS + multi-wall carbon nanotubes + β-cyclodextrin

In this paper, the impact of the mixture of sodium dodecyl sulfate (SDS) + multi-wall carbon nanotubes (MWCNTs) + β-cyclodextrin on the quantity and initial rate of methane dissolved in water is investigated. The experiments were performed at a temperature range of 278.15–303.15 K and an initial pressure of 0.5 MPa. The experimental results show that simultaneous utilization of β-cyclodextrin (0.01 mass fraction), MWCNTs (0.0005 mass fraction), and SDS (0.001 mass fraction) at 278.15 K increases the amount and the rate of methane dissolution in water by 29.90% and 173.78%, respectively, compared to pure water. An increase in the temperature decreases the quantity and the initial rate of methane dissolution in all solutions containing additives. However, no consistent relationship is observed between the temperature and the enhancement percentage of solubility of methane in solutions containing additives.     

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.