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.     

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.     

Axial offset control of PWR nuclear reactor core using intelligent techniques

Improved load following capability is one of the main technical performances of advanced PWR (APWR). Controlling the nuclear reactor core during load following operation encounters some difficulties. These difficulties mainly arise from nuclear reactor core limitations in local power peaking, while the core is subject to large and sharp variation of local power density during transients. Axial offset (AO) is the parameter usually used to represent of core power peaking, in form of a practical parameter. This paper, proposes a new intelligent approach to AO control of PWR nuclear reactors core during load following operation. This method uses a neural network model of the core to predict the dynamic behavior of the core and a fuzzy critic based on the operator knowledge and experience for the purpose of decision-making during load following operations. Simulation results show that this method can use optimum control rod groups maneuver with variable overlapping and may improve the reactor load following capability.     

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.     

Highly efficient synthesis of benzopyranopyridines via ZrP2O7 nanoparticles catalyzed multicomponent reactions of salicylaldehydes with malononitrile and thiols

ZrP₂O₇ nanoparticles as an efficient catalyst have been used for the preparation of benzopyrano[2,3-b]pyridines from the four-component condensation reaction of salicylalde-hydes, thiols, and 2 equiv. of malononitrile under reflux conditions in ethanol in excellent yields and short reaction times.     

Induction time,storage capacity,and rate of methane hydrate formation in the presence of SDS and silver nanoparticles

In this communication, the kinetic parameters of methane hydrate formation (induction time, quantity and rate of gas uptake, storage capacity (SC), and apparent rate constant) in the presence of sodium dodecyl sulfate (SDS), synthetized silver nanoparticles (SNPs), and mixture of SDS?+?SNPs have been studied. Experimental measurements were performed at temperature of 273.65?K and initial pressure of 7?MPa in a 460?cm³ stirred batch reactor. Our results show that adding SDS, SNPs and their mixture increases the quantity of gas uptake, water to hydrate conversion, and SC of methane hydrate formation, noticeably. Using 300?ppm SDS increases the SC and the quantity of methane uptake 615, and 770%, respectively, compared with pure water. Investigating the hydrate growth rate at the start of hydrate formation process shows that, using SNPs, SDS, and their mixture increases the initial apparent rate constant of hydrate rate, considerably. Our results show that the system of methane?+?water?+?SDS 500?ppm?+?SNPs 45?µM represents the maximum value of initial apparent rate constant, compared with other tested systems.     

Tracking Performance of an Adaptive MIMO-OFDM LS Estimator via MSE Criterion Over a Wireless Time Correlated Fading Channel

In this contribution, the performance of an adaptive least squares channel estimator for a time correlated MIMO-OFDM channel is presented in a closed form relation. As a criterion for performance evaluation, the well-known mean square error (MSE) is employed. The pilot aided channel estimation is used to extract the channel coefficients. To minimize the MSE and arrange phase shift orthogonally, the pilots are constructed with equal power and space. The model of the channel is developed by implementing a first order Markov model, and it is proved that the performance of the system is dependent upon the defined forgetting factor, the number of transmit antennas, the number of multipath channel taps, transmitted power, and Doppler shift. The effect of each parameter on the performance of the system is discussed separately. The computer simulation results follow with the analytical results comprehensively.