Attention control with reinforcement learning for face recognition under partial occlusion

In this paper a new method for handling occlusion in face recognition is presented. In this method the faces are partitioned into blocks and a sequential recognition structure is developed. Then, a spatial attention control strategy over the blocks is learned using reinforcement learning. The outcome of this learning is a sorted list of blocks according to their average importance in the face recognition task. In the recall mode, the sorted blocks are employed sequentially until a confident decision is made. Obtained results of various experiments on the AR face database demonstrate the superior performance of proposed method as compared with that of the holistic approach in the recognition of occluded faces.     

A Novel Model for the Time Dependent Competitive Vehicle Routing Problem: Modified Random Topology Particle Swarm Optimization

This paper presents a novel model for a time dependent vehicle routing problem when there is a competition between distribution companies for obtaining more sales. In a real-world situation many factors cause the time dependency of travel times, for example traffic condition on peak hours plays an essential role in outcomes of the planned schedule in urban areas. This problem is named as “Time dependent competitive vehicle routing problem” (TDVRPC) which a model is presented to satisfy the “non-passing” property. The main objectives are to minimize the travel cost and maximize the sale in order to serve customers before other rival distributors. To solve the problem, a Modified Random Topology Particle Swarm Optimization algorithm (RT-PSO) is proposed and the results are compared with branch and bound algorithm in small size problems. In large scales, comparison is done with original PSO. The results show the capability of the proposed RT-PSO method for handling this problem.     

Nano levels detection of beryllium by a novel beryllium PVC-based membrane sensor based on 2,3,5,6,8,9-hexahydro-1,4,7,10-benzotetra oxacyclododecine-12-carbaldehyde-12-(2,4-dinitrophenyl)hy

A novel poly(vinyl chloride)-based 2,3,5,6,8,9-hexahydro-1,4,7,10-benzotetra oxacyclododecine-12-carbaldehyde-12-(2,4-dinitrophenyl)hy (PBC) with sodium tetraphenyl borate (NaTPB) as an anion excluder, benzyl acetate (BA), acetophenon (AP) and o-nitrophenyloctyl ether (NPOE) as plasticizing solvent mediators was prepared and investigated as a beryllium selective sensor. The best performance was observed with the membrane having the PVC–NaTPB–NPOE–PBC composition 30%:3%:62%:5%, which worked well over a very wide concentration range (1.0×10⁻⁷ M to 1.0×10⁻¹ M). The sensor exhibits a Nernstian slope of 29.9 mV per decade of Be²⁺ activity. The detection limit of the sensor is 7.0×10⁻⁸ M (630 ppt). The proposed electrode shows excellent discriminating ability toward Be²⁺ ion with regard to alkali, alkaline earth, transition and heavy metal ions. It was successfully applied to the determination of beryllium in a mineral sample.     

Clearance angle and evolution of depth of cut in actuated disc cutting

This paper investigates the effect of cutter clearance angle on variation of depth of cut and cutting process with an actuated disc cutting(ADC). ADC is a cyclic cutting method with two main characteristics:(i) a disk-shape cutter is used to attack the rock in an undercutting mechanism; and(ii) the cutter is dynamically actuated as it is moved across the rock. Hence, the cutting process of such system is periodic, each recurrence known as actuation cycle. The first ADC model, developed in 2016, represented an idealization of the technology with a flat disc cutter, where no clearance angle was considered. The evolution of the contact between the disc and the rock was, therefore, computed only on horizontal x-y plane, ignoring the effect of normal component of the force acting on cartridge. This article reports on a study that incorporates the cutter inclination angle in derivation of cutter/rock interface laws. It extends the proposed kinematic and geometry based model to take into account the variable depth of cut in estimating the forces associated with cutting in one actuation cycle. Experiments were conducted using Wobble to test the predictions of the improved model at various operating conditions. The model predictions are matched with the experimental results and effects of various factors are analysed.     

Highly efficient supported AlCl3-based cationic catalysts to produce polyα-olefin oil base stocks

The main aim of this research is to decrease the amount of AlCl₃ content that is very corrosive and hazardous in the catalytic system, required for the α-olefin oligomerization without substantial change of final oil features. This was successfully achieved by supporting AlCl₃ on different carriers. More precisely, a series of supported bimetallic catalysts was synthesized by immobilization of AlCl₃ and TiCl₄ onto Al₂O₃, SiO₂, and mixed supports, that is, Al₂O₃/FeCl₃ and SiO₂/FeCl₃. It was found that silica and alumina-based catalysts had higher catalytic activities compared to support free AlCl₃; however, this enhancement for silica-based supports was more significant. According to gel permeation chromatography (GPC) results, the use of single supports, that is, Al₂O₃ and SiO₂, increased oligomer's molecular weight, while the application of mixed supports resulted in the decrease of molecular weight of the oligomers. Viscosity characteristics of the synthesized oligomers have also been studied at two different temperatures of 40 and 100°C (KV⁴⁰ and KV¹⁰⁰). The viscosity index (VI) values, derived from KV⁴⁰ and KV¹⁰⁰, of the prepared oligomers were in the range of 126–145. The molecular weight and termination mechanisms of the oligomers were studied by ¹H-NMR spectroscopy. The obtained results disclosed that the employed reaction conditions led to the production of oligomer chains with various structures including vinylidene (Vd), and di and three-substituted vinylene (2Vn, 3Vn) structures.     

Simultaneous Spectrophotometric Determination of 2-Thiouracil and 2-Mercaptobenzimidazole in Animal Tissue Using Multivariate Calibration Methods: Concerns and Rapid Methods for Detection

ABSTRACT: Two multivariate calibration methods, partial least squares (PLS) and principal component regression (PCR), were applied to the spectrophotometric simultaneous determination of 2-mercaptobenzimidazole (MB) and 2-thiouracil (TU). A genetic algorithm (GA) using partial least squares was successfully utilized as a variable selection method. The concentration model was based on the absorption spectra in the range of 200 to 350 nm for 25 different mixtures of MB and TU. The calibration curve was linear across the concentration range of 1 to 10 μg mL⁻¹ and 1.5 to 15 μg mL⁻¹ for MB and TU, respectively. The values of the root mean squares error of prediction (RMSEP) were 0.3984, 0.1066, and 0.0713 for MB and 0.2010, 0.1667, and 0.1115 for TU, which were obtained using PCR, PLS, and GA-PLS, respectively. Finally, the practical applicability of the GA-PLS method was effectively evaluated by the concurrent detection of both analytes in animal tissues. It should also be mentioned that the proposed method is a simple and rapid way that requires no preliminary separation steps and can be used easily for the analysis of these compounds, especially in quality control laboratories.     

Permeability prediction and construction of 3D geological model: application of neural networks and stochastic approaches in an Iranian gas reservoir

Determination of petrophysical parameters by using available data has a specific importance in exploration and production studies for oil and gas industries. Modeling of corrected permeability as a petrophysical parameter can help in decision making processes. The objective of this study is to construct a comprehensive and quantitative characterization of a carbonate gas reservoir in marine gas field. Artificial neural network is applied for prediction of permeability in accordance with other petrophysical parameters at well location. Correlation coefficient for this method is 84 %. In the study, the geological reservoir model is developed in two steps: First, the structure skeleton of the field is constructed, and then, reservoir property is distributed within it by applying new stochastic methods. Permeability is modeled by three techniques: kriging, sequential Gaussian simulation (SGS) and collocated co-simulation using modeled effective porosity as 3D secondary variable. This paper enhances the characterization of the reservoir by improving the modeling of permeability through a new algorithm called collocated co-simulation. Kriging is very simple in modeling the reservoir permeability, and also, original distribution of the data changes considerably in this model. In addition, the SGS model is noisy and heterogeneous, but it retains the original distribution of the data. However, the addition of a 3D secondary variable in third method resulted in a much more reliable model of permeability.     

Asymptotically optimum detection of primary user in cognitive radio networks

Traditionally, the frequency spectrum is licensed to users by government agencies in a fixed manner where the licensee has exclusive right to access the allocated band. However, with increasing demand for the spectrum and scarcity of vacant bands, a spectrum policy reform seems inevitable. Meanwhile, recent measurements suggest the possibility of sharing spectrum among different parties subject to interference-protection constraints. In order to enable access to an unused licensed spectrum, a secondary user has to monitor licensed bands and opportunistically transmit whenever no primary signal is detected. Spectrum-sharing between a primary licensee and a group of secondary users has been studied. The structure of an asymptotically optimum detector based on the measurements of all secondary users is derived and the effect of the quantisation error in such a system is evaluated. Also, it is shown that by using the proposed detector in a sequential detection structure, it is possible to shorten the decision time needed by the detector. The results show the superiority of the proposed detector to other schemes.