Malek-Ashtar University of Technology

About: Malek-Ashtar University of Technology is a education organization based out in Tehran, Iran. It is known for research contribution in the topics: Microstructure & Coating. The organization has 2294 authors who have published 3479 publications receiving 41563 citations.

A fuzzy VIKOR method for supplier selection based on entropy measure for objective weighting

Abstract: Recently, resolving the problem of evaluation and ranking the potential suppliers has become as a key strategic factor for business firms. With the development of intelligent and automated information systems in the information era, the need for more efficient decision making methods is growing. The VIKOR method was developed to solve multiple criteria decision making (MCDM) problems with conflicting and non-commensurable criteria assuming that compromising is acceptable to resolve conflicts. On the other side objective weights based on Shannon entropy concept could be used to regulate subjective weights assigned by decision makers or even taking into account the end-users' opinions. In this paper, we treat supplier selection as a group multiple criteria decision making (GMCDM) problem and obtain decision makers' opinions in the form of linguistic terms. Then, these linguistic terms are converted to trapezoidal fuzzy numbers. We extended the VIKOR method with a mechanism to extract and deploy objective weights based on Shannon entropy concept. The final result is obtained through next steps based on factors R, S and Q. A numerical example is proposed to illustrate an application of the proposed method.

Development of high-performance A356/nano-Al2O3 composites

Abstract: In this study, a new method was used in stir casting to fabricate nano-Al2O3 particulate reinforced aluminum composites and avoid agglomeration and segregation of particles. Different volume fractions of nano-alumina particles were incorporated into the A356 aluminum alloy by a mechanical stirrer and then cylindrical specimens were cast and tested. The microstructural characterization of the composite samples showed uniform distribution of reinforcement, grain refinement of aluminum matrix, and presence of the minimal porosity. The effects of nano-Al2O3 particle content on the mechanical properties of the composites were investigated. Based on experiments, it was revealed that the presence of nano-Al2O3 reinforcement led to significant improvement in hardness, 0.2% yield strength, UTS and ductility. This combination of enhancement in UTS and ductility exhibited by nano-Al2O3 reinforced aluminum is due to uniform distribution of reinforcement and grain refinement of aluminum matrix.

Deep-Cascade: Cascading 3D Deep Neural Networks for Fast Anomaly Detection and Localization in Crowded Scenes

Abstract: This paper proposes a fast and reliable method for anomaly detection and localization in video data showing crowded scenes. Time-efficient anomaly localization is an ongoing challenge and subject of this paper. We propose a cubic-patch-based method, characterised by a cascade of classifiers, which makes use of an advanced feature-learning approach. Our cascade of classifiers has two main stages. First, a light but deep 3D auto-encoder is used for early identification of “many” normal cubic patches. This deep network operates on small cubic patches as being the first stage, before carefully resizing the remaining candidates of interest, and evaluating those at the second stage using a more complex and deeper 3D convolutional neural network (CNN). We divide the deep auto-encoder and the CNN into multiple sub-stages, which operate as cascaded classifiers. Shallow layers of the cascaded deep networks (designed as Gaussian classifiers, acting as weak single-class classifiers) detect “simple” normal patches, such as background patches and more complex normal patches, are detected at deeper layers. It is shown that the proposed novel technique (a cascade of two cascaded classifiers) performs comparable to current top-performing detection and localization methods on standard benchmarks, but outperforms those in general with respect to required computation time.