Sadjad University of Technology

Mashhad, Iran

Sadjad University of Technology

Mashhad, Iran
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Khodadoust J.,Payame Noor University | Khodadoust A.M.,Sadjad University of Technology
Expert Systems with Applications | Year: 2017

Fingerprint indexing plays a key role in the automatic fingerprint identification systems (AFISs) which allows us to speed up the search in large databases without missing accuracy. In this paper, we propose a fingerprint indexing algorithm based on novel features of minutiae triplets to improve the performance of fingerprint indexing. The minutiae triplet based feature vectors, which are generated by ellipse properties and their relation with the triangles formed by the proposed expanded Delaunay triangulation, are used to generate indices and a recovery method based on k-means clustering algorithm is employed for fast and accurate retrieval. The proposed expanded Delaunay triangulation algorithm is based on the quality of fingerprint images and combines two robust Delaunay triangulation algorithms. This paper also employs an improved k-means clustering algorithm which can be applied over large databases, without reducing the accuracy. Finally, a candidate list reduction criteria is employed to reduce the candidate list and to generate the final candidate list for matching stage. Experimental results over some of the fingerprint verification competition (FVC) and national institute of standards and technology (NIST) databases show superiority of the proposed approach in comparison with state-of-the-art indexing algorithms. Our indexing proposal is very promising for the improvement of real-time AFISs efficiency and accuracy in the near future. © 2017 Elsevier Ltd

Kahrobaee S.,Sadjad University of Technology | Hejazi T.-H.,University of Management and Technology
Journal of Magnetism and Magnetic Materials | Year: 2017

Austenitizing and tempering temperatures are the effective characteristics in heat treating process of AISI D2 tool steel. Therefore, controlling them enables the heat treatment process to be designed more accurately which results in more balanced mechanical properties. The aim of this work is to develop a multiresponse predictive model that enables finding these characteristics based on nondestructive tests by a set of parameters of the magnetic Barkhausen noise technique and hysteresis loop method. To produce various microstructural changes, identical specimens from the AISI D2 steel sheet were austenitized in the range 1025–1130 °C, for 30 min, oil-quenched and finally tempered at various temperatures between 200 °C and 650 °C. A set of nondestructive data have been gathered based on general factorial design of experiments and used for training and testing the multiple response surface model. Finally, an optimization model has been proposed to achieve minimal error prediction. Results revealed that applying Barkhausen and hysteresis loop methods, simultaneously, coupling to the multiresponse model, has a potential to be used as a reliable and accurate nondestructive tool for predicting austenitizing and tempering temperatures (which, in turn, led to characterizing the microstructural changes) of the parts with unknown heat treating conditions. © 2017 Elsevier B.V.

Khodadoust J.,Payame Noor University | Khodadoust A.M.,Sadjad University of Technology
Pattern Recognition | Year: 2017

Fingerprint identification is an important issue for identifying fingerprints and plays a key role in the fingerprint recognition systems. However, performing a fingerprint identification over a large database can be an inefficient task due to the lack of scalability and high computing times of fingerprint matching algorithms. Fingerprint indexing is a key strategy in automatic fingerprint identification systems (AFISs) which allows us to reduce the number of candidates, the search space, and the occurrences of false acceptance in large databases. In this paper, an efficient indexing algorithm is proposed using minutiae pairs and convex core point which employs k-means clustering and candidate list reduction criteria to improve the identification performance. Our proposal can effectively reduce the search space and number of candidates for fingerprint matching, and thus achieves higher efficiency and significantly improves the system retrieval performance. Experimental results over some of the fingerprint verification competition (FVC) and the national institute of standards and technology (NIST) databases prove the superiority of the proposed approach against some of the well known indexing algorithms. © 2017 Elsevier Ltd

Del Hougne P.,Laue Langevin Institute | Rajaei B.,Laue Langevin Institute | Rajaei B.,Sadjad University of Technology | Daudet L.,Laue Langevin Institute | And 2 more authors.
Optics Express | Year: 2016

Transmission matrices (TMs) have become a powerful and widely used tool to describe and control wave propagation in complex media. In certain scenarios the TM is partially uncontrollable, complicating its identification and use. In standard optical wavefront shaping experiments, uncontrollable reflections or imperfect illumination may be the cause; in reverberating cavities, uncontrollable reflections off the walls have that effect. Here we employ phase retrieval techniques to identify such a partially uncontrollable TM solely based on random intensity-only reference measurements. We demonstrate the feasibility of our method by focusing both on a single target as well as on multiple targets in a microwave cavity, using a phase-binary Spatial-Microwave-Modulator. © 2016 Optical Society of America.

Mohammadalizadeh S.,Sadjad University of Technology | Ghayeni M.,Sadjad University of Technology
4th Iranian Conference on Renewable Energy and Distributed Generation, ICREDG 2016 | Year: 2017

Selective harmonic elimination technology (SHE) has been widely used in many medium and high power converters. Because of low switching frequency privilege in this method, low power loss is expected in the converters. Although it is still a crucial work to find optimum switching angle from a group of nonlinear transcendental equations, especially in multilevel inverters with multiple switching in each level. In this article a new strategy based on selective harmonic elimination concept (SHE-PWM) in photovoltaic multilevel inverter with multiple switching is proposed. In the proposed strategy, the solving process is divided into two steps to ease the complexity of the problem and increase the degree of control in each step. It also offers an exclusive searching scheme which makes convergence easily achievable. Simulation's result also proves validity of the proposed strategy for a wide range of modulation indices. © 2016 IEEE.

Hamidzadeh J.,Sadjad University of Technology | Sadeghi R.,Imam Reza University | Namaei N.,Sadjad University of Technology
Applied Soft Computing Journal | Year: 2017

Support Vector Data Description (SVDD) is a support vector based learning algorithm for anomaly detection. In this method, the target is to form a boundary around the normalcy data by building a hyper-sphere. To gain noticeable accuracy, a control parameter is used to regulate the hyper-sphere volume. The value of this parameter depends on the data characteristics. Thus, there is no proper way to estimate it. On the other hand, the number of free parameters increases in the more improved versions of SVDD. In this paper, an evolutionary algorithm, Chaotic Bat Algorithm, is used with the aim of designing effective description of data. The proposed method, weighted SVDD based on Chaotic Bat Algorithm (WSVDD-CBA) is constructed based on a new weight and ergodicity of chaotic functions and automatic switching between global and local searches of Bat Algorithm (BA). To evaluate this method several experiments have been conducted based on 10-fold cross-validation over some data sets from UCI repository. Experimental results show the superiority of the proposed algorithm to state-of-the-art methods in the terms of classification accuracy, precision and recall rate measures. © 2017 Elsevier B.V.

Kashefi M.,Ferdowsi University of Mashhad | Kahrobaee S.,Sadjad University of Technology
Journal of Alloys and Compounds | Year: 2017

Assessment of martensite start temperature in alloy steels is of critical importance for many industrial applications of hardenable steels. To determine this, dilatometry is often carried out by characterizing the volume changes in the sample during cooling from austenitizing temperature. The method is destructive and costly, thus, is not normally utilized in quality inspection of the heat treated parts in the related industries. In the present paper, an electromagnetic technique has been explored to be utilized as a nondestructive tool to determine martensite start temperature in AISI D2 cold work tool steels. To detect the temperature, a designed NDE method was applied during cooling (from about 450 °C to 25 °C) of the samples austenitized in a range of 1000–1150 °C. The results show an acceptable accuracy of the proposed nondestructive method in comparison to the dilatometry as well as Andrew empirical equation in determining Ms temperatures. © 2017 Elsevier B.V.

Sadeghi R.,Imam Reza University | Hamidzadeh J.,Sadjad University of Technology
Soft Computing | Year: 2016

Event handlers have wide range of applications such as medical assistant systems and fire suppression systems. These systems try to provide accurate responses based on the least information. Support vector data description (SVDD) is one of the appropriate tools for such detections, which should handle lack of information. Therefore, many efforts have been done to improve SVDD. Unfortunately, the existing descriptors suffer from weak data characteristic in sparse data sets and their tuning parameters are organized improperly. These issues cause reduction of accuracy in event handlers when they are faced with data shortage. Therefore, we propose automatic support vector data description (ASVDD) based on both validation degree, which is originated from fuzzy rough set to discover data characteristic, and assigning effective values for tuning parameters by chaotic bat algorithm. To evaluate the performance of ASVDD, several experiments have been conducted on various data sets of UCI repository. The experimental results demonstrate superiority of the proposed method over state-of-the-art ones in terms of classification accuracy and AUC. In order to prove meaningful distinction between the accuracy results of the proposed method and the leading-edge ones, the Wilcoxon statistical test has been conducted. © 2016 Springer-Verlag Berlin Heidelberg

Assadi M.T.,Sadjad University of Technology | Bagheri M.,Sadjad University of Technology
European Journal of Industrial Engineering | Year: 2016

Cross docking is a new strategy in logistics mainly consisting of unloading products from inbound trucks, resorting and loading directly into outbound trucks with minimum possible transitional storages. In this paper, we study the truck scheduling problem in a cross docking terminal with multiple receiving and shipping dock doors. The objective is to find the best door assignments, the docking sequences of both inbound and outbound trucks and also product assignments to trucks to minimise the weighted number of tardy trucks, when the ready times for inbound trucks, and different distances between the inbound and outbound doors are considered. The problem is formulated as a mixed-integer linear programming (MILP) model and since the optimisation problem is NP-hard, we suggest simulated annealing and genetic algorithms to solve the model. To evaluate the performance of meta-heuristics, we benefit from numerous different problem instances in the literature and compared the results to a pure random search algorithm and also to GAMS software results for the MILP model. Copyright © 2016 Inderscience Enterprises Ltd.

Assadi M.T.,Sadjad University of Technology | Bagheri M.,Sadjad University of Technology
Computers and Industrial Engineering | Year: 2016

Scheduling of inbound and outbound trucks, which decides on the succession of truck processing at the receiving and shipping dock, is particularly significant to ensure a rapid turnover and on-time deliveries. In this paper, we adopt Just-In-Time (JIT) philosophy in truck scheduling problem, where the interchangeability of products, ready times for both inbound and outbound trucks and also different transshipment time between receiving and shipping doors are considered. The objective is to minimize total earliness and tardiness for outbound trucks, in such systems. A mixed integer programming model is developed to formulate the problem and is solved optimally in small-sized instances with ILOG CPLEX solver. Also to solve medium to large-sized cases, two meta-heuristics called Differential evolution and Population-based simulated annealing are employed. The meta-heuristics are tuned by the response surface methodology. Finally, the performances of the meta-heuristics are compared with CPLEX solver in small-sizes instances, and also to each other and Pure Random search in medium to large-sized problems. The computational results demonstrates the efficiency of our meta-heuristics. © 2016 Elsevier Ltd. All rights reserved.

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