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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

Ravanshad N.,Sadjad University of Technology | Rezaee-Dehsorkh H.,Sadjad University of Technology | Lotfi R.,Ferdowsi University of Mashhad
Midwest Symposium on Circuits and Systems | Year: 2017

A fully-synchronous offset-insensitive structure is proposed for implementing level-crossing analog-To-digital converters (LC-ADCs). The proposed structure is designed and implemented for high-precision compressed electrocardiogram (ECG) monitoring applications. Synchronous implementation leads to less implementation complexity compared to the conventional asynchronous implementations. Also the major source of error, viz. the difference in comparators offsets is eliminated which additionally leads to considerable saving in silicon area. Designing and simulating in a 0.18 μm CMOS technology, the LC-ADC achieves an ENOB of 8.45 bits and occupies 0.038 mm2 silicon area. The average sampling rate is about 120 S/s when applied to the whole MIT/BIH arrhythmia database. Simulation results show a power consumption of 81 nW with a 1.8 V supply voltage, by testing the ADC using Tape 100 of the MIT/BIH arrhythmia database. © 2016 IEEE.

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

Ebrahimi M.,Sadjad University of Technology | Hasanpour S.,Sadjad University of Technology
2014 International Congress on Technology, Communication and Knowledge, ICTCK 2014 | Year: 2015

The reduction of greenhouse gases (GHGs) emission has become the greatest environmental concern worldwide. This paper analyses the operation of power system which is the major contributor of carbon emission, considering emission market, electricity market and renewable energy policies such as use of wind unit. Each generator is allocated certain amount of emission allowances, which they can use to cover emission during energy generation. Emission allowances are allocated to power producers based on their power outputs and previous levels of emission. In this paper two main policies to reduce greenhouse gases, emission quota trade and renewable energy policy are considered. Weibull probability density function is applied to wind power output probabilities. The strategic model is used to analyze the game between Gencos. The performance of the model has been demonstrated by applying it on a 6 generating units system. © 2014 IEEE.

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.

Rasouli O.,Sadjad University of Technology | Zarei M.H.,Technical University of Madrid
Total Quality Management and Business Excellence | Year: 2015

Patients' dissatisfaction with hospital services is a major indicator for the assessment of healthcare quality. This paper proposes an innovative framework to measure and decrease patient dissatisfaction with hospital services. First, a validated and verified SERVQUAL-based questionnaire is proposed to be distributed among patients. Then, according to the collected data, the level of dissatisfaction is monitored by deploying a p-chart and a Demerit chart. Finally, in order to identify long-term improvement opportunities, an improvement index and Pareto chart have been exploited. The usefulness of the proposed framework is illustrated by the application on a case study in a public hospital of Iran. The results revealed that both the Demerit chart and p-chart are quite competent in monitoring patients’ dissatisfaction and alarming out-of-control situations. In the studied hospital, food service was found to be the critical challenge that required both immediate and long-term improvements. Nurses’ criteria should receive immediate improvement while long-term efforts should be devoted to hospital environment and facilities. © 2015 Taylor & Francis

Bagheri M.,Sadjad University of Technology | Gholinejad Devin A.,Sadjad University of Technology | Izanloo A.,Razavi Hospital
Computers and Industrial Engineering | Year: 2016

Given its complexity and relevance in healthcare, the well-known Nurse Scheduling Problem (NSP) has been the subject of several researches and different approaches have been used for its solution. The importance of this problem comes from its critical role in healthcare processes as NSP assigns nurses to daily shifts while respecting both the preferences of the nurses and the objectives of hospital. Most models in NSP literature have dealt with this problem in a deterministic environment, while in the real-world applications of NSP, the vagueness of information about management objectives and nurse preferences are sources of uncertainties that need to be managed so as to provide a qualified schedule. In this study, we propose a stochastic optimization model for the Department of Heart Surgery in Razavi Hospital, which accounts for uncertainties in the demand and stay period of patients over time. Sample Average Approximation (SAA) method is used to obtain an optimal schedule for minimizing the regular and overtime assignment costs, with the numerical experiments demonstrating the convergence of statistical bounds and moderate sample size for a given numerical experiment. The results confirm the validity of the model. © 2016 Elsevier Ltd. All rights reserved.

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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