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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


Abedi M.,Sadjad University of Technology | Pourmohammad A.,Amirkabir University of Technology
Proceedings - 2015 11th International Conference on Innovations in Information Technology, IIT 2015 | Year: 2015

The super-gaussian non-stationary audio noises could not be well represented, feature extracted, and then classified in the stationary and linear transform domains as DFT (Discrete Fourier Transform) or STFT (Short Time Fourier Transform) especially in the very low SNR (Signal-to-Noise Ratio) data capturing times. DWT (Discrete Wavelet Transform) is the most commonly used and conventional transform for representing, feature extracting, and then classifying of such signals using data independent kernels. But the simulations confirm that the sparse representation transforms could well represent them than DWT because of using data dependent kernels (Atoms). In this paper it is investigated using MP-TFD (Matching-Pursuit Time-Frequency Decomposition) technique for the super-gaussian non-stationary audio noises representing, then applying NMF (non-Negative Matrix Decomposition) technique for decomposing of the TFM (Time-Frequency Matrix) into its significant components, and finally extracting MFCCs (Mel-Frequency Cepstral Coefficients) as the features in order to the sources classifying. © 2015 IEEE.


Azimi-Roein M.,Islamic Azad University | Golmakani A.,Sadjad University of Technology
Journal of Engineering and Applied Sciences | Year: 2016

Two ultra wideband Low Noise Amplifiers (LNAs) are presented. A common source topology is adopted for input stage to achieve wideband input matching while a cascode stage is used as the second stage to provide power gain at high frequencies. The first work is a LNA with resistive shunt feedback. It achieves a maximum power gain of 10.5 dB, a bandwidth of 10 GHz and 8.7 dB minimum noise fiqure. The power consumption is 14.28 mW from a 1.8 V supply. The second work is common source with a reuse pmos current source. © Medwell Journals, 2016.


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.


Hamidzadeh J.,Sadjad University of Technology | Monsefi R.,Ferdowsi University of Mashhad | Sadoghi Yazdi H.,Ferdowsi University of Mashhad
Pattern Recognition | Year: 2015

In instance-based classifiers, there is a need for storing a large number of samples as training set. In this work, we propose an instance reduction method based on hyperrectangle clustering, called Instance Reduction Algorithm using Hyperrectangle Clustering (IRAHC). IRAHC removes non-border (interior) instances and keeps border and near border ones. This paper presents an instance reduction process based on hyperrectangle clustering. A hyperrectangle is an n-dimensional rectangle with axes aligned sides, which is defined by min and max points and a corresponding distance function. The min-max points are determined by using the hyperrectangle clustering algorithm. Instance-based learning algorithms are often confronted with the problem of deciding which instances must be stored to be used during an actual test. Storing too many instances can result in a large memory requirements and a slow execution speed. In IRAHC, core of instance reduction process is based on set of hyperrectangles. The performance has been evaluated on real world data sets from UCI repository by the 10-fold cross-validation method. The results of the experiments have been compared with state-of-the-art methods, which show superiority of the proposed method in terms of classification accuracy and reduction percentage. © 2014 Elsevier Ltd. All rights reserved.


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


Samadi R.,Islamic Azad University at Ferdows | Hamidzadeh J.,Sadjad University of Technology
2014 International Congress on Technology, Communication and Knowledge, ICTCK 2014 | Year: 2015

The dynamic economic dispatch (DED) problem is an extension of the conventional static load dispatch problem in the context of electrical power generation. In this paper, issues related to the implementation of the several soft computing techniques are highlighted for a successful application to solve dynamic economic dispatch (DED) problem, which is a constrained optimization problem in power systems. First of all, a survey covering the basics of the techniques is presented and then implementation of the techniques in the DED problem is discussed. The soft computing techniques, namely multi-layered perceptron neural network (MLP NN), genetic algorithm (GA), Imperialist Competitive Algorithm(ICA), particle swarm (PSO) and are applied to solve the DED problem. The Evolutionary Algorithms are tested on power system consisting 3 generating units and the results are compared together. Suggestion is presented to improve techniques. © 2014 IEEE.


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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