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Azadeh A.,University of Tehran | Saberi M.,Islamic Azad University at Tafresh | Rouzbahman M.,University of Toronto | Saberi Z.,Amirkabir University of Technology
Journal of Loss Prevention in the Process Industries | Year: 2013

This study presents an intelligent algorithm based on Adaptive Neuro-Fuzzy Inference System (ANFIS) and statistical methods for measuring job stress in noisy and complex petrochemical plants. Job stress is evaluated against health, safety, environment and ergonomics (HSEE) program in the integrated algorithm. The algorithm is composed of seventeen distinct steps. To achieve the objectives of this study, standard questionnaires with respect to HSEE are completed by operators. The average results for each category of HSEE are used as inputs and job stress is used as output for the algorithm. Moreover, operators' stress level with respect to HSEE is evaluated by the algorithm. Finally, operators with weak stress level are identified. The advantage and superiority of the intelligent algorithm are shown by error analysis in contrast with conventional regression approaches. This is the first study that introduces an integrated intelligent algorithm for assessment and improvement of job stress and HSEE in noisy, complex and uncertain environment. © 2012 Elsevier Ltd. Source

Kaveh A.,Iran University of Science and Technology | Rahami H.,University of Tehran | Mehanpour H.,Islamic Azad University at Tafresh
Advances in Engineering Software | Year: 2012

The aim of this paper is two fold. In the first part, static analysis of structures with repeated patterns is presented. These structures are comprised of submodels each having different repeated pattern. As an example, considering a structure with two different repeated patterns, the nodal numbering is performed in such a manner that the resulting stiffness matrix of the structure contains two block diagonal matrices. Thus their inversion can easily be performed using regular matrices requiring smaller amount of computational time. In the second part, the modal analysis, free vibration and eigen-frequencies of such structures are studied. Here as well the stiffness and mass matrices are transformed into two block matrices forms and using dynamic condensation and the matrix inversion which is involved in this condensation, the eigensolution is performed on matrices of lower dimensions. The presented examples consist of 2D and 3D structures in which in some stories the stiffnesses are changed due to the addition of some members taking the structures out of regularity. Apart from these, the power transition towers often having additional bracings in some levels are investigated. Other applications correspond to calculating the buckling loads and natural frequencies of regular plates driven to irregular forms by having different support conditions and some added parts. © 2012 Elsevier Ltd. All rights reserved. Source

Azadeh A.,University of Tehran | Asadzadeh S.M.,University of Tehran | Saberi M.,Islamic Azad University at Tafresh | Nadimi V.,Tafresh University | And 2 more authors.
Applied Energy | Year: 2011

This paper presents an adaptive network-based fuzzy inference system-stochastic frontier analysis (ANFIS-SFA) approach for long-term natural gas (NG) consumption prediction and analysis of the behavior of NG consumption. The proposed models consist of input variables of Gross Domestic Product (GDP) and population (POP). Six distinct models based on different inputs are defined. All of trained ANFIS are then compared with respect to mean absolute percentage error (MAPE). To meet the best performance of the intelligent based approaches, data are pre-processed (scaled) and finally the outputs are post-processed (returned to its original scale). To show the applicability and superiority of the integrated ANFIS-SFA approach, gas consumption in four Middle Eastern countries i.e. Bahrain, Saudi Arabia, Syria, and United Arab Emirates is forecasted and analyzed based on the data of the time period 1980-2007. With the aid of autoregressive model, GDP and population are projected for the period 2008-2015. These projected data are used as the input of ANFIS model to predict the gas consumption in the selected countries for 2008-2015. SFA is then used to examine the behavior of gas consumption in the past and also to make insights for the forthcoming years. The ANFIS-SFA approach is capable of dealing with complexity, uncertainty, and randomness as well as several other unique features discussed in this paper. © 2011 Elsevier Ltd. Source

Azadeh A.,University of Tehran | Saberi M.,Islamic Azad University at Tafresh | Anvari M.,Iran University of Science and Technology
Computers and Industrial Engineering | Year: 2011

Efficiency frontier analysis has been an important approach of evaluating firms' performance in private and public sectors. There have been many efficiency frontier analysis methods reported in the literature. However, the assumptions made for each of these methods are restrictive. Each of these methodologies has its strength as well as major limitations. This study proposes two non-parametric efficiency frontier analysis sub-algorithms based on (1) Artificial Neural Network (ANN) technique and (2) ANN and Fuzzy C-Means for measuring efficiency as a complementary tool for the common techniques of the efficiency studies in the previous studies. Normal probability plot is used to find the outliers and select from these two methods. The proposed computational algorithms are able to find a stochastic frontier based on a set of input-output observational data and do not require explicit assumptions about the functional structure of the stochastic frontier. In these algorithms, for calculating the efficiency scores, a similar approach to econometric methods has been used. Moreover, the effect of the return to scale of decision-making unit (DMU) on its efficiency is included and the unit used for the correction is selected by notice of its scale (under constant return to scale assumption). Also in the second algorithm, for increasing DMUs' homogeneousness, Fuzzy C-Means method is used to cluster DMUs. Two examples using real data are presented for illustrative purposes. First example which deals with power generation sector shows the superiority of Algorithm 2 while the second example dealing auto industries of various developed countries shows the superiority of Algorithm 1. Overall, we find that the proposed integrated algorithm based on ANN, Fuzzy C-Means and Normalization approach provides more robust results and identifies more efficient units than the conventional methods since better performance patterns are explored. © 2010 Elsevier Ltd. All rights reserved. Source

Kheirkhah A.,Bu - Ali Sina University | Azadeh A.,University of Tehran | Saberi M.,Islamic Azad University at Tafresh | Azaron A.,University College Dublin | And 2 more authors.
Computers and Industrial Engineering | Year: 2013

Due to various seasonal and monthly changes in electricity consumption and difficulties in modeling it with the conventional methods, a novel algorithm is proposed in this paper. This study presents an approach that uses Artificial Neural Network (ANN), Principal Component Analysis (PCA), Data Envelopment Analysis (DEA) and ANOVA methods to estimate and predict electricity demand for seasonal and monthly changes in electricity consumption. Pre-processing and post-processing techniques in the data mining field are used in the present study. We analyze the impact of the data pre-processing and post-processing on the ANN performance and a 680 ANN-MLP is constructed for this purpose. DEA is used to compare the constructed ANN models as well as ANN learning algorithm performance. The average, minimum, maximum and standard deviation of mean absolute percentage error (MAPE) of each constructed ANN are used as the DEA inputs. The DEA helps the user to use an appropriate ANN model as an acceptable forecasting tool. In the other words, various error calculation methods are used to find a robust ANN learning algorithm. Moreover, PCA is used as an input selection method, and a preferred time series model is chosen from the linear (ARIMA) and nonlinear models. After selecting the preferred ARIMA model, the Mcleod-Li test is applied to determine the nonlinearity condition. Once the nonlinearity condition is satisfied, the preferred nonlinear model is selected and compared with the preferred ARIMA model, and the best time series model is selected. Then, a new algorithm is developed for the time series estimation; in each case an ANN or conventional time series model is selected for the estimation and prediction. To show the applicability and superiority of the proposed ANN-PCA-DEA-ANOVA algorithm, the data regarding the Iranian electricity consumption from April 1992 to February 2004 are used. The results show that the proposed algorithm provides an accurate solution for the problem of estimating electricity consumption. © 2012 Elsevier Ltd. All rights reserved. Source

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