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Search Results: 1 - 10 of 632 matches for " Naser Mehrdadi "
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Study of Biological Methods in Landfill Leachate Treatment  [PDF]
Parna Eskandari Payandeh, Naser Mehrdadi, Parisa Dadgar
Open Journal of Ecology (OJE) , 2017, DOI: 10.4236/oje.2017.79038
Abstract: Landfill leachate is mainly the result of precipitation of water into the layers of buried waste, and biochemical reactions of waste that has dangerous substances and pollutants that lead to the contamination of surface and groundwater resources. Therefore, it must be collected and treated properly. The investigation of various biological methods in leachate treatment, their advantages and disadvantages, and their effect on reduction of COD (chemical oxygen demand) are the objectives of this study. Reviewed processes include anaerobic and aerobic sequencing batch reactor, up-flow anaerobic sludge blanket, moving-bed biofilm reactor, membrane bioreactor, and aerated lagoons, lead to reduction of biodegradability pollutants in different circumstances. The present study has indicated that the most and the least reduction of COD has been through aerated lagoon (95%) and moving-bed biofilm reactor (8%), respectively.
Performance Simulation of H-TDS Unit of Fajr Industrial Wastewater Treatment Plant Using a Combination of Neural Network and Principal Component Analysis  [PDF]
Hamed Hasanlou, Naser Mehrdadi, Mohammad Taghi Jafarzadeh, Hamidreza Hasanlou
Journal of Water Resource and Protection (JWARP) , 2012, DOI: 10.4236/jwarp.2012.45034
Abstract: Nowadays, with regard to environmental issues, proper operation of wastewater treatment plants is of particular importance that in the case of inappropriate utilization, they will cause serious problems. Processes that exist in environmental systems and environmental engineers are dealing with them mostly have two major characteristics: they are dependent on many variables; and there are complex relationships between its components which make them very difficult to analyze. Being familiar with characteristics of industrial town effluents from various wastewater treatment units, which have high qualitative and quantitative variations and more uncertainties compared to urban wastewaters, plays very effective role in governing them. In order to achieve a better and efficient control over the operation of an industrial wastewater treatment plant, powerful mathematical tool can be used that is based on recorded data from some basic parameters of wastewater during a period of treatment plant operation. In this study, the multilayer perceptron (MLP) feed forward neural network with a hidden layer and stop training method was used to predict quality parameters of the industrial effluent. Data of this study are related to the Fajr Industrial Wastewater Treatment Plant located in Mahshahr—Iran that qualitative and quantitative characteristics of its units were used for training, calibration and evaluation of the neural model. Also, Principal Component Analysis technique was applied to modify and improve performance of generated models of neural networks. The results of this model showed good accuracy of the model in estimating qualitative pro- file of wastewater. This model facilitates evaluating the performance of each treatment plant units through comparing the results of prediction model with the standard amount of output.
Simulation of Low TDS and Biological Units of Fajr Industrial Wastewater Treatment Plant Using Artificial Neural Network and Principal Component Analysis Hybrid Method  [PDF]
Naser Mehrdadi, Hamed Hasanlou, Mohammad Taghi Jafarzadeh, Hamidreza Hasanlou, Hamid Abdolabadi
Journal of Water Resource and Protection (JWARP) , 2012, DOI: 10.4236/jwarp.2012.46042
Abstract: Being familiar with characteristics of industrial town effluents from various wastewater treatment units, which have high qualitative and quantitative variations and more uncertainties compared to urban wastewaters, plays very effective role in governing them. With regard to environmental issues, proper operation of wastewater treatment plants is of par- ticular importance that in the case of inappropriate utilization, they will cause serious problems. Processes that exist in environmental systems mostly have two major characteristics: they are dependent on many variables; and there are complex relationships between its components which make them very difficult to analyze. In order to achieve a better and efficient control over the operation of an industrial wastewater treatment plant (WWTP), powerful mathematical tool can be used that is based on recorded data from some basic parameters of wastewater during a period of treatment plant operation. In this study, the treatment plant was divided into two main subsystems including: Low TDS (Total Dissolved Solids) treatment unit and Biological unit (extended aeration). The multilayer perceptron feed forward neural network with a hidden layer and stop training method was used to predict quality parameters of the industrial effluent. Data of this study are related to the Fajr Industrial Wastewater Treatment Plant, located in Mahshahr—Iran that qualita- tive and quantitative characteristics of its units were used for training, calibration and validation of the neural model. Also, Principal Component Analysis (PCA) technique was applied to improve performance of generated models of neural networks. The results of L-TDS unit showed good accuracy of the models in estimating qualitative profile of wastewater but results of biological unit did not have sufficient accuracy to being used. This model facilitates evaluating the performance of each treatment plant units through comparing the results of prediction model with the standard amount of outputs.
Automorphism of Cyclic Codes  [PDF]
Naser Amiri
Intelligent Information Management (IIM) , 2012, DOI: 10.4236/iim.2012.425043
Abstract: We investigate how the code automorphism group can be used to study such combinatorial object as codes. Consider GF(qn) as a vector over GF(q). For any k = 0, 1, 2, 3, ???, n. Which GF(qn) exactly one subspace C of dimension k and which is invariant under the automorphism.
New Modeling for Generation of Normal and Abnormal Heart Rate Variability Signals  [PDF]
Naser Safdarian
Journal of Biomedical Science and Engineering (JBiSE) , 2014, DOI: 10.4236/jbise.2014.714110
Abstract: This research is performed based on the modeling of biological signals. We can produce Heart Rate (HR) and Heart Rate Variability (HRV) signals synthetically using the mathematical relationships which are used as input for the Integral Pulse Frequency Modulation (IPFM) model. Previous researches were proposed same methods such as one model of ECG signal synthetically based on RBF neural network, a model based on IPFM with random threshold, method was based on the estimation of produced signals which are dependent on autonomic nervous system using IPFM model with fixed threshold, a new method based on the theory of vector space that based on time-varying uses of IPMF model (TVTIPMF) and special functions, and two different methods for producing HRV signals with controlled characteristics and structure of time-frequency (TF) for using non-stationary HRV analysis. In this paper, several chaotic maps such as Logistic Map, Henon Map, Lorenz and Tent Map have been used. Also, effects of sympathetic and parasympathetic nervous system and an internal input to the SA node and their effects in HRV signals were evaluated. In the proposed method, output amount of integrator in IPFM model was compared with chaotic threshold level. Then, final output of IPFM model was characterized as the HR and HRV signal. So, from HR and HRV signals obtaining from this model, linear features such as Mean, Median, Variance, Standard Deviation, Maximum Range, Minimum Range, Mode, Amplitude Range and frequency spectrum, and non-linear features such as Lyapunov Exponent, Shanon Entropy, log Entropy, Threshold Entropy, sure Entropy and mode Entropy were extracted from artificial HRV and compared them with characteristics as extracted from natural HRV signal. Also, in this paper two patients that called high sympathetic Balance and Cardiovascular Autonomy Neuropathy (CAN) which is detected and evaluated by HRV signals were simulated. These signals by changing the values of the some coefficients of the normal simulated signal and with extracted frequency feature from these signals were simulated. For final generation of these abnormal signals, frequency features such as energy of low frequency band (EL), energy of high frequency band (HL), ratio of energy in low frequency band to the energy in high frequency band (EL/EH), ratio of energy in low frequency band to the energy in all frequency band (EL/ET) and ratio of energy in high frequency band to the energy in all frequency band (EH/ET) from abnormal signals were extracted and compared with these extracted values from normal signals. The
A New Solution for the Friedmann Equations  [PDF]
Naser Mostaghel
International Journal of Astronomy and Astrophysics (IJAA) , 2016, DOI: 10.4236/ijaa.2016.61010
Abstract: Assuming a flat universe expanding under a constant pressure and combining the first and the second Friedmann equations, a new equation, describing the evolution of the scale factor, is derived. The equation is a general kinematic equation. It includes all the ingredients composing the universe. An exact closed form solution for this equation is presented. The solution shows remarkable agreement with available observational data for redshifts from a low of z = 0.0152 to as high as z = 8.68. As such, this solution provides an alternative way of describing the expansion of space without involving the controversial dark energy.
Evaluate the Accuracy of Fargas and BLM Models for Identification of Erosion Intensity  [PDF]
Naser Abdi
Open Journal of Geology (OJG) , 2016, DOI: 10.4236/ojg.2016.611103
Abstract: Erosion process not only changes the land use to badlands, but also produces sediments that are dumped in the dam reservoirs, reduces the reservoir volume and finally makes it useless. So, it is necessary to do study on erosion intensity and the sediment production evaluation for which there are some methods and models. The experimental Fargas and BLM models are largely used for this issue, in Iran as well as many other countries, separately or together based on the data availability. These studies results are as data for sediment supply estimation in different watershed management studies. So, the result accuracy is important for determination of sediment occurrence. This study evaluates these models’ results accuracy, in order to find the limitations and any solutions. Therefore, these two models were used and run in the same area, Aghbolagh drainage basin, Iran; the results were compared and evaluated. These models are based on some factors like rock type, drainage density, surface erosion and litter cover. The study includes field and laboratory analysis. The data were combined in GIS software and processed. The results reveal that, Fargas model predicts 3.67%, 14.26% and 81.06% of the area susceptible for high, severe and very severe erosion respectively; whilst, referring to BLM model outcome, 42.96% area has high sensitivity; 42.96% and 24.94% of the area have high and severe sensitivity for erosion, respectively. Furthermore, both models show same severity for around 18% of the study area. So, for these two models results very low similarities are concluded, which could be an indication of low reliability of the results, especially when they are used separately without any combination or comparison. Finally, it is recommended to use both of them together, or use another method beside each mentioned models.
Effects of Time Dilation on the Measurements of the Hubble Constant  [PDF]
Naser Mostaghel
International Journal of Astronomy and Astrophysics (IJAA) , 2018, DOI: 10.4236/ijaa.2018.84024
Abstract: We show that, when measuring the Hubble constant by starting the evaluation from the time of the big bang era, the effect of time dilation results in a decrease in the value of the Hubble constant. But when evaluating the Hubble constant by starting the evaluation from the present time, the effect of time dilation results in an increase in the value of the Hubble constant. To elucidate the process, the time dilation is calculated both directly and through Schwarzschild solution of the Einstein equation for the gravitational time dilation. It is concluded that both measured values are valid but because of time dilation, different starting times for the evaluation of the Hubble constant have resulted in different measured values for the Hubble constant.
Performance Improvement of Wireless Communications Using Frequency Hopping Spread Spectrum  [PDF]
Naser Hossein Motlagh
Int'l J. of Communications, Network and System Sciences (IJCNS) , 2010, DOI: 10.4236/ijcns.2010.310108
Abstract: To improve the performance of short-range wireless communications, channel quality must be improved by avoiding interference and multi-path fading. Frequency hopping spread spectrum(FHSS) is a transmission technique where the carrier hops from frequency to frequency. For frequency hopping a mechanism must be designed so that the data can be transmitted in a clear channel and avoid congested channels. Adaptive frequency hopping is a system which is used to improve immunity toward frequency interference by avoiding using congested frequency channels in hopping sequence. In this paper mathematical modelling is used to simulate and analyze the performance improvement by using FHSS with popular modulation schemes, and also the hopping channel situations are investigated.
Evaluating Effects of Two Alternative Filters for the Incremental Pruning Algorithm on Quality of Pomdp Exact Solutions  [PDF]
Mahdi Naser-Moghadasi
International Journal of Intelligence Science (IJIS) , 2012, DOI: 10.4236/ijis.2012.21001
Abstract: Decision making is one of the central problems in artificial intelligence and specifically in robotics. In most cases this problem comes with uncertainty both in data received by the decision maker/agent and in the actions performed in the environment. One effective method to solve this problem is to model the environment and the agent as a Partially Observable Markov Decision Process (POMDP). A POMDP has a wide range of applications such as: Machine Vision, Marketing, Network troubleshooting, Medical diagnosis etc. In recent years, there has been a significant interest in developing techniques for finding policies for (POMDPs).We consider two new techniques, called Recursive Point Filter (RPF) and Scan Line Filter (SCF) based on Incremental Pruning (IP) POMDP solver to introduce an alternative method to Linear Programming (LP) filter for IP. Both, RPF and SCF have solutions for several POMDP problems that LP could not converge to in 24 hours. Experiments are run on problems from POMDP literature, and an Average Discounted Reward (ADR) is computed by testing the policy in a simulated environment.
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