TY - JOUR A1 - Homaei, Mohammad Hossein A1 - Soleimani, Faezeh A1 - Shamshirband, Shahaboddin A1 - Mosavi, Amir A1 - Nabipour, Narjes A1 - Varkonyi-Koczy, Annamaria R. T1 - An Enhanced Distributed Congestion Control Method for Classical 6LowPAN Protocols Using Fuzzy Decision System JF - IEEE Access N2 - The classical Internet of things routing and wireless sensor networks can provide more precise monitoring of the covered area due to the higher number of utilized nodes. Because of the limitations in shared transfer media, many nodes in the network are prone to the collision in simultaneous transmissions. Medium access control protocols are usually more practical in networks with low traffic, which are not subjected to external noise from adjacent frequencies. There are preventive, detection and control solutions to congestion management in the network which are all the focus of this study. In the congestion prevention phase, the proposed method chooses the next step of the path using the Fuzzy decision-making system to distribute network traffic via optimal paths. In the congestion detection phase, a dynamic approach to queue management was designed to detect congestion in the least amount of time and prevent the collision. In the congestion control phase, the back-pressure method was used based on the quality of the queue to decrease the probability of linking in the pathway from the pre-congested node. The main goals of this study are to balance energy consumption in network nodes, reducing the rate of lost packets and increasing quality of service in routing. Simulation results proved the proposed Congestion Control Fuzzy Decision Making (CCFDM) method was more capable in improving routing parameters as compared to recent algorithms. KW - Internet der dinge KW - IOT KW - Internet of things KW - wireless sensor network KW - congestion control KW - fuzzy decision making KW - back-pressure Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20200213-40805 UR - https://ieeexplore.ieee.org/document/8967114 IS - volume 8 SP - 20628 EP - 20645 PB - IEEE ER - TY - JOUR A1 - Jilte, Ravindra A1 - Ahmadi, Mohammad Hossein A1 - Kumar, Ravinder A1 - Kalamkar, Vilas A1 - Mosavi, Amir T1 - Cooling Performance of a Novel Circulatory Flow Concentric Multi-Channel Heat Sink with Nanofluids JF - Nanomaterials N2 - Heat rejection from electronic devices such as processors necessitates a high heat removal rate. The present study focuses on liquid-cooled novel heat sink geometry made from four channels (width 4 mm and depth 3.5 mm) configured in a concentric shape with alternate flow passages (slot of 3 mm gap). In this study, the cooling performance of the heat sink was tested under simulated controlled conditions.The lower bottom surface of the heat sink was heated at a constant heat flux condition based on dissipated power of 50 W and 70 W. The computations were carried out for different volume fractions of nanoparticles, namely 0.5% to 5%, and water as base fluid at a flow rate of 30 to 180 mL/min. The results showed a higher rate of heat rejection from the nanofluid cooled heat sink compared with water. The enhancement in performance was analyzed with the help of a temperature difference of nanofluid outlet temperature and water outlet temperature under similar operating conditions. The enhancement was ~2% for 0.5% volume fraction nanofluids and ~17% for a 5% volume fraction. KW - Nanostrukturiertes Material KW - Kühlkörper KW - Nasskühlung KW - nanofluid KW - Nanomaterials KW - Machine learning KW - heat sink Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20200401-41241 UR - https://www.mdpi.com/2079-4991/10/4/647 VL - 2020 IS - Volume 10, Issue 4, 647 PB - MDPI CY - Basel ER - TY - JOUR A1 - Kargar, Katayoun A1 - Samadianfard, Saeed A1 - Parsa, Javad A1 - Nabipour, Narjes A1 - Shamshirband, Shahaboddin A1 - Mosavi, Amir A1 - Chau, Kwok-Wing T1 - Estimating longitudinal dispersion coefficient in natural streams using empirical models and machine learning algorithms JF - Engineering Applications of Computational Fluid Mechanics N2 - The longitudinal dispersion coefficient (LDC) plays an important role in modeling the transport of pollutants and sediment in natural rivers. As a result of transportation processes, the concentration of pollutants changes along the river. Various studies have been conducted to provide simple equations for estimating LDC. In this study, machine learning methods, namely support vector regression, Gaussian process regression, M5 model tree (M5P) and random forest, and multiple linear regression were examined in predicting the LDC in natural streams. Data sets from 60 rivers around the world with different hydraulic and geometric features were gathered to develop models for LDC estimation. Statistical criteria, including correlation coefficient (CC), root mean squared error (RMSE) and mean absolute error (MAE), were used to scrutinize the models. The LDC values estimated by these models were compared with the corresponding results of common empirical models. The Taylor chart was used to evaluate the models and the results showed that among the machine learning models, M5P had superior performance, with CC of 0.823, RMSE of 454.9 and MAE of 380.9. The model of Sahay and Dutta, with CC of 0.795, RMSE of 460.7 and MAE of 306.1, gave more precise results than the other empirical models. The main advantage of M5P models is their ability to provide practical formulae. In conclusion, the results proved that the developed M5P model with simple formulations was superior to other machine learning models and empirical models; therefore, it can be used as a proper tool for estimating the LDC in rivers. KW - Maschinelles Lernen KW - Gaussian process regression KW - longitudinal dispersion coefficient KW - M5 model tree KW - random forest KW - support vector regression KW - rivers Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20200128-40775 UR - https://www.tandfonline.com/doi/full/10.1080/19942060.2020.1712260 VL - 2020 IS - Volume 14, No. 1 SP - 311 EP - 322 PB - Taylor & Francis ER - TY - JOUR A1 - Lashkar-Ara, Babak A1 - Kalantari, Niloofar A1 - Sheikh Khozani, Zohreh A1 - Mosavi, Amir T1 - Assessing Machine Learning versus a Mathematical Model to Estimate the Transverse Shear Stress Distribution in a Rectangular Channel JF - Mathematics N2 - One of the most important subjects of hydraulic engineering is the reliable estimation of the transverse distribution in the rectangular channel of bed and wall shear stresses. This study makes use of the Tsallis entropy, genetic programming (GP) and adaptive neuro-fuzzy inference system (ANFIS) methods to assess the shear stress distribution (SSD) in the rectangular channel. To evaluate the results of the Tsallis entropy, GP and ANFIS models, laboratory observations were used in which shear stress was measured using an optimized Preston tube. This is then used to measure the SSD in various aspect ratios in the rectangular channel. To investigate the shear stress percentage, 10 data series with a total of 112 different data for were used. The results of the sensitivity analysis show that the most influential parameter for the SSD in smooth rectangular channel is the dimensionless parameter B/H, Where the transverse coordinate is B, and the flow depth is H. With the parameters (b/B), (B/H) for the bed and (z/H), (B/H) for the wall as inputs, the modeling of the GP was better than the other one. Based on the analysis, it can be concluded that the use of GP and ANFIS algorithms is more effective in estimating shear stress in smooth rectangular channels than the Tsallis entropy-based equations. KW - Maschinelles Lernen KW - smooth rectangular channel KW - Tsallis entropy KW - genetic programming KW - artificial intelligence KW - machine learning KW - big data KW - computational hydraulics Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20210504-44197 UR - https://www.mdpi.com/2227-7390/9/6/596 VL - 2021 IS - Volume 9, Issue 6, Article 596 PB - MDPI CY - Basel ER - TY - JOUR A1 - Mosavi, Amir A1 - Hosseini Imani, Mahmood A1 - Zalzar, Shaghayegh A1 - Shamshirband, Shahaboddin T1 - Strategic Behavior of Retailers for Risk Reduction and Profit Increment via Distributed Generators and Demand Response Programs JF - Energies N2 - Following restructuring of power industry, electricity supply to end-use customers has undergone fundamental changes. In the restructured power system, some of the responsibilities of the vertically integrated distribution companies have been assigned to network managers and retailers. Under the new situation, retailers are in charge of providing electrical energy to electricity consumers who have already signed contract with them. Retailers usually provide the required energy at a variable price, from wholesale electricity markets, forward contracts with energy producers, or distributed energy generators, and sell it at a fixed retail price to its clients. Different strategies are implemented by retailers to reduce the potential financial losses and risks associated with the uncertain nature of wholesale spot electricity market prices and electrical load of the consumers. In this paper, the strategic behavior of retailers in implementing forward contracts, distributed energy sources, and demand-response programs with the aim of increasing their profit and reducing their risk, while keeping their retail prices as low as possible, is investigated. For this purpose, risk management problem of the retailer companies collaborating with wholesale electricity markets, is modeled through bi-level programming approach and a comprehensive framework for retail electricity pricing, considering customers’ constraints, is provided in this paper. In the first level of the proposed bi-level optimization problem, the retailer maximizes its expected profit for a given risk level of profit variability, while in the second level, the customers minimize their consumption costs. The proposed programming problem is modeled as Mixed Integer programming (MIP) problem and can be efficiently solved using available commercial solvers. The simulation results on a test case approve the effectiveness of the proposed demand-response program based on dynamic pricing approach on reducing the retailer’s risk and increasing its profit. In this paper, the decision-making problem of the retailers under dynamic pricing approach for demand response integration have been investigated. The retailer was supposed to rely on forward contracts, DGs, and spot electricity market to supply the required active and reactive power of its customers. To verify the effectiveness of the proposed model, four schemes for retailer’s scheduling problem are considered and the resulted profit under each scheme are analyzed and compared. The simulation results on a test case indicate that providing more options for the retailer to buy the required power of its customers and increase its flexibility in buying energy from spot electricity market reduces the retailers’ risk and increases its profit. From the customers’ perspective also the retailers’accesstodifferentpowersupplysourcesmayleadtoareductionintheretailelectricityprices. Since the retailer would be able to decrease its electricity selling price to the customers without losing its profitability, with the aim of attracting more customers. Inthiswork,theconditionalvalueatrisk(CVaR)measureisusedforconsideringandquantifying riskinthedecision-makingproblems. Amongallthepossibleoptioninfrontoftheretailertooptimize its profit and risk, demand response programs are the most beneficial option for both retailer and its customers. The simulation results on the case study prove that implementing dynamic pricing approach on retail electricity prices to integrate demand response programs can successfully provoke customers to shift their flexible demand from peak-load hours to mid-load and low-load hours. Comparing the simulation results of the third and fourth schemes evidences the impact of DRPs and customers’ load shifting on the reduction of retailer’s risk, as well as the reduction of retailer’s payment to contract holders, DG owners, and spot electricity market. Furthermore, the numerical results imply on the potential of reducing average retail prices up to 8%, under demand response activation. Consequently, it provides a win–win solution for both retailer and its customers. KW - Risikomanagement KW - demand response programs KW - stochastic programming KW - forward contracts KW - risk management KW - retailer KW - OA-Publikationsfonds2018 Y1 - 2018 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20180628-37546 UR - http://www.mdpi.com/1996-1073/11/6/1602 VL - 2018 IS - 11, 6 PB - MDPI CY - Basel ER - TY - INPR A1 - Mosavi, Amir A1 - Moeini, Iman A1 - Ahmadpour, Mohammad A1 - Alharbi, Naif A1 - E. Gorji, Nima T1 - Modeling the time-dependent characteristics of perovskite solar cells N2 - We proposed two different time-dependent modeling approaches for variation of device characteristics of perovskite solar cells under stress conditions. The first approach follows Sah-Noyce-Shockley (SNS) model based on Shockley–Read–Hall recombination/generation across the depletion width of pn junction and the second approach is based on thermionic emission model for Schottky diodes. The connecting point of these approaches to time variation is the time-dependent defect generation in depletion width (W) of the junction. We have fitted the two models with experimental data reported in the literature to perovskite solar cell and found out that each model has a superior explanation for degradation of device metrics e.g. current density and efficiency by time under stress conditions. Nevertheless, the Sah-Noyce-Shockley model is more reliable than thermionic emission at least for solar cells. KW - Solarzelle KW - Solar KW - Solar cells KW - Modeling KW - Time-dependent KW - Defect generation KW - Perovskite Y1 - 2018 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20180907-37573 N1 - Published in final form at https://doi.org/10.1016/j.solener.2018.05.082. ER - TY - JOUR A1 - Mosavi, Amir A1 - Najafi, Bahman A1 - Faizollahzadeh Ardabili, Sina A1 - Shamshirband, Shahaboddin A1 - Rabczuk, Timon T1 - An Intelligent Artificial Neural Network-Response Surface Methodology Method for Accessing the Optimum Biodiesel and Diesel Fuel Blending Conditions in a Diesel Engine from the Viewpoint of Exergy and Energy Analysis JF - Energies N2 - Biodiesel, as the main alternative fuel to diesel fuel which is produced from renewable and available resources, improves the engine emissions during combustion in diesel engines. In this study, the biodiesel is produced initially from waste cooking oil (WCO). The fuel samples are applied in a diesel engine and the engine performance has been considered from the viewpoint of exergy and energy approaches. Engine tests are performed at a constant 1500 rpm speed with various loads and fuel samples. The obtained experimental data are also applied to develop an artificial neural network (ANN) model. Response surface methodology (RSM) is employed to optimize the exergy and energy efficiencies. Based on the results of the energy analysis, optimal engine performance is obtained at 80% of full load in presence of B10 and B20 fuels. However, based on the exergy analysis results, optimal engine performance is obtained at 80% of full load in presence of B90 and B100 fuels. The optimum values of exergy and energy efficiencies are in the range of 25–30% of full load, which is the same as the calculated range obtained from mathematical modeling. KW - Biodiesel KW - ANN modeling KW - biodiesel KW - Artificial Intelligence KW - diesel engines KW - energy, exergy KW - mathematical modeling KW - OA-Publikationsfonds2018 Y1 - 2018 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20180507-37467 UR - http://www.mdpi.com/1996-1073/11/4/860 VL - 2018 IS - 11, 4 PB - MDPI CY - Basel ER - TY - JOUR A1 - Mosavi, Amir A1 - Shamshirband, Shahaboddin A1 - Esmaeilbeiki, Fatemeh A1 - Zarehaghi, Davoud A1 - Neyshabouri, Mohammadreza A1 - Samadianfard, Saeed A1 - Ghorbani, Mohammad Ali A1 - Nabipour, Narjes A1 - Chau, Kwok-Wing T1 - Comparative analysis of hybrid models of firefly optimization algorithm with support vector machines and multilayer perceptron for predicting soil temperature at different depths JF - Engineering Applications of Computational Fluid Mechanics N2 - This research aims to model soil temperature (ST) using machine learning models of multilayer perceptron (MLP) algorithm and support vector machine (SVM) in hybrid form with the Firefly optimization algorithm, i.e. MLP-FFA and SVM-FFA. In the current study, measured ST and meteorological parameters of Tabriz and Ahar weather stations in a period of 2013–2015 are used for training and testing of the studied models with one and two days as a delay. To ascertain conclusive results for validation of the proposed hybrid models, the error metrics are benchmarked in an independent testing period. Moreover, Taylor diagrams utilized for that purpose. Obtained results showed that, in a case of one day delay, except in predicting ST at 5 cm below the soil surface (ST5cm) at Tabriz station, MLP-FFA produced superior results compared with MLP, SVM, and SVM-FFA models. However, for two days delay, MLP-FFA indicated increased accuracy in predicting ST5cm and ST 20cm of Tabriz station and ST10cm of Ahar station in comparison with SVM-FFA. Additionally, for all of the prescribed models, the performance of the MLP-FFA and SVM-FFA hybrid models in the testing phase was found to be meaningfully superior to the classical MLP and SVM models. KW - Bodentemperatur KW - Algorithmus KW - Maschinelles Lernen KW - Neuronales Netz KW - firefly optimization algorithm KW - soil temperature KW - artificial neural networks KW - hybrid machine learning KW - OA-Publikationsfonds2019 Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20200911-42347 UR - https://www.tandfonline.com/doi/full/10.1080/19942060.2020.1788644 VL - 2020 IS - Volume 14, Issue 1 SP - 939 EP - 953 ER - TY - INPR A1 - Mosavi, Amir A1 - Torabi, Mehrnoosh A1 - Hashemi, Sattar A1 - Saybani, Mahmoud Reza A1 - Shamshirband, Shahaboddin T1 - A Hybrid Clustering and Classification Technique for Forecasting Short-Term Energy Consumption N2 - Electrical energy distributor companies in Iran have to announce their energy demand at least three 3-day ahead of the market opening. Therefore, an accurate load estimation is highly crucial. This research invoked methodology based on CRISP data mining and used SVM, ANN, and CBA-ANN-SVM (a novel hybrid model of clustering with both widely used ANN and SVM) to predict short-term electrical energy demand of Bandarabbas. In previous studies, researchers introduced few effective parameters with no reasonable error about Bandarabbas power consumption. In this research we tried to recognize all efficient parameters and with the use of CBA-ANN-SVM model, the rate of error has been minimized. After consulting with experts in the field of power consumption and plotting daily power consumption for each week, this research showed that official holidays and weekends have impact on the power consumption. When the weather gets warmer, the consumption of electrical energy increases due to turning on electrical air conditioner. Also, con-sumption patterns in warm and cold months are different. Analyzing power consumption of the same month for different years had shown high similarity in power consumption patterns. Factors with high impact on power consumption were identified and statistical methods were utilized to prove their impacts. Using SVM, ANN and CBA-ANN-SVM, the model was built. Sine the proposed method (CBA-ANN-SVM) has low MAPE 5 1.474 (4 clusters) and MAPE 5 1.297 (3 clusters) in comparison with SVM (MAPE 5 2.015) and ANN (MAPE 5 1.790), this model was selected as the final model. The final model has the benefits from both models and the benefits of clustering. Clustering algorithm with discovering data structure, divides data into several clusters based on similarities and differences between them. Because data inside each cluster are more similar than entire data, modeling in each cluster will present better results. For future research, we suggest using fuzzy methods and genetic algorithm or a hybrid of both to forecast each cluster. It is also possible to use fuzzy methods or genetic algorithms or a hybrid of both without using clustering. It is issued that such models will produce better and more accurate results. This paper presents a hybrid approach to predict the electric energy usage of weather-sensitive loads. The presented methodutilizes the clustering paradigm along with ANN and SVMapproaches for accurate short-term prediction of electric energyusage, using weather data. Since the methodology beinginvoked in this research is based on CRISP data mining, datapreparation has received a gr eat deal of attention in thisresear ch. Once data pre-processing was done, the underlyingpattern of electric energy consumption was extracted by themeans of machine learning methods to precisely forecast short-term energy consumption. The proposed approach (CBA-ANN-SVM) was applied to real load data and resulting higher accu-racy comparing to the existing models. 2018 American Institute of Chemical Engineers Environ Prog, 2018 https://doi.org/10.1002/ep.12934 KW - Data Mining KW - support vector machine (SVM) KW - Machine Learning KW - forecasting KW - Prediction KW - Electric Energy Consumption KW - clustering KW - artificial neural networks (ANN) Y1 - 2018 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20180907-37550 N1 - This is the pre-peer reviewed version of the following article: https://onlinelibrary.wiley.com/doi/10.1002/ep.12934, which has been published in final form at https://doi.org/10.1002/ep.12934. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. ER - TY - JOUR A1 - Mousavi, Seyed Nasrollah A1 - Steinke Júnior, Renato A1 - Teixeira, Eder Daniel A1 - Bocchiola, Daniele A1 - Nabipour, Narjes A1 - Mosavi, Amir A1 - Shamshirband, Shahaboddin T1 - Predictive Modeling the Free Hydraulic Jumps Pressure through Advanced Statistical Methods JF - Mathematics N2 - Pressure fluctuations beneath hydraulic jumps potentially endanger the stability of stilling basins. This paper deals with the mathematical modeling of the results of laboratory-scale experiments to estimate the extreme pressures. Experiments were carried out on a smooth stilling basin underneath free hydraulic jumps downstream of an Ogee spillway. From the probability distribution of measured instantaneous pressures, pressures with different probabilities could be determined. It was verified that maximum pressure fluctuations, and the negative pressures, are located at the positions near the spillway toe. Also, minimum pressure fluctuations are located at the downstream of hydraulic jumps. It was possible to assess the cumulative curves of pressure data related to the characteristic points along the basin, and different Froude numbers. To benchmark the results, the dimensionless forms of statistical parameters include mean pressures (P*m), the standard deviations of pressure fluctuations (σ*X), pressures with different non-exceedance probabilities (P*k%), and the statistical coefficient of the probability distribution (Nk%) were assessed. It was found that an existing method can be used to interpret the present data, and pressure distribution in similar conditions, by using a new second-order fractional relationships for σ*X, and Nk%. The values of the Nk% coefficient indicated a single mean value for each probability. KW - Maschinelles Lernen KW - Machine learning KW - mathematical modeling KW - extreme pressure KW - hydraulic jump KW - stilling basin KW - standard deviation of pressure fluctuations KW - statistical coeffcient of the probability distribution Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20200402-41140 UR - https://www.mdpi.com/2227-7390/8/3/323 VL - 2020 IS - Volume 8, Issue 3, 323 PB - MDPI CY - Basel ER -