Showing papers by "Batman University published in 2019"

Optimal Tuning of Fractional Order PID Controller for DC Motor Speed Control via Chaotic Atom Search Optimization Algorithm

Abstract: In this paper, atom search optimization (ASO) algorithm and a novel chaotic version of it [chaotic ASO (ChASO)] are proposed to determine the optimal parameters of the fractional-order proportional+integral+derivative (FOPID) controller for dc motor speed control. The ASO algorithm is simple and easy to implement, which mathematically models and mimics the atomic motion model in nature, and is developed to address a diverse set of optimization problems. The proposed ChASO algorithm, on the other hand, is based on logistic map chaotic sequences, which makes the original algorithm be able to escape from local minima stagnation and improve its convergence rate and resulting precision. First, the proposed ChASO algorithm is applied to six unimodal and multimodal benchmark optimization problems and the results are compared with other algorithms. Second, the proposed ChASO-FOPID, ASO-FOPID, and ASO-PID controllers are compared with GWO-FOPID, GWO-PID, IWO-PID, and SFS-PID controllers using the integral of time multiplied absolute error (ITAE) objective function for a fair comparison. Comparisons were also made for the integral of time multiplied squared error (ITSE) and Zwe-Lee Gaing's (ZLG) objective function as the most commonly used objective functions in the literature. Transient response analysis, frequency response (Bode) analysis, and robustness analysis were all carried out. The simulation results are promising and validate the effectiveness of the proposed approaches. The numerical simulations of the proposed ChASO-FOPID and ASO-FOPID controllers for the dc motor speed control system demonstrated the superior performance of both the chaotic ASO and the original ASO, respectively.

Improved Kidney-Inspired Algorithm Approach for Tuning of PID Controller in AVR System

Abstract: This paper proposes a novel tuning design of proportional integral derivative (PID) controller via an improved kidney-inspired algorithm (IKA) with a new objective function. The main objective of the proposed approach is to optimize the transient response of the AVR system by minimizing the maximum overshoot, settling time, rise time and peak time values of the terminal voltage, and eliminating the steady state error. After obtaining the optimal values of the three gains of the PID controller (KP, KI, and KD) with the proposed approach, the transient response analysis was performed and compared with some of the current heuristic algorithms-based approaches in literature to show the superiority of the optimized PID controller. In order to evaluate the stability of the automatic voltage regulator (AVR) system tuned by IKA method, the pole/zero map analysis and Bode analysis are performed. Finally, the robustness analysis of the proposed approach has been carried out with variations in the parameters of the AVR system. The numerical simulation results demonstrated that the proposed IKA tuned PID controller has better control performances compared to the other existing approaches. The essence of the presented study points out that the proposed approach may successfully be applied for the AVR system.

Extreme learning machine model for water network management

Abstract: A novel failure rate prediction model is developed by the extreme learning machine (ELM) to provide key information needed for optimum ongoing maintenance/rehabilitation of a water network, meaning the estimated times for the next failures of individual pipes within the network. The developed ELM model is trained using more than 9500 instances of pipe failure in the Greater Toronto Area, Canada from 1920 to 2005 with pipe attributes as inputs, including pipe length, diameter, material, and previously recorded failures. The models show recent, extensive usage of pipe coating with cement mortar and cathodic protection has significantly increased their lifespan. The predictive model includes the pipe protection method as pipe attributes and can reflect in its predictions, the effect of different pipe protection methods on the expected time to the next pipe failure. The developed ELM has a superior prediction accuracy relative to other available machine learning algorithms such as feed-forward artificial neural network that is trained by backpropagation, support vector regression, and non-linear regression. The utility of the models provides useful inputs when planning and budgeting for watermain inspection, maintenance, and rehabilitation.

Sine-cosine algorithm-based optimization for automatic voltage regulator system:

Abstract: A novel design method, sine-cosine algorithm (SCA) is presented in this paper to determine optimum proportional-integral-derivative (PID) controller parameters of an automatic voltage regulator (AV...

Parameter optimization of power system stabilizers via kidney-inspired algorithm

Abstract: This article describes the application of a new population-based meta-heuristic optimization algorithm inspired by the kidney process in the human body for the tuning of power system stabilizers (P...

Investigation of the effect of use of Nano-Al2O3, Nano-TiO2 and Nano-SiO2 powders on strength of single lap joints bonded with epoxy adhesive

Abstract: In this study, single lap joints were formed by adding nano-Al2O3, nano-TiO2 and nano-Al2O3 powders in various proportions to the epoxy adhesive and using the additive-free epoxy adhesive; and also the mechanical properties of the connections were experimentally investigated at 20, 25, 30, 50 and 70 mm overlap lengths under shear load. In the experimental work, DP460 epoxy adhesive was used as adhesive and AISI 304 stainless steel plate as adherent material. When the results obtained from the experiments were examined, it was revealed that the average damage load in connection with the use of nanoparticle-added adhesives increased considerably in general. As a result of the experiments, the most effective nanoparticle in increasing the failure strength of the adhesive joints with nano-Al2O3 particles and the maximum failure strength increase rate was 20 mm in overlap length and 97% in 4 wt% nano-Al2O3 reinforced specimens. It was also found that the nanoparticle strain was an important parameter in the tensile strength of the adhesive joints. In addition, it has been found that the addition of nanoparticles into the adhesive increases the elongation of the joints. When the adhesion surfaces of the samples were examined as in the case of plain adhesives, damage was observed as adhesion separation while nanoparticle reinforcement was observed as a mixture of adhesion and cohesion.

An artificial neural network model to predict the thermal properties of concrete using different neurons and activation functions

Abstract: Growing concerns on energy consumption of buildings by heating and cooling applications have led to a demand for improved insulating performances of building materials. The establishment of thermal property for a building structure is the key performance indicator for energy efficiency, whereas high accuracy and precision tests are required for its determination which increases time and experimental costs. The main scope of this study is to develop a model based on artificial neural network (ANN) in order to predict the thermal properties of concrete through its mechanical characteristics. Initially, different concrete samples were prepared, and their both mechanical and thermal properties were tested in accordance with ASTM and EN standards. Then, the Levenberg–Marquardt algorithm was used for training the neural network in the single hidden layer using 5, 10, 15, 20, and 25 neurons, respectively. For each thermal property, various activation functions such as tangent sigmoid functions and triangular basis functions were used to examine the best solution performance. Moreover, a cross-validation technique was used to ensure good generalization and to avoid overtraining. ANN results showed that the best overall R2 performances for the prediction of thermal conductivity, specific heat, and thermal diffusivity were obtained as 0.996, 0.983, and 0.995 for tansig activation functions with 25, 25, and 20 neurons, respectively. The performance results showed that there was a great consistency between the predicted and tested results, demonstrating the feasibility and practicability of the proposed ANN models for predicting the thermal property of a concrete.

An experimental study on milling of natural fiber (jute)- reinforced polymer composites:

Abstract: The interest in materials having natural, environmentally friendly, renewable and low density/cost is increasing day by day due to sanctions imposed to reduce the emission rates, especially the Kyo...

Determination of fatigue life of the unidirectional GFRP/Al hybrid composite laminates

Abstract: This paper investigates the effects of loading rate and fiber orientation on the fatigue behavior of the unidirectional glass-fiber reinforced-plastic/aluminum (GFRP/Al) hybrid laminated (GLARE-2) plates. Fatigue tests were performed at three kinds of stress ratios (R = 0.3, 0.1, and −0.1) on specimens with different fiber orientations, θ = 0°, 15°, 30°, 45°, 60°, 75°, and 90°, in the GFRP layers. All the fatigue results to be presented in this article were obtained in repeated tension-tension and tension-compression at stress ratios of 0.3, 0.1, and −0.1, and the results were compared with each other. It has been shown that the specimens have the highest fatigue life in the fiber orientation direction at R = 0.3 loading rate. Also, it has been shown that the fatigue life of the specimens decreases as the loading rate decreases.

Evaluation of drilling performances of nanocomposites reinforced with graphene and graphene oxide

Abstract: The use of graphene (G) and graphene oxide (GO) reinforced nanocomposites have a great importance since G and GO improve the interface conditions of composite materials. However, the effects of G and GO on some mechanical properties and machinability in nanocomposites are still a research topic. In this study, G was converted to GO by Hummers’ method. G and GO nanoparticles were added to epoxy at different ratios and the tensile strengths of nanocomposites were determined. By taking into account, the reinforcement ratio of nanocomposites having the highest tensile strength, epoxy with G and GO, and unreinforced epoxy were added to carbon fiber (CF) fabric by hand lay-up. Thus, fabrication of the carbon fiber-reinforced plastic (CFRP) composite, and the G/CFRP and GO/CFRP nanocomposites was carried out. The effects of the G and GO on the fabricated nanocomposites, and the effect of different drilling parameters (cutting speed and feed rate) on the cutting force, cutting torque, temperature, and delamination factor were investigated. In the drilling of these composites, drills with the different bit point angles and the diameter of 5 mm were used. As a result, it was observed that GO was successfully synthesized, and G and GO positively affected the tensile strength, and GO exhibited a more effective feature than G on the tensile strength. It was also seen that the increase of the cutting speed, feed rate, bit point angle caused the increase in the cutting forces, cutting torque, and delaminations.

A new hand finger movements’ classification system based on bicoherence analysis of two-channel surface EMG signals

Abstract: In this study, two-channel surface electromyography (sEMG) signals were used to classify hand finger movements. Bicoherence analysis of the sEMG signal recorded with surface electrodes for flexor and extensor muscle bundles on the front and back of the forearm, respectively, was classified by extreme learning machines (ELM) based on phase matches in the electromyography (EMG) signal. EMG signals belonging to 42 human, 22 males and 20 females, with an average age of 21.4 were used in the study. The finger movements were also classified by using different learning algorithms. The best classification was performed by using ELM algorithm with 98.95 and 97.83% accuracies in average for subjects individually and all together, respectively. On the other hand, a maximum of 95.81 and 94.30% accuracies were reached for subjects individually and all together, respectively, with other learning methods used in the present study. From the information obtained through this study, it is possible to control finger movements by using flexor and extensor muscle activities of the forearm. Furthermore, by this method, it may be possible controlling of the intelligent prosthesis hands with high degree of freedom.

Harris Hawks Optimization Approach for Tuning of FOPID Controller in DC-DC Buck Converter

Abstract: In this work, a new nature-inspired, population-based meta-heuristic optimization method, Harris Hawks Optimization (HHO), is applied for the first time for tuning the parameters of both the traditional PID controller and the fractional order PID (FOPID) controller used in a DC-DC buck converter. The PID and FOPID controllers, whose parameters optimally tuned by HHO algorithm, are used for improving the transient response of a DC-DC buck converter by minimizing the maximum overshoot, settling time and rise time of converter's output voltage and for eliminating the steady state error. In addition, to illustrate the superiority of the proposed HHO-PID and HHO-FOPID controllers, beside the transient response analysis, the frequency response analysis and performance indices analysis were also conducted and compared with GA-PID and WOA-PID controllers, which are available in literature and are tuned by genetic algorithm (GA) and whale optimization algorithm (WOA), respectively. The numerical and graphical results demonstrated that the proposed HHO-FOPID controller has better control performance than the proposed HHO-PID, GA-PID, and WOA-PID controllers. The obtained simulation results confirmed the superiority and effectiveness of the proposed HHO-FOPID controller in terms of both algorithm and controller structure.

The effect of Sn on electrical performance of zinc oxide based thin film transistor

Abstract: In this study, we have effectively fabricated undoped and Sn-doped zinc oxide thin film transistors (ZTO TFTs) with the back-gate structure on commercially purchased p-type Si with a thermally grown SiO2 layer (thickness = 100 nm). The ZTO TFTs were prepared via low-cost solution based spin coating method. We investigated the structural and morphological properties of thin films and the effect of Sn doping on the electrical performance of ZnO TFTs. It has been found that the electrical parameters of the ZnO based transistors have highly affected by Sn doping. The field-effect mobility and on/off ratio of doped TFT increased ~ 40 and 106 times by comparison with undoped TFT, respectively. The best results were obtained from 10% Sn doped ZTO TFT with 3.83 cm2 V−1 s−1 the field effect mobility (µsat), 1 V/dec subthreshold slope (SS), 9 V threshold voltage (Vth), 3 × 108 Ion/Ioff ratio and a high on-current of 1.3 mA.

Predicting power production from a photovoltaic panel through artificial neural networks using atmospheric indicators

Abstract: In this study, an artificial neural network was modeled in order to predict the power generated by a monocrystalline silicon photovoltaic panel. This experimental study measured and recorded the voltage and current generated by the photovoltaic panel for a year, along with environmental variables such as solar irradiance, air temperature, wind speed, wind direction, relative humidity, and angle of the sun’s elevation. In the results of the comparisons between measured and estimated power, a perfect estimation was found to have been conducted in which the root mean square error did not exceed 1.4% and the coefficient of correlation (R) ranged from 99.637 to 99.998%. These results were obtained from the testing dataset. In this study, achieved artificial neural network models are able to perform estimations for any location using the atmospheric indicators. These models are considered able to lead investors using extremely sensitive and robust estimations in order to learn solar energy’s potential in a location.

Machine Learning based Regression Model for Prediction of Soil Surface Humidity over Moderately Vegetated Fields

Abstract: The soil surface humidity parameter over vegetated fields is of great importance for controlling water consumption; prevention of salinity caused by over-irrigation; efficient use of irrigation system and improving the yield and quality of the cultivated crop. However, determination of the soil surface humidity is very difficult on vegetated fields. In order to overcome this problem, polarimetric decomposition models and machine learning based regression model were implemented. The main purpose of this study is to predict soil surface humidity on moderately vegetated fields. Thus, the study is conducted in agricultural fields of Dicle University and it consists of several stages. In the first stage, a Radarsat-2 data was obtained in 3 March 2016 and the local humidity samples were measured simultaneously with the Radarsat-2 acquisition. In the second stage, 10 polarimetric features were obtained from each cell (2x2 pixels) of ground sample by utilizing standard intensity-phase technique as well as Freeman-Durden and H/A/$\alpha$ polarimetric decomposition models. This step is repeated for all ground samples and as a result, a dataset with 156x10 lengths is formed. In the next stage, Extreme Learning Machine based Regression (ELM-R) model was used for predicting the soil surface humidity with the aid of polarimetric SAR features. For the validation of the proposed system, leave-one-out cross-validation method was applied and finally, 2.19% Root Mean Square Error (RMSE) were computed.

Hardness and Wear Behaviours of Al Matrix Composites and Hybrid Composites Reinforced with B 4 C and SiC

Abstract: The conversion into the desired shape of the metal powders using Powder Metallurgy (PM) method enables economically mass productions. This case allows producing parts with complex and high dimensional accuracy with no machining. In this study the composites and hybrid composites with Al matrix were produced using PM method with different ratios B4C and SiC. Microhardness and wear experiments of the produced composites were investigated. Wear experiments were performed at a constant speed of 0.5 m/s, application loads of 5, 10 and 15 N and sliding distances of 250, 500, and 750 m. Then, SEM images of composites and hybrid composites were captured. The increase of the reinforcement ratio in the composites contributed to the increase of the hardness. The highest hardness value was computed as 58.7 HV from 16% B4C reinforced composite. In addition, the increase in the reinforcement ratio contributed to the increase of the wear resistance. The increase in the load and sliding distance also increased the wear. The minimum weight loss was calculated as 18 mg from 5 N load, 250 m sliding distance and 16% SiC reinforced composite.

Project Surveys for Determining and Defining Key Performance Indicators in the Development of Smart Grids in Energy Systems

Abstract: With the increasing energy needs, it has become obligatory to use technology and computer technology in electricity networks with the advancement of technology. By using the possibilities of high technology, it is possible to carry out audits with two-way data flow and intelligent systems on the sides of energy production and consumption. Many smart grid projects have been implemented around the world, such as the establishment of advanced measurement infrastructure, intelligent meter assembly, generation of electricity from renewable energy sources, energy efficiency, use of electric vehicles and smart buildings . Many developed and developing countries have set up platforms for developing Smart Grids and have set the road map for smart grid. In particular, the main objective of the European Union; By 2020, it was possible to integrate renewable energy resources into the system by 20%, increase energy efficiency by 20%, and reduce carbon emissions by 20%. The American government finances many smart grid projects in its country with a certain amount of reimbursement. Japan has launched smart city pilot applications by taking smart grid projects one step further. Europe, the US and Japan except China, South Korea, Canada and Australia has become interested in smart grids. In this study, development and project examples of smart grids worldwide have been investigated.

Geochemical evidence on the depositional environment of Nummulites accumulations around Elazig, Sivas, and Eskişehir (Turkey) in the middle Eocene sub-epoch

Abstract: The main objective of this study was to investigate the major, trace, and rare earth element (REEs) contents, in order to reconstruct the physico-chemical parameters of the marine paleoenvironment which hosted the investigated Nummulites accumulations. The studied samples were collected from Kirkgecit Formation-Elazig, Mamuca Formation, and Bogazkoy Formation in Ipresian-Lutetian ages. All Nummulites samples for major oxides, trace, and rare earth elements were analyzed by using inductively coupled plasma atomic emission spectroscopy (ICP-MS). The CaO contents in Nummulites range from 52.52 to 54.41 wt%. The (Tb/Yb)n–(La/Yb)n ratios of Nummulites show that all rare earth element (REE) contents were depleted during the sedimentation. The PAAS-normalized REE patterns of the Nummulites show similar trends that indicate a weak decrease to heavy REEs from light REEs. All Nummulites have negative Ce and positive Eu anomalies. The Y/Ho values vary between 28 and 41, and these values are close to that of sea water and carbonate rocks. Our findings show that Nummulites had several physicochemical features such as increases in ƒO2 and pH or decrease in temperature in the shallow marine environment.