MATLAB R2013a Research Articles

CONTROLLING WATER LEVEL BY USING MODIFIED MODEL FREE ADAPTIVE CONTROLLER

This paper investigates a simple mathematical model for a water level system, which consists of a DC motor (water pump), and a Speed to Height transformation block, that relates the speed of the motor, to the height of the water level. The input signal is the applied voltage to the armature of the DC motor, while the output signal is the rotational speed of the shaft. A simple modified model-free adaptive controller is suggested, to control the level of water, by adjusting the rate of the incoming water flow to the container, by changing the speed of the water pump, that fills the container. The suggested controller consists of a conventional model free adaptive controller, combined with the proportional integral derivative controller. The parameters of the controller are tuned using two methods. The overall controlled water level system is simulated through MATLAB R2015a software. The results show the efficiency of the suggested controller, when compared to the tuned PID and the MFAC, due to its least fluctuation peak, fast response with a small settling time, and zero steady-state error.

PO-03-104 MRI VERSUS ULTRASOUND: EXPLORING THE INFLUENCE OF IMAGING MODALITY ON THE FIDELITY OF NON-CONTACT CHARGE DENSITY MAPPING

Non-contact charge density mapping of atrial fibrillation (AF) relies on an inverse algorithm and accurate representation of atrial geometry. Current clinical systems utilize intrachamber ultrasound (US) for endocardial surface mesh reconstruction. To compare intrachamber US and MRI derived left atrial (LA) reconstructions and investigate the impact of any geometric discrepancy on non-contact mapping derived conduction metrics. Five patients underwent LA ablation for persistent AF. Intrachamber US data was analyzed with AcQMap 8.5 (Acutus Medical) to produce individual LA surface meshes. Cardiac MRI was acquired up to 6 weeks prior to their procedure on a 3T clinical MR scanner. A proprietary graphical interface tool implemented in Matlab R2014a enabled 3D surface mesh reconstruction from 2D MRI slices. Prior to registration of MRI and US generated surface meshes, the mitral valve (MV), left atrial appendage, and pulmonary veins (PVs) were excluded. Registration was achieved with a two-step approach, using the iterative closest point algorithm followed by a rigid registration using 5 anatomic landmarks: center of 4 PVs and MV holes. Subsequently, non-contact intrachamber voltage measurements from the individuals’ procedures were processed to inversely derive cardiac activation on all surface meshes and measure the resulting conduction speeds (Figure 1A and B). The average distance between surfaces of intrachamber US and MRI reconstructed surface meshes was measured as 3.4±4.0mm. Histograms of non-contact mapping determined conduction speed over surface mesh area for each participant displayed similar overlapping distributions for intrachamber US and MRI (Figure 1C). Conduction speed statistics across the entire population did not show a significant difference between the two modalities (0.66±0.26m/s and 0.63±0.22m/s respectively) (Figure 1D). LA surface reconstructions produced by intrachamber US and cardiac MRI compare favorably. We demonstrate concordance between both modalities in subsequent application of the inverse algorithm required for non-contact charge density mapping and calculation of conduction speed.

Numerical and Analytical Solutions of Space-Time Fractional Partial Differential Equations by Using a New Double Integral Transform Method

This work discusses the beginning of fractional calculus and how the Sumudu and Elzaki transforms are applied to fractional derivatives. This approach combines a double Sumudu-Elzaki transform strategy to discover analytic solutions to space-time fractional partial differential equations in Mittag-Leffler functions subject to initial and boundary conditions. Where this method gets closer and closer to the correct answer, and the technique's efficacy is demonstrated using numerical examples performed with Matlab R2015a.

MONITORING PERKEMBANGAN ANAK USIA DINI DENGAN METODE PERCEPTRON PADA DI TK KASIH BUNDA BATANGHARI

Monitoring the development of early childhood is very important, especially for children aged 4-6 years, because at that time it was a golden age that required more attention in order to stimulate child development to the maximum. This research is expected to be able to help educators to determine the development that occurs in students. The method used in this research is the perceptron method. The perceptron method is a simple Artificial Neural Network (ANN) method that uses a training algorithm to classify linearly, while the input data that becomes the criteria for this research are Moral Religious Values (MRV), Physical Motoric (PM), Cognitive, Social Emotional, Language, and Art. While the output of the data that has been processed is in the form of selected development indicators, this indicator is divided into three, namely Very Good Development [1 1], Evolves According to Expectations [1 0], and Begins to Develop [0 1]. After analyzing the monitoring of early childhood development by applying the perceptron method and the processing it using the Matlab R2009a application, the research concluded that the perceptron method was proven to be able to determine the target output and the actual appropriate ouput reached 68%.

Support Vector Machine Based Classification of Leaf Diseases

Myanmar is well known for agricultural country; wherein about 65% of the labor force depends on agriculture. Since the leaf diseases are microscopic organism, cannot be detected normal human eyes. Leaves are special indicator to distinguish the diseases because the image information of the leaf are changed when the leaf surf the diseases. So, the image processing techniques can be used in agricultural sector. The research work presents a support vector machine classifier algorithm by using MATLAB R2017a for the classification of leaf diseases such as Alternaria Alternata, Cercospora leaf spot, Bacterial Blight and so on. In this research work, RGB color space is converted into HSI (Hue Saturation Intensity) color space. In segmentation step, k-means clustering is used to select the defected area, and it is extracted the features by using GLCM (Gray Level Co-occurrence Matrix). Prior to the features extraction, the median filter is used for getting noise free feature results. Finally, the leaf disease is classified by using support vector machine (SVM) and computes the accuracy. From the obtained results, the maximum accuracy of the system is 83%.

On King type modification of $(p,q)$-Lupas Bernstein operators with improved estimates

This paper aims to modify the $(p,q)$-Lupaş Bernstein operators using King's technique and to establish convergence results of these operators by using of modulus of continuity and Lipschitz class functions. Some approximation results for this new sequence of operators are obtained. It has been shown that the convergence rate of King type modification is better than the $(p,q)$-Lupaş Bernstein operators. King type modification of operators also provide better error estimation within some subinterval of $[0,1]$ in comparison to $(p,q)$-Lupaş Bernstein operators. In the last section, some graphs and tables provided for simulation purposes using MATLAB (R2015a).

Desain Simulasi dan Analisa Closed Loop Boost Converter Terintegrasi Solar PV Berbasis PID

The tendency for the price of using solar PV (photovoltaic) to become cheaper means that the portion of the use of this solar energy source is encouraged to be larger along with the addition of the portion of renewable energy (EBT) generators based on PLN's 2021-2030 Electricity Supply Business Plan (RUPTL). As a step to mitigate this problem, a power electronics circuit application is needed in the form of a boost converter or what can be called a step-up DC-DC converter. In this study, the boost converter design modeling is made a closed loop. There is feedback by adding a PID controller to adjust the duty cycle as a function of switching on or off MOSFET so that the output voltage is as desired (setpoint) and adjusts to needs of load. Open-loop design modeling is also included as a comparison. The design stage begins with determining the circuit design, calculating component specifications, and simulating using MATLAB R2013a software and hardware implementation. From the test results, it was found that the boost converter can increase the solar PV voltage varies (17.34-19.13 Volts). In the closed loop boost converter circuit with a constant load of 129.96 Ω with varying duty-cycle value set by PID controller with value of Kp=0.005 Ki=9.75 Kd=0.00000029, the average output voltage is 36. 0067 Volts and stable at the desired setpoint of 36 Volts.

An inventory model for deteriorating products under preservation technology with time-dependent quality demand

In many real-world inventory deterioration concerns, the rate of deterioration of individual commodities is based on the deteriorating items during the storage period. Inventory models for degrading commodities have become more important in the food production sectors due to the unpredictability of product life spans. This deterioration can be overcome by involving a preservation factor to reduce the effects of decaying objects. This model is applicable for food production, in particular, dairy product (ice cream) suppliers to make inventory decisions in the inventory system under the effect of optimal values. In this paper, a preservative-based inventory model for deteriorating products with quality demand is discussed. The proposed model with cost functions yields the best operating plan and total sales. The policy combinations for dairy products are generally designed to reduce the deterioration rate, the expenditures, and the total cost. To illustrate the value of the proposed model numerical examples involving three flavours of ice creams have been studied. The model was tested with the real-time data obtained from the ice cream factory. Also, the proposed model’s numerical results are compared with the existing model. Further, the sensitivity analysis is performed to study the effect of changes in different parameters of the model. The complex algebraic equations are solved using MATLAB R2013a.

A Reinforcement Learning-based Approach for Active Optimization of High Proportional Distributed Power Supply Access to Rural Distribution Networks

In the process of actively implementing the rural revitalization strategy and the “double carbon” goal, distributed power sources are connected to the rural distribution network on a large scale, resulting in a gradual transition to a new type of active distribution network, which has a greater impact on the distribution network in terms of voltage, network loss, and protection, thus posing greater demands on the distribution network in terms of planning and control, power quality management, and operation. This significantly impacts the voltage, network loss, and protection of the distribution network, thus putting higher requirements on the planning and control, power quality management, operation, and maintenance of distribution networks. Since the traditional OPF algorithm has great defects in solving large-scale decentralized, time-series, and stochastic nonlinear problems, in order to realize real-time control of distribution networks under more complex network conditions, this paper proposes an optimization algorithm based on reinforcement learning and applies it to real-time control of active distribution networks, taking the optimal network loss as the objective function and considering network voltage, line load factor, and distributed power supply. As a de-modeling artificial intelligence method, reinforcement learning has great advantages in solving large problems with complex mathematical models. The method’s control effect and convergence time can be improved by introducing imitation learning and migration learning, expanding the knowledge matrix, and introducing risk assessment mechanisms. Based on MATLAB R2017a simulation platform for the IEEE 33-node improvement model, the simulation results show that the reinforcement learning method has a better control effect in solving the real-time control problem of active distribution network, significantly alleviates the voltage crossing limit, effectively reduces the network loss, and has good real-time characteristics.

Forecasting method for sources and loads in virtual power plant considering uncertainties of multiple sources and loads

Because of to the lack of primary energy and the deteriorating global environment, the key to getting rid of the existing difficulties is the development of clean and pollution-free new energy. On the one hand, to reduce the use of fossil energy, and on the other hand, to reduce emissions of polluting gases. So with the development of technology, more and more distributed power and load began to access the virtual power plant. This paper introduces PV and EVs(EVs) from the perspective of new energy applications and expounds on the background and significance of load forecasting and output power prediction. This paper introduces a variety of load prediction methods. Finally, Monte Carlo simulation is used to predict the EVs’ charging load, and BP neural network is used to build a photovoltaic power forecasting model. Based on the MATLAB R2017a simulation platform, this paper analyzes the EVs’ travel features in Beijing and takes the historical data and meteorological information of a photovoltaic power plant in Australia as an instance to analyze the influencing factors of PV.

Predict the quality and safety of chicken sausage through computer vision technology

The aim of this study was to test the ability of image technology to predict quality and safety of chicken sausage. Chicken sausages were chosen for image capture. Traits evaluated were color indexes (L*, a*, b*), pH, drip loss, cooking loss, dry matter, moisture, crude protein, ether extract, ash, thiobarbituric acid reactive substances (TBARS), peroxide value (POV), free fatty acid (FFA), total coliform count (TCC), total yeast and mold count (TYMC) and total viable count (TVC). Images were analyzed using the software Matlab (R2015a). Conventional analytical technology i.e., proximate, bio-chemical and microbiological analyses were followed for reference value. Calibration and prediction model were fitted using The Unscrambler X software. Results of this work show that image technology may be a useful tool for prediction of meat quality traits in the laboratory and meat processing industries. The L* value from imaging analysis had medium correlation with a* (r=0.28), b* (r=0.29), pH (r=0.31). A medium correlation found in CP (0.29) with „a*‟ value obtained from imaging analysis. In this experiment we found lower calibration and prediction accuracy in a*, crude protein and ether extract value. From this study it may be recapitulated that image technology has a potentiality to replace analytical technology for meat laboratory and processing units.

Productive performances of native sheep in Bangladesh

The aim of this study was to test the ability of image technology to predict quality and safety of chicken sausage. Chicken sausages were chosen for image capture. Traits evaluated were color indexes (L*, a*, b*), pH, drip loss, cooking loss, dry matter, moisture, crude protein, ether extract, ash, thiobarbituric acid reactive substances (TBARS), peroxide value (POV), free fatty acid (FFA), total coliform count (TCC), total yeast and mold count (TYMC) and total viable count (TVC). Images were analyzed using the software Matlab (R2015a). Conventional analytical technology i.e., proximate, bio-chemical and microbiological analyses were followed for reference value. Calibration and prediction model were fitted using The Unscrambler X software. Results of this work show that image technology may be a useful tool for prediction of meat quality traits in the laboratory and meat processing industries. The L* value from imaging analysis had medium correlation with a* (r=0.28), b* (r=0.29), pH (r=0.31). A medium correlation found in CP (0.29) with „a*‟ value obtained from imaging analysis. In this experiment we found lower calibration and prediction accuracy in a*, crude protein and ether extract value. From this study it may be recapitulated that image technology has a potentiality to replace analytical technology for meat laboratory and processing units.

Interdependence of External Magnetic and Temperature Effect on Thermodynamic Properties of GaAs Two Electron Quantum Dot

The particular interest of this paper is to investigate the impact of various values of temperature exposed to weak and strong magnetic field strength. A thermodynamic property's oscillatory change as a function of magnetic field effect (B) intensifies the quantization of electron orbits in a constant magnetic field intensity and is the primary contributor to the de Haas-van Alphen effects due to cyclotron frequency and its impact on localizing electron at circular region imposed with the magnetic field that is in contrary to the result of the temperature effect. Thus the interdependent effects of external magnetic field and temperature on thermodynamic properties are studied with harmonic oscillator potentials considering material parameters of GaAs quantum dot. The finite energy state is analytically solved using Nikiforov-Uvarov mathematical formalism. Moreover, the direct impact of the external magnetic fields and temperature on thermodynamic properties of the system is analyzed, and numerically simulated using matlab R2017a version. The dominance of temperature over the external magnetic field and vice versa effect is investigated, thus the value specific heat capacity fluctuated, while the equiponderate impact of temperature and magnetic field shows similar steady values of the specific heat capacity. The study clearly shows the interdependence of magnetic field and temperature affect thermodynamic quantities: partition function, mean energy, entropy, and specific heat capacity.

Artificial neuro-fuzzy intelligent prediction of techno-economic parameters of computer-aided scale-up for palm kernel oil based biodiesel production

Palm kernel oil (PKO) is one of the promising starting materials for biodiesel production. Economic viability of large-scale biodiesel production from PKO happens to be the major challenge, as investors would like to know the overall cost-benefit value before making decisions. Therefore, this study develops artificial intelligence (AI) techno-economic models for predicting overall cost-benefit value which will provide fundamental investment decisions for potential investors. The two AI techniques used in this study were artificial neural networks (ANN) and adaptive neuro-fuzzy inference system (ANFIS). The input-output data for modelling was gotten from a previous work which based solely on experimental design for PKO for biodiesel production. The input variables are Methanol:oil ratio, temperature, catalyst quantity, residence time and catalyst calcination temperature, while return on investment (ROI), payback time (PBT), net present value (NPV) and production capacity (PC) are the responses. ANN and Fuzzy Logic Toolboxes in MATLAB R2013a were used for model implementation. The developed models were appraised using statistical indices such as coefficient of determination (R2) and root mean square error (RMSE). The results showed that, trimf based ANFIS models (ROI- R2: 0.9999; RMSE: 7.39 × 10−7; PBT- R2: 0.9999; RMSE: 5.32 × 10−7; NPV- R2: 0.9999; RMSE: 5.89 × 10−7; PC- R2: 0.9999; RMSE: 5.89 × 10−7) performed marginally better than ANN models (ROI- R2: 0.9496; RMSE: 0.0599; PBT- R2: 0.9945; RMSE: 0.0373; NPV- R2: 0.9957; RMSE: 0.0384; PC- R2: 0.9959; RMSE: 0.0376). Also, the relative significance of input parameters based on sensitivity analysis showed catalyst calcination temperature (CT) as the most significant input parameter. These findings show that both the ANFIS and ANN models are effective in predicting techno-economic parameters.

Number Recognition Techniques to Automate Revenue Collection at Dodoma Mini Bus Terminals in Tanzania

The manual revenue collection process currently operating in Tanzania, has deprived the Local Government Authority (LGA) of its potential income. Meanwhile, The Number Recognition (NR), is a modern technique that uses optical character recognition on images to obtain desired characters. The technique involves Image acquisition, pre-processing, character segmentation and Character recognition. This technique has been used in activities like license plate detection as Automatic Number Plate Recognition (ANPR). This research attempts to solve the problem by automating the current revenue collection process at the mini bus terminals in Tanzania taking a case of Dodoma Municipal using Number recognition techniques and Optical Character Recognition (OCR). The research adopted a case study research design and simulation. In addition, the study used key informant interviews and observations to acquire a good understanding of the current operations at the mini bus terminals and the necessary requirements to achieve the main goal of the study. Simulation of the Number Recognition System (NRS) was achieved by using Matlab R2017a as a simulation tool on a Dell computer running windows 7 professional, Hard Disk Drive (HDD) 500 Gigabyte (GB), Random Access Memory (RAM) 4 Gigabyte. The research, achieved a simulation for NRS with an accuracy of 0.988 for the character recognition of the captured Surface on mini buses. The researcher recommends a further study on the image acquisition process and messaging alert system, to completely automate the process.

Penaksiran Parameter Pada Distribusi Erlang Berdasarkan Metode Maksimum Likelihood Dengan Menggunakan Algoritma Newton Raphson Dan Fisher Scoring

The Erlang distribution is a special case of the Gamma distribution with the k shape parameter and the λ rate parameter. In this study, the parameter estimation of the Erlang distribution was carried out using the Maximum Likelihood method. In maximizing the function, an implicit and non-linear form is obtained, then it is solved using the Newton Raphson algorithm. Apart from Newton Raphon, the estimation of parameters was also carried out using the Fisher Scoring algorithm. The Fisher Scoring algorithm is similar to the Newton Raphson algorithm, the difference is that Fisher Scoring uses an matrix information. The result of parameter estimation in Erlang distribution using Newton Raphson algorithm which is applied to outgoing telephone call data that generated by Matlab R2010a software cannot be done simultaneously. Therefore, the parameter assessment is carried out on the k parameter first, then followed by the λ parameter estimation and the parameter and = 0.6886812 are obtained. Meanwhile, the parameter estimation using the Fisher Scoring algorithm produces an equation that is not different from the Newton Raphson algorithm

VARIABLE THERMAL CONDUCTIVITY AND VISCOSITY EFFECTS ON THIN FILM FLOW OVER AN UNSTEADY POROUS STRETCHING SHEET

The aim of our current investigation is to examine, heat and mass transfer rates of thin liquid film, considering a permeable stretching sheet. In this study, we assume that the medium is porous, the sheet is unstable, and the flow is two-dimensional with nonlinear radiation, variable viscosity, and variable thermal conductivity. Similarity variables are used to convert partial differential equations (PDEs) into dimensionless ordinary differential equations (ODEs), which are solved numerically by Runge-Kutta-Fehlberg (RKF45) method along with shooting technique. MATLAB R2017a is used to illustrate the effects of unsteady, magnetic field, permeability, radiation, suction, temperature ratio, and chemical reaction parameter on velocity, temperature, and concentration profiles. Numerical values of physical quantities are provided in tabular form. The velocity profile tends to diminish as the permeability parameter increases. Our current results show great agreement with the previously reported investigations.

Production Scheduling Model and Machine Assignment to Automotive Parts Manufacturing for Optimum Production

PT. XYZ is a company engaged in the field of Automotive Parts Manufacturer. In carrying out its production this company uses make to order. The company carries out simple planning and scheduling, namely FCFS (First Come First Serve). The company has a problem, namely there is no production scheduling with the appropriate machine assignment. This causes the company to sometimes experience delays in completing the product. The purpose of this study is to overcome scheduling problems in companies to minimize order completion time (makespan). The method used in this study is the Heuristic Pour method, namely the development of a new heuristic algorithm in completing flowshop scheduling with the aim of minimizing makespan based on a combination approach and Genetic Algorithm which will be assisted with the help of MATLAB R2016a software which is a computational algorithm commonly used to solve a search for values in an optimization problem. Based on the data processing that has been done, the results of production scheduling are obtained using the Heuristic Pour method in the order Job 4 (J4)-Job 2 (J2) -Job 7 (J7) -Job 6 (J6) -Job 5 (J5) -Job 3 (J3) ) -Job 1 (J1) gets a makespan of 19440 minutes or equal to 16 working days with a total delay of 1 working day. While production scheduling with the selected Genetic Algorithm method getsthe order Job 4 (J4) -Job 2 (J2) -Job 7 (J7) -Job 6 (J6) -Job 3 (J3) -Job 1 (J1) -Job 5 ( J5) with the best makespan of 18660 minutes or 15 days, and there was no delay in completing requests. The company is expected to consider or review the use of the appropriate production scheduling method because it can provide a more optimum demand completion time (makespan), so the company can reduce delays in fulfilling customer requests.

An Accelerated Three Term Efficient Algorithm for Numerical Optimization

Background:
 A new optimization algorithm is presented. The method is biased with new non monotone line search an accelerated three term conjugate gradient method damped of Quasi Newton method compared to previews method design efficiency in provident of more than one factor for different optimization problem are more dramatic due to the ability of the technique to utile existing data.
 Materials and Methods:
 New monotone line search, new monotone line search, new modification of Damped Quasi-Newton method, Motivation and New Quasi-Newton Algorithm (MQ) and Global convergence.
 Results:
 In this work, we have n tendency to compare our new algorithm with same classical strategies like [7] by exploiting of unconstrained nonlinear optimization problem the functions obtained from Andrei [5, 6] Waziri and Sabiu (2015)[10] and La couzetul (2004)[3]. The numerical experiments demonstrate the performance of the proposed method. We selected seven relatively unconstrained problems with the size varies from 10 to 100. We consider the three sizes of each problem so that the total number of problem is 21 test problems. We stop the iteration when is satisfied All codes were written in Matlab R2017a and run on a pc with Intel COREi4 with a processor with 4GB of Ram and CPU 2.3GHZ we solved test problems using two different initial starting points.
 Conclusion:
 In this research article, a project on an accelerated three-term efficient algorithm for numerical optimization has presented the method as completely a derivative-free algorithm with less NOI and NOF and CPU time computed to the existing methods .using classical assumption the global convergence was also proved. Numerical results using the three terms efficient algorithm show that the algorithm is promising.

Early detection of liver disorders using hybrid soft computing techniques for optimal feature selection and classification

SummaryHuman liver disease is a sort of illness that starts in the ovaries and is particularly dangerous for women. As a consequence, aberrant cells develop that have the potential to spread to other parts of the body. Liver disease is a serious disorder that affects women's ovaries and is difficult to identify early on, which is why it is still one of the leading causes of mortality. The significance of unequivocal confirmation of intrinsic and typical components in establishing new frameworks to detect and eliminate danger is substantial. In this study, we present a hybrid soft computing approach for early diagnosis of liver problems (EDLD‐HS). For high and low level feature extraction models, we first employ an improved ant swarm optimization (IASO) approach. Then, for optimum feature selection, we develop a modified whale search optimization (MWSO) technique that combines features that may reflect both texture patterns and semantic backdrop scattered in the data. After that, we employed a hybrid swallow swarm intelligent‐deep neural network (HSSI‐DNN) classifier to determine the stage of liver illness. Finally, we used MATLAB R2014a to test our proposed EDLD‐HS method using well‐known benchmark datasets including BUPA, ILPD, and MPRLPD. The simulation findings are compared to existing state‐of‐the‐art methodologies in terms of accuracy, specificity, sensitivity, precision, recall, F1‐score, G‐mean, and area under curve (AUC). The detection accuracy of the proposed HSSI‐DNN classifier is 83.26% (BUPA), 84.26% (ILPD), and 91.23% (ILPD) (MPRLPD).