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Design and simulation of video monitoring structure over TCP/IP system using MATLAB

10.11591/ijeecs.v24.i3.pp1840-1845
Amany Mohammad Abood , Maysam Sameer Hussein , Zainab G. Faisal , Zainab H. Tawfiq
Video monitoring systems are undergoing an evolution from conventional analog to digital clarification to provide better rate and security over internet protocols. In addition, analog surveillance becomes insufficient to face enormous demand of security of system contains more than hundreds of camera often deployed in hotels environments far away from room control. This paper presents the design and simulation of a video monitoring scheme in excess of a transmission control protocol/internet protocol (TCP/IP) system using MATLAB. Sophisticated cameras could record directly high-definition digital videos based on digital technology which simply communicate the control room relaying on ordinary internet protocol infrastructure networks. This technology provides a flexible network interface over a wide variety of heterogeneous technology networks. Though, the acceptance of IP designed for video monitoring pretense severe difficulties in terms of power processing, system dependability, required bandwidth, and security of networks. The advantage of IP based video monitoring system has been investigated over conventional analog systems and the challenges of the method are described. The open research issues are still requiring a final solution to permits complete abandon against conventional technology of analog methods. In conclusion, the method to tackle the purpose of video monitoring in actual operation is proposed and verified properly by means of model simulation.
Volume: 24
Issue: 3
Page: 1840-1845
Publish at: 2021-12-01

Forecasting model with machine learning in higher education ICFES exams

10.11591/ijece.v11i6.pp5402-5410
Daniel Esteban Martínez Cervera , Octavio José Salcedo Parra , Marco Antonio Aguilera Prado
In this paper, we proposed to make different forecasting models in the University education through the algorithms K-means, K-closest neighbor, neural network, and naïve Bayes, which apply to specific exams of engineering, licensed and scientific mathematical thinking in Saber Pro of Colombia. ICFES Saber Pro is an exam required for the degree of all students who carry out undergraduate programs in higher education. The Colombian government regulated this exam in 2009 in the decree 3963 intending to verify the development of competencies, knowledge level, and quality of the programs and institutions. The objective is to use data to convert into information, search patterns, and select the best variables and harness the potential of data (average 650.000 data per semester). The study has found that the combination of features was: women have greater participation (68%) in Mathematics, Engineering, and Teaching careers, the urban area continues to be the preferred place to apply for higher studies (94%), Internet use increased by 50% in the last year, the support of the family nucleus is still relevant for the support in the formation of the children.
Volume: 11
Issue: 6
Page: 5402-5410
Publish at: 2021-12-01

Analysis of WEKA data mining algorithms Bayes net, random forest, MLP and SMO for heart disease prediction system: A case study in Iraq

10.11591/ijece.v11i6.pp5229-5239
Rana Riad K. AL-Taie , Basma Jumaa Saleh , Ahmed Yousif Falih Saedi , Lamees Abdalhasan Salman
Data mining is defined as a search through large amounts of data for valuable information. The association rules, grouping, clustering, prediction, sequence modeling is some essential and most general strategies for data extraction. The processing of data plays a major role in the healthcare industry's disease detection. A variety of disease evaluations should be required to diagnose the patient. However, using data mining strategies, the number of examinations should be decreased. This decreased examination plays a crucial role in terms of time and results. Heart disease is a death-provoking disorder. In this recent instance, health issues are immense because of the availability of health issues and the grouping of various situations. Today, secret information is important in the healthcare industry to make decisions. For the prediction of cardiovascular problems, (Weka 3.8.3) tools for this analysis are used for the prediction of data extraction algorithms like sequential minimal optimization (SMO), multilayer perceptron (MLP), random forest and Bayes net. The data collected combine the prediction accuracy results, the receiver operating characteristic (ROC) curve, and the PRC value. The performance of Bayes net (94.5%) and random forest (94%) technologies indicates optimum performance rather than the sequential minimal optimization (SMO) and multilayer perceptron (MLP) methods.
Volume: 11
Issue: 6
Page: 5229-5239
Publish at: 2021-12-01

Modified multistep model predictive control for three-phase induction motor drive system considering the common-mode voltage minimization

10.11591/ijpeds.v12.i4.pp2251-2260
Bao Binh Pho , Nguyen Van Cao , Tran Minh Hoan , Phuong Vu
It is acknowledged that the common-mode voltage may have detrimental effects on an induction motor (IM) drive system if not properly addressed. Therefore, in this paper, a modified multistep model predictive control method for IM drive system considering the common-mode voltage minimization is proposed. This research uses a multi-objective cost function, before applying the Sphere Decoding Algorithm to find the optimal control input. The results show that the proposed control method not only reduces the common-mode voltage significantly but also mitigates the computational burden of the microprocessor without affecting the system performance. The proposed control method is simulated by MATLAB-Simulink for an IM drive system with an 11-level cascaded H-bridge inverter.
Volume: 12
Issue: 4
Page: 2251-2260
Publish at: 2021-12-01

Modeling of hybrid active power filter using artificial intelligence controller: hardware and software prospective

10.11591/ijpeds.v12.i4.pp2545-2556
Sandhya P. , Nagaraj R.
The power factor is a significant concern in power systems. The significant power loss occurred due to electronic and electrical equipment damages affected by the deviation of physical characteristics, including voltage, current, and frequency parameters.The power loss and quality issues were resolved by introducing filtering techniques in electronic and electrical equipment. Many filtering techniques include passive filtering (PF), Active power filter (APF), and many hybrid approaches are already available. Most of these methods use proper compensation controlling approaches and failed to minimize the total harmonic distortion (THD), and harmonic mitigation in power systems has its best. In this article, an efficient Hybrid-APF using Artificial-Neuro Fuzzy interface system (ANFIS) for software and hardware perspective is designed. The proposed approach uses hybrid controlling strategies which include PI with artificial intelligence (ANFIS) controller, to control the power losses for H-APF. Additionally, current compensation is achieved by PQ-theory, followed by Hysteresis-Current- Controller (HCC). The hardware architecture of ANFIS with HCC is designed to improve the chip-area for real-time power applications.The present work analyzed by simulating the voltage and current waveform. The proposed-H-APF using ANFIS controller, both software and hardware approaches, is compared with other control techniques like H-APF with PI and Fuzzy logic controller by concerning THD,Reactive power, and Different Harmonics and loads improvements.
Volume: 12
Issue: 4
Page: 2545-2556
Publish at: 2021-12-01

Mining knowledge graphs to map heterogeneous relations between the internet of things patterns

10.11591/ijece.v11i6.pp5066-5080
Vusi Sithole , Linda Marshall
Patterns for the internet of things (IoT) which represent proven solutions used to solve design problems in the IoT are numerous. Similar to object-oriented design patterns, these IoT patterns contain multiple mutual heterogeneous relationships. However, these pattern relationships are hidden and virtually unidentified in most documents. In this paper, we use machine learning techniques to automatically mine knowledge graphs to map these relationships between several IoT patterns. The end result is a semantic knowledge graph database which outlines patterns as vertices and their relations as edges. We have identified four main relationships between the IoT patterns-a pattern is similar to another pattern if it addresses the same use case problem, a large-scale pattern uses a small- scale pattern in a lower level layer, a large pattern is composed of multiple smaller scale patterns underneath it, and patterns complement and combine with each other to resolve a given use case problem. Our results show some promising prospects towards the use of machine learning techniques to generate an automated repository to organise the IoT patterns, which are usually extracted at various levels of abstraction and granularity.
Volume: 11
Issue: 6
Page: 5066-5080
Publish at: 2021-12-01

Multiclassification of license plate based on deep convolution neural networks

10.11591/ijece.v11i6.pp5266-5276
Masar Abed Uthaib , Muayad Sadik Croock
In the classification of license plate there are some challenges such that the different sizes of plate numbers, the plates' background, and the number of the dataset of the plates. In this paper, a multiclass classification model established using deep convolutional neural network (CNN) to classify the license plate for three countries (Armenia, Belarus, Hungary) with the dataset of 600 images as 200 images for each class (160 for training and 40 for validation sets). Because of the small numbers of datasets, a preprocessing on the dataset is performed using pixel normalization and image data augmentation techniques (rotation, horizontal flip, zoom range) to increase the number of datasets. After that, we feed the augmented images into the convolution layer model, which consists of four blocks of convolution layer. For calculating and optimizing the efficiency of the classification model, a categorical cross-entropy and Adam optimizer used with a learning rate was 0.0001. The model's performance showed 99.17% and 97.50% of the training and validation sets accuracies sequentially, with total accuracy of classification is 96.66%. The time of training is lasting for 12 minutes. An anaconda python 3.7 and Keras Tensor flow backend are used.
Volume: 11
Issue: 6
Page: 5266-5276
Publish at: 2021-12-01

MedicPlant: A mobile application for the recognition of medicinal plants from the Republic of Mauritius using deep learning in real-time

10.11591/ijai.v10.i4.pp938-947
Sameerchand Pudaruth , Mohamad Fawzi Mahomoodally , Noushreen Kissoon , Fadil Chady
To facilitate the recognition and classification of medicinal plants that are commonly used by Mauritians, a mobile application which can recognise seventy different medicinal plants has been developed. A convolutional neural network (CNN) based on the TensorFlow framework has been used to create the classification model. The system has a recognition accuracy of more than 90%. Once the plant is recognised, a number of useful information is displayed to the user. Such information includes the common name of the plant, its English name and also its scientific name. The plant is also classified as either exotic or endemic followed by its medicinal applications and a short description. Contrary to similar systems, the application does not require an internet connection to work. Also, there are no pre-processing steps, and the images can be taken in broad daylight. Furthermore, any part of the plant can be photographed. It is a fast and non-intrusive method to identify medicinal plants. This mobile application will help the Mauritian population to increase their familiarity of medicinal plants, help taxonomists to experiment with new ways of identifying plant species, and will also contribute to the protection of endangered plant species.
Volume: 10
Issue: 4
Page: 938-947
Publish at: 2021-12-01

Comparative techno-economic analysis of power system with and without renewable energy sources

10.11591/ijeecs.v24.i3.pp1260-1268
Hephzibah Jose Queen , Jayakumar J. , Deepika T. J.
The primary aim of this work is to feature the advantages of integrating natural source of energy from the solar and wind to the prevailing electric power systems. Two types of analysis are carried out in two test systems (standard and modified test systems) and the outcome of the test systems are compared. The two analyses are technical analysis and economic analysis. The stability of the voltage is analyzed under technical analysis and the price of energy consumed from the electric grid is calculated and analyzed under the economic analysis. Dynamic hourly load data, hourly solar radiation, hourly wind velocity, and dynamic electricity prices are considered for the standard IEEE system and modified test system (with the integration of RES). Voltage stability index (L-Index) and price of the electricity consumed from electric grid are found for standard test system and the outcome is compared with the outcome of modified test systems. MATLAB coding is done for techno-economic analysis for both test systems. It is inferred from the outcome that the integration of renewable energy sources fairly contributes to the economic benefit of the system by lowering the power purchased from the grid and enhance the stability of the system.
Volume: 24
Issue: 3
Page: 1260-1268
Publish at: 2021-12-01

Verification and comparison of MIT-BIH arrhythmia database based on number of beats

10.11591/ijece.v11i6.pp4950-4961
Akram Jaddoa Khalaf , Samir Jasim Mohammed
The ECG signal processing methods are tested and evaluated based on many databases. The most ECG database used for many researchers is the MIT-BIH arrhythmia database. The QRS-detection algorithms are essential for ECG analyses to detect the beats for the ECG signal. There is no standard number of beats for this database that are used from numerous researches. Different beat numbers are calculated for the researchers depending on the difference in understanding the annotation file. In this paper, the beat numbers for existing methods are studied and compared to find the correct beat number that should be used. We propose a simple function to standardize the beats number for any ECG PhysioNet database to improve the waveform database toolbox (WFDB) for the MATLAB program. This function is based on the annotation's description from the databases and can be added to the Toolbox. The function is removed the non-beats annotation without any errors. The results show a high percentage of 71% from the reviewed methods used an incorrect number of beats for this database.
Volume: 11
Issue: 6
Page: 4950-4961
Publish at: 2021-12-01

Least mean sixth control approach for three-phase three-wire grid-integrated PV system

10.11591/ijpeds.v12.i4.pp2131-2139
Touheed Khan , Mohammed Asim , Mohammad Saood Manzar , Md Ibrahim , Shaikh Sadaf Afzal Ahmed
This work proposes an adaptive filter based on a new least mean sixth control approach with incremental conductance method of MPP for 3-phase grid-incorporated photovoltaic (PV) system. The proposed system comprises a PV array, 3-phase DC to AC converter, maximum power point tracker (MPPT), three-phase electronic load, and a 3-phase grid. The combination of solar PV array and the voltage source converter (VSC) supplies power to the grid. The 3-phase inverter as a distribution static synchronous compensator (D-STATCOM) improves the quality of the system performance in case of zero solar irradiation. D-STATCOM also reduces total harmonic distortion (THD) in grid currents, improves power factor, and maintainsa constant voltage at the point of common coupling (PCC). The system modelling and simulation is achieved on MATLAB/Simulink. The proposed system performance has been found satisfactory and conform to IEEE-519 standards.
Volume: 12
Issue: 4
Page: 2131-2139
Publish at: 2021-12-01

A smart algorithm for fault detection and location in electric power distribution system

10.11591/ijpeds.v12.i4.pp2123-2130
Hamid Touijer , Mohammed El Alami , Mustapha Zahri , Mohamed Habibi
In an electric power distribution system (EPDS), fault location accuracy is critical for system stability. In the past, several algorithms have shown that they are inefficient. However, the results of these algorithms have been shown to be inefficient, and they should not be used for every sort of the faults. This paper presents a new algorithm capable to determine the location of fault accurately with low error rate. It is based on the voltage and current calculation at the source station for different types of faults by using either one power supply or double power supply. The work includes the formulation analytical development as simulation test results. The test results are produced by numerical simulation using data from a recognized distribution line in the literature.
Volume: 12
Issue: 4
Page: 2123-2130
Publish at: 2021-12-01

Development of cost-effective phasor measurement unit for wide area monitoring system applications

10.11591/ijece.v11i6.pp4731-4739
V. Vijaya Rama Raju , K. H. Phani Shree , S. V. Jayarama Kumar
Sustained growth in the demand with unprecedented investments in the transmission infrastructure resulted in narrow operational margins for power system operators across the globe. As a result, power networks are operating near to stability limits. This has demanded the electrical utilities to explore new avenues for control and protection of wide area systems. Present supervisory control and data acquisition/energy management systems (SCADA/EMS) can only facilitate steady state model of the network, whereas synchrophasor measurements with GPS time stamp from wide area can provide dynamic view of power grid that enables supervision, and protection of power network and allow the operator to take necessary control/remedial measures in the new regime of grid operations. Construction of phasor measurement unit (PMU) that provide synchrophasors for the assessment of system state is widely accepted as an essential component for the successful execution of wide area monitoring system (WAMS) applications. Commercial PMUs comes with many constraints such as cost, proprietary hardware designs and software. All these constraints have limited the deployment of PMUs at high voltage transmission systems alone. This paper addresses the issues by developing a cost-effective PMU with open-source hardware, which can be easily modified as per the requirements of the applications. The proposed device is tested with IEEE standards.
Volume: 11
Issue: 6
Page: 4731-4739
Publish at: 2021-12-01

Metaheuristic optimization in neural network model for seasonal data

10.12928/telkomnika.v19i6.20409
Budi; Diponegoro University Warsito , Rukun; Diponegoro University Santoso , Hasbi; Diponegoro University Yasin
The use of metaheuristic optimization techniques in obtaining the optimal weights of neural network model for the time series was the main part of this research. The three optimization methods used as experiments were genetic algorithm (GA), particle swarm optimization (PSO), and modified bee colony (MBC). Feed forward neural network (FFNN) was the neural network (NN) architecture chosen in this research. The limitations and weaknesses of gradient-based methods for learning algorithm inspired some researchers to use other techniques. A reasonable choice is non-gradient based method. Neural network is inspired by the characteristics of creatures. Therefore, the optimization techniques which are also resemble the patterns of life in nature will be appropriate. In this study, various scenarios on the three metaheuristic optimization methods were applied to get the best one. The proposed procedure was applied to the rainfall data. The experimental study showed that GA and PSO were recommended as optimization methods at FFNN model for the rainfall data.
Volume: 19
Issue: 6
Page: 1892-1901
Publish at: 2021-12-01

Improved sliding mode power control of doubly-fed-induction generator under wind speed variation

10.11591/ijpeds.v12.i4.pp2443-2450
Bouiri Abdesselam , Benoudjafer Cherif , Boughazi Othmane , Abdallah Abden , Chojaa Hamid
Due to drawbacks of classical linear controller like proportional-integral (PI), many studies have been used non-linear controller specially when it comes to robustness, but this is less efficient in sliding mode controller (SM) due to the sign function, this function is known as a problem chattering phenomenon, this main disadvantage it can be compensated by Lyapunov backstepping condition, This paper presents nonlinear power control strategy of the doubly-fed-induction generator (DFIG) for wind application system (WAS) using sliding mode combining with backstepping controller (SM-BS) to control produced statoric powers to mitigate unnecessary chattering effects inherent in traditional SMC, to check the effectiveness of the controller, we compare performance of sliding mode controller and sliding mode controller combining with backstepping (SM-BS) in terms of required reference tracking, robustness under parametric variations of the generator, sensitivity to perturbations and reaction to speed variations under investigating further of the chattering phenomenon.
Volume: 12
Issue: 4
Page: 2443-2450
Publish at: 2021-12-01
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