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INDONESIA
Indonesian Journal of Electrical Engineering and Informatics (IJEEI)
Published by IAES Indonesia Section and Partners
ISSN : 20893272     EISSN : -     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) is a peer reviewed International Journal in English published four issues per year (March, June, September and December). The aim of Indonesian Journal of Electrical Engineering and Informatics (IJEEI) is to publish high-quality articles dedicated to all aspects of the latest outstanding developments in the field of electrical engineering. Its scope encompasses the engineering of Telecommunication and Information Technology, Applied Computing & Computer, Instrumentation & Control, Electrical (Power), Electronics, and Informatics.
Arjuna Subject : -
Articles 22 Documents
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Search results for , issue "Vol 10, No 4: December 2022" : 22 Documents clear
Early Detection of Diabetic Retinopathy Based Artificial Intelligent Techniques Aseel Nusrat Abdullah; Ayca Kurnaz Turkben
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 10, No 4: December 2022
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v10i4.4156

Abstract

The eye is impacted by several disorders, either directly or indirectly. As a result, eye exams are a crucial component of general healthcare. One of the effects of diabetes is diabetic retinopathy (DR), which affects the blood vessels that supply and nourish the retina and causes severe visual loss. One of the prevalent eye conditions and a consequence of diabetes that affects the eyes is diabetic retinopathy. The symptoms of diabetic retinopathy may be absent or minimal. It may eventually result in blindness. Therefore, seeing symptoms early could aid in preventing blindness. This paper aims to research automatic methods for detecting diabetic retinopathy and create a reliable system for doing so. A modified extracted feature for the automatic identification of DR in digital fundus pictures is presented. The properties of exudates, blood vessels, and microaneurysms—three elements of diabetic retinopathy—are reported utilizing a variety of image processing techniques. Back Propagation Neural Networks (BPNN) and Support Vector Machine (SVM) classifiers are used to categorize the phases. SVM, which has accuracy, sensitivity, and specificity of 96.5, 97.2, and 93.3 percent, respectively, is the model that performs the best overall.The eye is impacted by several disorders, either directly or indirectly. As a result, eye exams are a crucial component of general healthcare. One of the effects of diabetes is diabetic retinopathy (DR), which affects the blood vessels that supply and nourish the retina and causes severe visual loss. One of the prevalent eye conditions and a consequence of diabetes that affects the eyes is diabetic retinopathy. The symptoms of diabetic retinopathy may be absent or minimal. It may eventually result in blindness. Therefore, seeing symptoms early could aid in preventing blindness. This paper aims to research automatic methods for detecting diabetic retinopathy and create a reliable system for doing so. A modified extracted feature for the automatic identification of DR in digital fundus pictures is presented. The properties of exudates, blood vessels, and microaneurysms—three elements of diabetic retinopathy—are reported utilizing a variety of image processing techniques. Back Propagation Neural Networks and Support Vector Machine classifiers are used to categorize the phases. SVM, which has accuracy, sensitivity, and specificity of 96.5, 97.2, and 93.3 percent, respectively, is the model that performs the best overall..
Measurement and Analysis with KPIs based on an AMI system Kevin Morgado; Álvaro Zambrano; Javier Rosero Garcia
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 10, No 4: December 2022
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v10i4.3864

Abstract

This paper presents the development of a series of key performance indicators (KPI´s) for the electrical system of the campus of the National University of Colombia based on the deployed smart metering infrastructure (AMI). In order to develop the proposed indicators, it was necessary to use different sources of information to complement the data provided by the AMI system. For each of the proposed indicators is presented the way in which each selected indicator is calculated, and an analysis of the behavior obtained for each KPI. It was possible to observe how, based on the results obtained from the different indicators proposed, periods of inefficiency in terms of electricity consumption were identified. Finally, the conclusions obtained during the development of the project are presented.
Multi-Channel Configuration for improving received signal strength in non-line-of-sight environments of indoor visible light communication localization Hilal A. Fadhil; Feras H. AlAttar; Tariq Adnan Fadil; Haider J. Abd
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 10, No 4: December 2022
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v10i4.3880

Abstract

In modern engineering technologies, energy conservation is a factor of primary concern. A feature of Light-emitting diode (LED) light sources is the ability to transmit information in addition to illumination at no additional cost. VLC (Visible Light Communication) is gaining an upper hand over the traditional RF data communication model, as it utilizes a technology by which light can be used to transmit data. It is commonly seen that dealing with non-line of sight (NLOS) is a major challenge for VLC systems as the light intensity is reflected in a variety of directions. To overcome this drawback, a new technique based on multichannel configuration is utilized to enhance the overall system performance. An indoor VLC model is designed and simulated on the basis of the eye-diagram, bit error rate, and received power of the proposed model. We also investigated the model under the influence of ambient light noise. The corresponding results are compared with the conventional NLOS system and an inference made shows the significant improvement for the next-generation optical communication system.
Novel Robust Control Using a Fractional Adaptive PID Regulator for an unstable system Yassine Bensafia; Abdelhakim Idir; Khatir Khettab; Muhammad Saeed Akhtar; Sarwat Zahra
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 10, No 4: December 2022
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v10i4.4034

Abstract

Recent advances in fractional order calculus led to the improvement of control theory and resulted in potential use of fractional adaptive PID controller in advanced academic and industrial applications as compared to the conventional adaptive PID controller. Basically, a fractional order  adaptive PID  controller  is  an  improved version of classical  integer  order  adaptive PID  controller that outperformed  its classical  counterpart. In case of a closed loop system, a minute change would result in instability of the overall system. An efficient PID controller can be used to control the response of such system.  Among various parameters of an instable system, speed of the system is an important parameter to be controlled efficiently. The current research work presents the speed controlling mechanism for an uncertain instable system by using fractional order adaptive PID controller.To validate the arguments, effectiveness and robustness of the proposed fractional order adaptive PID controller have been studied in comparison to the classical adaptive PID controller using The Criterion of quadratic error. Simulation findings and comparisons demonstrated that the proposed controller has superior control performance and outstanding robustness in terms of percentage overshoot, settling time, rising time, and disturbance rejection.
Review of Intelligent Control Systems with Robotics Ahmed Khudhair Abbas; Yousif Al Mashhadany; Mustafa Jameel Hameed; Sameer Algburi
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 10, No 4: December 2022
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v10i4.3628

Abstract

Interactive between human and robot assumes a significant job in improving the productivity of the instrument in mechanical technology. Numerous intricate undertakings are cultivated continuously via self-sufficient versatile robots. Current automated control frameworks have upset the creation business, making them very adaptable and simple to utilize. This paper examines current and up and coming sorts of control frameworks and their execution in mechanical technology, and the job of AI in apply autonomy. It additionally expects to reveal insight into the different issues around the control frameworks and the various approaches to fix them. It additionally proposes the basics of apply autonomy control frameworks and various kinds of mechanical technology control frameworks. Each kind of control framework has its upsides and downsides which are talked about in this paper. Another kind of robot control framework that upgrades and difficulties the pursuit stage is man-made brainpower. A portion of the speculations utilized in man-made reasoning, for example, Artificial Intelligence (AI) such as fuzzy logic, neural network and genetic algorithm, are itemized in this paper. At long last, a portion of the joint efforts between mechanical autonomy, people, and innovation were referenced. Human coordinated effort, for example, Kinect signal acknowledgment utilized in games and versatile upper-arm-based robots utilized in the clinical field for individuals with inabilities. Later on, it is normal that the significance of different sensors will build, accordingly expanding the knowledge and activity of the robot in a modern domain
Forecasting and Clustering of Cassava Price by Machine Learning (A study of Cassava prices in Thailand) Sayan Tepdang; Ratthakorn Ponprasert
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 10, No 4: December 2022
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v10i4.3946

Abstract

Forecasting and Clustering the price of cassava is essential for agriculture, but the difficult part of forecasting is price fluctuation, in which the fact of prices is going up and down and be changed monthly. The paper proposes to forecast  the price of Cassava by machine learning. The process had been calculated by the price of Cassava from January 2005 to February 2022, which has been collected for 17 years by the Office of Agricultural Economics, Ministry of Agriculture, and Cooperatives. The research on forecasting found that the method of Support Vector Machine including using add-on feature with Garlic Price and Potato Price showing the Root Mean Squared Error (RMSE) with the lowest point as of 0.10. If comparing to the Conventional method with the equal database. The result shows that the proposed method demonstrates the value of the Mean Absolute Percentage Error (MAPE) as 3.35%, it displays more effectively as 0.61%. For the final process of clustering the price by analyzing with K-mean, the result came up with a peak pricing period in December of 14.08%. Subsequently, the agricultures would apply the research result to implement their planting plan for profit-making.
Object Tracking in Augmented Reality: Enhancement Using Convolutional Neural Networks Nurhadi Nurhadi; Deris Stiawan; Mohd. Yazid Idris; Saparudin Saparudin
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 10, No 4: December 2022
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v10i4.4104

Abstract

Augmented reality (AR) has been used in maintenance, simulation, and remote assistance, among other applications. In AR systems, one of the significant issues is the placement of objects in augmented physical environments. Given the importance of object placement in AR systems, we proposed deep learning-based object placement, covering both object detection and object segmentation, to address relevant issues. Deep learning can help users complete tasks by providing the right information effectively, with the method taking into account dynamically changing environments and users’ situations in real time. The problem is that it is rarely used in AR, thereby prompting the combination of deep learning-based object detection and instance segmentation with wearable AR technology to improve the performance of complex tasks. This challenge was addressed in this work through the use of convolutional neural networks in the detection and segmentation of objects in actual environments. We measured the performance of AR technology on the basis of detection accuracy under environmental conditions of different intensities. Experimental results showed satisfactory segmentation and accurate detection
A Novel 2DOF Fractional Controller for Wind-Solar Integrated Power System Majahar Hussain Mahammad; Ch Ravi Kumar
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 10, No 4: December 2022
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v10i4.4164

Abstract

Power system is an integration of many power generating units with continuous load variation due to which the frequency of the power system changes. Using traditional proportional integral (PI) controllers, frequency transients are reduced, and with sufficient time delay zero steadystate error is obtained. In this proposed research article, a three-area thermal plant system with wind and solar photovoltaic power generating systems is considered. This integration of renewable system will lead to the frequency transients which has to be addressed seriously. To improve the frequency profile of this diverse-source interconnected power system, a novel two degree of freedom proportional fractional integral double derivative (2-DOF-PFIDD) controller is proposed. The integral square error (ISE) cost function is utilized to discover the best parameter gains of the proposed controller using the intelligent water drops algorithm (IWDs). The benefits of the proposed controller are evaluated using an IEEE-39 bus system with wind and solar photovoltaic (SPV) generation. Uncertainties in the wind and solar power system characteristics such as wind speed and irradiance are considered. Comparisons with typical proportional integral derivative (PID), two degree of freedom proportional integral derivative (2-DOF PID), and 2-DOF-PIDD controllers are presented to demonstrate the efficacy of proposed controller for improving the frequency and tie-line power profiles.
Restoration of Blurred and Noisy Images Using Inverse Filtering and Adaptive Threshold Method Zayed M. Ramadan
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 10, No 4: December 2022
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v10i4.4032

Abstract

A restoration scheme for images that are corrupted with both blur and impulsive noise is proposed in this paper to reconstruct an image with minimum degradation. The restoration scheme consists of two stages in sequence where the first stage is applied to the blurred image and the second stage is applied to de-blurred image that has been subject to noise through electronic transmission. The first stage uses frequency domain filtering while the second utilizes spatial filtering to reduce the indicated blur and noise, respectively. In particular, truncated inverse filtering is used for reducing the blur and an adaptive algorithm with an estimated threshold is used for minimizing the noise. Simulation of the introduced method uses several performance measuring indices such as mean absolute error (MAE) and peak signal-to-noise ratio (PSNR). Results of these simulations show great performance of the proposed method in terms of reducing the blur and noise significantly while keeping details and sharpness of the image edges.
Forward Body Biased Low Power 4.0-10.6 GHz Wideband Low Noise Amplifier Rohit Goel; Anil Kumar; Mahesh Kumar; Sandeep Kumar
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 10, No 4: December 2022
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v10i4.4267

Abstract

A forward body biased low power Low Noise Amplifier (LNA) is designed using Common Gate (CG) topology. By using current reuse technique between the first stage and second stage Common Source topology accompanied with forward body biasing leads to low power dissipation. A series to parallel tank circuit at this stage leads to wideband design. A shunt peaking inductor at the drain terminal of second stage causes the higher frequency peak to increase leading to wide bandwidth. Two CS cascade stages are used to increase the overall gain of the proposed LNA with a buffer stage at the output for output matching. The proposed LNA attained maximum gain of 26.39 dB with a gain greater than 16 dB over entire range. The circuit gives reflection coefficient less than – 10 dB with NF 2.7 dB. With Vdd of 0.925 V, a DC current of 8.32 mA is consumed giving 7.7 mW power consumption.

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