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INDONESIA
Indonesian Journal of Electrical Engineering and Computer Science
Published by Institute of Advanced Engineering and Science
ISSN : 25024752     EISSN : 25024760     DOI : -
Core Subject :
Arjuna Subject : -
Articles 64 Documents
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Search results for , issue "Vol 25, No 2: February 2022" : 64 Documents clear
Development of depth map from stereo images using sum of absolute differences and edge filters Rostam Affendi Hamzah; Muhd Nazmi Zainal Azali; Zarina Mohd Noh; Madiha Zahari; Adi Irwan Herman
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 2: February 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i2.pp875-883

Abstract

This article proposes a framework for the depth map reconstruction using stereo images. Fundamentally, this map provides an important information which commonly used in essential applications such as autonomous vehicle navigation, drone’s navigation and 3D surface reconstruction. To develop an accurate depth map, the framework must be robust against the challenging regions of low texture, plain color and repetitive pattern on the input stereo image. The development of this map requires several stages which starts with matching cost calculation, cost aggregation, optimization and refinement stage. Hence, this work develops a framework with sum of absolute difference (SAD) and the combination of two edge preserving filters to increase the robustness against the challenging regions. The SAD convolves using block matching technique to increase the efficiency of matching process on the low texture and plain color regions. Moreover, two edge preserving filters will increase the accuracy on the repetitive pattern region. The results show that the proposed method is accurate and capable to work with the challenging regions. The results are provided by the Middlebury standard dataset. The framework is also efficiently and can be applied on the 3D surface reconstruction. Moreover, this work is greatly competitive with previously available methods.
Uneven clustering and fuzzy logic based energy-efficient wireless sensor networks Mohammed Adnan Altaha; Ahmed Adel Alkadhmawee; Wisam Mahmood Lafta
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 2: February 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i2.pp1011-1019

Abstract

Clustering is the fundamental issue in terms of ensuring long-term operation of wireless sensor networks (WSNs). The problem of hot spots remains the most prominent research challenge relating to the design of energy-efficient clustering algorithm. This paper proposed a protocol, namely an uneven clustering and fuzzy logic-based energy-efficient (UCFLEE), for prolonging network lifetime. Depending on the communication distance, the UCFLEE protocol divides the network into uneven clusters for suppressing the hot spot problem. The fuzzy logic selects the optimal cluster head in accordance with certain parameters. The advocated method adopts a dynamic energy threshold to chnage the cluster head. The UCFLEE protocol is dependent on the iterative deepening A (IDA) star algorithm for identifying the routing path from the cluster heads to the base station. The IDA-star method is reliant upon a cost bounded method to select the optimal solution for the base station. The UCFLEE protocol is tested and subsequently contrasted with other protocols. The results obtained from the UCFLEE protocol enable an energy consumption equilibrium, eradicates the hot spot challenge, while also attaining maximum network lifetime.
Estimation of the transformer parameters from nameplate data using turbulent flow of water optimization technique Amir Yassin Hassan; Mokhtar Said; Saber Mohamed Saleh Salem
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 2: February 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i2.pp639-647

Abstract

The mismatch between the transformer and its model leads to deviation of the results during the study of the different abnormal phenomena. This paper presents an optimization technique using transformer nameplate data to minimize the difference in the estimation of the parameters between the model and the actual transformer data. The turbulent flow of water through a narrow path (TFWO) in a circular form technique is used for the optimization of the transformer parameters. The optimization algorithms are used in extracting the parameters of the different rating of transformers, this technique needs an objective function for performing the optimization process. Minimizing the sum of square error (SSE) is the objective function of the optimizer technique. The SSE function includes the summation of the square error for the primary current and secondary current and voltage referring to the primary. The proposed optimization transformer parameters evaluation based on the nameplate data is accurate and fulfilled compared with the other methods.
Performance enhancement of a high-speed railway supply system with multi module converter: a laboratory prototype model for Indian railways Venkatasupura Vemulapati; Yerram N. Vijaykumar; Nagalamadaka Visali
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 2: February 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i2.pp680-689

Abstract

The present Indian traction supply system’s complications (neutral sections of the catenary line and issues of power quality) restrict the growth of railway transportation, particularly high-speed rail networks that are fast growing globally. The neutral sections (NS) results in loss of speed, momentum and mechanical failures that are all threatening the fast and stable operation of trains and systems. In the meantime, issues with the power quality such as the negative sequence currents (NSC), the reactive power and harmonics may create problems on the three phase grid side that cannot be overlooked. To address these two issues concurrently, a new traction power supply system is designed in this paper. The proposal will also analyses the theory of operation, build the mathematical model and develop the control system for back to back converters. Small scale prototype is also made for validation of simulation results. The results shows that it can fulfil the practical requirements. The experimental results shows that the overall system is practically more appropriate for the high speed railway.
Design and simulation of a software defined networking-enabled smart switch for internet of things-based smart grid Mustafa Abdulkadhim; Noor Qusay Abdulmuhsen; Aymen M. Al-Kadhimi
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 2: February 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i2.pp780-787

Abstract

Using sustainable energy is the future of our planet earth, this became not only economically efficient but also a necessity for the preservation of life on earth. Because of such necessity, smart grids became a very important issue to be researched. Many literatures discussed this topic and with the development of internet of things (IoT) and smart sensors, smart grids are developed even further. On the other hand, software defined networking is a technology that separates the cntrol plane from the data plan of the network. It centralizes the management and the orchestration of the network tasks by using a network controller. The network controller is the heart of the SDN-enabled network, and it can control other networking devices using software defined networking (SDN) protocols such as OpenFlow. A smart switching mechanism called (SDN-smgrid-sw) for the smart grid will be modeled and controlled using SDN. We modeled the environment that interact with the sensors, for the sun and the wind elements. The Algorithm is modeled and programmed for smart efficient power sharing that is managed centrally and monitored using SDN controller. Also, all if the smart grid elements (power sources) are connected to the IP network using IoT protocols.
Efficient processing of continuous spatial-textual queries over geo-textual data stream Kalpana Vivek Metre; Madan Kharat
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 2: February 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i2.pp1094-1102

Abstract

Due to the extensive use of social media and mobile devices, unbounded and massive data is generated continuously. The need to process this big data is increasing day by day. The traditional data processing algorithms fail to cater to the need of processing data generated by various applications such as digital geo-based advertising, and recommendation systems. There has been a high demand to process continuous spatial fuzzy textual queries over data stream of spatial-textual objects with high density by present locationbased and social network-based service applications. For the spatialkeyword data stream, the performance plays a vital role as the geo information and keyword description matching is needed for every incoming streaming object. The various continuous geo-keyword query processing methods normally lack the support for fuzzy keyword matching when processing the objects from the geo-textual data stream. The edit distancebased approach with the adaptive partitioning tree index for the queries is used for fuzzy string matching and it outperforms than the existing approaches in storage cost and query performance cost.
A deep learning-based cardio-vascular disease diagnosis system Hamdan Bensenane; Djemai Aksa; Fawzi Walid Omari; Abdellatif Rahmoun
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 2: February 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i2.pp963-971

Abstract

Recently ehealth technologies are becoming an overwhelming aspect of public health services that provides seamless access to healthcare information. Machine learning tools associated with IoT technology play an important role in developing such health technologies. This paper proposes a decision support system-based system (DSS) to make diagnosis of cardiovascular diseases. It uses deep learning approaches that classify electrocardiogram (ECG) signals. Thus, a two-stage long-short term memory (LSTM) based neural network architecture, along with an adequate preprocessing of the ECG signals is designed as a diagnosis-aided system for cardiac arrhythmia detection based on an ECG signal analysis. This deep learning based cardio-vascular disease diagnosis system (namely ‘DLCVD’) is built to meet higher performance requirements in terms of accuracy, specificity, and sensitivity. This must also be capable of an online real-time classification. Experimental results using the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database show that DLCVD led to outstanding performance
3D chaos graph deep learning method to encrypt and decrypt digital image Daniah Abdul Qahar Shakir; Ali Jbaeer Dawood
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 2: February 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i2.pp941-951

Abstract

We live in technological age development’s where many important data transmitted electronically from one device to another and in every place. Deep learning algorithms have facilitated the process of encoding and decoding digital images. Chaotic graph systems, on the other hand, are one of the most recent techniques utilized to encode image data based on the methods of cryptography. The chaos maps are divided into two main aspects, first one deals with the 1D map which requires fewer features and can be developed easily, the second one is the high dimensional map which is more complex than the 1D graph and it requires more features, more parameters, and it is relatively hard to develop. In this paper, we present a method for image encoding and decoding electronically using deep learning, the proposed algorithm was developed by using the hybrid technique of 3D chaos map generation, the best case of the proposed technique gave the following results: The average entropy calculation was (7.4838) before image encryption and (7.9896) after image encryption with average number of pixels change rate (NPCR) of (99.7085%) and the unified average changing intensity (UACI) of (33.2030%) which are the best outcomes when compared to other similar works.
Control of a servo-hydraulic system utilizing an extended wavelet functional link neural network based on sine cosine algorithms Shaymaa Mahmood Mahdi; Omar Farouq Lutfy
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 2: February 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i2.pp847-856

Abstract

Servo-hydraulic systems have been extensively employed in various industrial applications. However, these systems are characterized by their highly complex and nonlinear dynamics, which complicates the control design stage of such systems. In this paper, an extended wavelet functional link neural network (EWFLNN) is proposed to control the displacement response of the servo-hydraulic system. To optimize the controller's parameters, a recently developed optimization technique, which is called the modified sine cosine algorithm (M-SCA), is exploited as the training method. The proposed controller has achieved remarkable results in terms of tracking two different displacement signals and handling external disturbances. From a comparative study, the proposed EWFLNN controller has attained the best control precision compared with those of other controllers, namely, a proportional-integralderivative (PID) controller, an artificial neural network (ANN) controller, a wavelet neural network (WNN) controller, and the original wavelet functional link neural network (WFLNN) controller. Moreover, compared to the genetic algorithm (GA) and the original sine cosine algorithm (SCA), the M-SCA has shown better optimization results in finding the optimal values of the controller's parameters.
Grid reactive voltage regulation and cost optimization for electric vehicle penetration in power network Farrukh Nagi; Aidil Azwin; Navaamsini Boopalan; Agileswari K. Ramasamy; Marayati Marsadek; Syed Khaleel Ahmed
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 2: February 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i2.pp741-754

Abstract

Expecting large electric vehicle (EV) usage in the future due to environmental issues, state subsidies, and incentives, the impact of EV charging on the power grid is required to be closely analyzed and studied for power quality, stability, and planning of infrastructure. When a large number of energy storage batteries are connected to the grid as a capacitive load the power factor of the power grid is inevitably reduced, causing power losses and voltage instability. In this work large-scale 18K EV charging model is implemented on IEEE 33 network. Optimization methods are described to search for the location of nodes that are affected most due to EV charging in terms of power losses and voltage instability of the network. Followed by optimized reactive power injection magnitude and time duration of reactive power at the identified nodes. It is shown that power losses are reduced and voltage stability is improved in the grid, which also complements the reduction in EV charging cost. The result will be useful for EV charging stations infrastructure planning, grid stabilization, and reducing EV charging costs.

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