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Bulletin of Electrical Engineering and Informatics
Published by Universitas Ahmad Dahlan
ISSN : -     EISSN : -     DOI : -
Core Subject : Engineering,
Electrical & Electronics Engineering
Bulletin of Electrical Engineering and Informatics (Buletin Teknik Elektro dan Informatika) ISSN: 2089-3191, e-ISSN: 2302-9285 is open to submission from scholars and experts in the wide areas of electrical, electronics, instrumentation, control, telecommunication and computer engineering from the global world. The journal publishes original papers in the field of electrical, computer and informatics engineering.
Arjuna Subject : -
Articles 64 Documents
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Search results for , issue "Vol 10, No 6: December 2021" : 64 Documents clear
Design of a dual-band antenna for energy harvesting application Maizatul Alice Meor Said; Syed Mohd Iqwan Naqiuddin Syed Jaya; Zahriladha Zakaria; Mohamad Harris Misran; Mohd Muzafar Ismail
Bulletin of Electrical Engineering and Informatics Vol 10, No 6: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i6.3203

Abstract

This report presents an investigation on how to improve the current dual-band antenna to enhance the better result of the antenna parameters for energy harvesting application. Besides that, to develop a new design and validate the antenna frequencies that will operate at 2.4 GHz and 5.4 GHz. At 5.4 GHz, more data can be transmitted compare to 2.4 GHz. However, 2.4 GHz has long distance of radiation, so it can be used when far away from the antenna module compare to 5 GHz that has short distance in radiation. The development of this project includes the scope of designing and testing of antenna using computer simulation technology (CST) 2018 software and vector network analyzer (VNA) equipment. In the process of designing, fundamental parameters of antenna are being measured and validated, in purpose to identify the better antenna performance.
Using particle swarm optimization to solve test functions problems Issa Ahmed Abed; May Mohammed Ali; Afrah Abood Abdul Kadhim
Bulletin of Electrical Engineering and Informatics Vol 10, No 6: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i6.3244

Abstract

In this paper the benchmarking functions are used to evaluate and check the particle swarm optimization (PSO) algorithm. However, the functions utilized have two dimension but they selected with different difficulty and with different models. In order to prove capability of PSO, it is compared with genetic algorithm (GA). Hence, the two algorithms are compared in terms of objective functions and the standard deviation. Different runs have been taken to get convincing results and the parameters are chosen properly where the Matlab software is used. Where the suggested algorithm can solve different engineering problems with different dimension and outperform the others in term of accuracy and speed of convergence.
The calculation of point quantity for lighting based on android OS using ionic framework and rule based expert system Mufadhol Mufadhol; Budi Hartono; Sulartopo Sulartopo; Maya Utami Dewi; Danang Danang; Guruh Aryotejo
Bulletin of Electrical Engineering and Informatics Vol 10, No 6: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i6.3183

Abstract

At this time, the Covid-19 pandemic has not been finished so all activities and expenditure of funds must be economical. On the each building will be required electricity resources as a means for lighting the room. Reduced activities carried out outside the room make people carry out activities indoors, this results in a significant increase in electricity consumption for lighting. PT. PLN as the provider in Indonesia of electricity services for the community experiences a shortage of electricity resources and most of the electricity resources are used for lighting purposes. Indonesia as a developing country has a very large of population growth, so it requires many buildings as a place to live or a place of business and other activities. This paper will be discussion how a smartphone with an Android operating system can be used to determining the lighting requirements using the Ionic Framework and Rule Based Expert Systems. Using this application system, so electricity to lighting can be optimal and not too wasteful, which in turn can save on the use of electrical energy resources and can save of the cost.
A single-phase simplified DC-AC converter using DC-link capacitors and an H-bridge Sai Divya Sindhura Nunna; Akhilesh Ketha; Srivastav Sai Goud Padamat; K. Rambabu; Ujwala Anil Kshirsagar; Abhilash Tirupathi
Bulletin of Electrical Engineering and Informatics Vol 10, No 6: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i6.2788

Abstract

This paper introduces a simplified inverter circuit using a single dc source and an H-bridge with a least possible number of “switching devices”. This topology does not employ multiple “dc sources”, which enhances the reliability of the configuration. The topology consists of two parts, namely: “Level generation parts” as well as “Polarity generation parts”, it is the mixture of some of the switching devices, DC-link capacitor and a single DC source completes the part of level generation. The H-bridge in the proposed structure produces the polarity generation part. A detailed explanation of the modulation system and operating modes of the proposed framework are discussed. Finally, in the MATLAB/SIMULINK platform, the projected network topology is simulated and the outcomes are presented.
Hyper-parameter optimization of convolutional neural network based on particle swarm optimization algorithm Zainab Fouad; Marco Alfonse; Mohamed Roushdy; Abdel-Badeeh M. Salem
Bulletin of Electrical Engineering and Informatics Vol 10, No 6: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i6.3257

Abstract

Deep neural networks have accomplished enormous progress in tackling many problems. More specifically, convolutional neural network (CNN) is a category of deep networks that have been a dominant technique in computer vision tasks. Despite that these deep neural networks are highly effective; the ideal structure is still an issue that needs a lot of investigation. Deep Convolutional Neural Network model is usually designed manually by trials and repeated tests which enormously constrain its application. Many hyper-parameters of the CNN can affect the model performance. These parameters are depth of the network, numbers of convolutional layers, and numbers of kernels with their sizes. Therefore, it may be a huge challenge to design an appropriate CNN model that uses optimized hyper-parameters and reduces the reliance on manual involvement and domain expertise. In this paper, a design architecture method for CNNs is proposed by utilization of particle swarm optimization (PSO) algorithm to learn the optimal CNN hyper-parameters values. In the experiment, we used Modified National Institute of Standards and Technology (MNIST) database of handwritten digit recognition. The experiments showed that our proposed approach can find an architecture that is competitive to the state-of-the-art models with a testing error of 0.87%.
A review paper on memory fault models and test algorithms Aiman Zakwan Jidin; Razaidi Hussin; Lee Weng Fook; Mohd Syafiq Mispan
Bulletin of Electrical Engineering and Informatics Vol 10, No 6: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i6.3048

Abstract

Testing embedded memories in a chip can be very challenging due to their high-density nature and manufactured using very deep submicron (VDSM) technologies. In this review paper, functional fault models which may exist in the memory are described, in terms of their definition and detection requirement. Several memory testing algorithms that are used in memory built-in self-test (BIST) are discussed, in terms of test operation sequences, fault detection ability, and also test complexity. From the studies, it shows that tests with 22 N of complexity such as March SS and March AB are needed to detect all static unlinked or simple faults within the memory cells. The N in the algorithm complexity refers to Nx*Ny*Nz whereby Nx represents the number of rows, Ny represents the number of columns and Nz represents the number of banks. This paper also looks into optimization and further improvement that can be achieved on existing March test algorithms to increase the fault coverage or to reduce the test complexity.
Quality of service performances of video and voice transmission in universal mobile telecommunications system network based on OPNET Sameer A. S. Lafta; Mohaned Mahdi Abdulkareem; Raed Khalid Ibrahim; Marwah M. Kareem; Adnan Hussein Ali
Bulletin of Electrical Engineering and Informatics Vol 10, No 6: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i6.3139

Abstract

The universal mobile telecommunications system (UMTS) has distinct benefits in that it supports a wide range of quality of service (QoS) criteria that users require in order to fulfill their requirements. The transmission of video and audio in real-time applications places a high demand on the cellular network, therefore QoS is a major problem in these applications. The ability to provide QoS in the UMTS backbone network necessitates an active QoS mechanism in order to maintain the necessary level of convenience on UMTS networks. For UMTS networks, investigation models for end-to-end QoS, total transmitted and received data, packet loss, and throughput providing techniques are run and assessed and the simulation results are examined. According to the results, appropriate QoS adaption allows for specific voice and video transmission. Finally, by analyzing existing QoS parameters, the QoS performance of 4G/UMTS networks may be improved.
Cucumber disease recognition using machine learning and transfer learning Md. Jueal Mia; Syeda Khadizatul Maria; Shahrun Siddique Taki; Al Amin Biswas
Bulletin of Electrical Engineering and Informatics Vol 10, No 6: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i6.3096

Abstract

Cucumber is grown, as a cash crop besides it is one of the main and popular vegetables in Bangladesh. As Bangladesh's economy is largely dependent on the agricultural sector, cucumber farming could make economic and productivity growth more sustainable. But many diseases diminish the situation of cucumber. Early detection of disease can help to stop disease from spreading to other healthy plants and also accurate identifying the disease will help to reduce crop losses through specific treatments. In this paper, we have presented two approaches namely traditional machine learning (ML) and CNN-based transfer learning. Then we have compared the performance of the applied techniques to find out the most appropriate techniques for recognizing cucumber diseases. In our ML approach, the system involves five steps. After collecting the image, pre-processing is done by resizing, filtering, and contrast-enhancing. Then we have compared various ML algorithms using k-means based image segmentation after extracted 10 relevant features. Random forest gives the best accuracy with 89.93% in the traditional ML approach. We also studied and applied CNN-based transfer learning to investigate the further improvement of recognition performance. Lastly, a comparison among various transfer learning models such as InceptionV3, MobileNetV2, and VGG16 has been performed. Between these two approaches, MobileNetV2 achieves the highest accuracy with 93.23%.
Wireless HART stack using multiprocessor technique with laxity algorithm A. Manjunathan; E. D. Kanmani Ruby; W. Edwin Santhkumar; A. Vanathi; P. Jenopaul; S. Kannadhasan
Bulletin of Electrical Engineering and Informatics Vol 10, No 6: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i6.3250

Abstract

The use of a real-time operating system is required for the demarcation of industrial wireless sensor network (IWSN) stacks (RTOS). In the industrial world, a vast number of sensors are utilised to gather various types of data. The data gathered by the sensors cannot be prioritised ahead of time. Because all of the information is equally essential. As a result, a protocol stack is employed to guarantee that data is acquired and processed fairly. In IWSN, the protocol stack is implemented using RTOS. The data collected from IWSN sensor nodes is processed using non-preemptive scheduling and the protocol stack, and then sent in parallel to the IWSN's central controller. The real-time operating system (RTOS) is a process that occurs between hardware and software. Packets must be sent at a certain time. It's possible that some packets may collide during transmission. We're going to undertake this project to get around this collision. As a prototype, this project is divided into two parts. The first uses RTOS and the LPC2148 as a master node, while the second serves as a standard data collection node to which sensors are attached. Any controller may be used in the second part, depending on the situation. Wireless HART allows two nodes to communicate with each other.
Prediction of passenger train using fuzzy time series and percentage change methods Solikhin Solikhin; Septia Lutfi; Purnomo Purnomo; Hardiwinoto Hardiwinoto
Bulletin of Electrical Engineering and Informatics Vol 10, No 6: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i6.2822

Abstract

In the subject of railway operation, predicting railway passenger volume has always been a hot topic. Accurately forecasting railway passenger volume is the foundation for railway transportation companies to optimize transit efficiency and revenue. The goal of this research is to use a combination of the fuzzy time series approach based on the rate of change algorithm and the Holt double exponential smoothing method to forecast the number of train passengers. In contrast to prior investigations, we focus primarily on determining the next time period in this research. The fuzzy time series is employed as the forecasting basis, the rate of change is used to build the set of universes, and the Holt's double exponential smoothing method is utilized to forecast the following period in this case study. The number of railway passengers predicted for January 2020 is 38199, with a tiny average forecasting error rate of 0.89 percent and a mean square error of 131325. It can also help rail firms identify future passenger needs, which can be used to decide whether to expand train cars or run new trains, as well as how to distribute tickets.

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