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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 2,901 Documents
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A smart guidance navigation robot using petri net, database location, and radio frequency identification Yudhi Gunardi; Jumadril J. N.; Dirman Hanafi
Bulletin of Electrical Engineering and Informatics Vol 10, No 4: August 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i4.3077

Abstract

The objective of this research is, to explain a new framework to navigate the movement of the robot towards a target goal. This involves the need for the robot to move from the initial position to 1 out of 30 rooms. Therefore the strategy used involves the combination of the room database stored in the RFID data using the petri net (PN) method to simulate and model the movement of the robot for navigation after which the dynamic behavior of the robot is moving to the desired location was analyzed. The process started from the creation of an environmental map determined by the user followed by modeling through PN and the result was used to produce a marking value which explains and navigates the movement of the robot towards the selected room. The marking value was also used as the database for the robot's movement and later substituted with the RFID to be used as the sensor input in the implementation stage. It was concluded that the robot has the ability to move to the target position according to the database stored in RFID and designed to move forward and turn left and right. For example, it followed the marking value M1 M2 M3M13M12 M11 M10 M9 M8 to Room 1 and M1 M2 M46 M47 to Room 29.
Microscopy images segmentation algorithm based on shearlet neural network Nemir Ahmed Al-Azzawi
Bulletin of Electrical Engineering and Informatics Vol 10, No 2: April 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i2.2743

Abstract

Microscopic images are becoming important and need to be studied to know the details and how-to quantitatively evaluate decellularization. Most of the existing research focuses on deep learning-based techniques that lack simplification for decellularization. A new computational method for the segmentation microscopy images based on the shearlet neural network (SNN) has been introduced. The proposal is to link the concept of shearlets transform and neural networks into a single unit. The method contains a feed-forward neural network and uses a single hidden layer. The activation functions are depending on the standard shearlet transform. The proposed SNN is a powerful technology for segmenting an electron microscopic image that is trained without relying on the pre-information of the data. The shearlet neural networks capture the features of full accuracy and contextual information, respectively. The expected value for specific inputs is estimated by learning the functional configuration of a network for the sequence of observed value. Experimental results on the segmentation of two-dimensional microscopy images are promising and confirm the benefits of the proposed approach. Lastly, we investigate on a challenging datasets ISBI 2012 that our method (SNN) achieves superior outcomes when compared to classical and deep learning-based methods.
A multi domains short message sentiment classification using hybrid neural network architecture Devi Munandar; Andri Fachrur Rozie; Andria Arisal
Bulletin of Electrical Engineering and Informatics Vol 10, No 4: August 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i4.2790

Abstract

Sentiment analysis of short texts is challenging because of its limited context of information. It becomes more challenging to be done on limited resource language like Bahasa Indonesia. However, with various deep learning techniques, it can give pretty good accuracy. This paper explores several deep learning methods, such as multilayer perceptron (MLP), convolutional neural network (CNN), long short-term memory (LSTM), and builds combinations of those three architectures. The combinations of those three architectures are intended to get the best of those architecture models. The MLP accommodates the use of the previous model to obtain classification output. The CNN layer extracts the word feature vector from text sequences. Subsequently, the LSTM repetitively selects or discards feature sequences based on their context. Those advantages are useful for different domain datasets. The experiments on sentiment analysis of short text in Bahasa Indonesia show that hybrid models can obtain better performance, and the same architecture can be directly used in another domain-specific dataset.
A streamlined 17-level cascaded H-bridge multilevel converter with solar PV integration Muhammad Hamza Shahbaz; Kashif Amjad; Naqash Ahmad; Arslan Ahmed Amin; Sajid Iqbal; Muhammad Gufran Khan; Muhammad Adnan
Bulletin of Electrical Engineering and Informatics Vol 10, No 2: April 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i2.2764

Abstract

The quest for a green electrical power system has increased the use of renewable energy resources and power electronic converters in the existing power system. These power electronic converters, however, are a major cause of harmonics and result in the degradation of power quality. In the last two decades, researchers have proposed various designs of multilevel converters to minimize these harmonic distortions, however, a comprehensive solution for stand-alone solar photovoltaic (PV) systems with low total harmonic distortion (THD) is still missing in the present body of knowledge. This paper proposes a single-phase 17-level cascaded H-bridge multilevel converter (CHMC) model for a stand-alone system using solar PV arrays. The proposed model employs eight different flexible PV arrays that can be replaced with DC voltage sources when required to meet the load demand. The proposed model does not include any capacitor and filter thus saving a lot of cost in the overall system. The model has been implemented in the Simulink environment using a model-based design approach. The simulation results show that the proposed model has reduced the THD to almost 7% as compared to the existing models. The cost comparison of the proposed converter also proved its economic benefit over other types.
An efficient and improved model for power theft detection in Pakistan Abid Afridi; Abdul Wahab; Shamsher Khan; Wasi Ullah; Sheharyar Khan; Syed Zia Ul Islam; Kashif Hussain
Bulletin of Electrical Engineering and Informatics Vol 10, No 4: August 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i4.3014

Abstract

This paper describes an improved model for the monitoring of power used by a party such as household users and different industries in Pakistan. The power theft detection was done using the intelligent internet of things (IoT) service system for calculating the user's power simultaneously. The power meter catches a theft detection device that is instantly transmitted to the central system which compares both the data by means of microcontroller and if there is any difference found, it informs the power utility about the hooking, meter relief or theft activities happen. Information of the theft detection through the global mobile communications system is transmitted and notified theft is displayed on the terminal monitor or won. As a result, although consumers continue to use excess fuel, the customer's power supply is cut in the electricity boards segment. The general radio package module system sends central circuit and meter data via an internet protocol address to a web server. GSM's IoT based perception is used to monitor the power supply and billing information calculated with a microcontroller continuously with the determination of the electricity table area. With this unit, the duplicate user can be located at the rear of the electricity office with the power meter status.
Real-time mask detection and face recognition using eigenfaces and local binary pattern histogram for attendance system Mohd Suhairi Md Suhaimin; Mohd Hanafi Ahmad Hijazi; Chung Seng Kheau; Chin Kim On
Bulletin of Electrical Engineering and Informatics Vol 10, No 2: April 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i2.2859

Abstract

Face recognition is gaining popularity as one of the biometrics methods for an attendance system in an organization. Due to the pandemic, the common face recognition system needs to be modified to meet the current needs, whereby facemask detection is necessary. The main objective of this paper is to investigate and develop a real-time face recognition system for the attendance system based on the current scenarios. The proposed framework consists of face detection, mask detection, face recognition, and attendance report generation modules. The face and facemask detection is performed using the haar cascade classifier. Two techniques for face recognition were investigated, the eigenfaces and local binary pattern histogram. The initial experimental results and implementation at Kuching Community College show the effectiveness of the system. For future work, an approach that is able to perform masked face recognition will be investigated.
DSP improvement of a vector speed induction motor control with a RST and adaptive fuzzy controller Mihoub Youcef; Toumi Djilali; Sandrine Moreau; Hassaine Said; Daoud Bachir
Bulletin of Electrical Engineering and Informatics Vol 10, No 3: June 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i3.1798

Abstract

The aim of this work is to improve the dynamics and to overcome the limitation of conventional fixed parameters PI controller used in induction motor (IM) field-oriented control (FOC). This study presents and implements a RST and an adaptive fuzzy controller (AFC) to enhance variable speed control. Theoretical background of theses controllers is outlined and then experimental results are presented. Practical implementation has been realized on a board with a 1.1 KW IM supplied by 10 KHz space vector pulse width modulation current regulated inverter used as power amplifier consisted of 300V, 10A IGBT and Matlab/Simulink environment. Test benches have been established under different operating conditions in order to evaluate and compare the performances of the PI, IP, and polynomial RST and adaptive fuzzy controllers. Parameter variations for the rotor and the inertia moment variation were done in order to compare and verify the robustness of each controller. High dynamic performances and robustness against parameters variation were obtained with the use of both RST and AFC.
Optimal resource allocation in networked control systems using viterbi algorithm Gökhan Çetin; M. Sami Fadali
Bulletin of Electrical Engineering and Informatics Vol 10, No 3: June 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i3.3022

Abstract

This paper presents an optimal bandwidth allocation method for a networked control system (NCS) which includes time-driven sensor, event-driven controller and random channels. A hidden markov model (HMM) with a discretized state space is formulated for the random traffic to predict the network states using a suitable data window. Network bandwidth is allocated based on the predicted traffic state subject to bounds on the deterministic traffic that guarantee acceptable NCS performance and do not exceed hardware limitations. Bandwidth allocation uses  minimization of unmet bandwidth demand. A stability condition is derived for a variable but bounded sampling period interval. Computer simulation results show the effect of varying the number of discrete states for the HMM and the window width on bandwidth allocation. The results compare favorably with a published approach based on fuzzy logic.
SentiHotel: a sentiment analysis application of hotel services using an optimized neural network Dyah Apriliani; Taufiq Abidin; Edhy Sutanta; Amir Hamzah; Oman Somantri
Bulletin of Electrical Engineering and Informatics Vol 10, No 3: June 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i3.3040

Abstract

An assessed hotel service is necessary for tourists and everyone who is traveling, however currently it is still difficult to find recommended hotel information. The solution provided in this research is to propose a smart application that has been developed by implementing machine learning in it. The purpose is to build a sentiment review smart application by applying the sentiment analysis hybrid model of the best neural network (NN) algorithm model that has been optimized using genetic algorithms. To get the right model, the research method was carried out with experiments starting from the initial stages of conducting data preprocessing, tokenization, weighting, modeling experiments, and conducting the system evaluation stage to determine the success of the proposed model. The progress of the application development system is by using the prototyping model. SentiHotel is a sentiment application that was successfully built to provide a solution for tourists in assessing a hotel service. The software validation test is carried out using the blackbox method and the results show that the SentHotel application is in accordance with the expected result; all system functions can run properly.
Optimal setting of DOC relay in distribution system in presence of D-FACTS Lazhar Bougouff; Abdelaziz Chaghi
Bulletin of Electrical Engineering and Informatics Vol 10, No 4: August 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i4.2809

Abstract

The process of selecting optimal settings for directional over-current relays (DOC relays) is a selection of time dial setting (TDS) and IP (backup current), So that changes in the system of electrical power distribution. In this work, a breeder genetic algorithm (BGA) has been applied to optimal settings of DOC relays with multisystem D-FACTS devices. The simulation consists of two network operation scenarios, scenario without D-FACTS which consisting of coordination of DOC relays against three phase faults, and the second scenarios with multi TCSC. In general, had been verified on optimal settings of relays that the impacts of TCSC insertion in 33-bus distribution system on DOC relays.

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