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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 70 Documents
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Search results for , issue "Vol 12, No 5: October 2023" : 70 Documents clear
Error performance analysis of forward error correction using convolutional encoding in the presence of (1/f) noise Yasin Yousif Al-Aboosi; Ammar Ali Sahrab; Amal Ibrahim Nasser; Hussein A. Abdulnabi
Bulletin of Electrical Engineering and Informatics Vol 12, No 5: October 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i5.4771

Abstract

Any communication scheme's principal goal is providing error-free data transmission. By increasing the rate at which data could be transmitted through a channel and maintaining a given error rate, this coding is advantageous. The message bits to be transmitted will gradually receive more bits thanks to the convolution (channel) encoder. At the receiver end of the channel, a Viterbi decoder is utilized in order to extract original message sequence from the received data. Widely utilized error correction approaches in communication systems for the enhancement of bit error rate (BER) performance are Viterbi decoding and convolutional encoding. The Viterbi decoder and convolution encoder rate for constraints with lengths of 2 and 6 and bit rates of 1⁄2 and 1⁄3 are shown in this study in the presence of (1/f) noise. The performance regarding the convolutional encoding/hard decision Viterbi decoding forward error correction (FEC) method affects the simulation outcomes. The findings demonstrate that the BER as function of signal to noise ratio (SNR) acquired for uncoded binary phase shift keying (BPSK) with the existence of additive white Gaussian noise (AWGN) is inferior to that acquired with the use of a hard decision Viterbi decoder.
Reducing waiting and idle time for a group of jobs in the grid computing Mahdi S. Almhanna; Firas Sabah Al-Turaihi; Tariq A. Murshedi
Bulletin of Electrical Engineering and Informatics Vol 12, No 5: October 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i5.4729

Abstract

Johnson's rule is a scheduling method for the sequence of jobs. Its primary goal is to find the perfect sequence of functions to reduce the amount of idle time, and it also reduces the total time required to complete all functions. It is a suitable method for scheduling the purposes of two functions in a specific time-dependent sequence for both functions and where the time factor is the only parameter used in this way. Therefore, it is not suitable for scheduling work for computers network, where there are many factors affecting the completion time such as CPU speed, memory, bandwidth, and size of data. In this research, Johnson's method will adopt by adding many factors that affect the completion time of the work so that it becomes suitable for the site’s job scheduling purposes to reduce the waiting and idle time for a group of jobs.
Arabic vowels characterization and classification using the normalized energy relating to formants and pitch Mohamed Farchi; Karim Tahiry; Ahmed Mouhsen
Bulletin of Electrical Engineering and Informatics Vol 12, No 5: October 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i5.4457

Abstract

Vowels are the primary units of a sound system of a language. The classification of these vowels is therefore very important for the recognition and synthesis of speech. In this paper, we propose a normalized energy-based approach in formants and pitch to characterize Arabic vowels (short vowels: / a /, / i /, / u /; long vowels: / a: /, / i: /, / u: /). The classification was performed using a developed algorithm on records extracted from an Arabic corpus after the extraction of the pitch and the first three formants and the computation of the normalized energy in these bands. The results showed that the algorithm distinguishes Arabic vowels by analyzing the normalized energy in the nucleus of F1, F2, and F3 formants and pitch F0 with a rate of 88.7% for long vowels and a rate of 90% for short vowels.
IoT based smart irrigation, control, and monitoring system for chilli plants using NodeMCU-ESP8266 Amirul Amin Abd Halim; Roslina Mohamad; Farah Yasmin Abdul Rahman; Harlisya Harun; Nuzli Mohamad Anas
Bulletin of Electrical Engineering and Informatics Vol 12, No 5: October 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i5.5266

Abstract

Traditionally, chilli plant irrigation relies solely on rainwater, which leads to uncontrolled and excessive water consumption and, in turn, unhealthy growth. Furthermore, existing cultivation systems lack systematic control and monitoring to sustain efficient crop growth. Much effort has been put into developing plant irrigation control and monitoring systems in recent decades, resulting in significant technological advancements in the agricultural sector. This paper describes the development of an internet of things-based irrigation control and monitoring system testbed for a chilli plantation. A DHT11 sensor, comprising of moisture, temperature and humidity sensors, were integrated with a node microcontroller NodeMCU ESP8266 unit interacting via wireless fidelity. A controller system that could remotely control the irrigation system was placed in the plantation area. Users interacted with the system through a user interface platform developed using Blynk and Thinger.io. Hence, real-time sensor data were sent to the user interface platform and represented in an easy-to-interpret manner. The results show that the irrigation system testbed can also control the amount of water used, ensuring efficient plant growth.
Towards a ROS2-based software architecture for service robots Yong Hwan Jo; Se Yeon Cho; Byoung Wook Choi
Bulletin of Electrical Engineering and Informatics Vol 12, No 5: October 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i5.5590

Abstract

This paper presents a scalable software architecture based on robot operating system 2 (ROS2) for service robots. ROS2 supports the data distribution service (DDS) protocol that provides benefits such as real-time operation and security and performance enhancements. However, ROS2 still lacks task management capabilities, essential for practical robotic applications consisting of multiple threads and processes. Moreover, integrating new devices into ROS2 requires additional development effort to create specific drivers for specific devices. The proposed software architecture addresses these drawbacks and provides a simple and user-friendly programming interface for easier integrating of various devices and existing ROS2 applications. Moreover, it is designed using python with multi-processing to avoid issues related to the python global interrupt lock (GIL). To verify the developed software architecture, an application for a custom-made service robot called the SeoulTech service robot (SSR) is implemented on a Jetson Xavier NX board with various features, such as ROS2 navigation and SLAM, text-to-speech (TTS), speech recognition, and face recognition.
Enhancing the maximum power of wind turbine using artificial neural network Bashar Mohammed Salih; Khaleel Nawafal; Safwan Assaf Hamoodi
Bulletin of Electrical Engineering and Informatics Vol 12, No 5: October 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i5.5019

Abstract

Wind energy conversion systems (WECS) now play a significant role in meeting the world's energy needs. Several different approaches are used to try to increase the reliability of these renewable energy systems. Smart systems are designed to be more proactive to improve the performance of renewable energy equipment. Artificial neural networks (ANNs) have a variety of applications, including controlling renewable energy systems. Using optimal torque control (OTC) system based prediction techniques, a controller to monitor a maximum point of wind turbine output power (MPPT) is designed and modeled in this paper. MATLAB/Simulink package tools for neural networks are also used to design the controller and simulate it to achieve the necessary results and to obtain an appropriate analysis for the controller. The results show that the ANN is more active and delivers better output than the traditional controller.
Single-channel speech enhancement by PSO-GSA with harmonic regeneration noise reduction Kalpana Ghorpade; Arti Khaparde
Bulletin of Electrical Engineering and Informatics Vol 12, No 5: October 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i5.5373

Abstract

Speech quality significantly affects the performance of speech dependent systems. Noise in the background lowers the clarity and intelligibility of speech. The augmentation of speech can increase its quality. We propose a single-channel speech improvement framework that combines particle swarm optimization (PSO), gravitational search algorithm (GSA), and harmonic regeneration noise reduction (HRNR) to minimize speech signal noise and increase speech intelligibility. The proposed hybrid algorithm optimizes the amount of overlap between the noisy speech frames. This helps in reducing the overlapped noise. Then HRNR algorithm is applied to retain the speech harmonics. The algorithm gives improvement in the speech intelligibility for babble, car and exhibition noise. The segmental signal to noise ratio (SNR) is also improved for these noise types. There is improvement in speech intelligibility with minimal speech distortion.
Backpropagation neural network based adaptive load scheduling method in an isolated power system Vijo M. Joy; Joseph John; Sukumarapillai Krishnakumar
Bulletin of Electrical Engineering and Informatics Vol 12, No 5: October 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i5.4511

Abstract

This work introduces an efficient load scheduling method for handling the day-to-day power supply needs. At peak load times, due to its instabilitythe power generation system fails and as a measure, the load shedding process is followed. The presented method overcomes this problem by scheduling the load based on necessity. For this load scheduling is handled with an artificial neural network (ANN). For the training purpose the backpropagation (BP) algorithm is used. The whole load essential is the input of the neural network (NN). The power generation of all resources and power losses at the instant of transmission is the NN output. The optimum scheduling of different power sources is important when considering all the available sources. Load scheduling shares the feasibility of entire load and losses. It is well-known as optimal scheduling if the constraints such as availability of power, load requirement, cost and power losses are considered. Training the system using a large number of parameters would be a difficult task. So, finest number of communally independent inputs is selected. The presented method aims to lower the power generation expenditure and formulate the power available on demand without alteration. The network is designed using MATLAB.
A comparative study of classification techniques in data mining algorithms used for medical diagnosis based on DSS Ahmed Shihab Ahmed; Hussein Ali Salah
Bulletin of Electrical Engineering and Informatics Vol 12, No 5: October 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i5.4804

Abstract

A significant amount of data is gathered by the healthcare sector, but it is not appropriately mined and utilized. Finding these hidden links and patterns is frequently underutilized. Our study focuses on this element of medical diagnostics by identifying patterns in the information gathered about kidney illness, liver disease, and chronic pancreatitis (CP) and designing adaptive medical decision support systems (MDSS) to assist doctors. This research compares a variety of data mining (DM) techniques, knowledge extraction tools, and software platforms for usage in a DSS for analysis using the Waikato environment for knowledge analysis (WEKA) mining tool (decision tree (DT)). The objective is to determine the most significant risk factors based on the extraction of the categorization criteria. The datasets used for this work are illustrates how successfully DM and DSS are integrated. In this research, we suggest using the C4.5 DT algorithm, Naïve Bayes (NB) algorithm, and the logistic regression (LR) algorithm to categorize these diseases and evaluate their performance and accuracy rates. It inferred that the C4.5 algorithm accuracy is 0.873% which is better than the other two algorithms in terms of rule generation and accuracy.
Controlling the effectiveness of STATCOM using ANFIS based on PI controller Maha Abdulrhman Al-Flaiyeh; Nagham Hikmat Aziz; Saraa Ismaeel Khalel
Bulletin of Electrical Engineering and Informatics Vol 12, No 5: October 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i5.5089

Abstract

This research achieves two goals, the first is the modelling of the static synchronous compensator (STATCOM) and design of the control circuits for current, voltage and d.c capacitor voltage (Vd.c) to improve the power factor (PF) by preparing the reactive power by STATCOM so that its work is similar to the work of a synchronous condenser, and the second goal is to use smart techniques to control the Vd.c loop. For comparison, smart technical methods such as fuzzy logic type 1 (FL-T1), fuzzy logic type 2 (FL-T2), and adaptive neuro fuzzy inference system (ANFIS) were used to regulate the Vd.c on the capacitor instead of the traditional controller proportional integral (PI). Simulation was performed in MATLAB 2021 to determine the efficiency of the suggested method or approach controllers for STATCOM. The use of the proposed method improves the signal value of both the current and the voltage and the phase difference between them, which reaches almost zero. Proved that an ANFIS technique provides best Vd.c response in different values of balance and unbalance load compared with the other methods by obtaining the minimum peak overshoot (P-ov) and minimum settling time (ST).

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