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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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Differential evolution with adaptive mutation and crossover strategies for nonlinear regression problems Wongsa, Watchara; Puphasuk, Pikul; Wetweerapong, Jeerayut
Bulletin of Electrical Engineering and Informatics Vol 13, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i5.6417

Abstract

This paper presents the differential evolution algorithm with adaptive mutation and crossover strategies (DEAMC) for solving nonlinear regression problems. The DEAMC algorithm adaptively uses two mutation strategies and two ranges of crossover rate. We evaluate its performance on the National Institute of Standards and Technology (NIST) nonlinear-regression benchmark containing many models of varying levels of difficulty and compare it with classic differential evolution (DE), enhanced differential evolution algorithm with an adaptation of switching crossover strategy (DEASC), and controlled random search methods (CRS4HC, CRS4HCe). We also apply the proposed method to solve parameter identification applications and compare it with enhanced chaotic grasshopper optimization algorithms (ECGOA), self-adaptive differential evolution with dynamic mutation and pheromone strategy (SDE-FMP), and JAYA and its variant methods. The experimental results show that DEAMC is more reliable and gives more accurate results than the compared methods.
Classification of human grasp forces in activities of daily living using a deep neural network Padilla-Magaña, Jesus Fernando; Sanchez-Suarez, Isahi; Peña-Pitarch, Esteban
Bulletin of Electrical Engineering and Informatics Vol 13, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i6.7181

Abstract

The study of human grasp forces is fundamental for the development of rehabilitation programs and the design of prosthetic hands in order to restore hand function. The purpose of this work was to classify multiple grasp types used in activities of daily living (ADLs) based on finger force data. For this purpose, we developed a deep neural network (DNN) model using finger forces obtained during the performance of six tests through a novelty force sensing resistor (FSR) glove system. A study was carried out with 25 healthy subjects (mean age: 35.4±11.6) all right handed. The DNN classifier showed high overall performance, obtaining an accuracy of 93.19%, a precision of 93.33%, and a F1-score of 91.23%. Therefore, the DNN classifier in combination with the FSR glove system is an important tool for physiotherapists and health professionals to determine and identify finger grasp forces patterns. The DNN model will facilitate the development of tailored and personalized rehabilitation programs for subjects recovering of hand injurie and other hand diseases. In future work, prosthetic hand devices can be optimized to more accurately reproduce natural grasping patterns.
Exploiting channel state information of WiFi signal for human activity detection: an experimental study Boudlal, Hicham; Serrhini, Mohammed; Tahiri, Ahmed
Bulletin of Electrical Engineering and Informatics Vol 13, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i2.6344

Abstract

Ubiquitous computing aims to seamlessly integrate computing into our daily lives, and requires reliable information on human activities and state for various applications. In this paper, we propose a device-free human activity recognition system that leverages the rich information behind WiFi signals to detect human activities in indoor environments, including walking, sitting, and standing. The key idea of our system is to use the dynamic features of activities, which we carefully examine and analyze through the characteristics of channel state information. We evaluate the impact of location changes on WiFi signal distribution for different activities and design an activity detection system that employs signal processing techniques to extract discriminative features from wireless signals in the frequency and temporal domains. We implement our system on a single off-the-shelf WiFi device connecting to a commercial wireless access point and evaluate it in laboratory and conference room environments. Our experiments demonstrate the feasibility of using WiFi signals for device-free human activity recognition, which could provide a practical and non-intrusive solution for indoor monitoring and ubiquitous computing applications.
Development of IoT based intelligent irrigation system using particle swarm optimization and XGBoost techniques Santosh, D. Teja; Anuradha, Nandula; Kolukuluri, Madhavi; Gupta, Gaurav; Pathak, Mrunal Kishor; Krishnan, V. Gokula; Raghuvanshi, Abhishek
Bulletin of Electrical Engineering and Informatics Vol 13, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i3.6332

Abstract

A crop needs regular watering throughout its life to grow well. Irrigation improves food growth. Machines irrigate plants. The dry Sahel, which gets a lot of rain during the summer season but is dry in winter, needs irrigation. When it doesn't rain enough, crops need watering. By constantly monitoring soil moisture, humidity, temperature, and pH, precision agriculture reduces water use and increases crop output. Precision gardening uses less water. In many wealthy nations, efficient farming requires the internet of things (IoT). Particle swarm optimization (PSO) and XGBoost are used in this IoT-based intelligent watering system. Humidity and moisture sensors gather soil data at grass roots. Sensors constantly gather this data. These data are useless for smart watering. PSOselects smart watering data. This reduces central cloud info storage. Then, machine learning methods are trained using soil humidity, moisture, crop, and weather data. These programs can calculate a crop's water requirements. IoT devices control irrigation system water flow and results in saving fresh water. XGBoost algorithm is saving water from 23% to 27% for different crops.
Integral-proportional derivative approach for brushless direct current motor speed control Panjaitan, Seno Darmawan; Priyatman, Hendro; Supriono, Supriono; Frizky, Muhammad Revaldi
Bulletin of Electrical Engineering and Informatics Vol 13, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i5.7755

Abstract

is paper proposed the integral-proportional-derivative (I-PD) as an extension of the conventional proportional-integral-derivative (PID) method that has been used in many brushless direct current (BLDC) applications to control the BLDC motor that can deal with desired speed (reference) changes. It has elucidated a comprehensive comparative analysis between PID, intending to delineate the most efficacious control approach based on a thorough evaluation. This paper scrutinizes four principal methods: proportional-integral (PI), integral-proportional (I-P), PID, and I-PD. Our findings indicate that in the presence of voltage spike constraints, I-P or I-PD emerges as the optimum choice for both four-pole and six-pole motors. Where maximum difference (MaxDiff) is the principal consideration, PI, and I-P are identified as the most suitable methods. Conversely, when the primary objective is to minimize root mean square error (RMSE), PI proves superior for four-pole motors, while PID is preferable for six-pole types. Notably, I-P demonstrates excellent performance in terms of settling time for both motor types. In summation, I-P stands out as the preeminent choice if the objective is to select a singular method that ensures optimal performance across all parameters for a four-pole or six-pole motor.
Multiple-node model of wind turbine generating system for unbalanced distribution system load flow analysis Gianto, Rudy; Khwee, Kho Hie
Bulletin of Electrical Engineering and Informatics Vol 13, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i2.5461

Abstract

This paper discusses a method to integrate a wind turbine generating system (WTGS) into a three-phase unbalanced distribution system load flow (DSLF) analysis. The proposed method is based on the single-phase multiple-node model. In the present work, the single-phase multiple-node model is extended to a three-phase multiple-node model to facilitate the load flow analysis of a three-phase unbalanced power system network. The multiple-node model (i.e., three-node model) will only modify the load flow analysis by introducing two lines and two load buses to the distribution system network where the WTGS is installed. Thus, a standard three-phase load flow program can be employed to compute the unknown quantities in the DSLF problem formulation. The proposed method is verified by incorporating the model into the load flow analysis of three-phase distribution networks. The investigation uses two representative distribution networks (i.e., 19-bus and 25-bus networks). The results of the study confirm the validity of the proposed method.
Enhancing radar applications: FPGA-driven phase estimation with floating point arithmetic Sivaprasad, Ponduri; Venkataraman, Anandi; Murty, P Satyanarayana
Bulletin of Electrical Engineering and Informatics Vol 13, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i5.7074

Abstract

This article introduces a paradigm shift in radar technology with field programmable gate array (FPGA)-driven Phase estimation using floating point arithmetic (FPA). Leveraging FPGA’s parallel processing and the precision of FPA, this work promises enhanced accuracy and efficiency. The proposed system’s key performance metrics include the following: number of slices: 20,941, number of look-up tables (LUTs): 22,371, number of digital signal processing (DSP) blocks: 2, delay: 112.9 ns, and power consumption: 7.2 mw. A comparative analysis showcases advantages in area utilization, LUT, and DSP blocks despite a trade-off with delay. The presented methodology and results demonstrate the feasibility of real-time phase estimation at GHz rates, positioning this approach as transformative for next-gen radar systems.
Observer-based single phase robustness load frequency sliding mode controller for multi-area interconnected power systems Nguyen, Cong-Trang; Trong Hien, Chiem; Phan, Van-Duc
Bulletin of Electrical Engineering and Informatics Vol 13, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i5.7893

Abstract

In multi-area interconnected power systems (MAIPS), all the plant state’s measurement is stiff due to the lack of a device or the cost of the sensor is expensive. To solve this restriction, a novel sliding mode control technique- based load frequency controller (LFC) is investigated for MAIPS where the estimation states of the system is utilized fully in the switching surface and controller. Initially, a single-phase switching function is suggested to dismiss the reaching phase in traditional sliding mode control (TSMC) approach. Secondly, the MAIPS’s unmeasurable variables is estimated by using the suggested observer tool. Next, a new single phase robustness load frequency sliding mode controller (SPRLFSMC) for the MAIPS is established based on the support of the observer instrument and output data only. The entire plant’s stability is ensured through the Lyapunov theory. Even though the plant’s variables are not measured, the obtained results in the simulation display that the frequency remains in the nominal domain under load instabilities on the MAIPS. The simulation results for a three-area interconnected electricity plant verify the preeminence of the anticipated SPRLFSMC over other current controllers with respect to settling time and overshoot.
Customizing the minimum number of replicas for achieving fault tolerance in a cloud/grid environment S. Almhanna, Mahdi; A. Murshedi, Tariq; Al-Turaihi, Firas Sabah; M. Almuttairi, Rafah
Bulletin of Electrical Engineering and Informatics Vol 13, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i1.5413

Abstract

Networks consist of numerous resources; it is crucial not to overlook fault tolerance and consider it during planning. This is because errors during implementation can result in wasted time and effort, thereby squandering these resources. One solution to address this issue effectively is to implement the task on multiple resources to minimize the occurrence of failed tasks. However, employing an unspecified or fixed number of resources can lead to the depletion of network resources and the overall failure of the network. Replication plays a pivotal role in enhancing data availability in distributed systems. By storing data in multiple locations, users can still access it even if some copies are unavailable due to site failure. Many replication-based algorithms utilize a predetermined number of iterations per function, which may consume excessive network resources, even if the ongoing task does not require such abundant resources. This paper proposes task replication as a viable mechanism for an efficient and fault-tolerant scheduling system. We introduce an algorithm that dynamically selects the optimal and minimal number of replicas based on the network's failure history. This approach aims to minimize the failure rate during task execution.
Early prediction of COVID-19 infection using data mining and multi machine learning algorithms Enad, Ahmed Jaddoa; Aksu, Mustafa
Bulletin of Electrical Engineering and Informatics Vol 13, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i3.6912

Abstract

The fields of artificial intelligence (AI) and machine learning (ML) have attracted significant interest and investment from a diverse range of industries, especially during the last several years. Despite the fact that AI methods have been used extensively and put through extensive testing in the healthcare industry, the recently discovered coronavirus disease (COVID-19) necessitates the use of these methods in order to prevent the emergence of the disease. The proposed system is based on six ML algorithms to predict COVID-19 infection as random forest (RF) algorithm, naive bayes (NB) algorithm, support vector machine (SVM) algorithm, decision tree (DT) algorithm, multi-layer perceptron (MLP), and k-nearest neighbor (KNN). It is based on two steps: first, we uploaded the dataset to train the model. Then, we test our model on those cases to work directly after making a trained classifier so it can directly discover with automatic COVID-19 prediction state of a patient suspected or not. The proposed system results showed the high accuracy of NB, DT, and SVM as 98.646%. Besides the better time to build the model and early predict the state of patients is 31 ms of the NB algorithm.

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