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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 comparative study of multiband mamdani fuzzy classification methods for west of Iraq satellite image Nezar Ismat Seno; Muntaser Abdul Wahed Salman; Rabah Nory Farhan
Bulletin of Electrical Engineering and Informatics Vol 11, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i3.3561

Abstract

In our paper, performance of four fuzzy membership function generation methods was studied. These methods were studied in the context of implementing Mamdani fuzzy classification on a set of satellite images for western Iraqi territory. The first method generate triangulate membership functions using mean, minimum (min) and maximum (max) of histogram attribute values (AV), while peak and standard deviation (STD) of these AV were used in the second. On the other hand, in the third and fourth methods, Gaussian membership functions are generated using same mentioned values in the first and second method respectively. The goal was to generate a Mamdani type fuzzy inference system the membership function (MF) of each fuzzy set and implementing the AV of western Iraqi territory training data sets. A pixel-by-pixel comparison of each method with traditional maximum likelihood method (ML) was made on data sets comprising six bands of satellite imagery of the western Iraqi region taken by the Landsat-5 satellite. Simulation results of these performance comparisons singled out that the method using Gaussian MFs together with peak and STD of the AV as the best achiever with a similarity of 83.16 percent for band (3) of the studied area.
Facial expression recognition using HOG and LBP features with convolutional neural network Nadia Shamsulddin Abdulsattar; Mohammed Nasser Hussain
Bulletin of Electrical Engineering and Informatics Vol 11, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i3.3722

Abstract

In computer vision, automatic facial expression recognition (FER) continued a difficult and interesting topic. The majority of extant techniques are based on traditional features descriptors such as local binary pattern (LBP) and histogram of oriented gradient (HOG), in which the classifier's hyperparameters are tailored to produce the best recognition accuracies across a single database or a small set of similar databases. This paper integrates the power of deep learning techniques with the LBP and HOG. The LBP and HOG are estimated from each image in the dataset. The resulting dataset is applied to a convolutional neural network (CNN). The architecture of this CNN constitutes three convolutional layers and three max-pooling layers. The output layers involve BatchNormalization, three dense layers, and two dropout layers. The proposed architecture is validated on the extended cohn-kanade dataset (CK+). We obtain improvement in the accuracy of the CNN model from 0.9593 to 0.967 and 0.975 after using the LBP and HOG respectively.
Geometric generative adversarial net based multiple methods for spectrum sensing in cognitive radio networks Sattar B. Sadkhan; Doaa Jabbar Mardaw Zaidawi
Bulletin of Electrical Engineering and Informatics Vol 11, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i3.3811

Abstract

The majority of recently developed approaches require a significant number of labelled samples. The proposed system are dedicated to using less marked samples for automatic modulation detection in the cognitive radio domain. The proposed signal classifier generative adversarial nets (GANs) methodology is a semi-supervised learning framework that focuses on adversarial analysis GANs are a major step forward in the development of competitive generative networks, and they've spawned a slew of apparently unrelated versions. The discovery of a single geometric form in GAN and its derivatives is one of the paper's key contributions. In three geometric stages, by demonstrate how to train an adversarial generative model: updating the discriminator parameter away from the separating hyperplane, looking for the separating hyperplane, and updating the generator along the usual vector route of the separating hyperplane. The shortcomings in current approaches are shown by this geometric intuition, leading us to suggest a new geometric GAN formulation that maximizes the margin using SVM separating hyperplane. An equilibrium is reached between the discriminator and generator in the geometric GAN, according to our theoretical research. Furthermore, detailed computational results showing the superior efficiency of the GAN engineering network were obtained.
Intelligent multiperiod wind power forecast model using statistical and machine learning model Manisha Galphade; Valmik Nikam; Biplab Banerjee; Arvind Kiwelekar
Bulletin of Electrical Engineering and Informatics Vol 11, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i3.3756

Abstract

With the rapidly increasing integration of wind energy into the modern energy grid system, wind energy prediction (WPP) is playing an important role in the planning and operation of an electrical distribution system. However, the time series data of wind energy always has nonlinear and non-stationary characteristics, which is still a great challenge to be accurately predicted. This paper proposes the intelligent wind power forecast model and evaluates to forecast long term, short term and medium term wind power. It uses statistical and machine learning approach for finding the best model for multiperiod forecasting. The model has been tested on Sotavento wind farm historical data, located in Galicia, Spain. The experimental results show that random forest has better accuracy than other models for long term, short term and medium term forecasting. The power prediction accuracy of the proposed model has been evaluated on RMSE, and MAE metrics. The proposed model has shown better accuracy for medium term and long term forecast. The accuracy is improved by 72.12% in case of medium term and 50.49% in case of long term.
Indonesian automatic short answer grading system Heinrich Reagan Salim; Chintya De; Nicholas Daniel Pratamaputra; Derwin Suhartono
Bulletin of Electrical Engineering and Informatics Vol 11, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i3.3531

Abstract

Short answer question is one of the methods used to evaluate student cognitive abilities, including memorizing, designing, and freely expressing answers based on their thoughts. Unfortunately, grading short answers is more complicated than grading multiple choices answers. For that problem, several studies have tried to build an artificial intelligence system called automatic short answer grading (ASAG). We tried to improve the accuracy of the ASAG system at scoring student answers in Indonesian by enhancing the earlier state-of-the-art models and methods. They were the bidirectional encoder representations from transformer (BERT) with fine-tuning approach and ridge regression models utilizing advanced feature extraction. We conducted this study by doing stages of literature review, data set preparation, model development, implementation, and comparison. Using two different ASAG data sets, the best result of this study was an achievement of 0.9508 in pearson’s correlation and 0.4138 in root-mean-square error (RMSE) by the BERT-based model with the fine-tuning approach. This result outperformed the results of the previous studies using the same evaluation metrics. Thus, it proved our ASAG system using the BERT model with fine-tuning approach can improve the accuracy of grading short answers.
FRIT-based integral action state feedback controller tuning using PSO for a liquid slosh suppression system Nurul Najihah Zulkifli; Mohd Syakirin Ramli; Hamzah Ahmad; Addie Irawan
Bulletin of Electrical Engineering and Informatics Vol 11, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i3.3262

Abstract

This paper presents a model-free approach of controller tuning to a liquid slosh suppression system. The sloshing is the usual occurrence happening to the liquid in a moving container. An integral action state feedback controller was proposed as the selected control structure. A fictitious reference signal was formulated using the recorded input-output data generated from a one-shot experiment and later be used to design the appropriate performance index. The minimization of the performance index of the controlled system was achieved by employing the PSO algorithm. Numerical analyses using MATLAB software have been conducted to evaluate the effectiveness of the proposed model-free approach. The results manifested that the tuned controller had exhibited good transient response performance regarding the trajectory tracking of the cart motion and reduction of slosh level motion.
Variations in phase conductor size and spacing on power losses on the Nigerian distribution network Abdulrasaq Jimoh; Samson Oladayo Ayanlade; Funso Kehinde Ariyo; Abdulsamad Bolakale Jimoh
Bulletin of Electrical Engineering and Informatics Vol 11, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i3.3753

Abstract

Most Nigerian distribution networks are examined on a single-phase basis, which fails to reflect the network's true features. Using three-phase power flow algorithms, this research explores the implications of variations in conductor sizes and spacing on power losses on a Nigerian network. Modified carson's equations were used to model the distribution lines to determine the network's impedance without presuming transposition of the lines. The conductor sizes and spacing were changed to see how they affected network power losses and how they contributed to the distribution network imbalance. The results showed that changing the conductor sizes of certain of the phases increased real power losses by 55.8 and 5.8%, respectively, in phases A and B. Phase C's was reduced by 13.04%. Furthermore, reactive power losses in phases A and B increased by 3.29 and 8.18%, respectively, whereas reactive losses in phase C dropped by 10.32%. Changing the conductor spacing in phases A, B, and C increased real power losses by 825.8, 136.2, and 13.2%, respectively, and reactive power losses by 72.86, 52.30, and 31.89%. Distribution networks should not be evaluated on a single-phase basis since losses differ in each of the three phases. Conductor size and spacing reductions cause huge losses.
Random forest and support vector machine based hybrid liver disease detection Tsehay Admassu Assegie; Rajkumar Subhashni; Napa Komal Kumar; Jijendira Prasath Manivannan; Pradeep Duraisamy; Minychil Fentahun Engidaye
Bulletin of Electrical Engineering and Informatics Vol 11, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i3.3787

Abstract

This study develops an automated liver disease detection system using a support vector machine and random forest detection techniques. These techniques are trained on data containing the information collected from the Mayo Clinic trial in primary biliary cirrhosis (PBC) of the liver conducted between 1974 and 1984. The proposed system can detect the presence of liver disease in the test set. The random forest model is used for recursive feature elimination at the pre-processing stage and the support vector machine is trained on the optimal feature set. The experimental result shows that the proposed support vector machine (SVM) model has achieved 78.3% accuracy.
The performance comparison of artificial intelligence based distance relays for the protection of transmission lines Muhammad Rameez Javed; Umar Siddique Virk; Aashir Waleed; Muhammad Yasir Jamal
Bulletin of Electrical Engineering and Informatics Vol 11, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i3.3305

Abstract

The safe and reliable generation and transmission of electricity is the desirable factor for utilities which arises the need of protection equipment such as relays in the power systems. According to recent studies, the conventional relays are not able to provide the required protection to the power systems, resulting in the emergence of various artificial intelligence (AI) techniques such as (i) artificial neural network (ANN); (ii) adaptive neuro fuzzy interface system (ANFIS); and (iii) fuzzy logic based relays for the protection. This work presents the protection scheme for transmission lines using various AI based distance relays along with performance comparison of these relays with a conventional numerical distance relay (NL). The comparison analysis has been performed by generating a test model in the MATLAB/Simulink environment and comparing “response time” against the same fault occurence for all relays. The comparison analysis found that AI based relays outperformed than conventional relay in terms of response time and accuracy level against the faults.
1×16 Rectangular dielectric resonator antenna array for 24 Ghz automotive radar system Abderrahim Haddad; Mohssin Aoutoul; Mohamed Essaaidi; Khalid Sabri; Abdelaziz Khoukh; Youssef Errami; Anas Had; Fadwa El Moukhtafi; Redouane Jouali
Bulletin of Electrical Engineering and Informatics Vol 11, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i4.3068

Abstract

This paper presents the design of a 1×16-elements RDRA array for anti-collision radar SRR application at 24 GHz. A single RDRA with high dielectric constant of 41, fed by a simple microstrip line feeding technique, is initially designed to operate around 24 GHz. The RDRA element is further used within an array network structure made up of 16 linear antenna elements to cover the same frequency band. The simulated 1×16 RDRA array can reach a high gain, up to18.6 dB, very high radiation efficiency (97%), and ensure enough directional radiation pattern properties for radar applications with a 3-dB angular beam width of 6°. To validate our design, RDRA array’ radiation pattern computed results are compared to an equivalent fabricated patch antenna array reported in the literature.

Page 141 of 291 | Total Record : 2901

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