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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 11, No 5: October 2022" : 64 Documents clear
A new modified grasshopper optimization algorithm Abdulkareem Y. Abdalla; Turki Y. Abdalla
Bulletin of Electrical Engineering and Informatics Vol 11, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i5.4083

Abstract

The grasshopper algorithm (GOA) is a recent algorithm. It is widely used in many applications and results in a good solution. The algorithm is simple and the accuracy in very high. The GOA has some limitations due to the use of linear comfort zone parameter that causes some difficulties in balancing between the exploration and exploitation which may lead to fall in a local optimum. In this paper a modification is made to improve the operation of GOA. A nonlinear function is developed to replace the linear comfort zone parameter. The benchmark of GOA authors is used for testing the performance improvement of the suggested modified GOA compared to the basic GOA. Results indicate that the MGOA outperforms original GOA, presenting a higher accuracy, faster convergence, and stronger stability. The proposed new modified GOA performs better than the original GOA.
Enhanced Taguchi’s T-method using angle modulated Bat algorithm for prediction Zulkifli Marlah Marlan; Faizir Ramlie; Khairur Rijal Jamaludin; Nolia Harudin
Bulletin of Electrical Engineering and Informatics Vol 11, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i5.4350

Abstract

Analysis of multivariate historical information in predicting future state or unknown outcomes is the core function of Taguchi’s T-method. Introduced by Dr. Genichi Taguchi under Mahalanobis-Taguchi system, the T-method combines regression principle and robust quality engineering element in formulating a predictive model and employs taguchi’s orthogonal array design in optimizing the model through feature or variable selection process. There is a concern regarding the sub-optimality of the T-method prediction accuracy, particularly when the orthogonal array failed to offer a significant number of combinations in search for an optimal subset of features. This is due to the fixed and limited combination offered for evaluation as well as the lack of higher-order interaction of combination. In response to this issue, this paper proposed an angle modulated Bat algorithm to be integrated with the T-method in optimizing the prediction model. A comparison study was conducted using energy efficiency benchmark datasets with the mean absolute error metric used as the performance measure. The results show that the proposed method improved the prediction accuracy by 10.74%, from 6.05 to 5.4, by integrating only four features over the original eight in the prediction model.
Vehicular impact analysis of driving for accidents using on board diagnostic II Siddhanta Kumar Singh; Ajay Kumar Singh
Bulletin of Electrical Engineering and Informatics Vol 11, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i5.3864

Abstract

A large number of people meet with an accident everyday around the world. One of the leading causes of death is traffic accidents. The reasons behind India's rising number of road accidents contribute to bad driving behavior, poor road design and infrastructure, lack of enforcement of traffic laws. The post accidental investigation report is very important to know the actual reason of collision for the concerned parties and the insurance company and the police. The proposed work effectively extracts interpretable features describing complex driving patterns. It will provide analytical report of the accidents to various parties involved in process. This work analyzes the type and cause of accident. The experiment has been simulated using on board diagnostic II (OBD II) and smart phone accelerometer for post accidental analysis of collision. As the electronic control unit (ECU) does not provide accelerometer sensor, so the smart phone accelerometer has been utilized in conjunction with another parameter of OBD II device. The gravitational force (G-force) values observed from accelerometer sensor along the different axes and speed, acceleration, fuel consumption rate, and are retrieved from OBD II device. The result shows that the parameters recorded are very helpful in finding the actual accidental status of the vehicle.
Prediction measuring local coffee production and marketing relationships coffee with big data analysis support Anita Sindar Ros Maryana Sinaga; Ricky Eka Putra; Abba Suganda Girsang
Bulletin of Electrical Engineering and Informatics Vol 11, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i5.4082

Abstract

Following the increasing enthusiasm of the coffee market in Indonesia, a machine learning model is developed to study the relationship between coffee producers, consumers, production, and the market. Machine learning work flow is constructed in various stages; explore, develop, and validate the models. In this research, the building model predicts the production and market of late departure coffee based on labeled and unlabeled variables. The best predictions from the trained type of model algorithms of machine learning like tree accuracy of 85.7%, support vector machine (SVM) accuracy of 82.9%, and k-nearest neighbors, the accuracy of 82.9%, which produce three categories, namely, high production of 2 provinces, medium production of 21 provinces, and low production of 11 provinces. The accuracy classification is supported by the AUC value obtained for a high class, a medium class, and a low class. In addition, local coffee marketing modeling used in logistic regression was found with an accuracy of 88.9%, aiming to classify coffee interests between Arabica coffee and Robusta coffee. We found that the AUC value logistic regression for arabica coffee is about 0.94, while for Robusta is 0.92. The analysis of the classification modeling results shows a high level of accuracy of 93.0%.
Comparison of different sparse dictionaries for compressive sampling Deepak M. Devendrappa; Karthik P.; Deepak N. Ananth; Aruna Kumar P.
Bulletin of Electrical Engineering and Informatics Vol 11, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i5.4014

Abstract

Compressive sampling/compressed sensing (CS) is building on the observation that most of the signals in nature are sparse or compressible concerning some transform domain. And by converse, the same can be reconstructed with high accuracy by making use of far fewer samples than what is required by violating Shannon-Nyquist theorem. Some of the transform techniques like discrete cosine transform, fast fourier transforms discrete wavelet transform, discrete fourier transforms. In this paper, novel CS techniques like FFTCoSAMP, DCTCoSaMP, and DWTCoSaMP are introduced and compared on different sparse transforms for CS in magnetic resonance (MR) images based on sparse signal sequences/dictionaries by means of transform techniques with respect to objective quality assessment algorithms like PSNR, SSIM and RMSE, where CoSaMP stands for compressive sampling matching pursuit. DWTCoSaMP is giving the PSNR values of 37.16 (DB4), 38.12 (Coif3), 38.5 (Sym8), for DCTCoSaMP and FFTCoSaMP, it’s 36.33 and 36.01 respectively. For DWTCoSaMP, SSIM value is 0.81, and for DCTCoSaMP and FFTCoSaMP, it’s 0.73 and 0.7 respectively. And finally, for DWTCoSaMP, RMSE value is 0.66, and for DCTCoSaMP and FFTCoSaMP, it’s 0.53 and 0.41 respectively. DWTCoSaMP reveals the best than rest of the methods and traditional CS techniques by the detailed comparison and analysis.
Estimation of concrete compression using regression models Tsehay Admassu Assegie; Ayodeji Olalekan Salau; Tayo Uthman Badrudeen
Bulletin of Electrical Engineering and Informatics Vol 11, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i5.4210

Abstract

The objective of this study is to evaluate the effectiveness of different regression models in concrete compressive strength estimation. A concrete compressive strength dataset is employed for the estimation of the regressor models. Regression models such as linear regressor, ridge regressor, k-neighbors regressor, decision tree regressor, random forest regressor, gradient boosting regressor, AdaBoost regressor, and support vector regressor are used for developing the model that predicts the concrete strength. Cross-validation techniques and grid search are used to tune the parameters for better model performance. Python 3.8 programming language is used to conduct the experiment. The Performance evaluation result reveals that the gradient boosting regressor has better performance as compared to other models using root mean square error (RMSE).
Noise resistance evaluation of spatial-field optical flow using modifying Lorentzian function Darun Kesrarat; Vorapoj Patanavijit
Bulletin of Electrical Engineering and Informatics Vol 11, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i5.3815

Abstract

This paper presents the evaluation of the modifying Lorentzian function on the spatial-field optical flow to examine the validity in the noisy domain of motion estimation. In the routine of the motion estimation, the frame’s motion vector is estimated by the optical flow approach where the flow of the image’s frames is caught to estimate the motion vector. Nevertheless, in the noisy domain, the preciseness of the motion vector is weakened. We operated the measurement along with several non-Gaussian noises standards through several styles of the standard image frame. The determination on error vector magnitude (EVM) was taken into account to consider the preciseness of direction and length of the motion vector (MV) in comparison with various noise resistance techniques in spatial-field optical flow approach. In the achievement results, we found that this modifying Lorentzian norm function added up in the optical flow strengthen the degree of preciseness in the estimation of the spatial-field optical flow approach in the noisy domain.
Effect of mixing ratio on the breakdown voltage of mineral and natural ester insulating oil blends Sharin Ab Ghani; Imran Sutan Chairul; Mohd Shahril Ahmad Khiar; Nor Hidayah Rahim; Syahrun Nizam Md Arshad@Hashim
Bulletin of Electrical Engineering and Informatics Vol 11, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i5.3723

Abstract

To date, the most common insulating oil used in oil-immersed transformers is mineral insulating (MI) oil, which is derived from petroleum. Owing to the depletion of petroleum over the years, it can be anticipated that petroleum-derived products such as MI oils will also deplete in the future. MI oils are not only non-renewable, but they are also non-biodegradable, where these oils are harmful to the environment in cases of oil spillage. Therefore, the aim of this study is to investigate the potential of mixing MI oil with natural ester insulating (NEI) oil in order to reduce the high dependency on MI oil for transformer applications. The MI and NEI oils were mixed with different mixing ratios. AC breakdown voltage test was conducted on the MI-NEI oil blends according to the ASTM D1816 standard. From the results, it is found that the following mixing ratios (30% of MI oil + 70% of NEI oil, 20% of MI oil + 80% of NEI oil) result in significant improvement in terms of the AC breakdown voltage compared with unused MI oil. The flash point and corrosivity levels of the oil blends were also examined.
Particle swarm optimization based multilevel MRI compression using compressive sensing Tariq Tashan; Ahmed K. Kadhim
Bulletin of Electrical Engineering and Informatics Vol 11, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i5.3873

Abstract

A multilevel compression method, for magnetic resonance imaging (MRI) images, is presented in this paper. First, the image is segmented into frames of equal size. Then, the sparsity of each frame is computed. Based on the sparsity index value, each frame is compressive sensing (CS) compressed/reconstructed at one level of four. Particle swarm optimization (PSO) is used to optimize the amount of information to be used in the CS reconstruction process, and to optimize the sparsity thresholds, that separate the different compression levels. Two-dimensional sigmoid function is suggested as a fitness function for the PSO. Six MRI images are used to evaluate the performance of the proposed method. The results show considerable gain in both peak signal to noise ratio (PSNR) and compression level (CL), when compared to single level compression, which is commonly considered in the literature.
SMPP-CBIR: shorted and mixed aggregated image features for privacy-preserving content-based image retrieval Ali Lazim Lafta; Ayad I. Abdulsada
Bulletin of Electrical Engineering and Informatics Vol 11, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i5.4323

Abstract

Thanks to recent breakthroughs in photographic and digital technology, enormous amounts of image data are generated daily. Many content-based image retrieval (CBIR) systems have been developed for searching image collections. However, these systems need more computer and storage resources that can be met by cloud servers, since they supply a lot of processing power at a reasonable price. The protection of users' personal information is a worry for image owners since cloud services are not exactly trustworthy. In this paper, we suggest and put into practice a CBIR (SMPP-CBIR) technique for searching and retrieving ciphertext information that protects security. Asymmetric scalar-product-preserving encryption process (ASPE) is used to preserve aggregated mixed feature vectors while still enabling computation between them to describe the related picture collection. The k-means clustering algorithm is used to recursively arrange all encrypted attributes into a tree index in order to speed up search times. The findings show that SMPP-CBIR is more scalable, more precise, and faster in indexing and retrieval than earlier systems.

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