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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 63 Documents
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Search results for , issue "Vol 11, No 2: April 2022" : 63 Documents clear
Classification improvement of gene expression for bipolar disorder using weighted sparse logistic regression Abdulnasir Younus Ahmed; Mohammed Abdulrazaq Kahya; Suhaib Abduljabbar Altamir
Bulletin of Electrical Engineering and Informatics Vol 11, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i2.3594

Abstract

The computer-aided diagnosis system plays an important role in the classification of diseases and genes such as psychological or other diseases. Bipolar disorder (BD) is a commond psychological disease nowdys. Genes that describe this type of disease may include irrelative values to bipolar disorder disease. These values may adversely impact the classification performance. Logistic regression (LR) and recently sparse logistic regression (SLR) were used as a common technique to solve such binary classification problems. Gene selection has been applied to be a successful technique to get better classification output by excluding the irrelative values of genes. In this work we go further in improving the classification accuracy by restoring to incorporating the weight of these genes utilizing integrating the standardization of T-test with the sparse logistic regression, aiming to accomplish high classification accuracy. A bipolar dataset of gene expressions measured for 22283 genes using Affymetrix technology was used. Two performance indicators; classification accuracy, and geometric-mean of specificity and sensitivity are considered in evaluating the proposed method. Experimental results show an improvement over the two competitor methods; SLR-smoothly clipped absolute deviation (SCAD) and SLR-lasso in three indicators: classification accuracy, geo-means, and area under the curve. Therefore, our technique is beneficial to predict and classify BD psychopaths.
Implementation of FOC algorithm using FPGA for GaN-based three phase induction motor drive Tung Duong Do; Nam Duong Le; Vu Hoang Phuong; Nguyen Tung Lam
Bulletin of Electrical Engineering and Informatics Vol 11, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i2.3569

Abstract

Induction motor is widely used in industrial applications due to its low cost, simple design, and reliability. In this paper, the induction motor control structure FOC will be implemented on the FPGA platform for the drive system using GaN devices. By using GaN technology, the switching frequency can be up to 100 kHz instead of 2 to 20 kHz when using IGBT transistors. It leads to a significant reduction in switching loss as well as increasing the power density of the power electronic converter. The control structure will be programmed in VHDL language on the system-on-chip environment of Xilinx Zybo z7010 FPGA development board. The validity of the research is verified by some results when operating with HIL device.
Prediction of internet user satisfaction levels in Bangladesh using data mining and analysis of influential factors Md. Hasan Imam Bijoy; Sumiya Alam Akhi; Md. Ali Ashraf Nayeem; Md. Mahbubur Rahman; Md. Jueal Mia
Bulletin of Electrical Engineering and Informatics Vol 11, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i2.3617

Abstract

Today the world has already acknowledged as a global village by the inter-net which has technologically evolved into a significant performance instrument for individuals, businesses, and countries seeking to achieve betterment. This study is based on data mining techniques to predict the satisfaction level of internet users from the context of Bangladesh. After conducting a public survey with 18 questions, we were able to acquire 451 responses from participants. Data for user satisfaction was associated with end-user characteristics including certain getting high speed, internet packages, cable type of Wi-Fi connection with targeting various age groups and occupations. The research's most key conceptual breakthrough was the reliability of magnitude predictions of user satisfaction level based on their experience with internet use. The empirical findings indicate that people in Bangladesh have high expectations in existing internet technology, and they are very dissatisfied with their facilities of internet use and to measure satisfaction level related with monthly limit of the Wi-Fi packages and the elements affecting internet speed. Several classifier models were applied to our dataset and among them, Random Forest (RF) performance reaches the top position with 91.53% accuracy.
AutoKeras and particle swarm optimization to predict the price trend of stock exchange Doaa A. Fattah; Amany A. Naim; Abeer S. Desuky; Mervat S. Zaki
Bulletin of Electrical Engineering and Informatics Vol 11, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i2.3373

Abstract

The stock price varies depending on time, so stock market data is time-series data. The prediction of the trend of a stock price is a more interesting topic for investors to take an investment decision in a specific stock. Prediction of stock price always depends on machine learning algorithms. In this work, optimizing deep neural network (DNN) is used for predicting if the close price is reached to the profit which is determined by the investor or not and improve the prediction accuracy. Particle swarm optimization (PSO) and auto machine learning (AutoML) are used as optimizers with DNNs. The methods are applied to data of nine companies in Indonesia and National Stock Exchange (NSE) of India. The data is got from yahoo finance. Based on the experimental results, AutoML of deep learning proved to have the best accuracy rate, which is varying from 81 percent to 92 percent across all companies, and the accuracy after optimizing DNNs using PSO is varying from 73 percent to 82 percent across all companies.
Dynamic response investigation of PV based CLCIS fed IMD applications using HC and SMC Rajamanickam Pazhanimurugan; Ramaiyan Bensraj; Chinnapettai Ramalingam Balamurugan
Bulletin of Electrical Engineering and Informatics Vol 11, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i2.3394

Abstract

This paper explains a solar photovoltaic (SPV) based cascaded landsman converter inverter structure (CLCIS) which feeds induction motor drive (IMD) using hysteresis controller (HC) and sliding mode controller (SMC). The aim is to create SPV based cascaded landsman converter (CLC) with an enhanced dynamic response. HC and SMC enabled systems are designed and created using MATLAB. The standards of procedure and simulation results are examined. The simulation results of HC and SMC enabled systems are analyzed concerning time-domain parameters and a comparison has been exhibited. The proposed SMC ensures a model reference robust dynamics against changing voltage conditions of photovoltaic (PV) based CLCIS fed IMD system. The consequences reveal that the dynamic response with SMC is better compared to the HC enabled CLCIS-IMD system.
Power analyzer of linear feedback shift register techniques using built in self test Kannadhasan Suriyan; Nagarajan Ramalingam; Kanagaraj Venusamy; Sathish Sivaraman; Kiruthiga Balasubramaniyan; Manjunathan Alagarsamy
Bulletin of Electrical Engineering and Informatics Vol 11, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i2.3331

Abstract

Wasteful patterns that don't lead to fault dropping squander a tone of energy in the linear-feedback shift register and circuit under examination in a random research region. Random switching actions in the CUT and scan pathways between applications with two consecutive vectors are another significant cause of energy loss. This study proposes a unique built-in self-test (BIST) technique for scan-based circuits that might help save energy. Only the available vectors are produced in a fixed series thanks to a mapping logic that alters the LFSR's state transitions. As a consequence, and without reducing fault coverage, the time it takes to execute trials has decreased. Experiments on circuits demonstrated that during random testing, the linear feedback shift register saves a significant amount of power.
Constrained self regulating particle swarm optimization Tayyab Ahmed Shaikh; Syed Sajjad Hussain Rizvi; Muhammad Rizwan Tanweer
Bulletin of Electrical Engineering and Informatics Vol 11, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i2.3564

Abstract

Self regulating particle swarm optimization (SRPSO) is a variant of particle swarm optimization (PSO) which has proved to be a very efficient algorithm for unconstrained optimization compared with other evolutionary algorithms (EAs) and utilized recently by the researchers for solving real-world problems. However, SRPSO has not been evaluated and analyzed for constrained optimization. In this work, SRPSO has been evaluated exhaustively for constrained optimization using the 24 constrained benchmark problems by coupling it with four efficient constraint handling techniques (CHTs). The results of constrained SRPSO algorithm have been compared with two other algorithms i.e. Differential evolution (DE) and PSO. DE and PSO have also been coupled with same four CHTs and evaluated on the 24 constrained benchmark problems. Statistical analysis on performance evaluation of three algorithms on the benchmark problems shows that constrained SRPSO algorithm performance is better than constrained PSO but it is found to be deficient when compared with constrained DE with 95% confidence level. Therefore, the objective of this work is to evaluate the SRPSO algorithm comprehensively for constrained optimization with different views to come up with suitability of constrained SRPSO algorithm when coupled with particular CHT for solving specific type of problems.
A novel phosphor structure for improving the luminous flux of white LEDs Nguyen Thi Phuong Thao; Jan Nedoma; Le Anh Vu; Dieu An Nguyen Thi
Bulletin of Electrical Engineering and Informatics Vol 11, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i2.3608

Abstract

This section focuses on the color uniformity and luminous production of multi-chip white-emitted LED lighting systems (MCW-LEDs) in improving illuminated performance. To accomplish the desired outcome, CaO:Sb3+ must be mixed with their phosphor compounding, which has been shown to have a massive impact on illuminating effectiveness. There is also evidence that the increasing of yellowish-green-emitted phosphorus CaO:Sb3+ concentration supports color homogeneity as well as luminescent effectiveness enhancements in MCW-LEDs featuring a 8500 K correlating colour temperature (CCT). Meanwhile, that rise in CaO:Sb3+ concentration leads to the gradually deteriorating color quality scale. Thus, if appropriate concentration and particle size of CaO:Sb3+ phosphor are determined, it is not hard to obtain such an excellent presentation in color uniformity, color quality scale and luminescence of MCW-LEDs.
Classification of good and damaged rice using convolutional neural network Dolly Indra; Hadyan Mardhi Fadlillah; Kasman Kasman; Lutfi Budi Ilmawan; Harlinda Lahuddin
Bulletin of Electrical Engineering and Informatics Vol 11, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i2.3385

Abstract

Rice production is massive in Indonesia, therefore maintaining the quality of the product is necessary. Detection and classification of objects have become a very important part in image processing. We performed object detection namely rice. After the object is found, it can be classified into two categories, namely good and damaged rice. We conducted a new study on rice which was carried out per group not per grain to obtain or classify good and damaged rice where we had carried out several steps, namely segmentation process using HSV (hue, saturation, value) color space. HSV is used because of its excellence in representing brightness of the image. We considered evaluating brightness because the tendency of damaged rice is darker or paler compared to good rice. To accomodate environment lighting ambiguity we perform the image acquisition in a controlled environment, so that all the images have the same light intensity. Here we use only channel V of HSV to be used in feature extraction using the gray-level co-occurrence matrix (GLCM) and finally convolutional neural network (CNN) is used for classification. From the test experiments that we have done, we have produced 83% prediction accuracy. Considering how similar the good rice is to the spoiled rice, the results are quite impressive.
A compact four port MIMO antenna for millimeterwave applications Mohini Narendra Naik; Hasanali Gulamali Virani
Bulletin of Electrical Engineering and Informatics Vol 11, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i2.3689

Abstract

A novel compact microstrip MIMO antenna with high isolation and low envelope correction coefficient has been proposed and presented in this paper. The proposed antenna is compact in size with 3.22x4.86 mm2, operating over the frequency band of 60GHz. The MIMO antennas have been designed andsimulated using electromagnetic design and verification platformIE3DTM. Two ports MIMO antenna with the size of 3.22x1.93 mm2 resonates at 58.1 GHz covering a band of 57-59.3 GHz, with the bandwidth of 2.3 GHz, gain of 5.2 dBi, and directivity of 8.67 dBi. It also provides aninput reflection coefficient of -18.41 dB with isolation of -19.98 dB. Proposed four port MIMO antenna resonates at 58 GHz covering a range of 56.9 GHz to 59.2 GHz, with a bandwidth of 2.3 GHz, gain of 5.44 dBi, and the directivity of 9.75dBi. It provides goodisolationranging from -19 dB to -30 dB with input reflection coefficient of -18.09 dB. The envelope correlation coefficient (ECC) of the proposed four-port MIMO antenna is below 0.06 in far-field radiation characteristics. The proposed MIMO antenna has been analyzed for its diversity performance in terms of ECC, mean effective gain, and diversity gain.

Page 5 of 7 | Total Record : 63

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