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Bulletin of Electrical Engineering and Informatics

EEI
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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.

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Articles 57 Documents

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Search results for , issue "Vol 9, No 5: October 2020" : 57 Documents clear

Modeling of Dirac voltage for highly p-doped graphene field-effect transistor measured at atmospheric pressure Muhamad Amri Ismail; Khairil Mazwan Mohd Zaini; Mohd Ismahadi Syono
Bulletin of Electrical Engineering and Informatics Vol 9, No 5: October 2020
Publisher : Institute of Advanced Engineering and Science

In this paper, the modeling approach of Dirac voltage extraction of highly p-doped graphene field-effect transistor (GFET) measured at atmospheric pressure is presented. The difference of measurement results between atmospheric and vacuum pressures was analyzed. This work was started with actual wafer-scale fabrication of GFET with the purposes of getting functional device and good contact of metal/graphene interface. The output and transfer characteristic curves were measured accordingly to support on GFET functionality and suitability of presented wafer fabrication flow. The Dirac voltage was derived based on the measured output characteristic curve using ambipolar virtual source model parameter extraction methodology. The circuit-level simulation using frequency doubler circuit shows the importance of accurate Dirac voltage value to the device practicality towards design integration.

A high-efficiency continuous class-F power amplifier design using simplified real frequency technique Md. Golam Sadeque; Zubaida Yusoff; Mardeni Roslee
Bulletin of Electrical Engineering and Informatics Vol 9, No 5: October 2020
Publisher : Institute of Advanced Engineering and Science

The fourth-generation (4G) wireless communication has been deployed in many countries. However, there are still some problems such as spectrum crisis due to the increase of wireless mobile devices and servicing. Therefore, the fifth-generation (5G) communication system will be employed at some different spectrum other than 4G frequency band. The radio frequency power amplifier (RFPA) is the key component of the 5G system. In this paper, a broadband continuous class-F (CCF) RFPA is designed for the 5G frequency band from 3.3-4.3 GHz. The input and output matching network are designed using the simplified real frequency technique (SRFT). Using a 10W GaN CGH40010F Cree device, the efficiency of the RFPA achieved greater than 70.7% for the whole frequency band with a maximum of 81.5%. The output power and the gain are more than 40 dBm and 10 dB respectively

Power flow control in parallel transmission lines based on UPFC Mohammed Y. Suliman; Mahmood T. Al-Khayyat
Bulletin of Electrical Engineering and Informatics Vol 9, No 5: October 2020
Publisher : Institute of Advanced Engineering and Science

The power flow controlled in the electric power network is one of the main factors that affected the modern power systems development. The unified power flow controller (UPFC) is a FACTS powerful device that can control both active and reactive power flow of parallel transmission lines branches. In this paper, modelling and simulation of active and reactive power flow control in parallel transmission lines using UPFC with adaptive neuro-fuzzy logic is proposed. The mathematical model of UPFC in power flow is also proposed. The results show the ability of UPFC to control the flow of powers components "active and reactive power" in the controlled line and thus the overall power regulated between lines.

Identification of post-stroke EEG signal using wavelet and convolutional neural networks Esmeralda C. Djamal; Rizkia I. Ramadhan; Miranti I. Mandasari; Deswara Djajasasmita
Bulletin of Electrical Engineering and Informatics Vol 9, No 5: October 2020
Publisher : Institute of Advanced Engineering and Science

Post-stroke patients need ongoing rehabilitation to restore dysfunction caused by an attack so that a monitoring device is required. EEG signals reflect electrical activity in the brain, which also informs the condition of post-stroke patient recovery. However, the EEG signal processing model needs to provide information on the post-stroke state. The development of deep learning allows it to be applied to the identification of post-stroke patients. This study proposed a method for identifying post-stroke patients using convolutional neural networks (CNN). Wavelet is used for EEG signal information extraction as a feature of machine learning, which reflects the condition of post-stroke patients. This feature is Delta, Alpha, Beta, Theta, and Mu waves. Moreover, the five waves, amplitude features are also added according to the characteristics of the post-stroke EEG signal. The results showed that the feature configuration is essential as distinguish. The accuracy of the testing data was 90% with amplitude and Beta features compared to 70% without amplitude or Beta. The experimental results also showed that adaptive moment estimation (Adam) optimization model was more stable compared to Stochastic gradient descent (SGD). But SGD can provide higher accuracy than the Adam model.

Wideband millimeter-wave substrate integrated waveguide cavity-backed antenna for satellites communications Najib AL-Fadhali; Huda A. Majid; Rosli Omar; M. F. Ismail; M. K. A. Rahim; Abdul Rashid O. Mumin; B. A. F. Esmail
Bulletin of Electrical Engineering and Informatics Vol 9, No 5: October 2020
Publisher : Institute of Advanced Engineering and Science

This paper presents a new type of wideband waveguide (SIW) cavity-backed patch antenna for millimeter wave (mmW). The antenna proposed applies to applications of 31-36 GHz Ka-band such as satellites communications. The SIW is intended with settings for particular slots. The antenna is constructed on Rogers RT5880 (lossy) with 2.2 dielectric constant, l.27 mm thickness, and 0.0009 loss tangent. It is simulated in the programming of computer simulation technology (CST) Microwave Studio. The simulated results show that the SIW antenna resonates across 31 to 36 GHz bands, which means that this new antenna covers all applications within this range. The reflection coefficients in targeting range are below 10 dB. The antenna achieves good efficiency and gain with 80% and 8.87 dBi respectively.

Data-driven adaptive predictive control for an activated sludge process Mashitah C. Razali; Norhaliza Abdul Wahab; Syahira Ibrahim; Azavitra Zainal; M. F. Rahmat; Ramon Vilanova
Bulletin of Electrical Engineering and Informatics Vol 9, No 5: October 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v9i5.2257

Data-driven control requires no information of the mathematical model of the controlled process. This paper proposes the direct identification of controller parameters of activated sludge process. This class of data-driven control calculates the predictive controller parameters directly using subspace identification technique. By updating input-output data using receding window mechanism, the adaptive strategy can be achieved. The robustness test and stability analysis of direct adaptive model predictive control are discussed to realize the effectiveness of this adaptive control scheme. The applicability of the controller algorithm to adapt into varying kinetic parameters and operating conditions is evaluated. Simulation results show that by a proper and effective excitation of direct identification of controller parameters, the convergence and stability of the implicit predictive model can be achieved.

Wideband frequency reconfigurable metamaterial antenna design with double H slots Adamu Y. Iliyasu; Mohamad Rijal Hamid; Mohamad Kamal A. Rahim; Mohd Fairus Mohd Yusoff; Murtala Aminu- Baba; Mohammed Mustapha Gajibo
Bulletin of Electrical Engineering and Informatics Vol 9, No 5: October 2020
Publisher : Institute of Advanced Engineering and Science

This paper presents the design of wideband frequency reconfigurable metamaterial antenna with double H slots. The design is based on the idea of composite right/left-handed transmission line (CRLH-TL) technique. Bandwidth enhancement was achieved by utilizing series left-handed capacitor CL transmission line parameter. The design has several outstanding advantages which include efficient bandwidth to cover many lower Application bands with multi frequency operation characteristics. A comprehensive analysis and simulation were done by using computer simulation technology (CST) software to determine the performance and efficiency of the proposed antenna. From the result obtained, the antenna aquired bandwidth range which covered (2.3-5.2) GHz which is equivalent to 77% fractional bandwidth. The wideband antenna was reconfigured by using frequency reconfiguration technique. From the reconfiguration results, the antenna can be switch from wideband to two single bands which resonate at 2.4 GHz and 4.2 GHz and to dual band which resonate at 2.4 GHz and 4.2 GHz. The realized peak gain at 2.4 GHz is 2.28 dBi and 2.58 dBi for E and H field respectively. The maximum efficiency of 96% was obtained. The antenna can be use for WLAN, proposed lower 5G band and cognitive radio system for frequency sencing.

Improved support vector machine using optimization techniques for an aerobic granular sludge Nur Sakinah Ahmad Yasmin; Norhaliza Abdul Wahab; Aznah Nor Anuar
Bulletin of Electrical Engineering and Informatics Vol 9, No 5: October 2020
Publisher : Institute of Advanced Engineering and Science

Aerobic granular sludge (AGS) is one of the treatment methods often used in wastewater systems. The dynamic behavior of AGS is complex and hard to predict especially when it comes to a limited data set. Theoretically, support vector machine (SVM) is a good prediction tool in handling limited data set. In this paper, an improved SVM using optimization approaches for better predictions is proposed. Two different types of optimization are built which are particle swarm optimization (PSO) and genetic algorithm (GA). The prediction of the models using SVM-PSO, SVM-GA and SVM-Grid Search are developed and compared prior to several feature analysis for verification purposes. The experimental data under hot temperature of 50˚C obtained from sequencing batch reactor is used. From simulation results, the proposed SVM with optimizations improve the prediction of chemical oxygen demand compared to the conventional grid search method and hence provide better prediction of effluent quality using AGS wastewater treatment systems.

H∞ mixed sensitivity optimization for high speed tilting trains Fazilah Hassan; Argyrios Zolotas; Shaharil Mohd Shah
Bulletin of Electrical Engineering and Informatics Vol 9, No 5: October 2020
Publisher : Institute of Advanced Engineering and Science

The industrial norm of tilting high speed trains, nowadays, is that of Precedence tilt (also known as Preview tilt). Precedence tilt, although succesfull as a concept, tends to be complex (mainly due to the signal interconnections between vehicles and the advanced signal processing required for monitoring). Research studies of early prior to that of precedence tilt schemes, i.e. the so-called Nulling-type schemes, utilized local-per-vehicle signals to provide tilt action (this was essentially a typical disturbance rejection-scheme) but suffered from inherent delays in the control). Nulling tilt may still be seen as an important research aim due to the simple nature and most importantly due to the more straightforward fault detection compared to precedence schemes. The work in this paper presents a substantial extension conventional to robust H∞ mixed sensitivity nulling tilt control in literature. A particular aspect is the use of optimization is used in the design of the robust controller accompanied by rigorous investigation of the conflicting deterministic/stochastic local tilt trade-off

Prediction of land-change using machine learning for the deforestation in Paraguay Max Muller; Shweta Vincent; Om Prakash Kumar
Bulletin of Electrical Engineering and Informatics Vol 9, No 5: October 2020
Publisher : Institute of Advanced Engineering and Science

Northwestern Paraguay is being deforested at a very rapid rate. This article studies the rate of deforestation that has happened in this area using satellite images of LandSAT5 and LandSAT7. The rate of the deforestation is detected from 1986 to 2011, graphically using which future prediction is made. The images of LandSAT8 are used to validate the prediction made until 2018. An extrapolation of the graph shows that the process of deforestation is already 3 years ahead of its forecast. An overall accuracy of 98% has been achieved using this technique. The root mean square error (RMSE) is around 0.011.

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