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INDONESIA
Indonesian Journal of Electrical Engineering and Computer Science
Published by Institute of Advanced Engineering and Science
ISSN : 25024752     EISSN : 25024760     DOI : -
Core Subject :
Arjuna Subject : -
Articles 68 Documents
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Search results for , issue "Vol 20, No 1: October 2020" : 68 Documents clear
Fault classification on transmission line using LSTM network Abdul Malek Saidina Omar; Muhammad Khusairi Osman; Mohammad Nizam Ibrahim; Zakaria Hussain; Ahmad Farid Abidin
Indonesian Journal of Electrical Engineering and Computer Science Vol 20, No 1: October 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v20.i1.pp231-238

Abstract

Deep Learning has ignited great international attention in modern artificial intelligence techniques. The method has been widely applied in many power system applications and produced promising results. A few attempts have been made to classify fault on transmission lines using various deep learning methods. However, a type of deep learning called long short-term memory (LSTM) has not been reported in literature. Therefore, this paper presents fault classification on transmission line using LSTM network as a tool to classify different types of faults. In this study, a transmission line model with 400 kV and 100 km distance was modelled. Fault free and 10 types of fault signals are generated from the transmission line model. Fault signals are pre-processed by extracting post-fault current signals. Then, these signals are fed as input to the LSTM network and trained to classify 10 types of faults. The white Gaussian noise of level 20 dB and 30 dB signal to noise ratio (SNR) is also added to the fault current signals to evaluate the immunity of the proposed model. Simulation results show promising classification accuracy of 100%, 99.77% and 99.55% for ideal, 30 dB and 20 dB noise respectively. Results has been compared to four different methods which can be seen that the LSTM leading with the highest classification accuracy. In line with the purpose of the LSTM functions, it can be concluded that the method has a capability to classify fault signals with high accuracy.
Zinc oxide-paper based sensor for photoconductive ultraviolet detection Mohammad Shafiq Che Soh; Mastura Shafinaz Zainal Abidin; Shaharin Fadzli Abd Rahman; Shuthish Elangkovan; Ahmad Bukhairi Md Rashid
Indonesian Journal of Electrical Engineering and Computer Science Vol 20, No 1: October 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v20.i1.pp60-66

Abstract

Paper based sensor is the new technology to fabricate a simple, portable, and low cost device that exhibits the comparable properties and functions with those fabricated using complex fabrication process. Paper based sensor is usually applied in environmental monitoring, health diagnostics, and food quality control.  This research is focusing on the paper based sensor that will contribute to the development of ultraviolet (UV) sensor. The fabrication of the sensor was done by using different grade of pencil, namely 6B and 2B on different type of paper. The different grade of pencil corresponds to different percentage of graphite and clay. As for the type of paper, A4 printing paper and Whatman filter paper were used. UV sensing operation was analyzed from the measurement of current-voltage (I-V) characteristics under the exposure of UV light. zinc oxide (ZnO) was coated on the sensor to facilitate the detection in the presence of UV. The sample fabricated using 6B pencil grade on A4 printing paper and with ZnO coating showed a better UV sensing performance compared to other samples. This is due to the high conduction of 6B pencil grade and smooth surface of A4 printing paper. The ZnO coating increased the sensor sensitivity and response towards the UV light. These findings provide valuable information which can be used in fabricating a low-cost and simple UV paper sensor.
Improvements of trapezoid antenna gain using artificial magnetic conductor and frequency selective surface Siti Adlina Md Ali; Maisarah Abu; Siti Normi Zabri
Indonesian Journal of Electrical Engineering and Computer Science Vol 20, No 1: October 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v20.i1.pp281-286

Abstract

This paper presents the performance enhancement of the trapezoid antenna with artificial magnetic conductor (AMC) and frequency selective surface (FSS). The antenna, AMC and FSS structures are printed on 0.254 mm of RT/Duroid 5880 high frequency laminate. The performances of the antenna with and without AMC and FSS were evaluated. Three cases are analyzed; antenna alone, antenna with AMC and antenna with AMC-FSS. The 2x3 arrays of AMC and AMC-FSS were positioned at the back of the antenna with 6 mm air gap. The antenna alone works at 12 GHz, and shifted to 12.35 GHz and 12.33 GHz for case 2 and case 3, respectively. Despite the shift in the resonance, the antenna is still operating well at 12 GHz with a return loss –16.70 dB for case 2 and–16.84 dB for case 3. Case 3 effectively enhanced the antenna gain from 4.43 dB to 6.74 dB and contributed to a directive antenna. Moreover, case 3 also successfully reduced the radiation of the antenna that penetrates into human body as the antenna is applied for on-body applications. 
Experiments on city train vibration anomaly detection Using deep learning approaches Taehee Kim; Cheolwoo Ro; Kiho Suh
Indonesian Journal of Electrical Engineering and Computer Science Vol 20, No 1: October 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v20.i1.pp329-337

Abstract

Anomaly detection is widely in demand in the field where automated detection of anomalous conditions in many observation tasks. While conventional data science approaches have shown interesting results, deep learning approaches to anomaly detection problems reveal new perspectives of possibilities especially where massive amount of data need to be handled. We develop anomaly detection applications on city train vibration data using deep learning approaches. We carried out preliminary research on anomaly detection in general and applied our real world data to existing solutions. In this paper, we provide a survey on anomaly detection and analyse our results of experiments using deep learning approaches.
Fast lightweight block cipher design with involution substitution permutation network (SPN) structure Omar A. Dawood
Indonesian Journal of Electrical Engineering and Computer Science Vol 20, No 1: October 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v20.i1.pp361-369

Abstract

In the present paper, a new cryptographic lightweight algorithm has been developed for the internet of things (IoT) applications. The submitted cipher designed with the involution Substitution Permutation Network SPN structure. The involution structure means that the same encryption algorithm is used in the decryption process except the ciphering key algorithm is applied in reverse order. The introduced algorithm encrypts the data with a block size of 128-bit 192-bit or 256-bit, which iterative with 10, 12 and 14-rounds respectively similar to the AES cipher. The design aspect supports an elegant structure with a secure involution round transformation. The main round is built without S-Box stage instead that it uses the on-fly immediate computing stage and the involution of mathematical invertible affine equations. The proposed cipher is adopted to work in a restricted environment and with limited resources pertaining to embedded devices. The proposed cipher introduces an accepted security level and reasonable gate equivalent (GE) estimation with fast implementation.
Image encryption scheme in public key cryptography based on cubic pells quadratic case Raghunandan K. R.; Ganesh Aithal; Surendra Shetty; Bhavya K.
Indonesian Journal of Electrical Engineering and Computer Science Vol 20, No 1: October 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v20.i1.pp385-394

Abstract

Cryptography systems face new threats with the transformation of time and technology. Each innovation tries to contest challenges posed by the previous system by analyzing approaches that are able to provide impressive outcomes. The prime aim of this work is to urge ways in which the concept of Pell’s equation can be used in Public key Cryptography techniques.The main aim of this approach is secure and can be computed very fast. Using Cubic Pell’s equation defined in Quadratic Case, a secure public key technique for Key generation process is showcased. The paper highlights that a key generation time of proposed scheme using Pell’s Quadratic case equation is fast compared to existing methods.The strength and quality  of the proposed method is proved and analyzed by obtaining the results of entropy, differential analysis, correlation analysis and avalanche effect. The superiority of the proposed method over the conventional AES and DES is confirmed by a 50% increase in the execution speed and shows that Standard diviation and Entropy analysis of proposed scheme gives immunity to guess the encryption key and also it is hard to deduce the private key from public key using  Diffrential analysis.
A coplanar waveguide tapered slot antenna with beam switching capabilities Delphine Abijuru; M. R. Hamid; N. Seman; M. Himdi
Indonesian Journal of Electrical Engineering and Computer Science Vol 20, No 1: October 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v20.i1.pp275-280

Abstract

A wideband tapered slot antenna (TSA) with three radiating elements for beam switching purpose is proposed in this study. The integrated radiating taper slots in assistance with metal strips acting as switches provided the proposed design with the capability of switching its beam into three different directions while maintaining the antenna performance stable. To validate the accuracy of the proposed design, the prototype was fabricated and measurements were conducted in terms of reflection coefficient (S11), radiation pattern and realized gain towards the three different operating modes. By sequentially, activating the switches, the antenna main beam rotated 90º in the XY coordinates. A realized gain ranging from 4.3 to 6.4 dBi and a wide operating bandwidth (|S11| ≤-10dB) from 3.3 GHz to 5 GHz  were observed throughout the antenna performance in simulation as well as in experiment. With the covered bands, the proposed antenna can be suitable for Sub -6GHz wireless communications systems. Improvements of Trapezoid Antenna Gain using Artificial Magnetic Conductor and Frequency Selective Surface.
FIBR-OSS: fault injection model for bug reports in open-source software Sundos Abdulameer Alazawi; Mohammed Najim Al-Salam
Indonesian Journal of Electrical Engineering and Computer Science Vol 20, No 1: October 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v20.i1.pp465-474

Abstract

For assessment of system dependability, fault injection techniques are used to expedite the presence of an error or failure in the system, which helps evaluate fault tolerance and system failure prediction. Defects classification and prediction is the principal significant advance in the trustworthiness evaluation of complex software systems such as open-source software since it can quickly be affected by the reliability of those systems, improves performance, and lessening the product cost. In this context, a new prototype of the fault injection model is presented, FIBR-OSS (fault injection for bug reports in open-source software). FIBR-OSS can support developers to evaluate the system performance during phase's development for its dependability attributes such as reliability and system dependability means such as fault prediction or forecasting. FIBR-OSS is used for fault speed-up to test the system's failure prediction performance. Some machine learning techniques are implemented on bug reports produced existing by the bug tracking system as datasets for failure prediction techniques, some of those machine learning techniques are used in our approach.
Pet dog disease pre-diagnosis system for caregiver with possibilistic C-means clustering and disease database Kwang Baek Kim; Doo Heon Song
Indonesian Journal of Electrical Engineering and Computer Science Vol 20, No 1: October 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v20.i1.pp300-305

Abstract

While the population of pet dogs and veterinary clinics are increasing, there is no reliable and useful software for pet owners/caregivers who have limited knowledge on the pet diseases. In this paper, we propose a pre-diagnosis system working on the mobile platform that the pet owner can take a pre-diagnosis from his/her observation of pet dog’s abnormality. Technically, the system needs a reliable databases for disease-symptom association thus we provide it based on the textbook and encyclopedia. Then, we apply Possibilistic C-Means algorithm that is an unsupervised machine learning algorithm to form the connections between disease and symptoms from database. The system outputs five most probable diseases from the observed symptoms of pet dog. The utility of this system is to alert the owner’s attention on the pet dog’s abnormal behavior and try to find the diseases as soon as possible.
Numerical simulation of DIC drying process on matlab distributed computing server Hafizah Farhah Saipan Saipol; Norma Alias
Indonesian Journal of Electrical Engineering and Computer Science Vol 20, No 1: October 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v20.i1.pp338-346

Abstract

Instant controlled pressure drop, also known as DIC, is one of the drying techniques that has been used for texturing, extracting and drying various food products. Mathematical model has been used to explain the drying process, although most of the studies focused on the statistical regression model approach. Due to the limitations of regression model, which neglects the fundamental of dehydration process, this paper presents the development of mathematical models to detect, solve and visualize the three-dimensional (3D) heat and mass transfer in DIC drying process. Finite difference method (FDM) with central difference formula is used to discretize the mathematical models. A large sparse of system of linear equation (SLE), which represents the actual drying process, is solved by using some numerical methods, such as jacobi (JB), red black gauss seidel (RBGS), alternating group explicit with douglas (AGED) and brian (AGEB) methods. Based on the numerical results, high execution time and high computational complexity have been shown. In order to reduce the execution time and computational complexity, the parallel algorithm based on domain decomposition technique has been implemented on the MATLAB distributed computing server (MDCS). The parallel algorithm of the numerical methods was evaluated and compared based on the parallel performance metrics: execution time, speed up, efficiency, effectiveness, temporal performance and granularity. From the parallel performance metrics, it was found that the PAGEB approach had better performance, followed by PAGED, PRBGS and PJB methods.

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