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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 9,174 Documents
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Multi-level inverter with novel carrier pulse width modulation technique for high voltage applications Sanka Sreelakshmi; Machineni Sanjeevappa Sujatha; Jammy Ramesh Rahul
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 2: May 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i2.pp667-674

Abstract

The present work deals with multilevel inverters (MLI) with modified carrier pulse width modulation technique and its application, especially in solar-based applications due to its modularity in structure and suitability for medium or high-power applications. Several multilevel inverters are introduced for various applications. One of the drawbacks of these inverters is high total harmonic distortion (THD) value in the output which impacts the power quality. The main objective of this work is to improve the power quality, thereby increasing the life and performance of the overall system at the consumer side. The most popular and compact cascaded multilevel DC-link inverter (CMDCLI) is considered for study and employed for 11-level operation. In general, sinusoidal pulse width modulation (SPWM) technique is used for control of inverter. However, the present work proposes a modified carrier-based hybrid pulse width modulation (HPWM) technique and has been tested with CMLDCLI. This novel technique compares the THD performance results with normal carrier wave considering R-L load. The results are analyzed in MATLAB environment and are validated with experimental results.
Improvised convolutional auto encoder for thyroid nodule image enhancement and segmentation Drakshaveni Gunjali; Prasad Naik Hansavath
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 1: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i1.pp342-351

Abstract

Thyroid ultrasonography and thermography are a widely used clinical technique for nodule diagnosis in thyroid regions. However, it remains difficult to detect and recognize the nodules due to low contrast, high noise, and diverse appearance of nodules. To alleviate doctors’ tremendous labor in the diagnosis procedure, we advocate a machine learning approach to the detection and recognition tasks in this paper. Moreover, this research mainly focuses on segmenting the image and finding the probable region. In this research work an improvised convolutional auto encoder (ICAE) is introduced for segmenting the image and finding the probable region of thyroid gland and it enhances image. ICAE comprises various layer and mechanism, each having their own task. Apart from the traditional approach, skip connection is applied for the image enhancement and dual frame is introduced for better feature extraction. Further optimization technique is used for increasing the learning rate. ICAE is evaluated considering digital database thyroid image (DDTI) dataset with performance metrics like accuracy, true positive rate, false positive rate, dice coefficient and similarity index (SI); also, comparative analysis is carried out with various existing model and proposed model simply outperforms the existing model.
Mobile Ad Hoc networks intrusion detection system against packet dropping attacks Oussama Sbai; Mohamed Elboukhari
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 2: May 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i2.pp819-825

Abstract

Due to the extreme lack of a stable infrastructure, also self-organization of network components, unpredictable network topologies, and the lack of a central authority for routing, security assurance in mobile ad hoc networks (MANETs) is an important and difficult challenge. Among the famous threat that MANETs suffer from: blackhole, grayhole, and selfishness attacks, because the target of these attacks is to drop packets and disturb the routing operation of the network. A scalable, reliable, and robust network intrusion detection system (NIDS) should be created to effectively combat these families of network layer routing assaults in order to offer high availability for MANETs. In this paper, we present a MANETs-IDS based on machine learning algorithm against blackhole, grayhole, and selfishness attacks with Ad Hoc on-demand distance vector (AODV) routing protocol (RFC 3561) and optimized link state routing (OLSR) potocol (RFC 3626), using ns-3 simulation platform. Our simulation took into consideration the density of the network and a random mobility model of nodes. The obtained experimental results show that the proposed detection algorithm reached very promoting performances (in term of accuracy, processing time, time to build the model, precision, recall, F-measure).
Face recognition based on Siamese convolutional neural network using Kivy framework Yazid Aufar; Imas Sukaesih Sitanggang
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 2: May 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i2.pp764-772

Abstract

Human face recognition is a vital biometric sign that has remained owing to its many levels of applications in society. This study is complex for free faces globally because human faces may vary significantly due to lighting, emotion, and facial stance. This study developed a mobile application for face recognition and implemented one of the convolutional neural network (CNN) architectures, namely the Siamese CNN for face recognition. Siamese CNN can learn the similarity between two object representations. Siamese CNN is one of the most common techniques for one-shot learning tasks. Our participation in this study determined the efficiency of the Siamese CNN architecture with the enormous quantity of face data employed. The findings demonstrated that the suggested strategy is both practical and accurate. The method with augmentation produces the best results with a total data set of 9000 face images, a buffer size of 10000, and epochs of 5, producing the minimum loss of 0.002, recall of 0.996, the precision of 0.999, and F1-score of 0.672. The proposed method gets the best accuracy of 98% with test data. The Siamese CNN model is successfully implemented in Python, and a user interface and executables are built using the Kivy framework.
Short term forecasting of electrical consumption using a neural network: joint approximate diagonal eigenvalue Mashitah Mohd Hussain; Zuhaina Zakaria; Nofri Yenita Dahlan; Nur Iqtiyani Ilham; Zhafran Hussin; Noor Hasliza Abdul Rahman; Md Azwan Md Yasin
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 1: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i1.pp56-66

Abstract

This article aims to estimate the load profiling of electricity that provides information on the electrical load demand. In achieving this research implemented the neural network algorithm of joint approximate diagonalisation of eigen-matrices (JADE) to describe the load profile pattern for each point. Nowadays, utility providers claim that natural sources are used to generate power by rising consumer demands for energy. However, occasionally utility workers need to know the demand at certain location, corresponding to maintenance issues or for any shutdown area involved. A distribution pattern based on the data can be predicted based on the incoming data profile without having detailed information of certain load bus, the concept of derivatives was relevant to forecast the types of distribution data. The model was constructed with load profile information based on three different locations, and the concept of derivative was recognized, including the type of incoming data. Historical data were captured from a selected location in Malaysia that was proposed to train the JADE algorithm from three different empirical distributions of consumers, recording every 15 minutes per day. The results were analyzed based on the error measurement and compared with the real specific load distribution feeder information of needed profiles.
Artificial neural network modeling for predicting the quality of water in the Sabak Bernam River Faqihah Affandi; Mohamad Faizal Abd Rahman; Adi Izhar Che Ani; Mohd Suhaimi Sulaiman
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i3.pp1616-1623

Abstract

Water quality prediction is aided by environmental monitoring, ecological sustainability, and aquaculture. Traditional prediction approaches capture the nonlinearity and non-stationarity of water quality well. Due to their rapid progress, artificial neural networks (ANNs) have become a hotspot in water quality prediction in recent years. ANNs are utilised in this study to predict water quality using soft computing techniques. The feedforward network and the standard back-propagation method of Levenberg-Marquardt and scaled conjugate gradient learning algorithm were employed in this research. One hidden layer has been recommended for the modelling, with the number of hidden neurons set at 3, 24, and 49. For this analysis, six different testing percentages were used, and the output data can be categorised as '0' for clean water and '1' for polluted water. From the results, it can be shown that the most optimised model was from the model of trainlm with a testing percentage of 18% and with 3 number of neurons. This most optimised model obtains an accuracy of 91.7%, the best validation performance of 0.073346 with 24 epochs, and having a receiver operating characteristic (ROC) curve that is closer to the true positive rate compared to other samples.
Datasets design of gate diffusion input based pipeline architecture for numerically controlled oscillator Ramana Reddy Gujjula; Chitra Perumal; Prakash Kodali; Bodapati Venkata Rajanna
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 1: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i1.pp253-260

Abstract

Gate diffusion input (GDI) is a technique, which enables reducing power consumption, area and delay in the digital circuits significantly, at the same time maintains low complexity of the logical design. This paper focuses on the analysis and interpretation of the design and implementation of GDI-based pipeline architecture for numerically controlled oscillator (NCO) using look up table (LUT). Based on the input signal and the alternate signal, this phase separation will separate the phase difference signal. The NCO generates a frequency and phase harmonized output signal with an antecedence fixed frequency clock. The 32-bit counter then compares the current count to the value stored in the compare register. Here the Coherent control comes into picture. It controls the carrier synchronizer by employing data from the 32-bit counter and the obtained data will be saved. It is updated and advanced using the third peer group of frequency synthesis technology. The test outcomes are accompanied with the theoretical concept and reproduced the results. The main objective of GDI-based pipeline architecture for NCO using LUT is to reduce the usage of metal oxide semiconductor field effect transistors (MOSFET’s). NCO is an indispensable component in many digital communication systems linked to modems, software-defined radios, and digital radio, digital down/upconverters for cellular and personal communications service base stations.
Knowledge discovery in manufacturing datasets using data mining techniques to improve business performance Amani Gomaa Shaaban; Mohamed Helmy Khafagy; Mohamed Abbas Elmasry; Heba El-Beih; Mohamed Hasan Ibrahim
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i3.pp1736-1746

Abstract

Recently due to the explosion in the data field, there is a great interest in the data science areas such as big data, artificial intelligence, data mining, and machine learning. Knowledge gives control and power in numerous manufacturing areas. Companies, factories, and all organizations owners aim to benefit from their huge; recorded data that increases and expands very quickly to improve their business and improve the quality of their products. In this research paper, the knowledge discovery in databases (KDD) technique has been followed, “association rules” algorithms “Apriori algorithm”, and “chi-square automatic interaction detection (CHAID) analysis tree” have been applied on real datasets belonging to (Emisal factory). This factory annually loses tons of production due to the breakdowns that occur daily inside the factory, which leads to a loss of profit. After analyzing and understanding the factory product processes, we found some breakdowns occur a lot of days during the product lifecycle, these breakdowns affect badly on the production lifecycle which led to a decrease in sales. So, we have mined the data and used the mentioned methods above to build a predictive model that will predict the breakdown types and help the factory owner to manage the breakdowns risks by taking accurate actions before the breakdowns happen.
A novel method for prediction of diabetes mellitus using deep convolutional neural network and long short-term memory Gorli L Aruna Kumari; Poosapati Padmaja; Jaya G Suma
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 1: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i1.pp404-413

Abstract

Hyperglycemia arises due to diabetes mellitus, which is a persistent and life-threatening ailment. In this paper deep convolution neural network can be embedded to long short-term memory networks to recognize early prediction of diabetes and to decrease the complications that can be occurred through diabetes irrespective to the age. Diabetes problem is being gradually growing and presently, it is reported as a significant cause of death in the top spot. According to the recent studies 48% of overall world population will be affected by diabetes by 2045. If diabetes unidentified in early stages, it may cause other additional cardiac problems. In the proposed based work, a deep learning framework deep combination of convolution neural network and long short-term memory is proposed by embedding both to leverage their respective advantages for diabetes recognition and to allow early prediction of diabetes to avoid other complications. The experimental evolution on the bunch mark of diabetes data set demonstrates the proposed model embedded deep long short-term memory outperforms other machine learning and conventional deep learning approaches. The proposed algorithm in this paper outperforms existing techniques and evaluates total effectiveness and accuracy of predicting whether a person will suffer from diabetes.
Word recognition and automated epenthesis removal for Indonesian sign system sentence gestures Erdefi Rakun; I Gusti Bagus Hadi Widhinugraha; Noer Fitria Putra Setyono
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i3.pp1402-1414

Abstract

This research focuses on building a system to translate continuous Indonesian sign system (SIBI) gestures into text. In a continuous gesture, a signer will add an epenthesis (transitional) gesture, which is hand movement with no meaning but needed to connect the hand movement of one word with the next word in a continuous gesture. Reducing the number of irrelevant inputs to the model through automated epenthesis removal can improve the system's ability to recognize the words in continuous gestures. We implemented threshold conditional random fields (TCRF) to identify epenthesis gestures. The dataset consists of 2,255 videos representing 28 common sentences in SIBI. The translation system consists of MobileNetV2 as a feature extraction technique, removing epenthesis gestures found by the TCRF, and a long short-term memory (LSTM) for the classifier. With the MobileNetV2-TCRF-bidirectional LSTM model, the best word error rate (WER) and sentence accuracy (SAcc) were 33.4% and 16.2%, respectively. Intermediate-stage processing steps consisting of sandwiched majority voting of the TCRF and the removal of word labels whose number of frames is less than two frames, along with LSTM output grouping, were able to reduce WER from 33.4% to 3.4% and increase SAcc from 16.2% to 80.2%.

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