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29,922 Article Results

Improvement the voltage stability margin of Iraqi power system using the optimal values of FACTS devices

10.11591/ijece.v11i2.pp984-992
Ghassan Abdullah Salman , Hatim G. Abood , Mayyadah Sahib Ibrahim
The detection of potential voltage collapse in power systems is essential to maintain the voltage stability in heavy load demand. This paper proposes a method to detect weak buses in power systems using two stability indices: the voltage stability margin factor (dS/dY) and the voltage collapse prediction index (VCPI). Hence, the paper aims to improve the voltage stability of Iraqi transmission grid by allocating FACTS devices in the optimal locations and optimal sizes. Two types of FACTS are used in this paper which are Thyristor controlled series compensator (TCSC) and static var compensator (SVC). The objective function of the problem is fitted using particle swarm optimization (PSO). The proposed method is verified using simulation test on Diyala-132 kV network which is a part of the Iraqi power system. The results observed that improvement the voltage stability margin, the voltage profile of Diyala-132 kV is increased and the power losses is decreased.
Volume: 11
Issue: 2
Page: 984-992
Publish at: 2021-04-01

Multi-constraints based RPL objective function with adaptive stability for high traffic IoT applications

10.11591/ijeecs.v22.i1.pp407-418
Abdelhadi Eloudrhiri Hassani , Aicha Sahel , Abdelmajid Badri , El Mourabit Ilham
The internet of things technology is classified as a Low power and lossy network. These kinds of networks require a trustworthy routing protocol considered as the backbone for management and high quality of service achievements. IPv6 routing protocol for Low power and lossy network (RPL) was able to gain popularity compared to other routing protocols dedicated to IoT for its great flexibility through the objective function. Default objective functions implemented in the RPL core are based on a single metric. Consequently, the routing protocol can’t cope with different constraints and show congestion issues in high traffics. For that, we proposed in our paper multi-constraints-based objective function with adaptive stability (MCAS-OF), which uses novel strategies for Radio strength indicator, node energy consumption, hop count and a designed work-metric combination, new rank processing, and parent selection procedure. The network stability was also taken into account, since the multi constraints can lead to frequent parent changes, using an adaptive threshold. The proposal, evaluated under the COOJA emulator against standard-RPL and EC-OF, showed a packet delivery ratio improvement by 24% in high traffics, a decrease in the power consumption close to 44%, achieved less latency and DIO control messages, it also gives a good workload balancing by reducing the standard deviation of node’s power consumption.
Volume: 22
Issue: 1
Page: 407-418
Publish at: 2021-04-01

Effects of BaSO4 nano-particles on the enhancement of the optical performance of white LEDs

10.12928/telkomnika.v19i2.16855
Huu Phuc; Industrial University of Ho Chi Minh City Dang , Phung Ton; Industrial University of Ho Chi Minh City That , Dao Huy; Ton Duc Thang University Tuan
The usage of BaSO4 nanoparticles on WLEDs luminous flux and color uniformity improvements have been analyzed and demonstrated in this manuscript. The mixture of BaSO4 and silicone placed on the yellow phosphor layer benefits the internal light scattering and thus enhances the angular correlated color temperature (CCT) homogeneity. Specifically, the blue-light intensity at large angles tend to increase and results in light intensity discrepancy, which can be corrected with added BaSO4. In addition to this, the BaSO4-silicone composite modifies the refractive index of the air-phosphor layer interface to an appropriate value, and thus, the luminous efficiency increases. The results show that the CCT deviations is reduced by 580 K, from 1000 K to 420 K, within the angle range from -700 to +700 with BaSO4 in the phosphor structure. The increase in luminous flux is also recorded by 2.25%, in comparison with that of the non-BaSO4 traditional structure, at the 120-mA driving current. Hence, integrating BaSO4 nanoparticles into the remote phosphor structure can contributes to the enhancement of both lumen output and CCT uniformity.
Volume: 19
Issue: 2
Page: 603-607
Publish at: 2021-04-01

Machine learning model for clinical named entity recognition

10.11591/ijece.v11i2.pp1689-1696
Ravikumar J. , Ramakanth Kumar P.
To extract important concepts (named entities) from clinical notes, most widely used NLP task is named entity recognition (NER). It is found from the literature that several researchers have extensively used machine learning models for clinical NER.The most fundamental tasks among the medical data mining tasks are medical named entity recognition and normalization. Medical named entity recognition is different from general NER in various ways. Huge number of alternate spellings and synonyms create explosion of word vocabulary sizes. This reduces the medicine dictionary efficiency. Entities often consist of long sequences of tokens, making harder to detect boundaries exactly. The notes written by clinicians written notes are less structured and are in minimal grammatical form with cryptic short hand. Because of this, it poses challenges in named entity recognition. Generally, NER systems are either rule based or pattern based. The rules and patterns are not generalizable because of the diverse writing style of clinicians. The systems that use machine learning based approach to resolve these issues focus on choosing effective features for classifier building. In this work, machine learning based approach has been used to extract the clinical data in a required manner
Volume: 11
Issue: 2
Page: 1689-1696
Publish at: 2021-04-01

High-speed radix-10 multiplication using partial shifter adder tree-based convertor

10.12928/telkomnika.v19i2.14991
Utsav Kumar; Government Engineering College Malviya
A radix-10 multiplication is the foremost frequent operations employed by several monetary business and user-oriented applications, decimal multiplier using in state of art digital systems are significantly good but can be upgraded with time delay and area optimization. This work is proposed a more area and time delay optimized new design of overloaded decimal digit set (ODDS) architecture-based radix-10 multiplier for signed numbers. Binary coded decimal (BCD) to binary followed by binary multiplication and finally binary to BCD conversion are 3 major modules employed in radix-10 multiplication. This paperwork presents a replacement technique for binary coded decimal (BCD) to binary and vice-versa convertors in radix-10 multiplication. A novel addition tree structure called as partial shifter adder (PSA) tree-based approach has been developed for BCD to binary conversion, and it is used to add partially generated products. To meet our major concern i.e. speed, we need particular high-speed multiplication, hence the proposed PSA based radix-10 multiplier is using vertical cross binary multiplication and concurrent shifter-based addition method. The design has been tested on 45nm technology-based Zynq-7 field programmable gate array (FPGA) devices with a 6-input lookup table (LUTs). A combinational implementation maps quite well into the slice structure of the Xilinx Zynq-7 families field programmable gate array. The synthesis results for a Zynq-7 device indicate that our design outperforms in terms of the area and time delay.
Volume: 19
Issue: 2
Page: 556-563
Publish at: 2021-04-01

Detection of citrus leaf diseases using a deep learning technique

10.11591/ijece.v11i2.pp1719-1727
Ahmed R. Luaibi , Tariq M. Salman , Abbas Hussein Miry
The food security major threats are the diseases affected in plants such as citrus so that the identification in an earlier time is very important. Convenient malady recognition can assist the client with responding immediately and sketch for some guarded activities. This recognition can be completed without a human by utilizing plant leaf pictures. There are many methods employed for the classification and detection in machine learning (ML) models, but the combination of increasing advances in computer vision appears the deep learning (DL) area research to achieve a great potential in terms of increasing accuracy. In this paper, two ways of conventional neural networks are used named Alex Net and Res Net models with and without data augmentation involves the process of creating new data points by manipulating the original data. This process increases the number of training images in DL without the need to add new photos, it will appropriate in the case of small datasets. A self-dataset of 200 images of diseases and healthy citrus leaves are collected. The trained models with data augmentation give the best results with 95.83% and 97.92% for Res Net and Alex Net respectively.
Volume: 11
Issue: 2
Page: 1719-1727
Publish at: 2021-04-01

Machine learning with multistage classifiers for identification of of ectoparasite infected mud crab genus Scylla

10.12928/telkomnika.v19i2.16724
Rozniza; Universiti Malaysia Terengganu Ali , Muhamad Munawarar; Universiti Malaysia Terengganu Yusro , Muhammad Suzuri; Universiti Malaysia Terengganu Hitam , Mhd Ikhwanuddin; Universiti Malaysia Terengganu Abdullah
Recently, the mud-crab farming can help the rural population economically. However, the existing parasite in the mud-crabs could interfere the long live of the mud-crabs. Unfortunately, the parasite has been identified to live in hundreds of mud-crabs, particularly it happened in Terengganu Coastal Water, Malaysia. This study investigates the initial identification of the parasite features based on their classes by using machine learning techniques. In this case, we employed five classifiers i.e logistic regression (LR), k-nearest neighbors (kNN), Gaussian Naive Bayes (GNB), support vector machine (SVM), and linear discriminant analysis (LDA). We compared these five classfiers to best performance of classification of the parasites. The classification process involving three stages. First, classify the parasites into two classes (normal and abnormal) regardless of their ventral types. Second, classified sexuality (female or male) and maturity (mature or immature). Finally, we compared the five classifiers to identify the species of the parasite. The experimental results showed that GNB and LDA are the most effective classifiers for carrying out the initial classification of the rhizocephalan parasite within the mud crab genus Scylla.
Volume: 19
Issue: 2
Page: 406-413
Publish at: 2021-04-01

Hybrid sliding PID controller for torsional vibrations mitigation in rotary drilling systems

10.11591/ijeecs.v22.i1.pp146-158
Chafiaa Mendil , Madjid Kidouche , Mohamed Zinelabidine Doghmane
During the drilling process, the drilling system devices can be exposed to several types of phenomena incited by lateral, axial, and torsional vibrations. The latter can lead to severe damages if they are not efficiently controlled and quickly mitigated. This research work is focused on the torsional vibrations, which are stimulated by the nonlinear dynamical interaction between the geological rocks and the drill bit. Wherein, a model with three degrees of freedom was designed to demonstrate the severity of the stick-slip phenomenon as consequence of torsional vibrations. The main objective of this study was to design a robust controller based on hybridizing a conventional PID controller with sliding mode approach in order to mitigate rapidly the torsional vibrations. Moreover, a comparative study between PI, PID and sliding mode controllers allowed us to emphasize the effectiveness of the new hybrid controller and improve the drilling system performances. Furthermore, the chattering phenomenon in the sliding surface was overcome by using the saturation function rather than the sign function. The obtained results proved the usefulness of the proposed controller in suppressing the stick-slip phenomenon for smart industrial drilling systems.
Volume: 22
Issue: 1
Page: 146-158
Publish at: 2021-04-01

Face Recognition with Frame size reduction and DCT compression using PCA algorithm

10.11591/ijeecs.v22.i1.pp168-178
Padmaja vijaykumar , Jeevan K Mani
Face recognition has become a very important study of research because it has a variety of applications in research field such as human computer interaction, pattern recognition (PR). A successful face recognition procedure, be it mathematical or numerical, depends on the particular choice of the features used by the classifier. Feature selection in pattern recognition consists of the derivation of salient features present in the raw input data in order to reduce the amount of data used for classification. For the successful face recognition, the database images must have sufficient information so that when presented with the probe image, the recognition must be possible. Majority of times, there is always excess information present in the database images, leads higher storage, hence optimum size of the images needs to be stored in the database for good performance, are compressed with reduction in frame size and then compressed with that of the DCT. 
Volume: 22
Issue: 1
Page: 168-178
Publish at: 2021-04-01

Coyote multi-objective optimization algorithm for optimal location and sizing of renewable distributed generators

10.11591/ijece.v11i2.pp975-983
E. M. Abdallah , M. I. El Sayed , M. M. Elgazzar , Amal A. Hassan
Research on the integration of renewable distributed generators (RDGs) in radial distribution systems (RDS) is increased to satisfy the growing load demand, reducing power losses, enhancing voltage profile, and voltage stability index (VSI) of distribution network. This paper presents the application of a new algorithm called ‘coyote optimization algorithm (COA)’ to obtain the optimal location and size of RDGs in RDS at different power factors. The objectives are minimization of power losses, enhancement of voltage stability index, and reduction total operation cost. A detailed performance analysis is implemented on IEEE 33 bus and IEEE 69 bus to demonstrate the effectiveness of the proposed algorithm. The results are found to be in a very good agreement.
Volume: 11
Issue: 2
Page: 975-983
Publish at: 2021-04-01

A 5G mm-wave compact voltage-controlled oscillator in 0.25 µm pHEMT technology

10.11591/ijece.v11i2.pp1036-1042
Abdelhafid Es-saqy , Maryam Abata , Mahmoud Mehdi , Mohammed Fattah , Said Mazer , Moulhime El Bekkali , Catherine Algani
A 5G mm-wave monolithic microwave integrated circuit (MMIC) voltage-controlled oscillator (VCO) is presented in this paper. It is designed on GaAs substrate and with 0.25 µm-pHEMT technology from UMS foundry and it is based on pHEMT varactors in order to achieve a very small chip size. A 0dBm-output power over the entire tuning range from 27.67 GHz to 28.91 GHz, a phase noise of -96.274 dBc/Hz and -116.24 dBc/Hz at 1 and 10 MHz offset frequency from the carrier respectively are obtained on simulation. A power consumption of 111 mW is obtained for a chip size of 0.268 mm2. According to our knowledge, this circuit occupies the smallest surface area compared to pHEMTs oscillators published in the literature.
Volume: 11
Issue: 2
Page: 1036-1042
Publish at: 2021-04-01

Aluminum based nanostructures for energy applications

10.12928/telkomnika.v19i2.18146
Mohammad Tariq; University of Mosul Yaseen , Abdalem A.; University of Mosul Rasheed
The plasmonic material properties of aluminum allow active plasmon resonances extending from the blue color in the visible range to the ultraviolet (UV) region of the spectrum. Whereas Al is usually avoided for applications of plasmonics due to its losses in the infrared spectrum region. In this work, the study of the scatter and absorption of disk nanoantennas (DNAs) using various types of materials Au, Ag, and Al is accomplished by using the CST microwave studio suite simulation. The results showed that Al can offer good plasmonic properties when DNA radius is 25 nm to 125 nm at 20 nm height and working wavelengths longer than 800 nm in the near-infrared (NIR) region. Al produces negative plasmonic features around 800 nm wavelength due to the interband transition in the imaginary part of epsilon. For Au and Ag, the plasmonic characteristics rapidly decayed when the DNA radius was higher than 60 nm, but in contrast, Al offers good plasmonic features at these large dimensions of DNAs. This extended response of Al in UV, visible, and NIR, incorporated with its low cost, natural abundance, low native oxide, and amenability to industrial processes, could make Al an extremely promising plasmonic metal candidate for energy applications.
Volume: 19
Issue: 2
Page: 683-689
Publish at: 2021-04-01

Real-time Arabic scene text detection using fully convolutional neural networks

10.11591/ijece.v11i2.pp1634-1640
Rajae Moumen , Raddouane Chiheb , Rdouan Faizi
The aim of this research is to propose a fully convolutional approach to address the problem of real-time scene text detection for Arabic language. Text detection is performed using a two-steps multi-scale approach. The first step uses light-weighted fully convolutional network: TextBlockDetector FCN, an adaptation of VGG-16 to eliminate non-textual elements, localize wide scale text and give text scale estimation. The second step determines narrow scale range of text using fully convolutional network for maximum performance. To evaluate the system, we confront the results of the framework to the results obtained with single VGG-16 fully deployed for text detection in one-shot; in addition to previous results in the state-of-the-art. For training and testing, we initiate a dataset of 575 images manually processed along with data augmentation to enrich training process. The system scores a precision of 0.651 vs 0.64 in the state-of-the-art and a FPS of 24.3 vs 31.7 for a VGG-16 fully deployed.
Volume: 11
Issue: 2
Page: 1634-1640
Publish at: 2021-04-01

Analysis of the influence of the ambient temperature on the energy efficiency of solar modules by application of empirical correlations for natural convection

10.12928/telkomnika.v19i2.16102
Shahir Fleyeh; Tikrit University Nawaf , Mohammad Omar; Tikrit University Salih , Younis Nather; Tikrit University Younis
In this paper, the effect of the ambient temperature on photovoltaic (PV) modules for different angles of inclinations and different intensities of the solar radiation on the surface of PV module is considered using empirical correlations for natural convection. The analysis used an analytical model based on the energy balance equilibrium between PV module and the environment. It has been shown that in real conditions of exploitation, the value of the solar conversion coefficient of the solar energy to be determined by the manufacturer, valid for the standard test conditions (STC) for PV module (25 °C -1000 W/m2). The results obtained indicates that in the case a smaller number of PV modules corresponding to the required number for average household. The proposed procedure can be applied in the techno-economic analysis for PV system with uniaxial monitoring of the sun position as well as static PV systems.
Volume: 19
Issue: 2
Page: 540-546
Publish at: 2021-04-01

Real time ear recognition using deep learning

10.12928/telkomnika.v19i2.18322
Ahmed M.; University of Mosul Alkababji , Omar H.; University of Mosul Mohammed
Automatic identity recognition of ear images represents an active area of interest within the biometric community. The human ear is a perfect source of data for passive person identification. Ear images can be captured from a distance and in a covert manner; this makes ear recognition technology an attractive choice for security applications and surveillance in addition to related application domains. Differing from other biometric modalities, the human ear is neither affected by expressions like faces are nor do need closer touching like fingerprints do. In this paper, a deep learning object detector called faster region based convolutional neural networks (Faster R-CNN) is used for ear detection. A convolutional neural network (CNN) is used as feature extraction. principal component analysis (PCA) and genetic algorithm are used for feature reduction and selection respectively and a fully connected artificial neural network as a matcher. The testing proved the accuracy of 97.8% percentage of success with acceptable speed and it confirmed the accuracy and robustness of the proposed system.
Volume: 19
Issue: 2
Page: 523-530
Publish at: 2021-04-01
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