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31,456 Article Results

Optimization of energy consumption in cloud computing datacenters

10.11591/ijece.v11i1.pp686-698
Ahmed Osman , Assim Sagahyroon , Raafat Aburukba , Fadi Aloul
Cloud computing has emerged as a practical paradigm for providing IT resources, infrastructure and services. This has led to the establishment of datacenters that have substantial energy demands for their operation. This work investigates the optimization of energy consumption in cloud datacenter using energy efficient allocation of tasks to resources. The work seeks to develop formal optimization models that minimize the energy consumption of computational resources and evaluates the use of existing optimization solvers in testing these models. Integer linear programming (ILP) techniques are used to model the scheduling problem. The objective is to minimize the total power consumed by the active and idle cores of the servers’ CPUs while meeting a set of constraints. Next, we use these models to carry out a detailed performance comparison between a selected set of Generic ILP and 0-1 Boolean satisfiability based solvers in solving the ILP formulations. Simulation results indicate that in some cases the developed models have saved up to 38% in energy consumption when compared to common techniques such as round robin. Furthermore, results also showed that generic ILP solvers had superior performance when compared to SAT-based ILP solvers especially as the number of tasks and resources grow in size.
Volume: 11
Issue: 1
Page: 686-698
Publish at: 2021-02-01

Smart fire monitoring system with remote control usingZigBee network

10.11591/ijeecs.v21.i2.pp1132-1139
Jung kyu Park , Jaeho Kim
There are several differences between the two types of alarm systems, conventionalsystems and addressable systems.  It is important to carefully determine the introduc-tion of a fire alarm system according to the installation environment.  Talking aboutthe main difference relates to how the connected device communicates with the maincontrol panel by sending a signal.  Cost is another factor that can be a determinant ofyour chosen fire alarm system. In this paper, we proposed smart addressable fire detec-tion system.  In the proposed system, IoT was used and the network was constructedusing ZigBee module.  In the configured network, it consists of a local server and acontrol server. The local server controls the addressing sensor and sends the informa-tion obtained from the sensor to the control server.  The control server receives datatransmitted from the local server and enables quick fire action.  In the actual imple-mentation, the local server used the Lycra controller and ZigBee module. In addition,the control server used the Raspberry Pi and ZigBee modules and connected to theEthernet so that the administrator could monitor or control the local server.
Volume: 21
Issue: 2
Page: 1132-1139
Publish at: 2021-02-01

Recursive least square and control for PUMA robotics

10.11591/ijeecs.v21.i2.pp1238-1246
Lafta E. Jumaa Alkurawy
The solution of inverse kinematics system based on recursive least square (RLS) theorem is improved this paper. The task in joints of robotics is inverse kinematics for PUMA robotics. The design the manipulator of robotics is not simple if due to model of algebraic method. I suggested a method of RLS method to get predicts the positions of robot and it is comfortable the applications in real-time. The RLS is used to find the solution of the inverse kinematics for the joints 6-dof of the robotics. This technique is important to compute the joints of each arm space with Cartesian axes in the end-effector. The identification will be in each joint for PUMA by RLS and applied PI controller on each joint to get the response follows the reference input by tuning the values of coefficients of PI.
Volume: 21
Issue: 2
Page: 1238-1246
Publish at: 2021-02-01

Investigation of dualband fan-shaped microstrip bandpass filter

10.12928/telkomnika.v19i1.16507
Seevan F.; Al-Mansour University College Abdulkareem , Zainab; Al-Mansour University College Faydh , Dhamyaa H.; Al-Mansour University College Al-Nuaimi
In this study, design and simulation of microtrip bandpass filter is presented using RT/Duroid 6010.2 lm substrate. This filter has fan-shaped topology with small dimensions of 12x12 mm2, designed for dual band frequencies at 3.41 and 6.14 GHz. The insertion loss and return loss of initial band at 3.41 GHz are -0.7 and -38.224 dB respectively and its bandwidth ranged from 3.3561 to 3.48 GHz. On the other hand, for 2nd band at 6.14 GHz, the insertion loss and return loss have been -1.377 and -14 dB respectively with bandwidth ranged from 6.0951 to 6.1782 GHz.
Volume: 19
Issue: 1
Page: 229-234
Publish at: 2021-02-01

Lower and upper bound form for outage probability analysis in two-way of half-duplex relaying network under impact of direct link

10.12928/telkomnika.v19i1.15265
Phu Tran; Industrial University of Ho Chi Minh City Tin , Van-Duc; Van Lang University Phan , Tan N.; Ton Duc Thang University Nguyen
In this paper, the system performance of the two-way of half-duplex (HD) relaying network under the impact of the direct link is studied. The model system has two sources (S) and one destination (D) communicate by direct link and via relay (R). For system performance analysis, we derived the lower and upper bound for outage probability (OP). Furthermore, the analytical expressions of the system performance are verified by using the Monte Carlo simulation in the effect of main parameters. As shown in the results, we can the simulation and analytical results have a good agreement.
Volume: 19
Issue: 1
Page: 206-212
Publish at: 2021-02-01

Cloud computing acceptance among public sector employees

10.12928/telkomnika.v19i1.17883
Mohd Talmizie; Universiti Teknologi MARA Amron , Roslina; Universiti Teknologi Malaysia Ibrahim , Nur Azaliah Abu; Universiti Teknologi Malaysia Bakar
Cloud computing is one of the platforms that drive organisations and users to be better prepared for a simpler computing platform and offers significant benefits to the quality of work. The transition from conventional computing to the virtual world helps organisations to maximise their potential. However, not all users can accept cloud computing adoption. Failure to understand the factors of user's acceptance will negatively impact the organisation's mission of empowering the technology. Therefore, this study proposes to assess to what extent the users are accepting cloud computing. This study adopts the unified theory of acceptance and use of technology (UTAUT) and six technological and human factors assessed for the Malaysian public sectors. Survey data from several ministries were analysed using partial least squares-structural equation modelling (PLS-SEM). The study found out that performance expectancy, compatibility, security, mobility, information technology (IT) knowledge, and social influence had a significant impact on the user's intention to accept cloud computing. The results of this study contribute to a clear understanding of the factors affecting the Malaysian public sectors about cloud computing.
Volume: 19
Issue: 1
Page: 124-133
Publish at: 2021-02-01

Maximum likelihood estimation-assisted ASVSF through state covariance-based 2D SLAM algorithm

10.12928/telkomnika.v19i1.16223
Heru; Universitas Mercu Buana Suwoyo , Yingzhong; Shanghai University Tian , Wenbin; Shenzhen Polytechnic Wang , Long; Shanghai University Li , Andi; Universitas Mercu Buana Adriansyah , Fengfeng; Ryerson University Xi , Guangjie; Shanghai University Yuan
The smooth variable structure filter (ASVSF) has been relatively considered as a new robust predictor-corrector method for estimating the state. In order to effectively utilize it, an SVSF requires the accurate system model, and exact prior knowledge includes both the process and measurement noise statistic. Unfortunately, the system model is always inaccurate because of some considerations avoided at the beginning. Moreover, the small addictive noises are partially known or even unknown. Of course, this limitation can degrade the performance of SVSF or also lead to divergence condition. For this reason, it is proposed through this paper an adaptive smooth variable structure filter (ASVSF) by conditioning the probability density function of a measurementto the unknown parameters at one iteration. This proposed method is assumed to accomplish the localization and direct point-based observation task of a wheeled mobile robot, TurtleBot2. Finally, by realistically simulating it and comparing to a conventional method, the proposed method has been showing a better accuracy and stability in term of root mean square error (RMSE) of the estimated map coordinate (EMC) and estimated path coordinate (EPC).
Volume: 19
Issue: 1
Page: 327-338
Publish at: 2021-02-01

Optimizing RPL performance based on the selection of best route between child and root node using E-MHOF method

10.11591/ijece.v11i1.pp224-231
Ahmed R. Zarzoor
IETF has proposed the routing protocol for low power and lossy networks (RPL) for IOT as view as light weight routing protocol. In RPL, the objective function (OF) is used to select the best route between child and root node. Several researches have been conducted in order to, enhance OF according to number parameters such as number of hops, remaining energy and expected number of transmissions (ETX), without a consideration to other challenges such as congestion node problem and latency. So, to overcome these challenges a new technique called “Enhance-Minimum Rank with Hysteresis Objective Function (MHOF)” is proposed in this paper, to select the ideal path between the child and root node. The technique is consisted of three layers: parent selection layer in which parent is selected based on three parameters (ETX, RSSI and nodes’ residual energy), path selection layer in which the best route is chosen according to the minimum of (average ETX value) and maximum of (average remaining energy value) of all nodes in the selected route. The last layer is child node minimization, which utilized to solve the congestion node energy problem by using two parameters (RSSI reference and threshold value). The proposed method has been implemented and evaluated by using Cooja simulator software. The simulation results have shown that selected path with E-MHOF is increased the network lifetime and reduced latency in comparison with MHOF.
Volume: 11
Issue: 1
Page: 224-231
Publish at: 2021-02-01

A novel collective health monitoring of a wind park

10.11591/ijeecs.v21.i2.pp625-634
Kritika Sodha , George Fernandez S. , Vijayakumar K. , Sattianadan D.
Compared to a time-based maintenance schedule, condition-based maintenance provides better diagnostic information on the health condition of the different wind turbine components and subsystems. Rather than using an offline condition monitoring technique, which require the WT to be taken out of service, online condition monitoring does not require any interruption on the WT operation. The online condition monitoring system uses different types of sensors such as vibration, acoustic, temperature, current/voltage etc. Using a machine learning approach, we aim to establish a data driven fault prognosis framework. Instead of traditional wired communications, wireless communication systems such as Wireless Sensor Network have the advantages of easier installation and lower capital cost. We propose the use of WSN for collecting and transmitting the condition monitoring data to enhance the reliability of Wind Parks. Using data driven approach the collective health of the WP can be represented based on the condition of the individual wind turbines, which can be used for predicting the Remaining Useful Life of the system.
Volume: 21
Issue: 2
Page: 625-634
Publish at: 2021-02-01

Predicting RNA-seq data using genetic algorithm and ensemble classification algorithms

10.11591/ijeecs.v21.i2.pp1073-1081
Micheal Olaolu Arowolo , Marion O. Adebiyi , Ayodele A. Adebiyi , Olatunji J. Okesola
Malaria parasites accept uncertain, inconsistent life span breeding through vectors of mosquitoes stratospheres. Thousands of different transcriptome parasites exist. A prevalent ribonucleic acid sequencing (RNA-seq) technique for gene expression has brought about enhanced identifications of genetical queries. Computation of RNA-seq gene expression data transcripts requires enhancements using analytical machine learning procedures. Numerous learning approaches have been adopted for analyzing and enhancing the performance of biological data and machines. In this study, a genetic algorithm dimensionality reduction technique is proposed to fetch relevant information from a huge dimensional RNA-seq dataset, and classification uses Ensemble classification algorithms. The experiment is performed using a mosquito Anopheles gambiae dataset with a classification accuracy of 81.7% and 88.3%.
Volume: 21
Issue: 2
Page: 1073-1081
Publish at: 2021-02-01

Half Gaussian-based wavelet transform for pooling layer for convolution neural network

10.12928/telkomnika.v19i1.16398
Aqeel M. Hamad; Al-Nahrain University Alhussainy , Ammar D.; Al-Nahrain University Jasim
Pooling methods are used to select most significant features to be aggregated to small region. In this paper, anew pooling method is proposed based on probability function. Depending on the fact that, most information is concentrated from mean of the signal to its maximum values, upper half of Gaussian function is used to determine weights of the basic signal statistics, which is used to determine the transform of the original signal into more concise formula, which can represent signal features, this method named half gaussian transform (HGT). Based on strategy of transform computation, Three methods are proposed, the first method (HGT1) is used basic statistics after normalized it as weights to be multiplied by original signal, second method (HGT2) is used determined statistics as features of the original signal and multiply it with constant weights based on half Gaussian, while the third method (HGT3) is worked in similar to (HGT1) except, it depend on entire signal. The proposed methods are applied on three databases, which are (MNIST, CIFAR10 and MIT-BIH ECG) database. The experimental results show that, our methods are achieved good improvement, which is outperformed standard pooling methods such as max pooling and average pooling.
Volume: 19
Issue: 1
Page: 163-172
Publish at: 2021-02-01

Comparative study of logistic regression and artificial neural networks on predicting breast cancer cytology

10.11591/ijeecs.v21.i2.pp1113-1120
Yosra Abdulaziz Mohammed , Eman Gadban Saleh
Currently, breast cancer is one of the most common cancers and a main reason of women death worldwide particularly in developing countries such as Iraq. our work aims to predict the type of tumor whether benign or malignant through models that were built using logistic regression and neural networks and we hope it will help doctors in detecting the type of breast tumor. Four models were set using binary logistic regression and two different types of artificial neural networks namely multilayer perceptron MLP and radial basis function RBF. Evaluation of validated and trained models was done using several performance metrics like accuracy, sensitivity, specificity, and AUC (area under receiver operating characteristic ROC).   Dataset was downloaded from UCI ml repository; it is composed of 9 attributes and 699 samples. The findings are clearly showing that the RBF NN classifier is the best in prediction of the type of breast tumors since it had recorded the highest performance in terms of correct classification rate (accuracy), sensitivity, specificity, and AUC (area under Receiver Operating Characteristic ROC) among all other models.
Volume: 21
Issue: 2
Page: 1113-1120
Publish at: 2021-02-01

Voltage collapse prediction using artificial neural network

10.11591/ijece.v11i1.pp124-132
Samuel Isaac , Soyemi Adebola , Awelewa Ayokunle , Katende James , Awosope Claudius
Unalleviated voltage instability frequently results in voltage collapse; which is a cause of concern in power system networks across the globe but particularly in developing countries. This study proposed an online voltage collapse prediction model through the application of a machine learning technique and a voltage stability index called the new line stability index (NLSI_1). The approach proposed is based on a multilayer feed-forward neural network whose inputs are the variables of the NLSI_1. The efficacy of the method was validated using the testing on the IEEE 14-bus system and the Nigeria 330-kV, 28-bus National Grid (NNG). The results of the simulations indicate that the proposed approach accurately predicted the voltage stability index with an R-value of 0.9975 with a mean square error (MSE) of 2.182415x10−5 for the IEEE 14-bus system and an R-value of 0.9989 with an MSE of 1.2527x10−7 for the NNG 28 bus system. The results presented in this paper agree with those found in the literature.
Volume: 11
Issue: 1
Page: 124-132
Publish at: 2021-02-01

Cluster-based information retrieval by using (K-means)-hierarchical parallel genetic algorithms approach

10.12928/telkomnika.v19i1.16734
Sarah; University of Al-Qadisiyah Hussein Toman , Mohammed Hamzah; University of Al-Qadisiyah/Ad-Diwaniah Abed , Zinah Hussein; University of Al-Qadisiyah/Ad-Diwaniah Toman
Cluster-based information retrieval is one of the information retrieval (IR) tools that organize, extract features and categorize the web documents according to their similarity. Unlike traditional approaches, cluster-based IR is fast in processing large datasets of document. To improve the quality of retrieved documents, increase the efficiency of IR and reduce irrelevant documents from user search. In this paper, we proposed a (K-means)-hierarchical parallel genetic algorithms approach (HPGA) that combines the K-means clustering algorithm with hybrid PG of multi-deme and master/slave PG algorithms. K-means uses to cluster the population to k subpopulations then take most clusters relevant to the query to manipulate in a parallel way by the two levels of genetic parallelism, thus, irrelevant documents will not be included in subpopulations, as a way to improve the quality of results. Three common datasets (NLP, CISI, and CACM) are used to compute the recall, precision, and F-measure averages. Finally, we compared the precision values of three datasets with Genetic-IR and classic-IR. The proposed approach precision improvements with IR-GA were 45% in the CACM, 27% in the CISI, and 25% in the NLP. While, by comparing with Classic-IR, (K-means)-HPGA got 47% in CACM, 28% in CISI, and 34% in NLP.
Volume: 19
Issue: 1
Page: 349-356
Publish at: 2021-02-01

Network loss reduction and voltage improvement by optimal placement and sizing of distributed generators with active and reactive power injection using fine-tuned PSO

10.11591/ijeecs.v21.i2.pp647-656
Eshan Karunarathne , Jagadeesh Pasupuleti , Janaka Ekanayake , Dilini Almeida
Minimization of real power loss and improvement of voltage authenticity of the network are amongst the key issues confronting power systems owing to the heavy demand development problem, contingency of transmission and distribution lines and the financial costs. The distributed generators (DG) has become one of the strongest mitigating strategies for the network power loss and to optimize voltage reliability over integration of capacitor banks and network reconfiguration. This paper introduces an approach for the optimizing the  placement and sizes of different types of DGs in radial distribution systems using a fine-tuned particle swarm optimization (PSO). The suggested approach is evaluated on IEEE 33, IEEE 69 and a real network in Malaysian Context. Simulation results demonstrate the productiveness of active and reactive power injection into the electric power system and the comparison depicts that the suggested fine-tuned PSO methodology could accomplish a significant reduction in network power loss than the other research works.
Volume: 21
Issue: 2
Page: 647-656
Publish at: 2021-02-01
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