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29,734 Article Results

Random access improvement for M2M communication in LTE-A using femtocell

10.11591/ijece.v9i2.pp1153-1162
Eric Sackey , Rajeev Paulus
When an area is highly populated with Machine-to-Machine devices and all these devices attempt to access the Random Access Network Simultaneously, congestion is created on the network which degrades the performance of the network to other users. In this paper, the researchers are seeking to improve network accessibility by deploying more Femtocell into the network. They engaged the use of Extended Access Barring to restrict the M2M devices from accessing the network via macrocell eNB when a minimum load threshold is attained, thereby preventing the macrocell eNB from being congested. Deploying these Femtocells underneath the macrocell eNB comes with the issue of Inter-Cell Interference which nullifies any gains made by this deployment. The researchers employed Fractional Frequency Reuse and Complete Frequency Reuse schemes to mitigate the negative effects of ICI to augment the throughput of the network, improve the system capacity and enhanced the user experience within the network.
Volume: 9
Issue: 2
Page: 1153-1162
Publish at: 2019-04-01

Performance analysis of PV powered multilevel inverter

10.11591/ijece.v9i2.pp753-760
K. Dhineshkumar , C. Subramani , A. Geetha , C. Vimala
This article deals with the PV based DC/DC boost chopper integrated nine level inverter. This topology requires 7 switches in minimum to obtain a nine level stepped wave output. So the main objective of this paper is to develop a 9 level AC output using PV based DC/DC boost chopper. In the case of conventional multi-level inverter, 16 switches were utilized and the number of sources needed was also more. Here the proposed system comprises of single PV panel and the switches used are also less. Also PV is integrated with DC/DC boost chopper is used to increase the source input level of the inverter. Using MATLAB platform, the proposed system is simulated with a resistive and inductive load. The similar results are obtained in prototype which validates the designed converter.
Volume: 9
Issue: 2
Page: 753-760
Publish at: 2019-04-01

Proposed algorithm for image classification using regression-based pre-processing and recognition models

10.11591/ijece.v9i2.pp1021-1027
Chanintorn Jittawiriyanukoon
Image classification algorithms can categorise pixels regarding to image attributes with the pre-processing of learner’s trained samples. The precision and classification accuracy are complex to compute due to the variable size of pixels (different image width and height) and numerous characteristics of image per se. This research proposes an image classification algorithm based on regression-based pre-processing and the recognition models. The proposed algorithm focuses on an optimization of pre-processing results such as accuracy and precision. To evaluate and validate, recognition model is mapped in order to cluster the digital images which are developing the problem of a multidimensional state space. Simulation results show that compared to existing algorithms, the proposed method outperforms with the optimal number of precision and accuracy in classification as well as results higher matching percentage based upon image analytics.
Volume: 9
Issue: 2
Page: 1021-1027
Publish at: 2019-04-01

ImageSubXSS: an image substitute technique to prevent Cross-Site Scripting attacks

10.11591/ijece.v9i2.pp1393-1398
PMD Nagarjun , Shaik Shakeel Ahamad
Cross-Site Scripting (XSS) is one of serious web application attack. Web applications are involved in every activity of human life. JavaScript plays a major role in these web applications. In XSS attacks hacker inject malicious JavaScript into a trusted web application, execution of that malicious script may steal sensitive information from the user. Previous solutions to prevent XSS attacks require a lot of effort to integrate into existing web applications, some solutions works at client-side and some solutions works based on filter list which needs to be updated regularly. In this paper, we propose an Image Substitute technique (ImageSubXSS) to prevent Cross-Site Scripting attacks which works at the server-side. The proposed solution is implemented and evaluated on a number of XSS attacks. With a single line, developers can integrate ImageSubXSS into their applications and the proposed solution is able to prevent XSS attacks effectively.
Volume: 9
Issue: 2
Page: 1393-1398
Publish at: 2019-04-01

Multi parametric model predictive control based on laguerre model for permanent magnet linear synchronous motors

10.11591/ijece.v9i2.pp1067-1077
Nguyen Hong Quang , Nguyen Phung Quang , Dao Phuong Nam , Nguyen Thanh Binh
The permanent magnet linear motors are widely used in various industrial applications due to its advantages in comparisons with rotary motors such as mechanical durability and directly creating linear motions without gears or belts. The main difficulties of its control design are that the control performances include the tracking of position and velocity as well as guarantee limitations of the voltage control and its variation. In this work, a cascade control strategy including an inner and an outer loop is applied to synchronous linear motor. Particularly, an offline MPC controller based on MPP method and Laguerre model was proposed for inner loop and the outer controller was designed with the aid of nonlinear damping method. The numerical simulation was implemented to validate performance of the proposed controller under voltage input constraints.
Volume: 9
Issue: 2
Page: 1067-1077
Publish at: 2019-04-01

Hybrid backpropagation neural network-particle swarm optimization for seismic damage building prediction

10.11591/ijeecs.v14.i1.pp360-367
Marina Yusoff , Faris Mohd Najib , Rozaina Ismail
The evaluation of the vulnerability of buildings to earthquakes is of prime importance to ensure a good plan can be generated for the disaster preparedness to civilians. Most of the attempts are directed in calculating the damage index of buildings to determine and predict the vulnerability to certain scales of earthquakes. Most of the solutions used are traditional methods which are time consuming and complex. Some of initiatives have proven that the artificial neural network methods have the potential in solving earthquakes prediction problems. However, these methods have limitations in terms of suffering from local optima, premature convergence and overfitting. To overcome this challenging issue, this paper introduces a new solution to the prediction on the seismic damage index of buildings with the application of hybrid back propagation neural network and particle swarm optimization (BPNN-PSO) method. The prediction was based on damage indices of 35 buildings around Malaysia. The BPNN-PSO demonstrated a better result of 89% accuracy compared to the traditional backpropagation neural network with only 84%. The capability of PSO supports fast convergence method has shown good effort to improve the processing time and accuracy of the results.
Volume: 14
Issue: 1
Page: 360-367
Publish at: 2019-04-01

Multi-objective whale optimization based minimization of loss, maximization of voltage stability considering cost of DG for optimal sizing and placement of DG

10.11591/ijece.v9i2.pp835-839
J. P. Sridhar , R. Prakash
Huge need in electricity causes placement of Distribution Generation (DG)s like Photovoltaics (PV) systems in distribution side for enhancing the loadability by improving the voltage stability and minimization of loss with minimum cost. Many optimal placements of DG have done in focus of minimum loss and improving voltage profile. This Whale optimization is a new optimization technique framed with mathematics of spiral bubble-net feeding behavior of humpback whales for solving a power system multi-objective problem considering cost of the power tariff and DG. Here main objectives are minimizing loss and cost with maximization of voltage stability index. IEEE 69 power system data is used for solution of the proposed method.
Volume: 9
Issue: 2
Page: 835-839
Publish at: 2019-04-01

MECC scheduling algorithm in vehicular environment for uplink transmission in LTE networks

10.11591/ijece.v9i2.pp1191-1200
Shafinaz Ismail , Darmawaty Mohd Ali , Azita Laily Yusof
Single Carrier Frequency Division Multiple Access (SC-FDMA) is chosen because of the lower peak-to-average power ratio (PAPR) value in uplink transmission. However, the contiguity constraint is one of the major constraint presents in uplink packet scheduling, where all RBs allocated to a single UE must be contiguous in the frequency-domain within each time slot to maintain its single carrier. This paper proposed an uplink-scheduling algorithm namely the Maximum Expansion with Contiguity Constraints (MECC) algorithm, which supports both the RT and NRT services. The MECC algorithm is deployed in two stages. In the first stage, the RBs are allocated fairly among the UEs. The second stage allocates the RBs with the highest metric value and expands the allocation on both sides of the matrix, M with respect to the contiguity constraint. The performance of the MECC algorithm was observed in terms of throughput, fairness, delay, and Packet Loss Ratio (PLR) for VoIP, video and best effort flows. The MECC scheduling algorithm is compared to other algorithms namely the Round Robin (RR), Channel-Dependent First Maximum Expansion (CD-FME), and Proportional Fairness First Maximum Expansion (PF-FME). From here, it can be concluded that the MECC algorithm shows the best results among other algorithms by delivering the highest throughput which is up to 81.29% and 90.04% than CD-FME and RR scheduler for RT and NRT traffic respectively, having low PLR and delay which is up to 93.92% and 56.22% of improvement than CD-FME for the RT traffic flow. The MECC also has a satisfactory level of fairness for the cell-edge users in a vehicular environment of LTE network.
Volume: 9
Issue: 2
Page: 1191-1200
Publish at: 2019-04-01

Numerical simulation of electromagnetic radiation using high-order discontinuous galerkin time domain method

10.11591/ijece.v9i2.pp1267-1274
Pranowo Pranowo , Djoko Budiyanto Setyohadi
In this paper, we propose the simulation of 2-dimensional electromagnetic wave radiation using high-order discontinuous Galerkin time domain method to solve Maxwell's equations. The domains are discretized into unstructured straight-sided triangle elements that allow enhanced flexibility when dealing with complex geometries. The electric and magnetic fields are expanded into a high-order polynomial spectral approximation over each triangle element. The field conservation between the elements is enforced using central difference flux calculation at element interfaces. Perfectly matched layer (PML) boundary condition is used to absorb the waves that leave the domain. The comparison of numerical calculations is performed by the graphical displays and numerical data of radiation phenomenon and presented particularly with the results of the FDTD method. Finally, our simulations show that the proposed method can handle simulation of electromagnetic radiation with complex geometries easily.
Volume: 9
Issue: 2
Page: 1267-1274
Publish at: 2019-04-01

Using real interpolation method for adaptive identification of nonlinear inverted pendulum system

10.11591/ijece.v9i2.pp1078-1089
Phu Tran Tin , Tran Hoang Quang Minh , Tran Thanh Trang , Nguyen Quang Dung
In this paper, we investigate the inverted pendulum system by using real interpolation method (RIM) algorithm. In the first stage, the mathematical model of the inverted pendulum system and the RIM algorithm are presented. After that, the identification of the inverted pendulum system by using the RIM algorithm is proposed. Finally, the comparison of the linear analytical model, RIM model, and nonlinear model is carried out. From the results, it is found that the inverted pendulum system by using RIM algorithm has simplicity, low computer source requirement, high accuracy and adaptiveness in the advantages.
Volume: 9
Issue: 2
Page: 1078-1089
Publish at: 2019-04-01

A novel approach for selective feature mechanism for two-phase intrusion detection system

10.11591/ijeecs.v14.i1.pp101-112
B Narendra Kumar , M S V Sivarama Bhadri Raju , B Vishnu Vardhan
Intrusion Detection is an important aspect to secure the computing systems from different intrusions. To improve the accuracy and to reduce the computational time, this paper proposes a two-phase hybrid method based on the SVM and RNN. In addition, this paper also had a proposal to obtain a few sets of features with a feature selection technique in which the detection performance increases. For the two-phase system, two different feature selection techniques were proposed which solves both the linear dependency and non-linear dependency between the features. In the first phase, the RNN combines with the proposed Joint Mutual Information Maximization (JMIM) based feature selection and in the second phase, the Support Vector Machine (SVM) combines with correlation based feature selection. Extensive simulations are carried out over the proposed system using two different datasets, NSL-KDD and Kyoto2006+. The performance is measured through the performance metrics such as Detection Rate (DR), Precision, False Alarm Rate (FAR), Accuracy and F-Score. Furthermore, a comparative analysis with few recent hybrid frameworks is also enumerated. The obtained results signify the effectiveness of proposed method.
Volume: 14
Issue: 1
Page: 101-112
Publish at: 2019-04-01

Prediction of overcurrent rRelay miscoordination time using artificial neural network

10.11591/ijeecs.v14.i1.pp319-326
S. Karupiah , M. H. Hussain , I Musirin , S.R.A. Rahim
Overcurrent relay plays an important role in the protection of power system. For protection, proper coordination of relays with an appropriate relay settings need to be done. Coordination can be done by selecting an optimal Time Multiplier Setting (TMS) and Plug Setting (PS) considering the fault current at the relay location. Continuous Time Intervals (CTI) must be maintained between primary relay and secondary relay to ensure correct sequential operation of the relays. However, miscoordination can occurs due to secondary relay trips faster than primary relay. This paper presents an approach for predicting overcurrent relay miscoordination time using Artificial Neural Network (ANN) algorithm in MATLAB software. The efficiency of the proposed approach have been tested successfully on 17 bus test system. The simulation results indicated that the ANN Levenber-Maequardt algorithm is capable to predict the miscoordination time occurs between the primary and secondary relay operating time.
Volume: 14
Issue: 1
Page: 319-326
Publish at: 2019-04-01

Displacement mechanical amplifiers designed on poly-silicon

10.11591/ijece.v9i2.pp894-901
Ramon Cabello-Ruiz , Margarita Tecpoyotl-Torres , Alfonso Torres-Jacome , Gerardo Vera-Dimas , Svetlana Koshevaya , Pedro Vargas-Chable
Using Poly-Silicon, the implementation of novel Displacement-amplifying Compliant Mechanisms (DaCM), in two geometries of accelerometers, allows for remarkable improvements in their operation frequency and displacement sensitivity, with different proportions. Similar DaCM´s geometries were previously implemented by us with Silicon. In all mentioned cases, the geometries of DaCM´s are adjusted in order to use them with Conventional Capacitive Accelerometer (CCA) and Capacitive Accelerometer with Extended Beams (CAEB), which operate in-plane mode, (y-axis). It should be noted that CAEB shows improvements (95.33%) in displacement sensitivity compared to ACC. Simulations results, carried out using Ansys Workbench software, validate the system’s performance designed with Poly-Silicon. Finally, a comparison with the similar systems, previously designed with Silicon, is also carried out.
Volume: 9
Issue: 2
Page: 894-901
Publish at: 2019-04-01

A hybrid bacterial foraging and modified particle swarm optimization for model order reduction

10.11591/ijece.v9i2.pp1100-1109
Hadeel N. Abdullah
This paper study the model reduction procedures used for the reduction of large-scale dynamic models into a smaller one through some sort of differential and algebraic equations. A confirmed relevance between these two models exists, and it shows same characteristics under study. These reduction procedures are generally utilized for mitigating computational complexity, facilitating system analysis, and thence reducing time and costs. This paper comes out with a study showing the impact of the consolidation between the Bacterial-Foraging (BF) and Modified particle swarm optimization (MPSO) for the reduced order model (ROM). The proposed hybrid algorithm (BF-MPSO) is comprehensively compared with the BF and MPSO algorithms; a comparison is also made with selected existing techniques.
Volume: 9
Issue: 2
Page: 1100-1109
Publish at: 2019-04-01

Improved power quality buck boost converter for SMPS

10.11591/ijece.v9i2.pp789-801
J. Jayachandran , S. Malathi
In this paper, a Neural Network (NN) controlled Buck-Boost Converter (BBC) based Switched Mode Power Supply (SMPS) for a PC application is proposed. The proposed BBC is analyzed, modeled and designed for the rated load. Generally, the utilization of Multiple Output SMPS (MOSMPS) for PC application introduces Power Quality (PQ) issues in the power system network. Unlike conventional SMPS the proposed NN controlled BBC can accomplish improvement of power quality. The NN controller reduces the Total Harmonic Distortion (THD) of source current below 5%, maintains input side Power Factor (PF) to be nearly unity and improves the output voltage regulation. In the proposed system, NN controller replaces the conventional PI controller and overcomes the drawbacks of the conventional system. The proposed BBC is validated adopting MATLAB/SIMULINK software. The simulation analysis validate that the proposed NN controlled BBC performs better than conventional converter in terms of PQ indices under fluctuating conditions.
Volume: 9
Issue: 2
Page: 789-801
Publish at: 2019-04-01
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