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31,456 Article Results

Design of spark ignition engine speed control using bat algorithm

10.11591/ijece.v11i1.pp794-801
Herlambang Setiadi , Karl O. Jones , Teguh Aryo Nugroho , Muhammad Abdillah , Herri Trilaksana , Tahta Amrillah
The most common problem in spark ignition engine is how to increase the speed performance. Commonly researchers used traditional mathematical approaches for designing speed controller of spark ignition engine. However, this solution may not be sufficient. Hence, it is important to design the speed controller using smart methods. This paper proposes a method for designing speed controller of a spark ignition engine using the bat algorithm (BA). The simulation is carried out using the MATLAB/SIMULINK environment. Time domain simulation is carried out to investigate the efficacy of the proposed method. From the simulation results, it is found that by designing speed controller of spark ignition engine using PI based bat algorithm, the speed performance of spark ignition engine can be enhanced both in no load condition and load condition compared to conventional PI controler.
Volume: 11
Issue: 1
Page: 794-801
Publish at: 2021-02-01

APMorph: finite-state transducer for Amazigh pronominal morphology

10.11591/ijece.v11i1.pp699-706
Rachid Ammari , Ahbib Zenkoua
Our work aims to present an amazigh pronominal morphological analyzer (APMorph) based on xerox’s finite-state transducer (XFST). Our system revolves around a large lexicon named “APlex” including the affixed pronoun to the noun and to the verb and the characteristics relating to each lemma. A set of rules are added to define the inflectional behavior and morphosyntactic links of each entry as well as the relationship between the different lexical units. The implementation and the evaluation of our approach will be detailed within this article. The use of XFST remains a relevant choice in the sense that this platform allows both analysis and generation. The robustness of our system makes it able to be integrated in other applications of natural language processing (NLP) especially spellchecking, machine translation, and machine learning. This paper presents a continuation of our previous works on the automatic processing of Amazigh nouns and verbs.
Volume: 11
Issue: 1
Page: 699-706
Publish at: 2021-02-01

Improved dynamic performance of photovoltaic panel using fuzzy Logic-MPPT algorithm

10.11591/ijeecs.v21.i2.pp617-624
Khalid W. Nasser , Salam J. Yaqoob , Zainab A. Hassoun
The nonlinear characteristics and intense credence dependence of photovoltaic (PV) panel on the solar irradiance and ambient temperature demonstrate important challenges for researchers in the PV panel topic. To overcome these problems, the maximum power point tracking (MPPT) controller is needed which can improve the PV panel efficiency. In other words, for maximum efficiency, the MPPT controller can help to extract the optimal and overall available output power from the PV panel at different output load conditions. Fuzzy logic (FL) is one of the strongest techniques in the extracting of MPP in the PV panel since it has several advantages; robust; no requirement to have an accurate mathematical model, and works with imprecise inputs. Therefore, in this paper, fuzzy logic (FL-MPPT) has been designed and simulated to improve dynamic performance PV panel at different solar irradiance and then increased the efficiency. Therefore, "MATLAB/Simulink software" has been used to build the proposed algorithm and the simulation results have been adequate as well. Besides, a robust FL-MPPT algorithm has been presented with high dynamic performance under different weather conditions. Finally, the proposed algorithm has a quicker response and less oscillatory comparison of the conventional algorithms in the subject of extracting the maximum PV power.
Volume: 21
Issue: 2
Page: 617-624
Publish at: 2021-02-01

A multiple handover method by using the guide of mobile node

10.11591/ijeecs.v21.i2.pp1090-1102
Radhwan Mohamed Abdullah , Radhwan Basher , Ayad Hussain Abdulqader
Today’s healthcare system can be characterised using the up-and-coming integral component of mobility management of wireless body area networks (WBANs). In general, remote sensor nodes of WBAN are positioned on the body of a subject. Meanwhile, recommendations for specific proxy mobile IPv6 (PMIP) approaches have emerged, but its comparatively unfeasible nature in terms of group mobility management with regards to WBAN. Therefore, it shows a likelihood for expansive registration and handover interruptions. Thus, this work offered an alternative aimed at curbing such restrictions via an enhanced group mobility management method. The approach underlined the integration of authentication, authorisation, and accounting (AAA) services into the local mobility anchor (LMA) as another option for independent practice. Moreover, the proxy binding update (PBU) and AAA inquiry messages were consolidated, whereas the AAA response and proxy binding acknowledge (PBA) message were amalgamated. The resulting outcomes depicted the proposed method’s superior performance in comparison with the current PMIP approaches in the context of registration delay time, handover interruption, and average signalling cost.
Volume: 21
Issue: 2
Page: 1090-1102
Publish at: 2021-02-01

Application of particle swarm optimization with ANFIS model for double scroll chaotic system

10.11591/ijece.v11i1.pp328-335
W. A. Wali
The predictions for the original chaos patterns can be used to correct the distorted chaos pattern which has changed due to any changes whether from undesired disturbance or additional information which can hide under chaos pattern. This information can be recovered when the original chaos pattern is predicted. But unpredictability is most features of chaos, and time series prediction can be used based on the collection of past observations of a variable and analysis it to obtain the underlying relationships and then extrapolate future time series. The additional information often prunes away by several techniques. This paper shows how the chaotic time series prediction is difficult and distort even if Neuro-Fuzzy such as Adaptive Neural Fuzzy Inference System (ANFIS) is used under any disturbance. The paper combined particle swarm (PSO) and (ANFIS) to exam the prediction model and predict the original chaos patterns which comes from the double scroll circuit. Changes in the bias of the nonlinear resistor were used as a disturbance. The predicted chaotic data is compared with data from the chaotic circuit.
Volume: 11
Issue: 1
Page: 328-335
Publish at: 2021-02-01

Robust adaptive controller for wheel mobile robot with disturbances and wheel slips

10.11591/ijece.v11i1.pp336-346
Nga Thi-Thuy Vu , Loc Xuan Ong , Nam Hai Trinh , Sen Thi Huong Pham
In this paper an observer based adaptive control algorithm is built for wheel mobile robot (WMR) with considering the system uncertainties, input disturbances, and wheel slips. Firstly, the model of the kinematic and dynamic loops is shown with presence of the disturbances and system uncertainties. Next, the adaptive controller for nonlinear mismatched disturbance systems based on the disturbances observer is presented in detail. The controller includes two parts, the first one is for the stability purpose and the later is for the disturbances compensation. After that this control scheme is applied for both two loops of the system. In this paper, the stability of the closed system which consists of two control loops and the convergence of the observers is mathematically analysed based on the Lyapunov theory. Moreover, the proposed model does not require the complex calculation so it is easy for the implementation. Finally, the simulation model is built for presented method and the existed one to verify the correctness and the effectiveness of the proposed scheme. The simulation results show that the introduced controller gives the good performances even that the desired trajectory is complicated and the working condition is hard.
Volume: 11
Issue: 1
Page: 336-346
Publish at: 2021-02-01

A deep learning framework to detect Covid-19 disease via chest X-ray and CT scan images

10.11591/ijece.v11i1.pp844-850
Mohammed Y. Kamil
COVID-19 disease has rapidly spread all over the world at the beginning of this year. The hospitals' reports have told that low sensitivity of RT-PCR tests in the infection early stage. At which point, a rapid and accurate diagnostic technique, is needed to detect the Covid-19. CT has been demonstrated to be a successful tool in the diagnosis of disease. A deep learning framework can be developed to aid in evaluating CT exams to provide diagnosis, thus saving time for disease control. In this work, a deep learning model was modified to Covid-19 detection via features extraction from chest X-ray and CT images. Initially, many transfer-learning models have applied and comparison it, then a VGG-19 model was tuned to get the best results that can be adopted in the disease diagnosis. Diagnostic performance was assessed for all models used via the dataset that included 1000 images. The VGG-19 model achieved the highest accuracy of 99%, sensitivity of 97.4%, and specificity of 99.4%. The deep learning and image processing demonstrated high performance in early Covid-19 detection. It shows to be an auxiliary detection way for clinical doctors and thus contribute to the control of the pandemic.
Volume: 11
Issue: 1
Page: 844-850
Publish at: 2021-02-01

Hardware based cryptography: technological advances for applications in Colombia using embedded systems

10.11591/ijece.v11i1.pp508-517
Edwar Jacinto Gomez , Caterinne Perilla Gutierrez , Lina Uyasaba Murillo
To have totally independent systems that offer a sufficient security scheme has become a necessity in Colombia, this because of the proliferation of IoT type systems and similar; In general, it is required to make stand-alone systems totally independent and distributed to offer users a solution to this need, this work offers the analysis and comparison of two security schemes type digital signature and/or hardware security module (HSM) and its variations, made on embedded platforms type microcontroller software, which shows the strategy to provide information protection, In addition, it is analyzed how each implementation was executed, in which devices and metrics of interest, in the first application the cryptography schemes were made using a deep programming that describes the algorithms in C++ language and in the second implementation the use of the dedicated hardware that the embedded platform type microcontroller had is detailed; In both cases, solutions with an acceptable throughput were generated, allowing to obtain comparable solutions and the same style as those made in a PC or similar hardware. On the other hand, an exhaustive review of this type of solutions in the country-region was made, in order to have a reference as to the possible use of this type of applications.
Volume: 11
Issue: 1
Page: 508-517
Publish at: 2021-02-01

A new parallel bat algorithm for musical note recognition

10.11591/ijece.v11i1.pp558-566
ِAnsam Nazar Younis , Fawzia Mahmood Ramo
Music is a universal language that does not require an interpreter, where feelings and sensitivities are united, regardless of the different peoples and languages, The proposed system consists of two main stages: the process of extracting important properties using the linear discrimination analysis (LDA) This step is carried out after the initial treatment process using various procedures to remove musical lines, The second stage describes the recognition process using the bat algorithm, which is one of the metaheuristic algorithms after modifying the bat algorithm to obtain better discriminating results. The proposed system was supported by parallel implementation using the (Developed Bat Algorithm DBA), which increased the speed of implementation significantly. The method was applied to 1250 different images of musical notes. The proposed system was implemented using MATLAB R2016a, Work was done on a Windows10 Processor OS (Intel ® Core TM i5-7200U CPU @ 2.50GHZ 2.70GHZ) computer.
Volume: 11
Issue: 1
Page: 558-566
Publish at: 2021-02-01

Commercial wind turbines modeling using single and composite cumulative probability density functions

10.11591/ijece.v11i1.pp47-56
Othman A. M. Omar , Hamdy M. Ahmed , Reda A. Elbarkouky
As wind turbines more widely used with newer manufactured types and larger electrical power scales, a brief mathematical modelling for these wind turbines operating power curves is needed for optimal site matching selections. In this paper, 24 commercial wind turbines with different ratings and different manufactures are modelled using single cumulative probability density functions modelling equations. A new mean of a composite cumulative probability density function is used for better modelling accuracy. Invasive weed optimization algorithm is used to estimate different models designing parameters. The best cumulative density function model for each wind turbine is reached through comparing the RMSE of each model. Results showed that Weibull-Gamma composite is the best modelling technique for 37.5% of the reached results.
Volume: 11
Issue: 1
Page: 47-56
Publish at: 2021-02-01

Performance enhancement of audio transmission based on LMMSE method

10.11591/ijeecs.v21.i2.pp903-910
Marwa Al-Sultani , Haraa Raheem Hatem , Wurod Qasim Mohamed
The research in wireless communication has developed rapidly for the last decades as a result of raising the demand for efficient data transmission with more security and accuracy. This paper proposed a system based on the special multiplexing (SM) technique and linear minimum mean square error (LMMSE) detection method with the assistance of the hamming code as well as the interleaving techniques for a better enhanced performance of an audio transmission. Moreover, the comparison was done between the two systems for different antenna configurations and with the presence of two types of modulation: binary phase shift key and quateradure phase shift key. These systems are employed by Matlab simulation to show significant results in terms of enhancing the Rayleigh fading channel capacity, bit error rate (BER) and security as well as in recovering the transmitting audio signals. Each system has advantages than the others in one performance term respect to the other terms. The Simulation results have provided to prove and discuss our analysis.
Volume: 21
Issue: 2
Page: 903-910
Publish at: 2021-02-01

Impact of rail impedance intrinsic variability on railway system operation, EMC and safety

10.11591/ijece.v11i1.pp17-26
Andrea Mariscotti
Running rails in electrified transportation systems are the interface element for several phenomena related to system performance, electromagnetic compatibility and safety: useful voltage at rolling stock, short circuit current, induced voltage, stray current, and track circuit operation. This work presents the physical and mathematical groundings of rail electrical parameters (dc and ac resistance, ac internal and external inductance) and experimental results available in the literature, discussing variability and reliability for each interface. The results consist thus of the identification of the relevant rails longitudinal electrical parameters, the presentation of a set of reliable experimental values, and the discussion of the best approach to manage their variability and uncertainty.
Volume: 11
Issue: 1
Page: 17-26
Publish at: 2021-02-01

A hybrid feature selection on AIRS method for identifying breast cancer diseases

10.11591/ijece.v11i1.pp728-735
Achmad Ridok , Nashi Widodo , Wayan Firdaus Mahmudy , Muhaimin Rifa’i
Breast cancer may cause a death due to the late diagnosis. A cheap and accurate tool for early detection of this disease is essential to prevent fatal incidence. In general, the cheap and less invasive method to diagnose the disease could be done by biopsy using fine needle aspirates from breast tissue. However, rapid and accurate identification of the cancer cell pattern from the cell biopsy is still challenging task. This diagnostic tool can be developed using machine learning as a classification problem. The performance of the classifier depends on the interrelationship between sample sizes, some features, and classifier complexity. Thus, the removal of some irrelevant features may increase classification accuracy. In this study, a new hybrid feature selection fast correlation based feature (FCBF) and information gain (IG) was used to select features on identifying breast cancer using AIRS algorithm. The results of 10 times the crossing (CF) of our validation on various AIRS seeds indicate that the proposed method can achieve the best performance with accuracy =0.9797 and AUC=0.9777 at k=6 and seed=50.
Volume: 11
Issue: 1
Page: 728-735
Publish at: 2021-02-01

Modified hotelling’s T2 control charts using modified mahalanobis distance

10.11591/ijece.v11i1.pp284-292
Firas Haddad
This paper proposed new adjusted Hotelling’s T^2 control chart for individual observations. For this objective, bootstrap method for producing the individual observations were employed. To do so, both arithmetic mean vector and the covariance matrix in the traditional Hotelling’s T^2 chart were substituted by the trimmed mean vector and the covariance matrix of the robust scale estimators〖 Q〗_n, respectively which, in turn, its performance is carried out by simulated. In fact, the calculation of false alarms and the probability of detection outlier is used for determining the validity of this modified chart. The findings revealed a considerable significance in its performance.
Volume: 11
Issue: 1
Page: 284-292
Publish at: 2021-02-01

Automation of the burner of a pirotubular boiler to improve the efficiency in the generation of steam

10.11591/ijeecs.v21.i1.pp101-109
Omar Chamorro-Atalaya , Diego Goicochea-Vilela , Dora Arce Santillan , Martin Diaz-Choque , Teodoro Diaz-Leyva
The purpose of this article is to describe the automation developed by means of a programmable logic controller for the burner of a fire tube boiler, in order to improve the efficiency in the process of generating steam quantified through the percentage of oxygen generated. The implementation was developed under a control strategy based on the use of servomotors that allow to gradually open or close the solenoid valves that dose the amount of air and fuel; Considering that there are multiple elements, I limited the analysis to one of its main components, called the burner. After developing this automation, it was possible to demonstrate the decrease in the variability of the oxygen percentage in relation to the optimal or desired value in the boiler. The experimentally obtained data of the oxygen percentage, were subjected to a statistical analysis of dispersion, in which the value of the Pearson's correlation coefficient was determined, whose value was equal to 0.298; This result allowed to conclude that the degree of variability of the indicator in analysis with respect to time is very low, which means that the variable maintains a constant value, equal to the desired value, after automating the fire tube boiler.
Volume: 21
Issue: 1
Page: 101-109
Publish at: 2021-01-01
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