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31,456 Article Results

A review of adaptive overcurrent protection in distribution networks with integration of distributed energy resources

10.11591/ijeecs.v19.i1.pp140-148
Nur Aisyah Mohd Yusof , Zaipatimah Ali , Mohd Zainal Abidin Ab Kadir
This paper presents a review of an adaptive overcurrent protection scheme in a distribution network with the integration of distributed energy resources. The rapid integration of distributed energy resources brings challenges to the existing overcurrent protection system. The impacts are change in fault current magnitude and direction of current flow from radial to the multi-source dynamic network, which causes changes in network topology, inaccurate fault detection, and loss of coordination among protection systems. In this paper, the solution identified for reliable operation of overcurrent protection in the distribution network are network topology estimation, fault detection, and adaptive coordination.
Volume: 19
Issue: 1
Page: 140-148
Publish at: 2020-07-01

Modified FCS-MPC algorithm for five-leg voltage source inverter

10.11591/ijeecs.v19.i1.pp47-57
Riyadh Ghanem Omar
This paper represents a test of a modified algorithm to minimize the cost function in the traditional finite control-set model predictive current control (FCS-MPC) to control the (five-leg) DC voltage input inverter. A Matlab/Simulink description of a system presents a certain deviation limits between the reference and the actual measured phase currents, also the model implements a load current limitation. The algorithm picks out a proper switching state, which makes the lower error value between the wanted and the prognosticated currents; the proposed technique sets the chosen switching state as a driving signal to the ten switches. The modified program eliminates the switching combination with error values above the requested ones. Thereafter the system response enhanced by lowering the overshoots. The rigidness of the model is examined by using a step change in reference signals.
Volume: 19
Issue: 1
Page: 47-57
Publish at: 2020-07-01

Detecting learning disabilities based on neuro-physiological interface of affect (NPIoA)

10.11591/ijeecs.v19.i1.pp164-171
Nurul Izzati Mat Razi , Abdul Wahab Abdul Rahman , Norhaslinda Kamaruddin
Learning disability (LD) is a neurological processing disorder that causes impediment in processing and understanding information. LD is not only affecting academic performance but can also influence on relationship with family, friends and colleagues. Hence, it is important to detect the learning disabilities among children prior to the school year to avoid from anxiety, bully and other social problems. This research aims to implement the learning disabilities detection based on the emotions captured from electroencephalogram (EEG) to recognize the symptoms of Autism Spectrum Disorder (ASD), Attention Deficit Hyperactivity Disorder (ADHD) and dyslexia in order to have early diagnosis and assisting the clinician evaluation.  The results show several symptoms that ASD children have low alpha power with the Alpha-Beta Test (ABT) power ratio and ASD U-shaped graph, ADHD children have high Theta-Beta Test (TBT) power ratio while Dyslexia have high Left-over-Right Theta (LRT) power ratio.  This can be concluded that the learning disabilities detection methods proposed in this study is applicable for ASD, ADHD and also Dyslexia diagnosis.
Volume: 19
Issue: 1
Page: 164-171
Publish at: 2020-07-01

An immune memory and negative selection to visualizing clinical pathways from electronic health record data

10.11591/ijeecs.v19.i1.pp336-343
Mouna Berquedich , Oulaid Kamach , Malek Masmoudi , Laurent Deshayes
Clinical pathways indicate the applicable treatment order of interventions. In this paper we propose a data-driven methodology to extract common clinical pathways from patient-centric Electronic Health Record data (EHR). The analysis of  patient's, can lead to better regarding pathologies. The proposed algorithmic methodology consist to designing a system of control and analysis of patient records based on an analogy between the elements of the new EHRs and the biological immune systems. The detection of patient profiles ensured by biclustering Matrix. We rely on biological immunity to develop a set of models for structuring knowledge extracted from EHR and to make pathway analysis decisions. A specific analysis of the functional data leds to the detection of several types of patients who share the same EHR information. This methodology demonstrates its ability to simultaneously processing data, and is able to providing information for understanding and identifying the path of patients as well as predicting the path of future patients.
Volume: 19
Issue: 1
Page: 336-343
Publish at: 2020-07-01

Estimation of the refractivity gradient from measured essential climate variables in Iyamho-Auchi, Edo State, South-South Region of Nigeria

10.11591/ijeecs.v19.i1.pp276-284
Kingsley Eghonghon Ukhurebor , Wilson Nwankwo
Meteorological variables are crucial constituents in the estimation of refractivity disseminations and the uncharacteristic radio wave propagation situations of the troposphere as a result of their impact on radio wave communication relations over the atmosphere. In this study the measurement and assessment of air temperature, relative humidity and atmospheric pressure was carried out for a period of one year; 2018, so as to estimate the refractivity gradient over Iyamho-Auchi, Edo State, Nigeria using a self-implemented inexpensive portable meteorological monitoring device. The measurements of the essential climate variables were done at the administrative building of Edo University Iyamho by placing the meteorological monitoring device on a fixed position. The results show that the monthly estimated refractivity gradient values which would be useful in the prediction of the local radio propagation range from -20.00 N-units/km to -190.00 N-units/km with an average value of -60.67 N-units/km for the period under consideration. The findings also show that the months with limited relative humidity have greater refractivity gradient values compared to the ones with higher relative humidity. It was also observed from the results that the measured essential climate variables were having significant impacts on the estimated refractivity gradient during all the months in 2018, and these impacts were more noticeable in the months with higher relative humidity compared with the months that were having limited relative humidity. 
Volume: 19
Issue: 1
Page: 276-284
Publish at: 2020-07-01

Climate change detection in penang island using deterministic interpolation methods

10.11591/ijeecs.v19.i1.pp412-419
Chang Kok Yang , Fam Pei Shan , Tay Lea Tien
This paper presents detection of climate change in Penang Island by using precipitation data based on interpolation technique. Climate change brings about vast and everlasting effects on all living creatures on the Earth. These effects are especially detrimental towards heritage sites, landscapes and businesses based in Penang Island, Malaysia. This study focuses mainly on investigating the indication of climate change in Penang Island over the period of 2003-2018 by utilising sound application procedures of proven analysis methods. Two deterministic interpolation methods are used to produce new estimation points based on the precipitation data to enrich the monitoring network of rainfall stations in Penang Island. Monthly and monthly-average precipitation maps for Penang Island are produced by using inverse distance weighting interpolation method. Results reveal that seven out of twelve months of a year show increasing precipitation trends over the period of study and March is the only month that shows a decreasing trend in precipitation. Monthly-average precipitation in Penang Island also displays a gradual trend of precipitation increase over the period of study, further conforming the finding of monthly precipitation increase over the period of study. The finding of this study provides insight for local agriculturists and ministry to make better decision in response to climate change in Penang.
Volume: 19
Issue: 1
Page: 412-419
Publish at: 2020-07-01

Design and simulation of interdigitated electrode for Graphene-SnO2 sensor on acetone gas

10.11591/ijeecs.v19.i1.pp119-125
Nur Hikmah , Huzein Fahmi Hawari , Monika Gupta
This paper presents the design and simulation of interdigitated electrode for graphene-SnO2 sensor on acetone gas. This study focuses on designing and simulating a sensor platform based on IDE with different configuration parameters to obtain the most ideal and efficient layout concerning sensitivity. Eventhough the sensor platform can be easily fabricated by using photolithography, screen-printing and other methods, the simulation is preferable as it provides low cost, secure and quick analysis tools with required sensitivity analysis. The design is important before developing a hybrid gas sensor based on metal oxide and graphene to detect acetone for diabetic mellitus at room temperature. IDE is one of the sensor platforms which provide simplicity, miniaturization and offers an economical mass-fabrication as an alternative to large systems for a sensor. The sensitivity of this IDE can be improved by altering the parameters of the IDE configuration. Herein, COMSOL Multiphysics® 5.4 software is used for simulation where the IDE-based sensor is constructed, and the electrical field is simulated with dependence on several parameters such as width, gap, finger's number and thickness of the electrode. The electrical field that is generated by the simulation results were analyzed and discussed to find the ideal design with the highest sensitivity. From the simulation, it was found that the optimum sensitivity with electrical field of 58808 V/m was the design of IDE configuration with 14 fingers, 0.15 mm spacing size between fingers, 0.15 mm width of the finger and 0.7mm thickness of fingers and electrode.
Volume: 19
Issue: 1
Page: 119-125
Publish at: 2020-07-01

Optimization of parameters of neuro-fuzzy model

10.11591/ijeecs.v19.i1.pp229-232
Lyalya Bakievna Khuzyatova , Lenar Ajratovich Galiullin
The need for increasing the efficiency of the neuron-fuzzy model in the formation of knowledge bases is being updated. The task is to develop methods and algorithms for presetting and optimizing the parameters of a fuzzy neural network. To solve difficult formalized tasks, it is necessary to develop decision support systems - expert systems based on a knowledge base. ES developers are constantly faced with the problems of “extraction” and formalization of knowledge, as well as the search for new ways to obtain it. To do this, use the extraction, acquisition and formation of knowledge. Currently, the formation of knowledge bases is relevant for the creation of hybrid technologies - fuzzy neural networks that combine the advantages of neural network models and fuzzy systems. The analysis of the efficiency of the fuzzy neural network carried out in the work showed that the quality of training of the NN largely depends on the choice of the number of fuzzy granules for input drugs. In addition, to use fuzzy information formalized by the mathematical apparatus of fuzzy logic, procedures are required for selecting optimal forms and presetting the parameters of the corresponding membership functions (MF).
Volume: 19
Issue: 1
Page: 229-232
Publish at: 2020-07-01

Detecting abnormal movement of driver's head based on spatial-temporal features of video using deep neural network DNN

10.11591/ijeecs.v19.i1.pp344-352
Noor D. Al-Shakarchy , Israa Hadi Ali
The development of tracking and surveillance devices makes extracting useful information efficiently. Head tracking is an efficient method to obtain then analyze trajectory data and make a decision based on the spatiotemporal information of videos. Many applications are based on head tracking such as diseases some diagnosis,  the gestures languages, and drowsiness detection and so on. Abnormal head movement detection can be achieved using spatial information based on a single image (one frame) at a time without considering the temporal information over time. In this paper, a new method based on multi-images is proposed to track head in order to detect abnormal head movement depending on spatiotemporal Feature using Deep Neural Network DNN that employed the 3-Dimensional Convolution Neural Networks 3D CNN. The proposed method extracts the spatial information as well as the temporal information available in a video then analysis this information to make the decision based on time series (sequences of frames); these time series provides the tracking to the head overtime to make the decision. The new dataset created and gathered to implement with the proposed system and called Normal Abnormal Head Movement Dataset (NAHM) video dataset. The new dataset provides different subjects with different conditions that give more efficiency in the implementation of the proposed system. The accuracy of the training set that achieves by the proposed system reach to 88% and of validation set reaches to 86%. The values of loss function reach to 0.3 for the training set and 0.4 for the validation set.
Volume: 19
Issue: 1
Page: 344-352
Publish at: 2020-07-01

Mobility management for RPL protocol in internet of things

10.11591/ijeecs.v19.i1.pp451-458
Zohreh Royaee , Hamid Mirvaziri , Amid Khatibi bardsiri
The IPv6 Routing Protocol for Low Power and Lossy Networks (RPL) was proposed for various applications of IPv6 low power wireless networks. While RPL supports various routing metrics and is designed to be suitable for wireless sensor network environments, it does not consider the mobility of nodes. Therefore, there is a need for a method that is energy efficient and that provides stable and reliable data transmission by considering the mobility of nodes in RPL networks. In this paper a new heuristic flabellum algorithm inspired by physical and biological behaviour of flabella in the sea is presented, and bottleneck and swarm problems are resolved through managing the moving nodes by flabellum algorithm. Finally, the proposed algorithm’s performance is evaluated using the Cooja simulator. The proposed algorithm;Flabellum RPL; shows significant improvements with regards to packet delivery, and convergence and lifetime.
Volume: 19
Issue: 1
Page: 451-458
Publish at: 2020-07-01

Stator winding fault detection of induction generator based wind turbine using ANN

10.11591/ijeecs.v19.i1.pp126-133
N. F. Fadzail , S. Mat Zali , M. A. Khairudin , N. H. Hanafi
This paper presents a stator winding faults detection in induction generator based wind turbines by using artificial neural network (ANN). Stator winding faults of induction generators are the most common fault found in wind turbines. This fault may lead to wind turbine failure. Therefore, fault detection in induction generator based wind turbines is vital to increase the reliability of wind turbines. In this project, the mathematical model of induction generator based wind turbine was developed in MATLAB Simulink. The value of impedance in the induction generators was changed to simulate the inter-turn short circuit and open circuit faults. The simulated responses of the induction generators were used as inputs in the ANN model for fault detection procedures. A set of data was taken under different conditions, i.e. normal condition, inter-turn short circuit and open circuit faults as inputs for the ANN model. The target outputs of the ANN model were set as ‘0’ or ‘1’, based on the fault conditions. Results obtained showed that the ANN model can detect different types of faults based on the output values of the ANN model. In conclusion, the stator winding faults detection procedure for induction generator based wind turbines by using ANN was successfully developed.
Volume: 19
Issue: 1
Page: 126-133
Publish at: 2020-07-01

An optimal nonlinear control for anti-synchronization of Rabinovich hyperchaotic system

10.11591/ijeecs.v19.i1.pp380-387
Shaymaa Al-hayali , Saad Fawzi AL-Azzawi
This work derives new results for the anti-synchronization of 4D identical Rabinovich hyperchaotic systems by using two strategies: active and nonlinear control. The stabilization results of error dynamics systems are established based on Lyapunov second method. Control is designed via the relevant variables of drive and response systems.  In comparison with previous strategies, the current controller (Nonlinear control) focused on the minimum possible limits for relevant variables. The better performance is realizing the anti- synchronization by designing a control with low terms. After obtaining analytical results of the proposed controller, numircal simulation is carried out using Matlab. The graphical results prove validity and applicability of proposed control without know any parameter.The proposed control has certain significance for reducing the time and complexity for strategy implementation.
Volume: 19
Issue: 1
Page: 380-387
Publish at: 2020-07-01

Exploring permissions in android applications using ensemble-based extra tree feature selection

10.11591/ijeecs.v19.i1.pp543-552
Howida Abuabker Alkaaf , Aida Ali , Siti Mariyam Shamsuddin , Shafaatunnur Hassan
The fast development of mobile apps and its usage has led to increase the risk of exploiting user privacy. One method used in Android security mechanism is permission control that restricts the access of apps to core facilities of devices. However, that permissions could be exploited by attackers when granting certain combinations of permissions. So, the aim of this paper is to explore the pattern of malware apps based on analyzing permissions by proposing framework utilizing feature selection based on ensemble extra tree classifier method and machine learning classifier. The used dataset had 25458 samples (8643 malware apps & 16815 benign apps) with 173 features. Three dataset with 25458 samples and 5, 10 and 20 features respectively were generated after using the proposed feature selection method. All the dataset was fed to machine learning. Support Vector machine (SVM), K Neighbors Classifier, Decision Tree, Naïve bayes and Multilayer Perceptron (MLP) classifiers were used. The classifiers models were evaluated using true negative rate (TNR), false positive rate (FNR) and accuracy metrics. The experimental results obtained showed that Support Vector machine and KNeighbors Classifiers with 20 features achieved the highest accuracy with 94 % and TNR with rate of 89 % using KNeighbors Classifier. The FNR rate is dropped to 0.001 using 5 features with support vector machine (SVM) and Multilayer Perceptrons (MLP) classifiers. The result indicated that reducing permission features improved the performance of classification and reduced the computational overhead.
Volume: 19
Issue: 1
Page: 543-552
Publish at: 2020-07-01

A survey: the role of the internet of things in the development of education

10.11591/ijeecs.v19.i1.pp215-221
Ridhab Sami Abd-Ali , Sarah Abbas Radhi , Zaid Ibrahim Rasool
There is a demand to change the contents and activities, and adapt the methods for higher education institutions, especially, universities to let researchers and educational to act more efficiently in a digital context. A well-designed campus, finally combining technology, is basic for developing digital university by facilities for learning, teaching, and research, enhancing the student trials, and supplying the convenient settings. Within digital universities, technology can improve security, reduce costs, and offer devices for faculty, scholars, academics, and students. These advantages give more attention to university processes and evolutions, the experience of researchers, and students. In this research, we have done a study on the Internet of things and what its role in the development of education through the review for a group of previous research. In addition, we have studied the smart class and its components and the difference from the traditional class, and then we have displayed the smart laboratories and its applications. At the end of the research, the great importance of Internet things in universities and its importance to the teacher and the student was concluded by learning faster and developing and improving the educational process.
Volume: 19
Issue: 1
Page: 215-221
Publish at: 2020-07-01

Towards electronic learning features in education 4.0 environment: literature study

10.11591/ijeecs.v19.i1.pp442-450
Nor Azah Mansor , Natrah Abdullah , Hayati Abd Rahman
This paper presents electronic learning features in Education 4.0 environment. Malaysian government encourages Higher Education Institution (HEIs) to embed technologies in order to prepare future education (Education 4.0). Besides, developing skills among students is important to be more adaptable in changing the environment. Current HEIs using Learning Management System (LMS) has lack of interactive features and non-personalized learning. Therefore, this article set out to analyze the existing literature on e-learning practices in Education 4.0 and to propose e-learning features suits in Education 4.0 environment. Guided by the PRISMA Statement (Preferred Reporting Items for Systematic reviews and Meta-Analyses) review method, a systematic review of the Scopus 24 related studies. Further review of these articles resulted in seven principles of e-learning features based on the constructivism principle – Self-regulation, Personal perspective, Experiential learning, Social learning, Learning Community, Creating and Sharing Knowledge, Structure and layout which can be used to upgraded and redesigned LMS.
Volume: 19
Issue: 1
Page: 442-450
Publish at: 2020-07-01
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