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31,456 Article Results

Smooth transition from grid to standalone solar diesel mode hybrid generation system with a battery

10.11591/ijpeds.v10.i4.pp2065-2075
Aya M Elsherbiny , Adel S. Nada , Mohammed Kamal Ahmed
This paper described a self-supply smart microgrid which may be a low voltage or medium voltage  distribution network. It considered an efficient energy system in which different renewable sources as photo-voltaic array, wind, or conventional as diesel generator, and storage called distributed generators  which organized to chance the load power demand at any time with reliability. It can operate either  on-grid or off-grid configuration, especially limited areas far from the utility grid needed another backup power. By using Matlab/Simulink, a solar PV system is modeled, simulated, and determined the characteristic of a particular photovoltaic cell panel under the influence of different values of ambient conditions. Taking into consideration MPPT algorithm which increases the solar energy efficiency by Incremental conductance technique to track MPP correctly with fast response. Power electronic DC/DC converter and DC/AC inverter are used with PWM technique. To compensate the power fluctuations of the system under different possible transient cases, a high performance control based on an improved virtual synchronous generator is added to confirm smooth variants in voltage, frequency and active power during transient conditions. The results show the PV/Diesel generator/battery/power/frequency and voltage performance at two different conditions.
Volume: 10
Issue: 4
Page: 2065-2075
Publish at: 2019-12-01

A performance of comparative study for semi-structured web data extraction model

10.11591/ijece.v9i6.pp5463-5470
Ily Amalina Ahmad Sabri , Mustafa Man
The extraction of information from multi-sources of web is an essential yet complicated step for data analysis in multiple domains. In this paper, we present a data extraction model based on visual segmentation, DOM tree and JSON approach which is known as Wrapper Extraction of Image using DOM and JSON (WEIDJ) for extracting semi-structured data from biodiversity web. The large number of information from multiple sources of web which is image’s information will be extracted using three different approach; Document Object Model (DOM), Wrapper image using Hybrid DOM and JSON (WHDJ) and Wrapper Extraction of Image using DOM and JSON (WEIDJ). Experiments were conducted on several biodiversity website. The experiment results show that WEIDJ approach promising results with respect to time analysis values. WEIDJ wrapper has successfully extracted greater than 100 images of data from the multi-source web biodiversity of over 15 different websites.
Volume: 9
Issue: 6
Page: 5463-5470
Publish at: 2019-12-01

A computational analysis of short sentences based on ensemble similarity model

10.11591/ijece.v9i6.pp5386-5394
Arifah Che Alhadi , Aziz Deraman , Masita Masila Abdul Jalil , Wan Nural Jawahir Wan Yussof , Rosmayati Mohemad
The rapid development of Internet along with the wide use of social media applications produce huge volume of unstructured data in short text form such as tweets, text snippets and instant messages. This form of data rarely contains repeated word. It presents challenge in sentences similarity analysis as the standard text similarity models merely rely on the number of word occurrence, often resulting unreliable similarity value. Besides, the use of abbreviation, acronyms, slang, smiley, jargon, symbol or non-standard short form also contributes to the difficulty in similarity analysis. Thus, an extended ensemble similarity model approach is proposed. An experimental study has been conducted using datasets of English short sentences. The findings are very encouraging in improving the similarity value for short sentences.
Volume: 9
Issue: 6
Page: 5386-5394
Publish at: 2019-12-01

IQ level prediction and cross-relational analysis with perceptual ability using EEG-based SVM classification model

10.11591/ijai.v8.i4.pp436-442
Noor Hidayah Ros Azamin , Mohd Nasir Taib , Aisyah Hartini Jahidin , Dyg Suzana Awang , Megat Syahirul Amin Megat Ali
This paper presents IQ level prediction and cross-relational analysis with perceptual ability using EEG-based SVM classification model. The study hypothesized that measure of perceptual ability and intelligence is strongly connected through the brain’s attention regulatory mechanism. Therefore, an intelligent classification model should be able to predict and map IQ levels from a dataset associated with varying levels of perception. 115 samples of resting EEG is acquired from the left prefrontal cortex. Sixty-five is used for perceptual ability analysis via CTMT, while another fifty is used in the development of IQ level classification model using SVM. The mean pattern of theta, alpha and beta bands show positive correlation between perceptual ability and IQ level datasets. Meanwhile, the developed SVM model outperforms the previous ANN method; yielding 100% accuracy for training and testing. Subsequently, the classification model successfully predicts and mapped samples from the perceptual ability dataset to its corresponding IQ levels with 98.5% accuracy. Therefore, validity of the study is confirmed through positive correlation demonstrated by both traits of cognition using the pattern of mean power ratio features, and successful prediction of IQ level for perceptual ability dataset via SVM classification model.
Volume: 8
Issue: 4
Page: 436-442
Publish at: 2019-12-01

Heat transfer and efficiency of dual channel PVT air collector: a review

10.11591/ijpeds.v10.i4.pp2037-2045
Ahmad Fudholi , Muhammad Zohri , Ivan Taslim , Fitrotun Aliyah , Arthur Gani Koto
Solar energy is free, renewable and environment friendly and has been widely used in electricity generation and thermal energy through photovoltaic thermal (PVT) system. A PVT collector is a combination of a PV panel and a thermal collector in a single unit to simultaneously generate electricity and thermal energy. In this review, mathematical models for dual channel PVT air collectors is presented. This review presents various research and development, as well as heat transfer and thermal modelling of dual channel PVT air collectors. Moreover, various mathematical models that evaluate the performances base on energy and exergy analysis of dual channel PVT air collectors are presented. Energy balance is the basic concept in developing the mathematical models. Generally, steady-state one-dimensional linear first-order differential equations were reported for solution of mathematical model. Energy and exergy efficiencies of dual channel PVT air collectors were 22.5%–67% and 3.9%-58%, respectively.
Volume: 10
Issue: 4
Page: 2037-2045
Publish at: 2019-12-01

Design and implementation of a three dimensions (3D) printer for modeling and pre-manufacturing applications

10.11591/ijece.v9i6.pp4749-4757
Ghazi Qaryouti , Abdel Rahman Salbad , Sohaib A. Tamimi , Anwar Almofleh , Wael A. Salah , Qazem Jaber
The three-dimensional (3D) printing technologies represent a revolution in the manufacturing sector due to their unique characteristics. These printers arecapable to increase the productivitywithlower complexity in addition tothe reduction inmaterial waste as well the overall design cost prior large scalemanufacturing.However, the applications of 3D printing technologies for the manufacture of functional components or devices remain an almost unexplored field due to their high complexity. In this paper the development of 3D printing technologies for the manufacture of functional parts and devices for different applications is presented. The use of 3D printing technologies in these applicationsis widelyused in modelingdevices usually involves expensive materials such as ceramics or compounds. The recent advances in the implementation of 3D printing with the use of environmental friendly materialsin addition to the advantages ofhighperformance and flexibility. The design and implementation of relatively low-cost and efficient 3D printer is presented. The developed prototype was successfully operated with satisfactory operated as shown from the printed samples shown.
Volume: 9
Issue: 6
Page: 4749-4757
Publish at: 2019-12-01

Backstepping nonlinear control to maximize energy capture in a variable speed wind turbine

10.11591/ijece.v9i6.pp4758-4766
Ouadia El Maguiri , Driss Moussaif , El alami Smma , Farchi Abdelmajid , Aziz Akhiate
We are considering the problem of maximum power point tracking MPPT in wind energy conversion system (WECS). The paper proposes a new control strategy to maximize the wind aerodynamic energy captured in variable speed wind turbine with a separately excited DC-Generator and transformed to the battery through a controlled DC-DC converter. The proposed strategy controls the stip speed ratio via the rotor angular speed to an optimum point at wich the power coefficient is maximal. The controller is designed using the backstepping technique. A formal analysis based on lyapunov stability is developed to describe the control system performances. In addition to closed-loop global asymptotic stability, it is proved that the controller actually meets the MPPT requirement. The above results are confirmed by simulations. 
Volume: 9
Issue: 6
Page: 4758-4766
Publish at: 2019-12-01

Self-organizing map (SOM) for species distribution modelling of birds species at Kenyir landscape

10.11591/ijece.v9i6.pp5235-5243
Salwana Mohamad Asmara , Gertrude David , Mohd Tajuddin Abdullah , Wan Isni Sofiah Wan Din , Danakorn Nincarean a/l Eh Phon , Ahmad Firdaus Zainal Abidin
Identifying which biodiversity species are more dominant than others in any area is a very challenging task. This is because of the abundant of biodiversity species that may become the majority species in any particular region. This situation create a large dataset with a complex variables to be analysed. Moreover, the responds of organisms and environmental factors are occurred in a non-linear correlation. The effort to do so is really important in order to conserve the biodiversity of nature. To understand the complex relationships that exist between species distribution and their habitat, we analysed the interactions among bird diversity, spatial distribution and land use types at Kenyir landscape in Terengganu, Malaysia by using artificial neural network (ANN) method of self-organizing map (SOM) analysis. SOM performs an unsupervised and non-linear analysis on a complex and large dataset. It is capable to handle the non-linear correlation between organism and environmental factors because SOM identifies clusters and relationships between variables without the fixed assumptions of linearity or normality. The result suggested that SOM analysis was suited for understanding the relationships between bird species assemblages and habitat characteristics.
Volume: 9
Issue: 6
Page: 5235-5243
Publish at: 2019-12-01

Dynamic voltage restorer (DVR) in a complex voltage disturbance compensation

10.11591/ijpeds.v10.i4.pp2222-2230
Muhammad Alif Mansor , Muhammad Murtadha Othman , Ismail Musirin , Siti Zaliha Mohammad Noor
Nowadays, a distribution network is operating in a stressful manner because of a complex voltage disturbance stirred by its nonlinear, intensified, sensitive and complex loading condition with vast proliferation of electronic equipment required for the integration of renewable energy. A distribution network that mostly inflicted by the complex voltage disturbance can be referred to as the merge of stationary voltage disturbances with a short duration voltage disturbance under a nonlinear loading condition. Therefore, the dynamic voltage restorer (DVR) integrating with the battery bank will have enough energy storage to overcome long and deep complex voltage disturbance that occurs in a distribution network installed with the photovoltaic (PV) system. The results are obtained with satisfactorily findings in compensating the complex voltage disturbance using DVR.
Volume: 10
Issue: 4
Page: 2222-2230
Publish at: 2019-12-01

IoT based heart monitoring and alerting system with cloud computing and managing the traffic for an ambulance in India

10.11591/ijece.v9i6.pp5068-5074
Khushboo Bhagchandani , D. Peter Augustine
Global Burden of Disease Report, released in Sept 2017, shows that Cardio- vascular Diseases caused 1.7 million deaths (17.8%) in 2016 and it is the leading cause of deaths in India [1]. According to the Indian Heart Association, 25% of all heart attacks happen under the age of 40. In most cases, the initial heart attacks are often ignored. Even post-diagnosis, as per government data [2], 50% of heart attack cases reach the hospital in more than 400 minutes against the ideal window time of 180 minutes; post which damage is irreversible. The delay is often attributed to delay in reaching a hospital or receiving primary aid. In India, traffic conditions also add to the grimace of the situation. Although the government is taking various measures; a holistic solution is required to minimize the delay at each of the steps like accessing the patient situation, contacting the Medical aid or making available the nearest aid possible. In this paper, we aim at providing the holistic solution using the Internet of Things technology (IOT) along with data analytics. IoT enables real-time capturing and computation of medical data from smart sensors built-in wearable devices. The amalgamation of Internet-based services with Medical Things (Smart sensors) enhance the chances of survival of patients. The proposed system analyses the inputs collected from the sensors fit with the patients prone to cardiovascular diseases to ascertain the emergency situation. In addition, to these data, the system also considers age, maximum and minimum heart rate. Based on computational results received from the input parameters, the system triggers the alert to emergency contacts such as the close relatives of the patient, doctors, the hospitals and nearby ambulance. The proposed system combines with the optimized navigation platform to guide the medical assistance to find the fastest route.
Volume: 9
Issue: 6
Page: 5068-5074
Publish at: 2019-12-01

Analysis study on R-Eclat algorithm in infrequent itemsets mining

10.11591/ijece.v9i6.pp5446-5453
Mustafa Man , Julaily Aida Jusoh , Syarilla Iryani Ahmad Saany , Wan Aezwani Wan Abu Bakar , Mohd Hafizuddin Ibrahim
There are rising interests in developing techniques for data mining. One of the important subfield in data mining is itemset mining, which consists of discovering appealing and useful patterns in transaction databases. In a big data environment, the problem of mining infrequent itemsets becomes more complicated when dealing with a huge dataset. Infrequent itemsets mining may provide valuable information in the knowledge mining process. The current basic algorithms that widely implemented in infrequent itemset mining are derived from Apriori and FP-Growth. The use of Eclat-based in infrequent itemset mining has not yet been extensively exploited. This paper addresses the discovery of infrequent itemsets mining from the transactional database based on Eclat algorithm. To address this issue, the minimum support measure is defined as a weighted frequency of occurrence of an itemsets in the analysed data. Preliminary experimental results illustrate that Eclat-based algorithm is more efficient in mining dense data as compared to sparse data.
Volume: 9
Issue: 6
Page: 5446-5453
Publish at: 2019-12-01

Ensemble deep learning for tuberculosis detection using chest X-Ray and canny edge detected images

10.11591/ijai.v8.i4.pp429-435
Stefanus Kieu Tao Hwa , Mohd Hanafi Ahmad Hijazi , Abdullah Bade , Razali Yaakob , Mohammad Saffree Jeffree
Tuberculosis (TB) is a disease caused by Mycobacterium Tuberculosis. Detection of TB at an early stage reduces mortality. Early stage TB is usually diagnosed using chest x-ray inspection. Since TB and lung cancer mimic each other, it is a challenge for the radiologist to avoid misdiagnosis. This paper presents an ensemble deep learning for TB detection using chest x-ray and Canny edge detected images. This method introduces a new type of feature for the TB detection classifiers, thereby increasing the diversity of errors of the base classifiers. The first set of features were extracted from the original x-ray images, while the second set of features were extracted from the edge detected image. To evaluate the proposed approach, two publicly available datasets were used. The results show that the proposed ensemble method produced the best accuracy of 89.77%, sensitivity of 90.91% and specificity of 88.64%. This indicates that using different types of features extracted from different types of images can improve the detection rate.
Volume: 8
Issue: 4
Page: 429-435
Publish at: 2019-12-01

Optimization of discrete wavelet transform features using artificial bee colony algorithm for texture image classification

10.11591/ijece.v9i6.pp5253-5262
Fthi M. Albkosh , Muhammad Suzuri Hitam , Wan Nural Jawahir Hj Wan Yussof , Abdul Aziz K Abdul Hamid , Rozniza Ali
Selection of appropriate image texture properties is one of the major issues in texture classification. This paper presents an optimization technique for automatic selection of multi-scale discrete wavelet transform features using artificial bee colony algorithm for robust texture classification performance. In this paper, an artificial bee colony algorithm has been used to find the best combination of wavelet filters with the correct number of decomposition level in the discrete wavelet transform.  The multi-layered perceptron neural network is employed as an image texture classifier.  The proposed method tested on a high-resolution database of UMD texture. The texture classification results show that the proposed method could provide an automated approach for finding the best input parameters combination setting for discrete wavelet transform features that lead to the best classification accuracy performance.
Volume: 9
Issue: 6
Page: 5253-5262
Publish at: 2019-12-01

Modelling of three phase SVPWM AC-AC converter using unity power factor control

10.11591/ijpeds.v10.i4.pp1823-1832
Saidah Saidah , Hari Sutiksno , Bambang Purwahyudi , Taufik Taufik
This paper introduces the modelling of a novel three phase AC-AC converter with indirect use of a capacitor as DC voltage link. The proposed converter has high efficiency because it uses Space Vector PWM (SVPWM) technique at both rectificier and inverter stages to operate the pulse width modulation in IGBT switches. The novel converter is equipped with a power factor control to shape the rectifier input current waveform to be sinusoidal and to be in phase with the input voltage. To keep the DC voltage stable, the converter utilizes PI controllers. Simulations are conducted for output voltage from 120 to 300 Volts with output frequency ranging from 30 Hz to 60 Hz. The simulation results show that the converter is able to maintain stable the DC voltage and current. Furthermore, the model demonstrates the benefits of proposed converter in terms of acquiring high input power factor and sinusoidal current waveform at the output side of the inverter.
Volume: 10
Issue: 4
Page: 1823-1832
Publish at: 2019-12-01

Automatic segmentation of wrist bone fracture area by K-means pixel clustering from X-ray image

10.11591/ijece.v9i6.pp5205-5210
Kwang Baek Kim , Doo Heon Song , Sang-Seok Yun
Early detection of subtle fracture is important particularly for the senior citizens’ quality of life. Naked eye examination from X-ray image may cause false negatives due to operator subjectivity thus computer vision based automatic detection software is much needed in practice.  In this paper, we propose an automatic extraction method for suspisious wrist fracture regions. We apply K-means in pixel clustering to form the candidate part of possible fracture from wrist X-ray image automatically. This method can recover previously detected patterned false cases with edge detection method after fuzzy stretching. The proposed method is successful in 16 out of 20 tested cases in experiment.
Volume: 9
Issue: 6
Page: 5205-5210
Publish at: 2019-12-01
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