Articles
A bidirectional resonant converter based on wide input range and high efficiency for photovoltaic application
Ibrahim Alhamrouni;
M. R. Bin Hamzah;
Mohamed Salem;
Awang Jusoh;
Azhar Bin Khairuddin;
Tole Sutikno
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 10, No 3: September 2019
Publisher : Institute of Advanced Engineering and Science
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DOI: 10.11591/ijpeds.v10.i3.pp1469-1475
This work highlights a modular power conditioning system (PCS) in photovoltaic (PV) applications which consists with a DC-DC converter. The converter is able to regulate and amplify the input DC voltage produced by the PV panal. The implementation of Mosfet as bidirectional switch on the converter yields greater conversion ratio and better voltage regulation than a conventional DC-DC step up converter and PWM resonant converter. It also reduces the switching losses on the output DC voltage of the converter, as the MOSFET switches on primary winding of converter switch on under ZVS conditions. The proposed resonant converter has been designed, with the modification of series resonant converter and PWM boost converter that utilizes the high frequency of AC bidirectional switch to eliminate the weaknesses of used converters. The topology of the proposed converter includes the mode of operations, designing procedure and components selection of the new converter elements. This topology provides a DC output voltage to the inverter at range of about 120Vac-208 Vac.
The Coordinated Control of FACTS and HVDC Using H-infinity Robust Method to Stabilize the Inter-regional Oscillations in Power Systems
Mahmoud Zadehbagheri;
Mehrdad Pishavaie;
Rahim Ildarabadi;
Tole Sutikno
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 8, No 3: September 2017
Publisher : Institute of Advanced Engineering and Science
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DOI: 10.11591/ijpeds.v8.i3.pp1274-1284
This paper presents a new resistant control method for synchronized connection of FACTS & HVDC aiming to get the stability of small signal of the power system. The efficiency of the proposed controller on the stability of the entire tested system has been proved and also guarantees the stability against uncertainty and turmoil. Applying this method can also reduce the difficulties of oscillations between adjacent areas to generator without strengthening transmission lines or costly constraints on system performance. The simulation results on a system of 68 buses, 16 generators and 5 areas show that the mentioned controller with embedded HVDC and SVC has significant performance despite changes in parameters.
Switched Reluctance Motor Initial Design for Electric Vehicle using RMxprt
Kasrul Abdul Karim;
Nurfaezah Abdullah;
Auzani Jidin;
Tole Sutikno
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 8, No 3: September 2017
Publisher : Institute of Advanced Engineering and Science
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DOI: 10.11591/ijpeds.v8.i3.pp1080-1086
This paper presents a design and development of 8/6 switched reluctance motor for small electric vehicle using analytical method. The absent of permanent magnet, inherent fault tolerance capabilities, simple and robust construction make this motor become more attractive for small electric vehicle application such as electric scooter and go-kart. The switched reluctance motor is modelled using analytical formula in designing process. Later, the designed model is analyzed using ANSYS RMxprt software. In order to achieve 5kW power rating and to match with the design requirement, the switched reluctance motor model has been analyzed using RMxprt tools for the preliminary parameters design process. This tools is able to predict the output performance of motor in term of speed, flux linkage characteristic, output torque and efficiency.
Enhanced Torque Control and Reduced Switching Frequency in Direct Torque Control Utilizing Optimal Switching Strategy for Dual-Inverter Supplied Drive
M. Khairi Rahim;
Auzani Jidin;
Tole Sutikno
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 7, No 2: June 2016
Publisher : Institute of Advanced Engineering and Science
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DOI: 10.11591/ijpeds.v7.i2.pp328-339
Direct Torque Control (DTC) of induction machine has received wide acceptance in many adjustable speed drive applications due to its simplicity and high performance torque control. However, the DTC using a common two-level inverter poses two major problems such as higher switching frequency (or power loss) and larger torque ripple. These problems are due to inappropriate voltage vectors which are selected among a limited number of voltage vectors available in two-level inverter. The proposed research aims to formulate an optimal switching strategy using Dual-Inverter Supplied Drive for high performances of DTC. By using dual-inverter supplied, it provides greater number of voltage vectors which can offer more options to select the most appropriate voltage vectors. The most appropriate voltage vectors should able to produce minimum torque slope but sufficient to satisfy torque demands. The identification is accomplished by using an equation of rate of change of torque which is derived from the induction machine equations. The proposed strategy also introduces a block of modification of torque error status which is responsible to modify the status such that it can determine the most optimal voltage vectors from a look-up table, according to motor operating conditions. The improvements obtained are as follows; 1) minimization of switching frequency (reduce power loss), and 2) reduction of torque ripple. Some improvements obtained in the proposed strategy were verified via experimentations.
Breast cancer disease classification using fuzzy-ID3 algorithm based on association function
Nur Farahaina Idris;
Mohd Arfian Ismail;
Mohd Saberi Mohamad;
Shahreen Kasim;
Zalmiyah Zakaria;
Tole Sutikno
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 2: June 2022
Publisher : Institute of Advanced Engineering and Science
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DOI: 10.11591/ijai.v11.i2.pp448-461
Breast cancer is the second leading cause of mortality among female cancer patients worldwide. Early detection of breast cancer is considerd as one of the most effective ways to prevent the disease from spreading and enable human can make correct decision on the next process. Automatic diagnostic methods were frequently used to conduct breast cancer diagnoses in order to increase the accuracy and speed of detection. The fuzzy-ID3 algorithm with association function implementation (FID3-AF) is proposed as a classification technique for breast cancer detection. The FID3-AF algorithm is a hybridisation of the fuzzy system, the iterative dichotomizer 3 (ID3) algorithm, and the association function. The fuzzy-neural dynamicbottleneck-detection (FUZZYDBD) is considered as an automatic fuzzy database definition method, would aid in the development of the fuzzy database for the data fuzzification process in FID3-AF. The FID3-AF overcame ID3’s issue of being unable to handle continuous data. The association function is implemented to minimise overfitting and enhance generalisation ability. The results indicated that FID3-AF is robust in breast cancer classification. A thorough comparison of FID3-AF to numerous existing methods was conducted to validate the proposed method’s competency. This study established that the FID3-AF performed well and outperform other methods in breast cancer classification.
Brain stroke computed tomography images analysis using image processing: A Review
Nur Hasanah Ali;
Abdul Rahim Abdullah;
Norhashimah Mohd Saad;
Ahmad Sobri Muda;
Tole Sutikno;
Mohd Hatta Jopri
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 10, No 4: December 2021
Publisher : Institute of Advanced Engineering and Science
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DOI: 10.11591/ijai.v10.i4.pp1048-1059
Stroke is the second-leading cause of death globally; therefore, it needs immediate treatment to prevent the brain from damage. Neuroimaging technique for stroke detection such as computed tomography (CT) has been widely used for emergency setting that can provide precise information on an obvious difference between white and gray matter. CT is the comprehensively utilized medical imaging technology for bone, soft tissue, and blood vessels imaging. A fully automatic segmentation became a significant contribution to help neuroradiologists achieve fast and accurate interpretation based on the region of interest (ROI). This review paper aims to identify, critically appraise, and summarize the evidence of the relevant studies needed by researchers. Systematic literature review (SLR) is the most efficient way to obtain reliable and valid conclusions as well as to reduce mistakes. Throughout the entire review process, it has been observed that the segmentation techniques such as fuzzy C-mean, thresholding, region growing, k-means, and watershed segmentation techniques were regularly used by researchers to segment CT scan images. This review is also impactful in identifying the best automated segmentation technique to evaluate brain stroke and is expected to contribute new information in the area of stroke research.
Comparison of meta-heuristic algorithms for fuzzy modelling of COVID-19 illness’ severity classification
Nur Azieta Mohamad Aseri;
Mohd Arfian Ismail;
Abdul Sahli Fakharudin;
Ashraf Osman Ibrahim;
Shahreen Kasim;
Noor Hidayah Zakaria;
Tole Sutikno
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 1: March 2022
Publisher : Institute of Advanced Engineering and Science
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DOI: 10.11591/ijai.v11.i1.pp50-64
The world health organization (WHO) proclaimed the COVID-19, commonly known as the coronavirus disease 2019, was a pandemic in March 2020. When people are in close proximity to one another, the virus spreads mostly through the air. It causes some symptoms in the affected person. COVID-19 symptoms are quite variable, ranging from none to severe sickness. As a result, the fuzzy method is seen favourably as a tool for determining the severity of a person’s COVID-19 sickness. However, when applied to a large situation, manually generating a fuzzy parameter is challenging. This could be because of the identification of a large number of fuzzy parameters. A mechanism, such as an automatic procedure, is consequently required to identify the right fuzzy parameters. The metaheuristic algorithm is regarded as a viable strategy. Five meta-heuristic algorithms were analyzed and utilized in this article to classify the severity of COVID-19 sickness data. The performance of the five meta-heuristic algorithms was evaluated using the COVID-19 symptoms dataset. The COVID-19 symptom dataset was created in accordance with WHO and the Indian ministry of health and family welfare criteria. The findings provide the average classification accuracy for each approach.
A systematic literature review of machine learning methods in predicting court decisions
Nur Aqilah Khadijah Rosili;
Noor Hidayah Zakaria;
Rohayanti Hassan;
Shahreen Kasim;
Farid Zamani Che Rose;
Tole Sutikno
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 10, No 4: December 2021
Publisher : Institute of Advanced Engineering and Science
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DOI: 10.11591/ijai.v10.i4.pp1091-1102
Envisaging legal cases’ outcomes can assist the judicial decision-making process. Prediction is possible in various cases, such as predicting the outcome of construction litigation, crime-related cases, parental rights, worker types, divorces, and tax law. The machine learning methods can function as support decision tools in the legal system with artificial intelligence’s advancement. This study aimed to impart a systematic literature review (SLR) of studies concerning the prediction of court decisions via machine learning methods. The review determines and analyses the machine learning methods used in predicting court decisions. This review utilised RepOrting Standards for Systematic Evidence Syntheses (ROSES) publication standard. Subsequently, 22 relevant studies that most commonly predicted the judgement results involving binary classification were chosen from significant databases: Scopus and Web of Sciences. According to the SLR’s outcomes, various machine learning methods can be used in predicting court decisions. Additionally, the performance is acceptable since most methods achieved more than 70% accuracy. Nevertheless, improvements can be made on the types of judicial decisions predicted using the existing machine learning methods.
FPGA Based a PWM Technique for Permanent Magnet AC Motor Drives
Tole Sutikno;
Nik Rumzi Nik Idris;
Nuryono Satya Widodo;
Auzani Jidin
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 1, No 2: July 2012
Publisher : Institute of Advanced Engineering and Science
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DOI: 10.11591/ijres.v1.i2.pp43-48
The permanent magnet AC motor trapezoidal (BLDC motor) is not strictly DC motor, which uses a pulsed DC fed to the stator field windings to create a rotating magnetic field. Therefore, the motor needs an electronic commutation to provide the rotating field. A pair of switches must be turned on sequentially in the correct order to energize a pair of windings. If the incorrect order is applied, then the BLDC motor will not operate properly. This paper presents a smart guideline to ensure that the order to energize a pair of windings is correct. To ensure the guideline, FPGA based a simple commutation state machine scheme to control BLDC motor is presented. The experiment results have shown that the guideline is correct. The commutation scheme was successfully realized using Altera's APEX20KE FPGA to control BLDC motor in both of forward/reverse rotations or forward/reverse regenerative braking properly.
Simplified VHDL Coding of Modified Non-Restoring Square Root Calculator
Tole Sutikno;
Aiman Zakwan Jidin;
Auzani Jidin;
Nik Rumzi Nik Idris
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 1, No 1: March 2012
Publisher : Institute of Advanced Engineering and Science
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DOI: 10.11591/ijres.v1.i1.pp37-42
Square root calculation is one of the most useful and vital operation in digital signal processing which in recent generations of processors, the operation is performed by the hardware. The hardware implementation of the square root operation can be achieved by different means, but it is very dependent on programmer's sense and ability to write efficient hardware designs. This paper offers universal and shortest VHDL coding of modified non-restoring square root calculator. The main principle of the method is similar with conventional non-restoring algorithm, but it only uses subtract operation and append 01, while add operation and append 11 is not used. The strategy has conducted to implement successfully in FPGA hardware, and offer an efficient in hardware resource, and it is superior.