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Periodica Polytechnica Electrical Engineering and Computer Science
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  This is an Open Access Journal Open Access journal
ISSN (Print) 2064-5260 - ISSN (Online) 2064-5279
Published by Budapest University of Technology and Economics Homepage  [7 journals]
  • Detection of Lung Cancer Stages on Computed Tomography Image Using
           Laplacian Filter and Marker Controlled Watershed Segmentation Technique

    • Authors: Tamanna Tajrin, Mamun Ahmed, Sabina Zaman
      Pages: 105 - 115
      Abstract: Lung cancer is a form of malignant tumor distinguished by aggressive multiplication of abnormal cells in lung tissues. If we can assure the detection of lung cancer in the early stage, then we have a chance to increase the survival rate by five years as effective treatment is still available at this stage. Many researchers in the field of image processing sector have built various systems to detect cancer by using image processing techniques. Internationally TNM (Tumor, Nodule, Metastases respectively) method is followed by a physician and radiologist to describe the stage of lung cancer. Our proposed system uses image processing techniques to detect and classify the tumor according to the TNM staging method. First, a series of image processing techniques are performed in a Computed tomography (CT) image. Then, features are extracted to identify the region of interest (ROI). In our proposed system, the classification approach is different from the reviewed existing systems, and the detection rate is comparatively high.
      PubDate: 2022-05-17
      DOI: 10.3311/PPee.19755
      Issue No: Vol. 66, No. 2 (2022)
  • Multiband Handset Antenna System for UMTS/LTE/WLAN/Sub-6 5G and mmWave 5G
           Future Smartphones

    • Authors: Ahmed M. A. Sabaawi, Karam Mudhafar Younus
      Pages: 116 - 121
      Abstract: In this paper, a new antenna system for rapidly emerging multifunction devices is presented. The proposed antenna system consists of four antenna components each one operating at different frequency bands separately. The designed antennas are isolated and integrated on a single substrate. The first antenna is designed to operate at 1920–2170 MHz covering the UMTS band, whereas the second antenna is proposed for the lower band 5G systems and WiMAX operating within the frequency range of 3.4–4.2 GHz. Furthermore, another antenna is designed to cover the higher band 5G system and the IEEE 820.11a WLAN within the frequency range of 5.1–5.85 GHz. Finally, a 28 GHz bowtie-based MIMO antenna array is designed and simulated for the mmWave future 5G mobile networks. The proposed antennas were designed and simulated by using CST microwave studio. The results showed that all of the proposed antennas exhibited excellent reflection characteristics below −20 dB at the resonant frequency and achieved high radiation efficiency reached 99% in some cases with a peak gain ranging between 4–6 dBi. The proposed antenna system helps smartphones to perform multitasks and achieve a better-quality operation especially with the enormous growth of IoT techniques.
      PubDate: 2022-05-17
      DOI: 10.3311/PPee.19679
      Issue No: Vol. 66, No. 2 (2022)
  • Spectroscopy-Based Partial Prediction of In Vitro Dissolution Profile
           Using Artificial Neural Networks

    • Authors: Mohamed Azouz Mrad, Kristóf Csorba, Dorián László Galata, Zsombor Kristóf Nagy, Brigitta Nagy
      Pages: 122 - 131
      Abstract: In pharmaceutical industry, dissolution testing is part of the target product quality that essentials are in the approval of new products. The prediction of the dissolution profile based on spectroscopic data is an alternative to the current destructive and time-consuming method. RAMAN and Near Infrared (NIR) spectroscopy are two complementary methods, that provide information on the physical and chemical properties of the tablets and can help in predicting their dissolution profiles. This work aims to use the information collected by these methods to support the decision of how much of the dissolution profile should be measured and which methods to use, so that by estimating the remaining part, the accuracy requirement of the industry is met. Artificial neural network models were created, in which parts of the measured dissolution profiles, along with the spectroscopy data and the measured compression curves were used as an input to estimate the remaining part of the dissolution profiles. It was found that by measuring the dissolution profiles for 30 minutes, the remaining part was estimated within the acceptance limits of the f2 similarity factor. Adding further spectroscopy methods along with the measured parts of the dissolution profile significantly increased the prediction accuracy.
      PubDate: 2022-05-17
      DOI: 10.3311/PPee.18552
      Issue No: Vol. 66, No. 2 (2022)
  • Analyzing the Overfitting of Boosted Decision Trees for the Modelling of
           Stencil Printing

    • Authors: Péter Martinek, Oliver Krammer
      Pages: 132 - 138
      Abstract: Stencil printing is one of the key steps in reflow soldering technology, and by the spread of ultra-fine-pitch components, analysis of this process is essential. The process of stencil printing has been investigated by a machine learning technique utilizing the ensemble method of boosted decision trees. The phenomenon of overfitting, which can alter the prediction error of boosted decision trees has also been analyzed in detail. The training data set was acquired experimentally by performing stencil printing using different printing speeds (from 20 to 120 mm/s) and various types of solder pastes with different particle sizes (particle size range 25–45 µm, 20–38 µm, 15–25 µm) and different stencil aperture sizes, characterized by their area ratio (from 0.35 to 1.7). The overfitting phenomenon was addressed by training by using incomplete data sets, which means that a subset of data corresponding to a particular input parameter value was excluded from the training. Four cases were investigated with incomplete data sets, by excluding the corresponding data subsets for: area ratios of 0.75 and 1.3, and printing speeds of 70 mm/s and 85 mm/s. It was found that the prediction error at input parameter values that have been excluded from the training can be lowered by eliminating the overfitting; though, the decrease in the prediction error depends on the rate of change in the output parameter in the vicinity of the respective input parameter value.
      PubDate: 2022-05-17
      DOI: 10.3311/PPee.19274
      Issue No: Vol. 66, No. 2 (2022)
  • Predictive-DPC Based on Duty Cycle Control of PWM Rectifier under
           Unbalanced Network

    • Authors: Tarik Mohammed Chikouche, Kada Hartani, Tahar Terras
      Pages: 139 - 147
      Abstract: This work proposes a predictive direct power control (P-DPC) technique of a PWM rectifier, based on a duty cycle control method, using a new definition of the instantaneous reactive power in the predefined cost function, able to operate under balanced and unbalanced grid voltages. In conventional DPC, the use of a single voltage vector during a control period leads to high power ripples and variable switching frequency. To overcome these problems, a duty cycle control is introduced in P-DPC to achieve performance improvement in terms of power ripple reduction, dynamic response and robustness against unbalanced grid voltages. Its main characteristic is the use of several voltage vectors applied during a control period. In effect, the duration of the selected vector is determined by minimizing the active power ripple during a control period. Simulation results are presented to confirm the theoretical study developed.
      PubDate: 2022-05-17
      DOI: 10.3311/PPee.20048
      Issue No: Vol. 66, No. 2 (2022)
  • A Review on Electric Vehicles Charging Strategies Concerning Actors

    • Authors: Shahab Sabzi, Laszlo Vajta, Tayebeh Faghihi
      Pages: 148 - 162
      Abstract: Electric vehicles are becoming increasingly popular in societies and an important part of smart grids. Utility companies should be able to provide them with vital energy as they need electric energy instead of fuel, and this is where new challenges emerge in the network. In order to avoid causing utilities to incur additional energy and economic losses, researchers have proposed smart charging as a way to provide adequate energy to vehicles. When developing a charging schedule for a fleet of EVs, special considerations are made on variables such as energy, cost, and EVs milage. In this review paper, the importance of EVs integration into smart grids is studied, and then different methods to develop EVs charging scheduling are investigated. These methods can vary from optimization algorithms to learning-based, and game theory-based approaches. Then, as the considered system consists of three main actors, including EV users, the utility operator, and aggregators, a systematic review is conducted on these actors, and objectives related to each one are analyzed. Finally, research gaps related to the problem are studied. Researchers can use this review to conduct further research on the integration of EVs into smart grids.
      PubDate: 2022-05-17
      DOI: 10.3311/PPee.19625
      Issue No: Vol. 66, No. 2 (2022)
  • A Novel Model Predictive Control for Stability Improvement of Small Scaled
           Zero-inertia Multiple DGs Micro-grid

    • Authors: Abdulrahman J. Babqi
      Pages: 163 - 173
      Abstract: A zero-inertia micro-grid is a power system consisting of multiple renewable energy power sources and energy storage systems without the presence of conventional synchronous generators. In such a system, a large variation of the load or source sides during the islanded mode of operation extremely degrades the micro-grid's voltage and frequency stability. This study presents a virtual inertia-based predictive control strategy for a small-scale zero-inertia multiple distributed generators (DGs) micro-grid. In islanded mode, Voltage Model Predictive Control (VMPC) was implemented to control and maintain the voltage and frequency of the micro-grid. However, instabilities in frequency and voltage may rise at the Point of Common Coupling (PCC) due to large variations at both source and load sides. Therefore, the proposed virtual inertia loop calculates the amount of active power to be delivered or absorbed by each DG, and its effect is reflected in the estimated d current component of the VMPC, thus providing better frequency regulation. In grid-connected mode, Direct Power Model Predictive Control (DPMPC) was implemented to manage the power flow between each DG and the utility grid. The control approach also enables the DG plug and play characteristics. The performance of the control strategy was investigated and verified using the PSCAD/EMTDC software platform.
      PubDate: 2022-05-17
      DOI: 10.3311/PPee.19232
      Issue No: Vol. 66, No. 2 (2022)
  • Experimental Validation of Direct Predictive Control of Variable Speed
           Wind Energy Conversion System Based on DFIG

    • Authors: Said Chikha, Kamel Barra, Abd Allatif Reama
      Pages: 174 - 190
      Abstract: The paper presents the design and the implementation of a direct predictive control of a variable speed wind energy conversion system. The conversion chain uses a Doubly Fed Induction Generator DFIG whereas the control method is based on a Finite States Model Predictive Control FS-MPC. The proposed control method selects the optimal switching state of the two levels back to back power converter that minimizes the cost function, where this optimal voltage vector is applied on the output of the power converter in next sampling time. The proposed predictive control strategy uses only one sample time prediction and it is intuitive since it is very simple for implementation. In order to adjust the measured rotor currents to track their references, the error between orthogonal rotor current components predictions to their computed values used to select the optimal vector and applied on the power converter in rotor side CSR in next sampling time. On other side, based on the error between the active and reactive power prediction and their references of the electrical grid, the predictive algorithm control of the gird side converter CSG kept the Dc-link voltage constant and guarantee that the whole system functioning with unity power factor. The experimental results confirm the advantages of using this structure for wind energy conversion system and the effectiveness of the proposed control strategy.
      PubDate: 2022-05-17
      DOI: 10.3311/PPee.18874
      Issue No: Vol. 66, No. 2 (2022)
  • Robust Neural Control of Wind Turbine Based Doubly Fed Induction Generator
           and NPC Three Level Inverter

    • Authors: Khadraoua Narimene, Mendaz Kheira, Flitti Mohamed
      Pages: 191 - 204
      Abstract: This paper presents dynamic modeling and control of Doubly Fed Induction Generator (DFIG) based on wind turbine systems, where the stator of DFIG is directly connected to the grid and the rotor was fed by a three level PWM NPC inverter. The active and reactive power control of the DFIG is based on the feedback technique by vector control method by using a classical regulator of Proportional-Integral (PI) type which allows us, in association with the looping of powers, to obtain an efficient and robust system. This approach is a very attractive solution for devices using DFIG as wind energy conversion systems; because, it is a simple, practical implementation, commonly applied in the wind turbine industry and it presents very acceptable performance, However, this control approach has certain limitations and has several causes, vector command with NPC three-level inverter pulse width modulation (PWM) is used to control the reactive power and active power of the generator. Then, use the neural network design to replace the traditional proportional-integral (PI) controller. Finally, the Matlab/Simulink software is used for simulation to prove the effectiveness of the command strategy.
      PubDate: 2022-05-17
      DOI: 10.3311/PPee.19921
      Issue No: Vol. 66, No. 2 (2022)
  • Parameter Determination and Drive Control Analysis of Axial Flux Permanent
           Magnet Synchronous Motors

    • Authors: Attila Nyitrai, Gergely Szabó, Sándor R. Horváth
      Pages: 205 - 214
      Abstract: Axial flux electric motors have received a lot of attention in recent years due to successful implementations in industrial or traction applications. Particularly, axial flux permanent magnet synchronous motors (AFPMSM) can be an attractive choice in case of high torque-density requirements or when the drive environment (packaging) is geometrically limited to a disc-shaped motor. However, compared to radial flux motors, axial flux machine modeling possibilities are much less documented. In the present study, different electromagnetic modeling approaches have been compared through an example AFPMSM design. The motor parameters were determined by analytical and finite element methods. A 2D equivalent model (2D Linear Motor Modeling Approach – 2D-LMMA) and a 3D model results have been compared. The calculated values were used to carry out a drive control analysis of the axial flux motor.
      PubDate: 2022-05-17
      DOI: 10.3311/PPee.19714
      Issue No: Vol. 66, No. 2 (2022)
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