AEEE. 2026, vol. 24

Permanent URI for this collectionhttp://hdl.handle.net/10084/158428

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Now showing 1 - 7 out of 7 results

Fractional order proportional integral dertivative dased swarm optimization for three-tank system
(Vysoká škola báňská - Technická univerzita Ostrava, 2026) Dulaimi, Hussien; Al-Khazraji, Huthaifa; K. Hamzah, Mohammed
The control of a liquid level in the most of the industrial process gains high importance, par- ticularly in petrochemical, food processing and phar- maceutical. This paper deals with the designing of a fractional-order proportional-integral-derivative (FOPID) technique to managing liquid level in a three- tank system. To get the best performance from the FOPID controller, its adjustable parameters were fine- tuned using the crow search algorithm (CSA) and mine blast algorithm (MBA). The effectiveness of the pro- posed optimized FOPID controller was further exam- ined with standalone PID controller based on computer simulation using MATALB. Based on results of the time-based metrics like overshoot and how quickly the system settles, the FOPID-CSA controller outperforms the FOPID-MBA, the PID-CSA and the PID-MBA controllers. Moreover, the results obtained from robust- ness analysis showed that the FOPID-CSA controller is more robust under disturbances.
Particle swarm optimization in the field control of a novel electric vehicle design based on a linear induction motor
(Vysoká škola báňská - Technická univerzita Ostrava, 2026) Berrahal, Sebti; Chikhi, Abdesslem; Khettache, Laid
This work aims to improve the performance of electric vehicles (EVs) based on linear induction mo- tors (LIM). The Particle Swarm Optimization (PSO) method is proposed to tune the PID regulator of the Field-Oriented Control (FOC) technique. The main objective of this study is to develop innovative solutions that maximize the efficiency and precision of electric vehicles on various paths. The LIM model is imple- mented using the d-q synchronous reference frame and takes into account the end-effect phenomenon. This phenomenon occurs due to the termination of the mo- tor’s physical structure, which leads to distortion in the magnetic field at the ends of the motor’s primary (sta- tor). It is also highly nonlinear, which increases its complexity and makes control difficult. To overcome this issue, the Field-Oriented Control (FOC) technique is suggested to achieve better efficiency, dynamic per- formance, and greater control flexibility of the motor. Furthermore, the use of the (PSO) optimization tech- nique enables the determination of optimal control pa- rameters to maximize the performance of the (FOC- LIM) system under different operating conditions, such as speed variation and disturbance load. A compari- son between the PSO-PID and conventional methods in terms of response stability, steady-state error, and rise time is conducted using MATLAB/Simulink. The results demonstrate a more efficient, precise, and high- performing electric vehicle system.
Performance analysis of energy harvesting–enabled lora networks under hardware impirments with diversity and learning techniques
(Vysoká škola báňská - Technická univerzita Ostrava, 2026) Nguyen, Thi Tuyet-Hai; Nguyen, Hong Son; Huynh, Trong Thua; Phan, Nghia Hiep
This paper investigates the performance of energy harvesting (EH)–enabled long range (LoRa) networks with diversity techniques under the impact of hardware impairments (HI). In particular, we analyze the coverage probability (Pcov) of a network where LoRa end devices (EDs) rely solely on harvested energy supplied by power beacons (PBs). Both the gateway and PBs are equipped with multiple antennas and employ maximal ratio combining (MRC) and maximal ratio transmission (MRT) techniques, respectively, to enhance system performance. Due to the complexity of the considered network, conventional mathematical analysis becomes intractable. To overcome this challenge, we leverage machine learning and deep learning approaches including support vector machines (SVMs), random forest (RF), gradient boosting (GB), and neural networks (NNs) with different normalization strategies to estimate the coverage probability of the system. Extensive simulation results show that neural networks provide the most accurate performance predictions, followed by SVMs and RF, while GB exhibits the weakest performance. Nonetheless, the performance gaps among these models remain moderate to minor, indicating that all are suitable for most Internet of Things (IoT) applications. The accompanying parameter sensitivity analysis highlights the critical roles of the path-loss exponent and PB transmit power, offering valuable design and optimization insights for EHenabled LoRa networks under practical hardware constraints.
Bacteria in blood identification using electronic nose data based on LSTM and BILSTM deep neural network models
(Vysoká škola báňská - Technická univerzita Ostrava, 2026) Sedhane, Mouna; Hafs, Toufik; Daas, Sara; Hatem, Hatem
Bacteria are single-celled organisms that en- ter the body, grow, and release toxins that harm cells, causing sepsis and other diseases. Because bacteria cause various diseases in humans, prompt diagnosis is required to adapt antibiotic medication and prevent disease spread. This study presents a promising de- vice that can distinguish between different types of bac- teria commonly found in the blood. Electronic nose technology is now regarded as a quick tool for detect- ing pathologies based on volatile organic compounds (VOCs). The use of classical bacteriology takes time to give the practitioner or biologist a diagnosis. The bacterial species is detected from VOCs released by bac- teria in a few minutes using a multi-sensor system for the detection of VOCs. The goal of this study was to test and identify ten different types of bacteria in blood by an electronic nose. The proposed models achieved accuracies of 96.77% (LSTM) and 98.91% (Bi-LSTM), demonstrating the superiority of Bi-LSTM for bacterial classification.
Lightweight deep learning for autonomous human counting system on low-cost hardware
(Vysoká škola báňská - Technická univerzita Ostrava, 2026) Le, Anh Vu; Le, Nhat Tan; Nguzen, Anh Dung; Nguzen, Ngoc Nghia; Le, Hai Dang; Tran, Minh Dang; Minh, Bui Vu; Huynh, Lam Dong; Elara, Mohan Rajes
Accurate and efficient human counting is essen- tial for optimizing public transportation and advancing smart city infrastructure. This paper evaluates pro- posed lightweight deep learning models for autonomous human counting system on low-cost hardware, ensur- ing real-time monitoring and enhanced operational ef- ficiency. While existing methods, such as DeepSORT, Kalman Filters, and YOLO variants, are often im- plemented on high-end hardware, they typically prior- itize accuracy over computational efficiency. Few ob- ject detection and tracking techniques can run in real- time on low-end hardware. This work advances the field by utilizing optimized deep learning models suit- able for embedded systems with constrained resources. Specifically, fine-tuned YOLOv8 is employed for head detection, combined with ByteTrack for robust track- ing, outperforming YOLOv5 and YOLOv11 in accu- racy and efficiency. Archiving the 15 FPS and more then 90% accuracy on the real environment deployment on both RISC-V architecture with an integrated NPU (MaixCAM) and ARM v8 (Raspberry Pi), The pro- posed system demonstrates its suitability for real-time, cost-effective, and scalable autonomous human count- ing in public transit environments.
Resilient control scheme for LVRT enhancement in DFIG system with thyristor-contolled LC compensator
(Vysoká škola báňská - Technická univerzita Ostrava, 2026) Roy, Anushree; Debnath, Sudipta
This paper presents a multi-functional thyristor-controlled LC (TCLC) compensator for en- hancement of low voltage ride through (LVRT) capabil- ity of wind farms and active power flow management along with grid current harmonic mitigation. The pro- posed TCLC controller designed on generalized reactive power theory, enables the compensator to deliver reac- tive power in case of severe voltage dip during fault condition. It also controls the flow of active power under normal circumstance. In the presence of har- monics in the utility grid, the TCLC compensator can act as harmonic compensator. The compensator ele- ments have been designed depending on the range of injected reactive power and harmonic current rejection analysis. Simulation results and comparative assessme nt establish the improved performance of the compen- sator over other state-of-the-art techniques for LVRT improvement. Results obtained from the real-time dig- ital simulator (RTDS) prove the efficacy and reliability of the TCLC compensator.
A Solution map for extended economic load dispatch problem by secretary bird algorithm
(Vysoká škola báňská - Technická univerzita Ostrava, 2026) Pham, Ly Huu; Dinh, Bach Hoang; Phan, Tai Thanh; Dang, Tuu Kim; Luong, Bao Thien; Giang, Trung Thanh Nguyen
The paper introduces three applied methods - Secretary Bird Optimization Algorithm (SBOA), Par- ticle Swarm Optimization (PSO), and Tunicate Swarm Algorithm (TSA) - to address economic load dispatch problem (ELD) and the extended ELD problem with renewable energy resources (RES_ELD). These meth- ods were rigorously evaluated using various test systems with complex restrictions and objective functions. The test cases were ranged from simple to complex, with the most challenging involving load demands ranging from the minimum to the maximum load demand based on the total power of all units. The study’s results indi- cated that SBOA consistently outperformed PSO and TSA across all test systems, offering the best cost so- lutions in a shorter time. Also, SBOA demonstrates comparable or superior results as well as improved searchability compared to previous methods. Further- more, comparing these results highlighted SBOA’s ef- fectiveness in solving these problems and its potential for addressing engineering problems beyond ELD. Fi- nally, the study aimed to provide valuable insights for operators by suggesting solution map that operators can use it to make quick decisions to ensure safe and effi- cient system operation when generating capacity from power plants quickly meets load demand

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