https://etalpykla.vilniustech.lt/handle/123456789/153435 Sat, 11 Apr 2026 17:56:08 GMT 2026-04-11T17:56:08Z https://etalpykla.vilniustech.lt/handle/123456789/159727 Research of the Frequency Characteristics of the Semiconductor Linear Microstrip Patch Antenna Breivė, Valentinas; Katkevičius, Andrius; Pomarnacki, Raimondas; Belova-Plonienė, Diana; Krukonis, Audrius; Sledevič, Tomyslav This study evaluates the multiband performance of a five-element, series-fed microstrip patch antenna fabricated on n-type germanium substrates. Full-wave simulations were carried out in CST Microwave Studio across the 2.4 GHz and 5 GHz Wi-Fi bands. Three doping densities: 3.2 × 1015, 2.7 × 1016 and 1.2 × 1017 cm–3 were implemented by introducing group 5 donor impurities. The resulting S11 characteristics confirm stable dual-band operation, while far-field patterns reveal that increasing free-carrier density progressively broadens the main beam. These findings demonstrate the feasibility of germanium-based, doped-substrate antennas for Wi-Fi generations 4, 5 or 6 provide quantitative guidance on doping limits for maintaining radiation efficiency and directivity in semiconductor-integrated radio frequency (RF) modules. Wed, 01 Jan 2025 00:00:00 GMT https://etalpykla.vilniustech.lt/handle/123456789/159727 2025-01-01T00:00:00Z https://etalpykla.vilniustech.lt/handle/123456789/159726 An Approach for Building IT Support Dataset for Machine Learning Models Jevsejev, Roman; Mažeika, Dalius; Bereiša, Mindaugas This study investigates the challenges of preparing datasets for machine learning models based on the data of a centralized system for managing IT incidents within an organization. Key challenges include data quality issues, class imbalance, the need for anonymization, and redundancy in the information. Various data preparation techniques are analyzed, such as handling missing values, encoding categorical and textual data, balancing datasets, anonymizing sensitive information, and performing feature selection. The paper highlights its structural complexities and processing difficulties by examining the state enterprise's Service Desk incident data. Furthermore, the impact of data engineering and cleaning techniques on the accuracy and reliability of machine learning models is assessed. Finally, specific techniques to improve data preparation and to optimize model performance are analyzed. Wed, 01 Jan 2025 00:00:00 GMT https://etalpykla.vilniustech.lt/handle/123456789/159726 2025-01-01T00:00:00Z https://etalpykla.vilniustech.lt/handle/123456789/159725 Polynomial Approximation Degree Influence on Implicit Network Regularization for Impedance Signal Reconstruction Ivinskij, Vadimas; Morkvėnaitė-Vilkončienė, Inga A scanning electrochemical microscope (SECM) with artificial intelligence could generate the sample's activity image from approach curves measured at several points, minimizing the time of measurement and calculating sample activity in points of interest. The time used to shape the separation between the feature space and regression line in the convolutional process for CNNs and DNNs constitutes a significant contribution to the accuracy of the AI model performance. Kernel functions and pre-processing of synthetic data can achieve higher efficiency and localization precision by applying them to the initial or deeper hidden layers of an MLP network; assuming an infinite-width network, we can use the theory from NTK to approximate the kernel shape function to influence the dynamics of the vector flow in gradient descent update for back-propagation. In this paper, we compare various exponential polynomial and trigonometric degree kernel approximations and determine the effectiveness of the Taylor series kernel interpolation filtering to detect high-frequency features for non-image data in multi-layer perceptron (MLP) networks. The results show how engineered feature mapping and data shaping can affect the convergence and dynamics of several types of Gaussian and Fourier mapping method based NN algorithms for training and validation convergence, as well as model learning of non-periodic function approximation mapping in implicitly initialized neural networks. Wed, 01 Jan 2025 00:00:00 GMT https://etalpykla.vilniustech.lt/handle/123456789/159725 2025-01-01T00:00:00Z https://etalpykla.vilniustech.lt/handle/123456789/159724 Deep Learning-Based PID Controller Tuning for Effective Speed Control of DC Shunt Motors Pavan Kumar, Y. V.; Pradeep, D. John; Chakravarthi, M. Kalyan; Pradeep Reddy, G. Electric vehicles (EVs) have become essential due to the depletion of fuel energy resources. DC machines and their role in EVs are gaining significant attention. The speed of DC motor-driven wheels in EVs is usually controlled by proportional-integral-derivative (PID) controllers. But, when the EV is running, the mechanical noises, reduction in tire air volume, the corrugated and rugged surface on which it is driven, etc., lessen the robustness of the PID controllers. This continuous disturbance and variation in speed could result in the exertion of EV circuits, which can be fatal for passengers. Thus, this paper proposes artificial neural network (ANN) based control strategies for enhanced speed regulation in DC motor-driven EVs. Initially, different ANN architectures namely radial basis function (RBF) neural network, nonlinear autoregressive network with exogenous inputs (NARX), nonlinear autoregressive (NAR) network, Elman network, recurrent neural network (RNN), feedforward (FF) network, and probabilistic neural network (PNN) are implemented to design the PID. Of these, it is identified that the FF network is the best choice to design the PID based on its superior time-domain performance index. Further, the efficacy of this proposed ANN-PID controller is compared with the conventional Fuzzy-PID controller subjected to various disturbances namely sine, ramp, step, and chirp. The transient response and steady-state response simulation results proved that the proposed ANN-PID controller delivers superior performance compared to the conventional Fuzzy-PID controller. Wed, 01 Jan 2025 00:00:00 GMT https://etalpykla.vilniustech.lt/handle/123456789/159724 2025-01-01T00:00:00Z