Control design for trajectory tracking of multi-rotor aerial vehicles (MAVs) represents a challenging task due to the under-actuated property, highly nonlinear and cross-coupled dynamics, modeling errors, parametric uncertainties and external disturbances. This paper presents the design of the first order sliding mode control (FOSMC) algorithm for trajectory tracking of the octo-rotor unmanned aerial vehicle (UAV) in the presence of various disturbances. The highly nonlinear octo-rotor UAV dynamics is considered via the generalized framework for MAVs modeling. The stability analysis of the closed-loop system is presented using the Lyapunov based approach. The developed FOSMC exhibits finite-time convergence of the octo-rotor trajec-tories to the sliding manifold and the asymptotic stability of the equilibrium in the presence of vanishing disturbances. Simulation studies show a superior tracking performance and robustness properties of the FOSMC in comparison with the concurrent techniques for trajectory tracking of the octo-rotor UAV in the presence of internal and external disturbances.

The cloud has become an essential part of modern computing, and its popularity continues to rise with each passing day. Currently, cloud computing is faced with certain challenges that are, due to the increasing demands, becoming urgent to address. One such challenge is the problem of load balancing, which involves the proper distribution of user requests within the cloud. This paper proposes a genetic algorithm for load balancing of the received requests across cloud resources. The algorithm is based on the processing of individual requests instantly upon arrival. The conducted test simulations showed that the proposed approach has better response and processing time compared to round robin, ESCE and throttled load balancing algorithms. The algorithm outperformed an existing genetic based load balancing algorithm, DTGA, as well.

This paper presents a fine-tuned implementation of the quicksort algorithm for highly parallel multicore NVIDIA graphics processors. The described approach focuses on algorith-mic and implementation-level improvements to achieve enhanced performance. Several fine-tuning techniques are explored to identify the best combination of improvements for the quicksort algorithm on GPUs. The results show that this approach leads to a significant reduction in execution time and an improvement in algorithmic operations, such as the number of iterations of the algorithm and the number of operations performed compared to its predecessors. The experiments are conducted on an NVIDIA graphics card, taking into account several distributions of input data. The findings suggest that this fine-tuning approach can enable efficient and fast sorting on GPUs for a wide range of applications.

In this study we demonstrate the appropriate use of statistically based filtering methods for feature selection and describe the application to Heart Rate Variability (HRV) features used to distinguish between arrhythmia and normal sinus rhythm electrocardiogram (ECG) signals. The initial set of HRV features is evaluated using both correlation and statistical significance tests. Normality assumption is assessed for each feature in order to select appropriate correlation methods and significance tests. In addition, the impact of outliers on the statistical test results is illustrated by an explorative analysis of correlation before and after outlier removal. Finally, a reduced set of features is selected, and the decision process guided by correlation and statistical significance test results is described and discussed.

The paper presents an algorithm for determining the optimal connection location and power of a photovoltaic plant in a distribution network. The proposed algorithm is based on the use of the fuzzy logic and power flow calculation method. The fuzzy logic is used for the selection of candidate buses for the photovoltaic plant connection, while load flow analysis is used for the verification of voltage conditions and power losses in the distribution network. For each of the candidate buses photovoltaic plant of a certain power range was considered. The practical application of the considered algorithm was demonstrated on a part of Sarajevo's 10 kV distribution network.

This paper presents the use of the Hilbert-Huang Transform (HHT) to identify low-frequency electromechanical oscillatory modes, their characteristics, and damping. As these oscillations can have varying features, locations, and impacts on power systems, identifying and monitoring them is crucial for the monitoring, protection, and control of modern power systems. The Hilbert-Huang transform (HHT) is a technique used to analyze nonlinear and non-stationary time series data. It involves breaking down the data into components using Empirical Mode Decomposition (EMD), which generates components with varying amplitudes and frequencies. The EMD process includes an inner loop called sifting, which produces an Intrinsic Mode Function (IMF) until the signal reaches a mean value of zero or a maximum number of iterations. The obtained IMF is a characteristic function of a fundamental oscillation that is symmetrical around the abscissa. The dominant oscillatory mode's frequency can be determined by applying the Hilbert transformation to the first IMF, and the damping ratio and damping can be calculated by fitting a least square line to the logarithmic instantaneous amplitude of the first IMF. To demonstrate the efficacy of the methodology, three case studies are examined. The first case involves generating a synthetic signal to simulate a load angle change with a defined frequency and damping. In the second case, a small disturbance in mechanical power change in the Single Machine System is simulated. The third case simulates a three-phase short circuit on the transmission line using the IEEE 39 bus test system. The results are compared to modal analysis conducted in DigSilent PowerFactory software. The application of HHT yielded satisfactory and promising results in identifying the dominant mode's oscillation frequency and damping.

Due to the significant growth in the number of devices, the range of services it provides, and strict air conditioning requirements, the telecommunications infrastructure is becoming an increasingly important electricity consumer. The efficiency of the power supply system and the power quality are significant challenges in the design and maintenance of telecommunications infrastructure elements. In such systems, power electronic converters play an indispensable role. This paper discusses the results of power quality measurements for supply systems of telecommunications devices. The power supply systems of telecommunications devices with different power converters were analyzed. Also, the power supply of a mobile telephony base station at a remote location was considered, with special reference to the reaction of battery storage in the event of a power outage. Obtained results demonstrate that it is necessary to treat such consumers with special care and take measures to limit their emission of current harmonics.

This paper presents KF-RRT algorithm: a novel approach to path planning for robotic manipulators in dynamic environments. It is based on a modified RRT algorithm combined with Kalman filtering technique. RRT modification implies two aspects. The first one is related to continuous update of struc-ture/ordering within the tree to accommodate for online execution of the algorithm. The second one relies on forest-based replanning by combining connected components. On the other hand, Kalman filter is used to track/predict the motion of obstacles. Virtually augmented obstacles influence the growth of trees, which yields the improved safety margin of the resulting motion. KF-RRT is validated within a simulation study, where it is compared to comneting algorithms,

This paper provides an overview of the influential parameters for the power circuit breaker condition assessment based on the vibration fingerprint. By creating the feature subsets based on the domain of computation originating from the vibration fingerprint, the features are firstly ranked by four features ranking algorithms. To confirm the ranked feature contribution to the classification performance, 11 different machine learning classifiers are trained. The training of the classifier is performed on the complete feature set where afterward the same classifiers are trained with the subset of the features ordered by the ranking algorithms. The ranking and the classifier performance yield the concept of kurtosis in the time and frequency domain as a highly promising feature for binary classification which credibly reflects the circuit breaker's mechanical condition.

The main focus of this study is early-stage flame detection, where the number of flame pixels in the image is very scarce. To address this challenge, a custom-made dataset was created specifically for early-stage flame detection, encom-passing challenging environmental conditions. The DeepLabv3+ architecture with ResNet-50 backbone was employed for training and weighted cross-entropy was used to effectively handle the imbalanced nature of the dataset. As a result, the model achieved a mean Intersection over Union (mIoU) value of 0.7519, demonstrating robust performance in challenging conditions. The model exhibited accurate flame pixel detection and flame shape identification in images with low flame content but high smoke levels. Additionally, the model performed well in night-time conditions, accurately identifying flame regions and shapes. An important aspect of the model's performance was its ability to correctly identify images with no flames, thereby reducing false alarms and making it suitable for UAV-based flame detection tasks.

With the advancements of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL), it is now possible to greatly speed up the processes of predicting certain anomalies and prevent unforeseen situations and disasters. One example of such an environmental disaster is the problem of early-stage flame segmentation. It is not only important to create a model capable of pattern recognition with high accuracy but also to optimize it for real-time execution. In this paper, we demonstrate the capabilities of Deeplabv3+ for early-stage flame segmentation on a custom-made dataset with challenging conditions, and near real-time execution with the adoption of the Open VINO toolkit. Acceleration of the inference process in the range of 70.46% to 93.46% is achieved, while the speed of the inference process when using the GPU with FP16 precision is increased by almost 2 times when compared to FP32 precision. The impact of our findings is significant, as early-stage flame segmentation is a critical component of disaster prevention in environmental settings. Our results demonstrate the potential of using the OpenVINO toolkit for the acceleration of the inference process.

The prediction of the dynamics of High-Speed Craft (HSC) with prismatic hulls is commonly performed by designers using semi-empirical formulations based on Savitsky’s classic method. However, the accuracy of this prediction decreases with the presence of warp, when the deadrise of the hull change along its length, which is typical for small passenger ferries, even when considering the effective deadrise and trim angle concept proposed by Savitsky in 2012. The present work assessed the dynamics of three planing warped hulls and one prismatic monohull developed by the University of Naples, using a morphing grid approach implemented in OpenFOAM to capture the motion of the vessel. Numerical results on resistance, wetted area, dynamic trim angle, wall shear stress, and pressure distribution were compared with the method proposed by Savitsky, and previously published results where possible. Results suggested that it is possible to improve Savitsky prediction by changing the location where the equivalent deadrise angle is evaluated. This single modification will allow to extend the application of Savitsky method for a wider range of warp rates.

In this paper, the control of the electric vehicle with in-wheel motor drives is presented. Electric vehicle control is implemented through a drive motor control strategy based on the theory of discrete-time sliding mode. The speed controller is obtained as a combination of discrete-time first order sliding mode control and discrete-time realization of super twisting control algorithm that is commonly used in second-order sliding mode. The design of the proposed speed controller is performed using a discrete-time model of electrical drive. Various tests were performed in Matlab/Simulink software to validate the electronic differential system, vehicle model and engine control algorithm for different types of vehicle movement.

In unstructured environments like parking lots or construction sites, due to the large search-space and kinodynamic constraints of the vehicle, it is challenging to achieve real-time planning. Several state-of-the-art planners utilize heuristic search-based algorithms. However, they heavily rely on the quality of the single heuristic function, used to guide the search. Therefore, they are not capable to achieve reasonable computational performance, resulting in unnecessary delays in the response of the vehicle. In this work, we are adopting a Multi-Heuristic Search approach, that enables the use of multiple heuristic functions and their individual advantages to capture different complexities of a given search space. Based on our knowledge, this approach was not used previously for this problem. For this purpose, multiple admissible and non-admissible heuristic functions are defined, the original Multi-Heuristic A* Search was extended for bidirectional use and dealing with hybrid continuous-discrete search space, and a mechanism for adapting scale of motion primitives is introduced. To demonstrate the advantage, the Multi-Heuristic A* algorithm is benchmarked against a very popular heuristic search-based algorithm, Hybrid A*. The Multi-Heuristic A* algorithm outperformed baseline in both terms, computation efficiency and motion plan (path) quality.

Due to increasingly widespread electoral corruption, citizens are slowly starting to lose trust in the fairness of democratic elections. The main objective of VoteChain is the elimination of the aspect of trust from the electoral process, in order to make voting more secure, transparent, and easily accessible. This paper proposes and implements a robust system that enhances voting efficiency by creating an electronic platform on top of a distributed Bitcoin Cash blockchain ledger. Blockchain represents a time-stamped series of immutable data records shared across a distributed network. When utilized in the context of voting, it guarantees full anonymity, vote integrity, and a fair, incontrovertible ledger with verifiable election results to all voters. Moreover, the system offers the ability to vote via any Internet-enabled computer or smartphone, dramatically decreasing the overall election organization costs. The system is envisioned as an application that connects to the Bitcoin Cash blockchain network via a custom feature-rich library. After discussing the system's characteristics, design, and underlying technology, this paper presents an example election scenario explaining how VoteChain works in-depth. In the end, the system's possible shortcomings are outlined, along with its prospective evolution and potential improvements that can be implemented.