Mircea cel Batran Naval Academy

In the contemporary digital landscape, AI (Artificial Intelligence) emerged as a pivotal tool in enhancing the defense technologies developed across the entire network infrastructure. As reliance on AI-based decision-making grew, so did the imperative need for interpretability, transparency, and trustworthiness, leading to the development and integration of XAI (eXplainable Artificial Intelligence). This research paper provides a comprehensive overview of the current state of the art in XAI approaches that can be effectively implemented for network traffic monitoring, especially in critical digital infrastructures. The main contribution of this research article consists of the comparative analysis of the XAI SHAP (Shapley Additive Explanation) method applied to different datasets obtained from real-time network traffic monitoring, utilizing several representative parameters, which demonstrates the performance, vulnerabilities, and limitations of the proposed method, and also the security implications of the system resources from a cybersecurity perspective. Experimental results show that Ethernet networks offer higher predictability and clearer decision boundaries. Consequently, they are a safer solution for deployment in sensitive network architectures. In contrast, BYOD (Bring Your Own Device) Wi-Fi environments exhibit greater randomness.

This paper addresses Region-3 control of a 2.5 MW three-bladed HAWT using a data-driven workflow that links empirical modeling to implementable pitch control. To focus on fundamental regulation dynamics, the turbine is modeled as a rigid single-mass drivetrain driven by identified quasi-steady aerodynamics. First, we identify a compact shaft-power surface P(ω,V,β) and recover the associated MPP condition, which clarifies why the optimal rotor speed rises with wind and motivates a comparison between capped-MPP operation and constant-speed regulation. We then synthesize a practical Region-3 loop—PI in rate with a first-order pitch servo and saturation handling—and evaluate proportional (P), PI, and PI + servo controllers under sinusoidal and Kaimal-turbulent inflow. Finally, we propose an adaptive PI variant that keeps a fixed acceleration feed-through but retunes the integral path online via ARX(1,1) + RLS to maintain a target closed-loop bandwidth. Performance metrics computed over the full simulation window (t ∈ [0, 50] s) show that P-only control exhibits large steady bias and cap violations; PI recenters speed and power around their targets; adding a pitch servo further trims peaks and ripple. In steady-state turbulent tests, PI + servo achieves tight regulation, Δωpeak ≈ 0.033% (0.079 rad/s), PRMS ≈ 0.62%, while the adaptive PI maintains similar tightness with the lowest variability overall Δωpeak ≈ 0.0104% (0.025 rad/s), PRMS ≈ 0.17. The workflow yields a practically implementable β(V) schedule and a lightweight adaptation mechanism that compensates for slow aerodynamic performance drift without changing the control structure. While structural loads and aeroelastic modes are not explicitly modeled, the proposed controller enforces strict speed and power constraints via a rigid-body dynamic analysis. Extensions to IPC, preview/forecast augmentation, and validation on higher-fidelity aeroelastic/drivetrain models are identified as future work.

It is well known that the analysis of the consequences that the interaction of the ship's body with the navigation environment has on its behaviour is one of the difficult problems of naval architecture. The responsibility of a naval architect is to develop design methodologies and building technologies that, considering the complex actions of the navigation environment, allow the efficient operation of ships, in accordance with their functionality. For ships operating in the proximity of offshore drilling platforms, more than in the case of others, it is essential to anticipate, well know and control the consequences that the navigation environment has on the general behaviour of the ship. The paper aims to make some contributions to solving the complex problem of the movements of a supply ship (understood as a rigid solid with a complex architecture) in the hydrometeorological conditions of the North Sea and Baltic Sea. The aim is to substantiate a model for the analysis of static and dynamic nautical qualities, computer-assisted by advanced software instruments (AutoHydro and Octopus Office). To exemplify the study, a supply ship built in Tulcea Shipyard was used. The authors were interested in modifying the architecture of the original hull by introducing a cylindrical portion in the stern area of the ship. This architectural choice was justified by the need to provide, on the deck, additional spaces for various equipment intended for work near the offshore drilling platforms, and, below deck, additional storage spaces for various purposes. Using the analysis facilities offered by the two software tools mentioned, the consequences that these changes have on nautical qualities have been analysed (the study exemplifies, with comments and interpretations, the hydrostatic diagram and the stability diagrams). The study was computerized by AutoHydro software for the analysis of static nautical qualities. For the analysis of the dynamic behaviour of the wave profile characteristic of the North Sea, with the help of Octopus software, the oscillatory movements of roll and pitch were analysed, both for the original hull and for the modified version. The paper aimed to support the advantages of computerized studies in the initial design phases, without minimizing the importance of tests in towing tanks or sea trials. For scenarios such as the one proposed in this paper (modification of the length of a ship already built, while maintaining the rest of the initial dimensions) the computer-aided study, with software tools specific to the field of naval engineering and navigation, highlights the following advantages: elaboration of various architectural variants, precision in design, speed in the realization of constructive solutions, simulations of the dynamic behaviour on the wave according to various parameters (draft, speed, encounter angle).

High-speed propellers are essential components in the propulsion of high-performance boats, directly influencing the vessel's energy efficiency, stability, and maneuverability. This study presents a numerical analysis of a three-blade propeller designed for high-speed boats, using the Computational Fluid Dynamics (CFD) method implemented in ANSYS CFX. The propeller geometry is modeled in 3D and subjected to simulations to evaluate hydrodynamic parameters such as thrust force, torque, propulsion efficiency, and pressure distribution on the blade area. The results in the paper provide insight into the propeller's behavior under various operating conditions and can serve as a foundation for design optimization. The study highlights the advantages of using CFD in the geometry and optimization of high-speed propellers for boats.

Electromagnetic disturbances influence the proper functioning of electrical and electronic equipment on board, with negative effects on communications, target location, navigation and overall ship control. Based on the electromagnetic map of the ship, the most effective anti-perturbative protection measures, methods and solutions can be established to ensure the functionality of the ship. The sources of electromagnetic disturbances on a seagoing vessel are presented. Also, the different types of attenuations are underlined.

Romanian maritime ports play a pivotal role in the national and regional economy, acting as crucial logistical and commercial hubs in the Black Sea region. This study investigates the ongoing digital transformation of these ports, focusing on the shift from traditional fourth-generation ports to innovative smart ports. It examines the challenges, opportunities, and strategies employed in this transformation, emphasizing the integration of cutting-edge technologies such as automation, digitalization, and green innovations. In light of global trends in the logistics sector, alongside the increasing demand for efficiency, sustainability, and resilience, the research highlights how these advancements contribute to enhancing the operational performance of Romanian ports. Furthermore, the study explores the broader implications of this transition on the global logistics ecosystem, analyzing its potential to improve connectivity, reduce environmental impact, and stimulate economic growth. By evaluating the evolution of Romanian maritime ports over the past two decades, the study offers strategic insights into how these ports can evolve into intelligent, sustainable transport hubs capable of meeting the future demands of a globalized and environmentally conscious supply chain.

This paper analyzes the fact that the parts obtained through drawing processes are characterized by a quality resulting from the interaction of two factors: the technological system and the processing process. All these three elements are influenced by a multitude of factors, which will be analyzed. Furthermore, an analysis of the thickness accuracy of an A3K steel strip mirrored in obtaining the thickness frequency histogram will be presented, as this represented a factor influencing the drawing force in the production of small cylindrical parts.

Maritime transport has adapted to recent political and economic shifts by addressing stringent pollution reduction requirements, redrawing transport routes for safety, reducing onboard technical incidents, managing data security risks and transitioning to autonomous vessels. This paper presents a novel approach to predictive maintenance in the maritime industry, leveraging Artificial Intelligence (AI) and Machine Learning (ML) techniques to enhance fault detection and maintenance planning for naval systems. Traditional maintenance strategies, such as corrective and preventive maintenance, are increasingly ineffective in meeting the high safety and efficiency standards required by maritime operations. The proposed model integrates AI-driven methods to process operational data from shipboard systems, enabling more accurate fault diagnosis and early identification of system failures. By analyzing historical operational data, ML algorithms identify patterns and estimate the functional states, helping prevent unplanned failures and costly downtime. This approach is critical in environments where technical failures are a leading cause of incidents, as demonstrated by the high rate of machinery-related accidents in maritime operations. Our study highlights the growing importance of AI and ML in predictive maintenance and offers a practical tool for improving operational safety and efficiency in the naval industry. The paper discusses the development of a fault detection approach, evaluates its performance on real shipboard data-through tests on a seawater cooling system from an oil tanker and concludes with insights into the broader implications of AI-driven maintenance in the maritime sector.