Hasil untuk "Naval architecture. Shipbuilding. Marine engineering"

Mayuranath SureshKumar, Hanumanthrao Kannan

Classic problem-space theory models problem solving as a navigation through a structured space of states, operators, goals, and constraints. Systems Engineering (SE) employs analogous constructs (functional analysis, operational analysis, scenarios, trade studies), yet still lacks a rigorous systems-theoretic representation of the problem space itself. In current practice, reasoning often proceeds directly from stakeholder goals to prescriptive artifacts. This makes foundational assumptions about the operational environment, admissible interactions, and contextual conditions implicit or prematurely embedded in architectures or requirements. This paper addresses that gap by formalizing the problem space as an explicit semantic world model containing theoretical constructs that are defined prior to requirements and solution commitments. These constructs along with the developed axioms, theorems and corollary establish a rigorous criterion for unambiguous boundary semantics, context-dependent interaction traceability to successful stakeholder goal satisfaction, and sufficiency of problem-space specification over which disciplined reasoning can occur independent of solution design. It offers a clear distinction between what is true of the problem domain and what is chosen as a solution. The paper concludes by discussing the significance of the theory on practitioners and provides a dialogue-based hypothetical case study between a stakeholder and an engineer, demonstrating how the theory guides problem framing before designing any prescriptive artifacts.

Daniel Rodriguez-Cardenas, Xiaochang Li, Marcos Macedo et al.

Large language models for code are advancing fast, yet our ability to evaluate them lags behind. Current benchmarks focus on narrow tasks and single metrics, which hide critical gaps in robustness, interpretability, fairness, efficiency, and real-world usability. They also suffer from inconsistent data engineering practices, limited software engineering context, and widespread contamination issues. To understand these problems and chart a path forward, we combined an in-depth survey of existing benchmarks with insights gathered from a dedicated community workshop. We identified three core barriers to reliable evaluation: the absence of software-engineering-rich datasets, overreliance on ML-centric metrics, and the lack of standardized, reproducible data pipelines. Building on these findings, we introduce BEHELM, a holistic benchmarking infrastructure that unifies software-scenario specification with multi-metric evaluation. BEHELM provides a structured way to assess models across tasks, languages, input and output granularities, and key quality dimensions. Our goal is to reduce the overhead currently required to construct benchmarks while enabling a fair, realistic, and future-proof assessment of LLMs in software engineering.

Paloma Guenes, Rafael Tomaz, Maria Teresa Baldassarre et al.

The Impostor Phenomenon (IP) impacts a significant portion of the Software Engineering workforce, yet it is often viewed primarily through an internal individual lens. In this position paper, we propose framing the prevalence of IP as a form of Human Debt and discuss the relation with the ICSE2026 Pre Survey on the Future of Software Engineering results. Similar to technical debt, which arises when short-term goals are prioritized over long-term structural integrity, Human Debt accumulates due to gaps in psychological safety and inclusive support within socio-technical ecosystems. We observe that this debt is not distributed equally, it weighs heavier on underrepresented engineers and researchers, who face compounded challenges within traditional hierarchical structures and academic environments. We propose cultural refactoring, transparency and active maintenance through allyship, suggesting that leaders and institutions must address the environmental factors that exacerbate these feelings, ensuring a sustainable ecosystem for all professionals.

Jiawen Hu, Jingbao Hou, Tenglong Yang et al.

Main shaft bearings are among the critical rotating components of subsea drilling rigs, and their health status directly affects the efficiency and reliability of the drilling system. However, in the high-pressure liquid environment of the deep sea, with intense noise, the vibration signals of the bearings attenuate rapidly. As a result, fault-related features have a low signal-to-noise ratio (SNR), which poses a challenge for bearing health monitoring. In recent years, Deep Neural Network (DNN)-based fault diagnosis methods for subsea drilling rig bearings have become a research hotspot in the field due to their strong potential for deep fault feature mining. Nevertheless, their reliance on high-power-consumption computational resources restricts their widespread application in subsea monitoring scenarios. To address the above issues, this paper proposes a fault diagnosis method for the main-spindle bearings of subsea drilling rigs that combines population coding with an adaptive-threshold k-winner-take-all (k-WTA) mechanism. The method exploits the noise robustness of population coding and the sparse activation induced by the adaptive k-WTA mechanism, achieving a noise-robust and energy-efficient fault diagnosis scheme for the main-spindle bearings of subsea drilling rigs. The experimental results confirm the effectiveness of the proposed method. In accuracy and generalization experiments on the CWRU benchmark dataset, the proposed method achieves good diagnostic accuracy that is not inferior to other SOTA methods, indicating relatively strong generalization and robustness. On the Paderborn real-bearing benchmark dataset, the results highlight the importance of selecting features that are adapted to specific operating conditions. Additionally, in the noise robustness and energy efficiency experiments, the proposed method shows advantages in both noise resistance and energy efficiency.

Oz Levy, Ilya Dikman, Natan Levy et al.

This paper explores how generative AI can help automate and improve key steps in systems engineering. It examines AI's ability to analyze system requirements based on INCOSE's "good requirement" criteria, identifying well-formed and poorly written requirements. The AI does not just classify requirements but also explains why some do not meet the standards. By comparing AI assessments with those of experienced engineers, the study evaluates the accuracy and reliability of AI in identifying quality issues. Additionally, it explores AI's ability to classify functional and non-functional requirements and generate test specifications based on these classifications. Through both quantitative and qualitative analysis, the research aims to assess AI's potential to streamline engineering processes and improve learning outcomes. It also highlights the challenges and limitations of AI, ensuring its safe and ethical use in professional and academic settings.

Lekshmi Murali Rani

The study of behavioral and social dimensions of software engineering (SE) tasks characterizes behavioral software engineering (BSE);however, the increasing significance of human-AI collaboration (HAIC) brings new directions in BSE by presenting new challenges and opportunities. This PhD research focuses on decision-making (DM) for SE tasks and collaboration within human-AI teams, aiming to promote responsible software engineering through a cognitive partnership between humans and AI. The goal of the research is to identify the challenges and nuances in HAIC from a cognitive perspective, design and optimize collaboration/partnership (human-AI team) that enhance collective intelligence and promote better, responsible DM in SE through human-centered approaches. The research addresses HAIC and its impact on individual, team, and organizational level aspects of BSE.

Max Neuwinger, Dirk Riehle

The design of effective programming languages, libraries, frameworks, tools, and platforms for data engineering strongly depends on their ease and correctness of use. Anyone who ignores that it is humans who use these tools risks building tools that are useless, or worse, harmful. To ensure our data engineering tools are based on solid foundations, we performed a systematic review of common programming mistakes in data engineering. We focus on programming beginners (students) by analyzing both the limited literature specific to data engineering mistakes and general programming mistakes in languages commonly used in data engineering (Python, SQL, Java). Through analysis of 21 publications spanning from 2003 to 2024, we synthesized these complementary sources into a comprehensive classification that captures both general programming challenges and domain-specific data engineering mistakes. This classification provides an empirical foundation for future tool development and educational strategies. We believe our systematic categorization will help researchers, practitioners, and educators better understand and address the challenges faced by novice data engineers.

Marvin Wyrich, Lloyd Montgomery

A well-rounded software engineer is often defined by technical prowess and the ability to deliver on complex projects. However, the narrative around the ideal Software Engineering (SE) candidate is evolving, suggesting that there is more to the story. This article explores the non-technical aspects emphasized in SE job postings, revealing the sociotechnical and organizational expectations of employers. Our Thematic Analysis of 100 job postings shows that employers seek candidates who align with their sense of purpose, fit within company culture, pursue personal and career growth, and excel in interpersonal interactions. This study contributes to ongoing discussions in the SE community about the evolving role and workplace context of software engineers beyond technical skills. By highlighting these expectations, we provide relevant insights for researchers, educators, practitioners, and recruiters. Additionally, our analysis offers a valuable snapshot of SE job postings in 2023, providing a scientific record of prevailing trends and expectations.

Zeheng DONG, Xiangyu LI, Gaojie CHEN

ObjectiveAiming at the urgent need for research on vulnerability analysis methods for ship targets, a vulnerability analysis method for supply ship targets under anti-ship missile strikes is proposed.MethodsTaking a typical supply ship target as the research object, anti-ship missiles are selected as the strike weapon, and the structure-activity relationship of the target is analyzed. The dynamic response of a supply ship under the action of anti-ship missile internal explosion load is obtained through numerical simulation. The damage and damage criteria of key components under the action of anti-ship missile internal explosion load are studied, and the function damage degree of the supply ship is obtained. The target vulnerability of the supply ship is analyzed, and the attack method that causes the maximum damage to its navigation function is determined.ResultsA vulnerability analysis method flow is formed which includes an analysis of target structure-activity relationships, damage modes of key components, damage criteria and degree of target functional damage. Based on the vulnerability distribution information, the target should be targeted at the location where non-redundant components are concentrated, achieving the goal of causing maximum range damage. ConclusionThe findings of this study can provide technical support for future research on ship target vulnerability.

Ahmed Nader, Muhayy Ud Din, Mughni Irfan et al.

Performing intervention tasks in the maritime domain is crucial for safety and operational efficiency. The unpredictable and dynamic marine environment makes the intervention tasks such as object manipulation extremely challenging. This study proposes a robust solution for object manipulation from a dock in the presence of disturbances caused by sea waves. To tackle this challenging problem, we apply a deep reinforcement learning (DRL) based algorithm called Soft. Actor-Critic (SAC). SAC employs an actor-critic framework; the actors learn a policy that minimizes an objective function while the critic evaluates the learned policy and provides feedback to guide the actor-learning process. We trained the agent using the PyBullet dynamic simulator and tested it in a realistic simulation environment called MBZIRC maritime simulator. This simulator allows the simulation of different wave conditions according to the World Meteorological Organization (WMO) sea state code. Simulation results demonstrate a high success rate in retrieving the objects from the dock. The trained agent achieved an 80 percent success rate when applied in the simulation environment in the presence of waves characterized by sea state 2, according to the WMO sea state code

Gabriel C. Ullmann, Yann-Gaël Guéhéneuc, Fabio Petrillo et al.

Game engines are tools to facilitate video game development. They provide graphics, sound, and physics simulation features, which would have to be otherwise implemented by developers. Even though essential for modern commercial video game development, game engines are complex and developers often struggle to understand their architecture, leading to maintainability and evolution issues that negatively affect video game productions. In this paper, we present the Subsystem-Dependency Recovery Approach (SyDRA), which helps game engine developers understand game engine architecture and therefore make informed game engine development choices. By applying this approach to 10 open-source game engines, we obtain architectural models that can be used to compare game engine architectures and identify and solve issues of excessive coupling and folder nesting. Through a controlled experiment, we show that the inspection of the architectural models derived from SyDRA enables developers to complete tasks related to architectural understanding and impact analysis in less time and with higher correctness than without these models.

Tao Jiang, Yize Sun, Hai Huang et al.

Autonomous underwater manipulation is very important for the robotic and intelligence operations of oceanic engineering. However, a small target often involves limited features and results in inaccurate visual matching. In order to improve visual measurement accuracy, this paper has proposed an improved unsharp masking algorithm to further enhance the weak texture region of blurred and low contrast images. Moreover, an improved ORB feature-matching method with adaptive threshold, non-maximum suppression and improved random sample consensus has also been proposed. To overcome unknown underwater disturbances and uncertain system parameters in the underwater robotic manipulations, an adaptive non-singular terminal sliding mode controller has been proposed with a quasi-barrier function to suppress the chattering problem and improve grasp accuracy for small target. Oceanic experiments have been conducted to prove the performance of the proposed method.

Yuzhi Zhang, Shumin Zhang, Yang Wang et al.

Accurate channel estimation is the fundamental requirement for recovering underwater acoustic orthogonal time–frequency space (OTFS) modulation signals. As the Doppler effect in the underwater acoustic channel is much more severe than that in the radio channel, the channel information usually cannot strictly meet the compressed sensing sparsity assumption in the orthogonal matching pursuit channel estimation algorithm. This deviation ultimately leads to a degradation in system performance. This paper proposes a novel approach for OTFS channel estimation in underwater acoustic communications, utilizing a model-driven deep learning technique. Our method incorporates a residual neural network into the OTFS channel estimation process. Specifically, the orthogonal matching pursuit algorithm and denoising convolutional neural network (DnCNN) collaborate to perform channel estimation. The cascaded DnCNN denoises the preliminary channel estimation results generated by the orthogonal matching pursuit algorithm for more accurate OTFS channel estimation results. The use of a lightweight DnCNN network with a single residual block reduces computational complexity while still preserving the accuracy of the neural network. Through extensive evaluations conducted on simulated and experimental underwater acoustic channels, the outcomes demonstrate that our proposed method outperforms traditional threshold-based and orthogonal matching pursuit channel estimation techniques, achieves superior accuracy in channel estimation, and significantly reduces the system’s bit error rate.

Arnida L. Latifah, Henokh Lugo Hariyanto, Durra Handri et al.

This paper investigates the wind wave deformations above two isolated shallow seamounts using a phase-resolved wave model simulation using the HAWASSI-AB software. The first seamount is located some 8 km from the south coast of Jawa, Indonesia, near Glagah, with its top area about 2 m from the water level, while the second is the Socotra Rock, in the East China Sea, which has a top 4.6 m under the sea surface. The simulations found that isolated shallow bathymetry may generate a crossing sea region endangering ships. In both domains, short-crested wave simulations of second order show strong refraction and diffraction effects when waves run towards and downstream of the top of the seamount. Waves near the summit embrace the seamount and form a focal area with larger waves downstream. After crossing the Socotra Rock, the interaction waves lead to a crossing sea in the deep water. On the other hand, having passed the Glagah, waves further downstream are partly absent over a substantial stretch of the coast. For both cases, the phase-resolved wave simulation results determine detailed wind wave conditions and wave spectra over the whole area, compensating for a lack of experimental data.

Mohammad Jamous, Reza Marsooli

The current understanding of wind-generated wave climate from buoy-based measurements is mainly focused on a limited number of locations and has not been updated to include measurements in the past decade. This study quantifies wave climate variability and change during the historical period of 1980–2020 through a comprehensive analysis of wave height measurements at 43 buoys off the U.S. Pacific, Atlantic, and Gulf of Mexico Coasts. Variabilities and trends in the annual and monthly mean and 95th percentile significant wave heights (<i>SWH</i>) and the number of extreme wave events are quantified for the cold and warm seasons. We calculate the <i>SWH</i> long-term and decadal trends, and temporal variabilities using the ordinary least squares regression and coefficient of variation, respectively. Independent extreme wave events are identified using a method based on the peaks-over-threshold and the autocorrelation function, which accounts for the geographical variation in the timespan between independent extreme events. Results show that the warm season’s interannual variabilities in monthly and annual <i>SWH</i> are smaller in the Pacific while larger in the Atlantic and Gulf, with the largest variabilities observed at buoys in the Gulf and lower latitudes of the Atlantic. Strong significant alternating decadal trends in <i>SWH</i> are found in the Pacific and Atlantic regions. Buoys in the Atlantic and Gulf regions have experienced higher numbers of extreme wave events (anomalies) compared to the Pacific region. In general, the long-term trend in the number of extreme events during the cold season is positive at buoys located at higher latitudes but negative at lower latitudes.

HUANG Tong, GAO Qinhe, LIU Zhihao, WANG Dong

In order to investigate the driving characteristics of multi-axis special vehicle under the limit condition of missing tires, a five-axis special vehicle dynamics simulation test model including vehicle parameters, power transmission and braking system, axle and suspension system, steering system, and tire system was established based on the vehicle dynamics software TruckSim. Focusing on the analysis of the effect of tire deficiency and based on the tire six component test, the simulation test model of tire parameter was modified and the 0—80—0 km/h linear acceleration brake comfort simulation test and double line operating stability simulation test were conducted to study the smooth features and stability characteristics under the condition of tire deficiency at different positions. Based on the deviation of the centroid of the vehicle, the maximum number of missing tires at different driving speeds was analyzed, and the tire layout methods as well as the degree of influence of each axle tire on the vehicle at different driving speeds were proposed. The results show that the multi-axle special vehicle has the limit condition of driving under the condition of tire deficiency, and the tire deficiency at different positions has little effect on the driving speed of the vehicle. The influence of each axle tire on the driving of this type of vehicle is ranked in order of importance, which are the first axle, the fifth axle, the third axle, the second axle, and the fourth axle. When the vehicle travels at 50 km/h, 30 km/h, and 20 km/h, the maximum numbers of missing tires are 1, 2, and 3, respectively. This paper can provide theoretical support for the assessment of driving safety of multi-axle special vehicles.

Bishal Lakha, Kalyan Bhetwal, Nasir U. Eisty

Context: On top of the inherent challenges startup software companies face applying proper software engineering practices, the non-deterministic nature of machine learning techniques makes it even more difficult for machine learning (ML) startups. Objective: Therefore, the objective of our study is to understand the whole picture of software engineering practices followed by ML startups and identify additional needs. Method: To achieve our goal, we conducted a systematic literature review study on 37 papers published in the last 21 years. We selected papers on both general software startups and ML startups. We collected data to understand software engineering (SE) practices in five phases of the software development life-cycle: requirement engineering, design, development, quality assurance, and deployment. Results: We find some interesting differences in software engineering practices in ML startups and general software startups. The data management and model learning phases are the most prominent among them. Conclusion: While ML startups face many similar challenges to general software startups, the additional difficulties of using stochastic ML models require different strategies in using software engineering practices to produce high-quality products.

Humood Abdulla Naser

Costal reclamation has been carried out extensively along the coastlines of the Arabian Gulf during the last decades. As a small archipelago country, coastal reclamation continues to be a major option for securing land to meet the needs of the expanding population and economic development in Bahrain. Macrobenthic communities often reflect the integrity of ecosystems as they respond to natural and anthropogenic stressors. This study characterized the community structures of macrobenthic invertebrates in three reclaimed intertidal areas and a protected natural mudflat in Bahrain (August 2019 and December 2020). Macrobenthic community structures and sediment characteristics differed significantly between natural and reclaimed areas. A total of 43 species were recorded in the four study areas, of which 38 were collected from the natural mudflat. Polychaetes dominated macrobenthic communities, followed by molluscs and crustaceans. Polychaetes accounted for more than 90% of the communities in the reclaimed coastal areas. Macrobenthic monitoring is considered essential for detecting changes in coastal and marine ecosystems due to dredging and reclamation activities along the coastlines of the Arabian Gulf. The findings of this study can provide insights into the ecological dynamics of macrobenthic communities in reclaimed coastal areas for environmental monitoring and coastal planning and management in the Arabian Gulf.

Xingkun Zhou, Jinghao Chen, Zhengguang Ge et al.

Deepwater subsea wellheads may be significantly threatened under extreme sea conditions and operations, especially when the seabed is composed of very soft clay properties. A numerical model of a deepwater wellhead system is established using the classic ocean pipe element and nonlinear spring element of ANSYS to examine the behaviors of subsea wellheads in diverse seabed soil. Nonlinear spring elements coded in the APDL language are used to model three types of seabed soils: very soft soil, soft soil, and firm soil. The dynamic and quasi-static behaviors of the wellhead system in the typical coupled and decoupled models of the drilling riser system are particularly investigated in depth. The effects of the nonlinear seabed soil properties on the detailed wellhead are realistically simulated using time domain and extremum analysis. The results show that the softer the seabed soil, the greater the displacement, rotation angle, curvature, and bending moment of deepwater subsea wellheads. When the seabed soil reaches a particular depth, the mechanical characteristics of the wellheads under the three types of seabed soil conditions are almost simultaneously close to zero. Overall, several conclusions reached in this study may provide some useful references for design and stability analysis.

Florian Schummer, Maximilian Hyba

Model-Based Systems Engineering aims at creating a model of a system under development, covering the complete system with a level of detail that allows to define and understand its behavior and enables to define any interface and workpackage based on the model. Once such a model is established, further benefits can be reaped, such as the analysis of complex technical correlations within the system. Various insights can be gained by displaying the model as a formal graph and querying it. To enable such queries, a graph schema needs to be designed, which allows to transfer the model into a graph database. In the course of this paper, we discuss the design of a graph schema and MBSE modelling approach, enabling deep going system analysis and anomaly resolution in complex embedded systems. The schema and modelling approach are designed to answer questions such as what happens if there is an electrical short in a component? Which other components are now offline and which data cannot be gathered anymore? Or if a condition cannot be met, which alternative routes can be established to reach a certain state of the system. We build on the use case of qualification and operations of a small spacecraft. Structural and behavioral elements of the MBSE model are transferred to a graph database where analyses are conducted on the system. The schema is implemented by an adapter for MagicDraw to Neo4j. A selection of complex analyses are shown on the example of the MOVE-II space mission.