Wyniki wyszukiwania - BazTech

1

Full-Scale Identification of Baseline and Extended Voith–Schneider Propulsion Models from Sparse Data

Socik Paweł, Zadrąg Ryszard, Zacharewicz Marcin, Bogdanowicz Artur, Sigiel Norbert

Polish Maritime Research

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2026

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nr 3

50--60

EN

This paper describes the identification of low-order input–output models of a Voith–Schneider propulsion system based on a very limited set of full-scale data from a minehunter vessel. An analysis was conducted based on steady operating points at which data on the shaft speed, ship speed, hourly fuel consumption, and longitudinal blade setting angle were recorded. Using the measured values for the blade setting angle β and a monotonic mapping, an equivalent pitch ratio, h = (H/D)eq, was determined and introduced as a second coordinate of the propulsion state. A baseline model that depended solely on shaft speed was compared with an extended model that also took into account the equivalent pitch ratio. Both models were zero-dimensional input–output models and described the relationship between the propulsion setting and the vessel’s operational response. The quality of the model was assessed using leave-one-out cross-validation, prediction error metrics, and a resampling-based uncertainty analysis. The results showed that the baseline model had predictive usefulness for both ship speed and fuel consumption, whereas the extended model yielded more significant improvements in the representation of fuel consumption than for ship speed. These findings indicate that the equivalent pitch ratio is a physically meaningful model variable, although its predictive value depends on how clearly the effects of shaft speed and blade setting are separated in the available data. The paper also provides practical guidance for planning future research.

2

Comparative analysis of artificial neural networks for classification of real and generated hydroacoustic signals

Powarzyński Daniel, Łarzewski Bartosz, Sigiel Norbert

Polish Maritime Research

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2026

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nr 2

156--166

EN

In this article, we present the results of a study of the use of selected artificial neural network architectures for the classification of marine objects generating acoustic signals. The training data were acquired from various types of platform, including manned underwater vehicles, autonomous underwater vehicles, diver propulsion vehicles, surface vessels, and high-speed motorboats. A total of 14 models were trained and evaluated on two complementary datasets, consisting of a simulated submarine dataset and a real-world hydroacoustic dataset acquired with a DigitalHyd TP-1 hydrophone within the NARLUGA system. We considered the following neural network architectures: multi-layer perceptron, long short-term memory, gated recurrent unit, and convolutional neural network. This paper provides a detailed description of the model architectures, the training parameters, and the preprocessing steps used to adapt the data representation to each type of model. On the simulated dataset, feature-based models (based on gammatone cepstral coefficients) achieved a test accuracy of above 99%, whereas recurrent models trained directly on long raw sequences did not converge under the training settings used here. On the real-world dataset, the best feature-based model reached a weighted test accuracy of approximately 85%. The results confirm the validity of deep-learning-based algorithms for passive hydroacoustic classification, and highlight the importance of feature representation for robust performance and practical deployment.

3

Analysis of biodegradable additives in marine fuels and their effect on toxic exhaust emissions

Zadrąg Ryszard, Socik Paweł, Zacharewicz Marcin, Bogdanowicz Artur, Sigiel Norbert

Combustion Engines

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2026

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R. 65, nr 2

71--81

EN

This study investigates the influence of biodegradable additives, specifically butyl alcohol, ethyl alcohol, and demineralized water, on the emission of toxic compounds from marine diesel engines. Experiments were conducted using a Sulzer 6AL20/24 engine fueled with conventional diesel and two fuel blends containing alcohols and water. Emissions of NOₓ, CO, CO₂, and O₂ were measured and analyzed via statistical modeling. Results indicate that component fuels can reduce NOₓ emissions by up to 7.5%, mainly due to the cooling effect of water in the fuel mixture. However, effects on CO emissions were less consistent, varying with engine load and fuel composition. CO₂ emissions remained similar across fuels, while the presence of oxygenated compounds influenced oxygen levels. The findings support the potential of biodegradable fuel additives in reducing marine engine emissions. However, further studies are necessary to optimize combustion and meet environmental regulations.

4

Impact of sars-cov-2 on Inpost's service perception in Sosnowiec

Jeziorski Damian, Cieśla Maria, Markowska Katarzyna, Socik Paweł, Sigiel Norbert

Zeszyty Naukowe. Organizacja i Zarządzanie / Politechnika Śląska

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2024

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z. 197

183--205

EN

Purpose: The SARS–CoV–2 pandemic, which spread worldwide in early 2020, has had a significant impact on the global economy and on the operation of many sectors, including the logistics industry. In Poland, as in other countries, the sector has had to adapt quickly to changing conditions and newly introduced sanitary restrictions. InPost, a key player in the courier services market in Poland, faced the challenge of ensuring business continuity while maintaining a high level of customer satisfaction. This article focuses on an analysis of the impact of the SARS–CoV–2 pandemic on the perception of the quality of services provided by InPost in the city of Sosnowiec. The study was conducted the basis of data collected by questionnaires and analysis of InPost's operational data. The results are aimed not only at assessing the direct effects of the pandemic on the company's operations, but also at understanding long–term changes in consumer attitudes and business strategy. Design/methodology/approach: For the article, a survey was conducted using statistical analysis. The survey research was conducted on a population of 6424 respondents. Findings: The empirical research conducted made it possible to determine the impact of pandemics on the quality of service provision. The main issues identified were: the availability of service infrastructure to respondents, the density of its deployment, the impact of computerization on the quality of service delivery, and overall customer satisfaction with the services provided. Research limitations/implications: Despite efforts to collect as large a group of respondents as possible, the sample represented represents only about 3.3% of Sosnowiec's population. Additionally, the survey faces limitations due to the data voluntarily reported by respondents, which may lead to potential bias in their responses. Focusing on a specific percentage of Sosnowiec's population, may not reflect overall satisfaction with the services provided. Practical implications: The research will provide useful insights for courier companies developing their operations in urban areas. They offer guidance on concepts for expanding service infrastructure as well as mobile applications. Originality/value: Based on the research conducted, this study can make a distinctive contribution to the current literature. At the same time, it provides a valuable insight into the adaptation of InPost's logistics organizations in terms of the dynamic changes taking place in society, in the context of the COVID–19 pandemic.

5

Automatic classification of Unexploded Ordnance (UXO) based on Deep Learning Neural Networks (DLNNs)

Sigiel Norbert, Chodnicki Marcin, Socik Paweł, Kot Rafał

Polish Maritime Research

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2024

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nr 1

77--84

EN

This article discusses the use of a deep learning neural network (DLNN) as a tool to improve maritime safety by classifying the potential threat to shipping posed by unexploded ordnance (UXO) objects. Unexploded ordnance poses a huge threat to maritime users, which is why navies and non-governmental organisations (NGOs) around the world are using dedicated advanced technologies to counter this threat. The measures taken by navies include mine countermeasure units (MCMVs) and mine-hunting technology, which relies on the use of sonar imagery to detect and classify dangerous objects. The modern mine-hunting technique is generally divided into three stages: detection and classification, identification, and neutralisation/disposal. The detection and classification stage is usually carried out using sonar mounted on the hull of a ship or on an underwater vehicle. There is now a strong trend to intensify the use of more advanced technologies, such as synthetic aperture sonar (SAS) for high-resolution data collection. Once the sonar data has been collected, military personnel examine the images of the seabed to detect targets and classify tchem as mine-like objects (MILCO) or non mine-like objects (NON-MILCO). Computer-aided detection (CAD), computeraided classification (CAC) and automatic target recognition (ATR) algorithms have been introduced to reduce the burden on the technical operator and reduce post-mission analysis time. This article describes a target classification solution using a DLNN-based approach that can significantly reduce the time required for post-mission data analysis during underwater reconnaissance operations.

6

Model systemu detekcji przeszkód i mapowania środowiska dla autonomicznych pojazdów podwodnych

Kot Rafał, Piskur Paweł, Sigiel Norbert

Pomiary Automatyka Robotyka

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2023

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R. 27, nr 4

19--26

PL

W artykule przedstawiono model systemu detekcji przeszkód i mapowania środowiska, bazujący na przetwarzaniu obrazowym w czasie rzeczywistym, przeznaczony dla autonomicznego pojazdu podwodnego. Model został zrealizowany w oparciu o parametry pracy rzeczywistego sonaru z mechanicznym przestawianiem wiązki Tritech Micron Sonar. Działanie systemu detekcji zostało zweryfikowane z wykorzystaniem matematycznego modelu autonomicznego pojazdu podwodnego poruszającego się w środowisku podwodnym wyrażonym mapą testową przygotowaną na bazie rzeczywistych pomiarów zrealizowanych za pomocą ww. sonaru. Model systemu pozwala wykrywać i zapisywać w postaci mapy, przeszkody znajdujące się w polu widzenia sonaru w czasie rzeczywistym.

EN

This article presents a real-time model of an obstacle detection and environmental mapping system based on image processing for an autonomous underwater vehicle (AUV). The model was based on Tritech Micron Sonar operating parameters with mechanical beam adjustment. The operation of the detection system was verified using a mathematical model of an autonomous underwater vehicle moving in the underwater environment, expressed by a test map prepared based on actual measurements of the above-mentioned sonar. The system model allows for detecting and mapping obstacles in the sonar’s field of view in real-time.

7

A method for the interpretation of sonar data recorded during autonomous underwater vehicle missions

Zieja Mariusz, Wawrzyński Wojciech, Tomaszewska Justyna, Sigiel Norbert

Polish Maritime Research

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2022

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nr 3

176--186

EN

Image acquisition from autonomous underwater vehicles (AUVs) is useful for mapping objects on the seabed. However, there are few studies on the interpretation of data collected with side-scan sonar during autonomous underwater vehicle missions. By recording the seabed with 3D multibeam sonar, a large number of survey points can be obtained. The collected data are processed using applications based on remote sensing image processing. The data collected during AUV missions (or other sonar carriers) needs to be pre-processed to reach the proper effectiveness level. This process includes corrections of signal amplification (Time Varying Gain, or TVG) and geometric distortions of sonar images (Slant Range Corrections). It should be mentioned that, when carrying out the interpretation process for structures on the sea floor, sonar users need to understand the process of visualising seabed projections and depressions, as well as the resolution limitations of the sonar sensors.

8

Optimization of autonomous underwater vehicle mission planning process

Wawrzyński Wojciech, Zieja Mariusz, Żokowski Mariusz, Sigiel Norbert

Bulletin of the Polish Academy of Sciences. Technical Sciences

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2022

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Vol. 70, nr 2

art. no. e140371

EN

This article presents the information concerning aspects of the autonomous underwater vehicle (AUV) mission planning process, emphasizing maritime security monitoring and surveillance, and using side-looking sonars as a primary data source. The paper describes characteristic mission plan phases and gives suggestions for the operators, mainly concerning the safety and effectiveness of the AUV mission. The article describes the coverage path planning algorithm, which could be used to create an effective AUV mission plan, considering AUV manoeuvrability, sonar characteristics, and environmental factors. The results of the algorithms have been verified during the real mission of the AUV vehicle.

9

Methods of autonomous underwater vehicles positioning

Sigiel Norbert

Zeszyty Naukowe Akademii Marynarki Wojennej

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2019

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R. 60 nr 1 (216)

31--43

EN

Article presented below is a prelude to the process of autonomous underwater systems positioning. An article include description of positioning methods and guidelines for the operators of autonomous underwater vehicles (AUVs), concerning above mentioned process.

PL

Artykuł stanowi wprowadzenie w tematykę pozycjonowania podwodnych systemów autonomicznych. Ujęto w nim charakterystykę obecnie wykorzystywanych metod pozycjonowania oraz wytyczne dla operatorów pojazdów autonomicznych w zakresie realizacji tego procesu.

10

Methods of autonomous underwater vehicles positioning

Sigiel Norbert

Scientific Journal of Polish Naval Academy

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2019

|

R. 60 nr 1 (216)

31--43

EN

Article presented below is a prelude to the process of autonomous underwater systems positioning. An article include description of positioning methods and guidelines for the operators of autonomous underwater vehicles (AUVs), concerning above mentioned process.

PL

Artykuł stanowi wprowadzenie w tematykę pozycjonowania podwodnych systemów autonomicznych. Ujęto w nim charakterystykę obecnie wykorzystywanych metod pozycjonowania oraz wytycznedla operatorów pojazdów autonomicznych w zakresie realizacji tego procesu.