IIn this study, which can be considered a contribution to the global effort to produce sustainable materials and to search new manufacturing methods for the boat building industry, the performance of a 3D printable polylactic acid and recycled wood (PLAW) composite was investigated under the simulated operational conditions of a boat. The wood used in the composite was yellow pine (Pinus sylvestris), a local wood widely used in boat building and 8% by weight in the composite. For the study, tensile and compressive strength tests were performed in both atmospheric and post-aging conditions, using composite samples produced by the additive manufacturing method. The durations of the accelerated aging before the experiments were one, two and four weeks. During these aging periods, water spraying, a salty fog environment and a drying cycle were applied at elevated temperatures and at equal time intervals, daily. The effect of wood additive on the composite and the joining efficiency of the components were also examined with scanning and optical microscopes. The performance of the obtained composite and the effects of aging on performance were measured using two different thermal analyses: differential scanning calorimetry and thermogravimetric analysis. From the results obtained, it can be seen that PLAW composite can be used in the manufacture of structural elements subjected to relatively low loads in boats. It is an option that will provide integrity in the future interior design of wooden boats.
{"title":"Environmental Marine Degradation of PLA/Wood Composite as an Alternative Sustainable Boat Building Material","authors":"Selin Barış Çamlı, G. Neşer, Ayberk Sözen","doi":"10.2478/pomr-2024-0013","DOIUrl":"https://doi.org/10.2478/pomr-2024-0013","url":null,"abstract":"\u0000 IIn this study, which can be considered a contribution to the global effort to produce sustainable materials and to search new manufacturing methods for the boat building industry, the performance of a 3D printable polylactic acid and recycled wood (PLAW) composite was investigated under the simulated operational conditions of a boat. The wood used in the composite was yellow pine (Pinus sylvestris), a local wood widely used in boat building and 8% by weight in the composite. For the study, tensile and compressive strength tests were performed in both atmospheric and post-aging conditions, using composite samples produced by the additive manufacturing method. The durations of the accelerated aging before the experiments were one, two and four weeks. During these aging periods, water spraying, a salty fog environment and a drying cycle were applied at elevated temperatures and at equal time intervals, daily. The effect of wood additive on the composite and the joining efficiency of the components were also examined with scanning and optical microscopes. The performance of the obtained composite and the effects of aging on performance were measured using two different thermal analyses: differential scanning calorimetry and thermogravimetric analysis. From the results obtained, it can be seen that PLAW composite can be used in the manufacture of structural elements subjected to relatively low loads in boats. It is an option that will provide integrity in the future interior design of wooden boats.","PeriodicalId":49681,"journal":{"name":"Polish Maritime Research","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140404873","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Norbert Sigiel, Marcin Chodnicki, Paweł Socik, Rafał Kot
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 them as mine-like objects (MILCO) or non mine-like objects (NON-MILCO). Computer-aided detection (CAD), computer-aided 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.
{"title":"Automatic Classification of Unexploded Ordnance (UXO) Based on Deep Learning Neural Networks (DLNNS)","authors":"Norbert Sigiel, Marcin Chodnicki, Paweł Socik, Rafał Kot","doi":"10.2478/pomr-2024-0008","DOIUrl":"https://doi.org/10.2478/pomr-2024-0008","url":null,"abstract":"\u0000 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 them as mine-like objects (MILCO) or non mine-like objects (NON-MILCO). Computer-aided detection (CAD), computer-aided 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.","PeriodicalId":49681,"journal":{"name":"Polish Maritime Research","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140398507","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
It is critical to estimate the workforce requirements for the production of blocks in shipbuilding. In this study, the number of workforce (man-day) required for the production of a passenger ship’s double bottom block was estimated. Initially, the production of the block was observed, and the average working performance of the mounting, welding, and grinding workers was recorded. Block drawings were examined and the work required was calculated. The amount of work increased, depending on any revisions required due to incorrect or incomplete designs. The average working performance of an employee is uncertain due to environmental factors, including the weather and working conditions, as well as health (both physical and mental). A two-stage stochastic programming model with recourse was established to estimate man-day required and a Sample Average Approximation (SAA) technique was used to obtain a near-optimum solution. The results of the study were compared with shipyard records and an agreement of approximately 90% was achieved.
{"title":"Approximate Estimation of Man-Day in Ship Block Production: A Two-Stage Stochastic Program","authors":"Yusuf Genç, Mustafa Kafali, U. Çelebi","doi":"10.2478/pomr-2024-0015","DOIUrl":"https://doi.org/10.2478/pomr-2024-0015","url":null,"abstract":"\u0000 It is critical to estimate the workforce requirements for the production of blocks in shipbuilding. In this study, the number of workforce (man-day) required for the production of a passenger ship’s double bottom block was estimated. Initially, the production of the block was observed, and the average working performance of the mounting, welding, and grinding workers was recorded. Block drawings were examined and the work required was calculated. The amount of work increased, depending on any revisions required due to incorrect or incomplete designs. The average working performance of an employee is uncertain due to environmental factors, including the weather and working conditions, as well as health (both physical and mental). A two-stage stochastic programming model with recourse was established to estimate man-day required and a Sample Average Approximation (SAA) technique was used to obtain a near-optimum solution. The results of the study were compared with shipyard records and an agreement of approximately 90% was achieved.","PeriodicalId":49681,"journal":{"name":"Polish Maritime Research","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140403690","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Despite the fact that there is an existing body of literature addressing the computation of Coupling Loss Factors (CLFs) via the Finite Element Method (FEM), no publications have sufficiently taken into account real structural joints in their approach. Previous research has focused on academic cases of trivial connections, rarely involving more than two steel plates. To enable Statistical Energy Analysis (SEA) on a real ship, a methodology for determining CLFs for non-trivial systems is proposed, considering realistic boundary conditions and irregularities that can occur in marine structures. Based on the method, a library of CLFs is created by selecting the tested connections to enable modelling of about 90% of the acoustic paths on an existing jack-up vessel. Boundary conditions were set by introducing spring elements with a stiffness calibrated to the type of connection and taking the adjacent structure into account. In previous works, CLFs were determined for basic connections of rectangular plates. The lack of scantling variations, ignoring discontinuities and only defining parallel edges in the considered models, lead to the overestimation of energy transmission in real structures. To consider the influence of the above, random deviations from the initial stiffness of the springs at individual edges and point restraints at random points are introduced in this paper.
{"title":"Universal Sea/Fem Based Method for Estimation of Vibroacoustic Coupling Loss Factors in Realistic Ship Structures","authors":"Michał Drężek, Marek Augustyniak","doi":"10.2478/pomr-2024-0006","DOIUrl":"https://doi.org/10.2478/pomr-2024-0006","url":null,"abstract":"\u0000 Despite the fact that there is an existing body of literature addressing the computation of Coupling Loss Factors (CLFs) via the Finite Element Method (FEM), no publications have sufficiently taken into account real structural joints in their approach. Previous research has focused on academic cases of trivial connections, rarely involving more than two steel plates. To enable Statistical Energy Analysis (SEA) on a real ship, a methodology for determining CLFs for non-trivial systems is proposed, considering realistic boundary conditions and irregularities that can occur in marine structures. Based on the method, a library of CLFs is created by selecting the tested connections to enable modelling of about 90% of the acoustic paths on an existing jack-up vessel. Boundary conditions were set by introducing spring elements with a stiffness calibrated to the type of connection and taking the adjacent structure into account. In previous works, CLFs were determined for basic connections of rectangular plates. The lack of scantling variations, ignoring discontinuities and only defining parallel edges in the considered models, lead to the overestimation of energy transmission in real structures. To consider the influence of the above, random deviations from the initial stiffness of the springs at individual edges and point restraints at random points are introduced in this paper.","PeriodicalId":49681,"journal":{"name":"Polish Maritime Research","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140408115","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Fayou Liu, Weijia Li, Yaozhong Wu, Yuhang He, Tianyun Li
Rotor-bearing systems are important components of rotating machinery and transmission systems, and imbalance and misalignment are inevitable in such systems. At present, the main challenges faced by state-of-the-art fault diagnosis methods involve the extraction of fault features under strong background noise and the classification of different fault modes. In this paper, a fault diagnosis method based on an improved deep residual shrinkage network (IDRSN) is proposed with the aim of achieving end-to-end fault diagnosis of a rotor-bearing system. First, a method called wavelet threshold denoising and variational mode decomposition (WTD-VMD) is proposed, which can process original noisy signals into intrinsic mode functions (IMFs) with a salient feature. These one-dimensional IMFs are then transformed into two-dimensional images using a Gramian angular field (GAF) to give datasets for the deep residual shrinkage network (DRSN), which can achieve high levels of accuracy under strong background noise. Finally, a comprehensive test platform for a rotor-bearing system is built to verify the effectiveness of the proposed method in the field. The true test accuracy of the model at a 95% confidence interval is found to range from 84.09% to 86.51%. The proposed model exhibits good robustness when dealing with noisy samples and gives the best classification results for fault diagnosis under misalignment, with a test accuracy of 100%. It also achieves a higher testing accuracy compared to fault diagnosis methods based on convolutional neural networks and deep residual networks without improvement. In summary, IDRSN has significant value for deep learning engineering applications involving the fault diagnosis of rotor-bearing systems.
{"title":"Fault Diagnosis of Imbalance and Misalignment in Rotor-Bearing Systems Using Deep Learning","authors":"Fayou Liu, Weijia Li, Yaozhong Wu, Yuhang He, Tianyun Li","doi":"10.2478/pomr-2024-0011","DOIUrl":"https://doi.org/10.2478/pomr-2024-0011","url":null,"abstract":"\u0000 Rotor-bearing systems are important components of rotating machinery and transmission systems, and imbalance and misalignment are inevitable in such systems. At present, the main challenges faced by state-of-the-art fault diagnosis methods involve the extraction of fault features under strong background noise and the classification of different fault modes. In this paper, a fault diagnosis method based on an improved deep residual shrinkage network (IDRSN) is proposed with the aim of achieving end-to-end fault diagnosis of a rotor-bearing system. First, a method called wavelet threshold denoising and variational mode decomposition (WTD-VMD) is proposed, which can process original noisy signals into intrinsic mode functions (IMFs) with a salient feature. These one-dimensional IMFs are then transformed into two-dimensional images using a Gramian angular field (GAF) to give datasets for the deep residual shrinkage network (DRSN), which can achieve high levels of accuracy under strong background noise. Finally, a comprehensive test platform for a rotor-bearing system is built to verify the effectiveness of the proposed method in the field. The true test accuracy of the model at a 95% confidence interval is found to range from 84.09% to 86.51%. The proposed model exhibits good robustness when dealing with noisy samples and gives the best classification results for fault diagnosis under misalignment, with a test accuracy of 100%. It also achieves a higher testing accuracy compared to fault diagnosis methods based on convolutional neural networks and deep residual networks without improvement. In summary, IDRSN has significant value for deep learning engineering applications involving the fault diagnosis of rotor-bearing systems.","PeriodicalId":49681,"journal":{"name":"Polish Maritime Research","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140404454","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Sang-Do Lee, H. Kim, S. You, Jeong-Hum Yeon, B. Phuc
This paper deals with two topics: roll predictions of marine vessels with machine-learning methods and parameter estimation of unknown ocean disturbances when the amplitude, frequency, offset, and phase are difficult to estimate. This paper aims to prevent the risky roll motions of marine vessels exposed to harsh circumstances. First of all, this study demonstrates complex dynamic phenomena by utilising a bifurcation diagram, Lyapunov exponents, and a Poincare section. Without any observers, an adaptive identification applies these four parameters to the globally exponential convergence using linear second-order filters and parameter estimation errors. Then, a backstepping controller is employed to make an exponential convergence of the state variables to zero. Finally, this work presents the prediction of roll motion using reservoir computing (RC). As a result, the RC process shows good performance for chaotic time series prediction in future states. Thus, the poor predictability of Lyapunov exponents may be overcome to a certain extent, with the help of machine learning. Numerical simulations validate the dynamic behaviour and the efficacy of the proposed scheme.
{"title":"Roll Prediction and Parameter Identification of Marine Vessels Under Unknown Ocean Disturbances","authors":"Sang-Do Lee, H. Kim, S. You, Jeong-Hum Yeon, B. Phuc","doi":"10.2478/pomr-2024-0001","DOIUrl":"https://doi.org/10.2478/pomr-2024-0001","url":null,"abstract":"\u0000 This paper deals with two topics: roll predictions of marine vessels with machine-learning methods and parameter estimation of unknown ocean disturbances when the amplitude, frequency, offset, and phase are difficult to estimate. This paper aims to prevent the risky roll motions of marine vessels exposed to harsh circumstances. First of all, this study demonstrates complex dynamic phenomena by utilising a bifurcation diagram, Lyapunov exponents, and a Poincare section. Without any observers, an adaptive identification applies these four parameters to the globally exponential convergence using linear second-order filters and parameter estimation errors. Then, a backstepping controller is employed to make an exponential convergence of the state variables to zero. Finally, this work presents the prediction of roll motion using reservoir computing (RC). As a result, the RC process shows good performance for chaotic time series prediction in future states. Thus, the poor predictability of Lyapunov exponents may be overcome to a certain extent, with the help of machine learning. Numerical simulations validate the dynamic behaviour and the efficacy of the proposed scheme.","PeriodicalId":49681,"journal":{"name":"Polish Maritime Research","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140404467","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
J. Mindykowski, Łukasz Wierzbicki, Mariusz Górniak, Andrzej Piłat
In this study, the authors present a theoretical analysis and experimentally verified methods to improve the Energy Efficiency Design Index (EEDI) of ships. The improvements were studied via the application of an innovative solution of a thruster supplied by a hybrid power system on board a passenger-car ferry. The authors performed sea trials of a ship’s electrical power system supplied by battery packs with diesel generating set power units. The experimental study focused on energy balance and management, which were considered together with related power quality issues. The authors found that the application of an energy storage system to the ferry, such as batteries, with the simultaneous adaption of the operation modes of the electrical power system for current exploitation, significantly improved energy efficiency. Fuel consumption and CO2 emission were reduced, while adequate parameters of electrical power quality were maintained to meet classification standards.
{"title":"Analysis and Experimental Verification of Improving the EEDI of a Ship using a Thruster Supplied by a Hybrid Power System","authors":"J. Mindykowski, Łukasz Wierzbicki, Mariusz Górniak, Andrzej Piłat","doi":"10.2478/pomr-2024-0005","DOIUrl":"https://doi.org/10.2478/pomr-2024-0005","url":null,"abstract":"\u0000 In this study, the authors present a theoretical analysis and experimentally verified methods to improve the Energy Efficiency Design Index (EEDI) of ships. The improvements were studied via the application of an innovative solution of a thruster supplied by a hybrid power system on board a passenger-car ferry. The authors performed sea trials of a ship’s electrical power system supplied by battery packs with diesel generating set power units. The experimental study focused on energy balance and management, which were considered together with related power quality issues. The authors found that the application of an energy storage system to the ferry, such as batteries, with the simultaneous adaption of the operation modes of the electrical power system for current exploitation, significantly improved energy efficiency. Fuel consumption and CO2 emission were reduced, while adequate parameters of electrical power quality were maintained to meet classification standards.","PeriodicalId":49681,"journal":{"name":"Polish Maritime Research","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140406736","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Cunfeng Wei, Guohe Jiang, Gang Wu, Yu Zhou, Yuanyuan Liu
The effects of heavy fuel oil and biodiesel blends on engine combustion and emissions were studied in a marine two-stroke diesel engine. The engine was operated under propeller conditions using five different fuels with biodiesel blends of 10% (B10), 30% (B30), 50% (B50), and sulphur contents of 0.467% low sulphur fuel oil (LSFO) and 2.9% high sulphur fuel oil (HSFO). Tests have shown that using a biodiesel blend increases the engine fuel consumption due to its lower calorific value. Heavy fuel oil has a high Polycyclic aromatic hydrocarbons (PAH) content, which leads to higher exhaust temperatures due to severe afterburning in the engine. A comparison of engine soot emissions under different fuel conditions was carried out, and it was found that the oxygen content in biodiesel promoted the oxidation of soot particles during the combustion process, which reduced the soot emissions of biodiesel. Compared to HSFO, B10, B30, B50 and LSFO, the soot emission concentrations were reduced by 50.2%, 56.4%, 61% and 37.4%, respectively. In our experiments, the soot particles in the engine exhaust were sampled with a thermal float probe. Using Raman spectroscopy analysis, it was found that as the biodiesel ratio increased, the degree of carbonisation of the soot particles in the exhaust became less than that in the oxygenation process, resulting in a decrease in the degree of graphitisation.
{"title":"Effects on of Blended Biodiesel and Heavy Oil on Engine Combustion and Black Carbon Emissions of a Low-Speed Two-Stroke Engine","authors":"Cunfeng Wei, Guohe Jiang, Gang Wu, Yu Zhou, Yuanyuan Liu","doi":"10.2478/pomr-2024-0010","DOIUrl":"https://doi.org/10.2478/pomr-2024-0010","url":null,"abstract":"\u0000 The effects of heavy fuel oil and biodiesel blends on engine combustion and emissions were studied in a marine two-stroke diesel engine. The engine was operated under propeller conditions using five different fuels with biodiesel blends of 10% (B10), 30% (B30), 50% (B50), and sulphur contents of 0.467% low sulphur fuel oil (LSFO) and 2.9% high sulphur fuel oil (HSFO). Tests have shown that using a biodiesel blend increases the engine fuel consumption due to its lower calorific value. Heavy fuel oil has a high Polycyclic aromatic hydrocarbons (PAH) content, which leads to higher exhaust temperatures due to severe afterburning in the engine. A comparison of engine soot emissions under different fuel conditions was carried out, and it was found that the oxygen content in biodiesel promoted the oxidation of soot particles during the combustion process, which reduced the soot emissions of biodiesel. Compared to HSFO, B10, B30, B50 and LSFO, the soot emission concentrations were reduced by 50.2%, 56.4%, 61% and 37.4%, respectively. In our experiments, the soot particles in the engine exhaust were sampled with a thermal float probe. Using Raman spectroscopy analysis, it was found that as the biodiesel ratio increased, the degree of carbonisation of the soot particles in the exhaust became less than that in the oxygenation process, resulting in a decrease in the degree of graphitisation.","PeriodicalId":49681,"journal":{"name":"Polish Maritime Research","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140402180","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
J. C. Priya, K. Rudzki, Xuan Huong Nguyen, Hoang Phuong Nguyen, Naruphun Chotechuang, Nguyen Dang Khoa Pham
With the increasing demand for efficient maritime logistic management, industries are striving to develop automation software. However, collecting data for analytics from diverse sources like shipping routes, weather conditions, historical incidents, and cargo specifications has become a challenging task in the distribution environment. This challenge gives rise to the possibility of faulty products and traditional testing techniques fall short of achieving optimal performance. To address this issue, we propose a novel decentralised software system based on Transfer Learning and blockchain technology named as BETL (Blockchain -Enabled Transfer Learning). Our proposed system aims to automatically detect and prevent vulnerabilities in maritime operational data by harnessing the power of transfer learning and smart contract-driven blockchain. The vulnerability detection process is automated and does not rely on manually written rules. We introduce a non-vulnerability score range map for the effective classification of operational factors. Additionally, to ensure efficient storage over the blockchain, we integrate an InterPlanetary File System (IPFS). To demonstrate the effectiveness of transfer learning and blockchain integration for secure logistic management, we conduct a testbed-based experiment. The results show that this approach can achieve high precision (98.00%), detection rate (98.98%), accuracy (97.90%), and F-score (98.98), which highlights its benefits in enhancing the safety and reliability of maritime logistics processes. Additionally, the computational time of BETL (the proposed approach) was improved by 18.9% compared to standard transfer learning.
随着对高效海运物流管理的需求日益增长，各行业都在努力开发自动化软件。然而，在配送环境中，从航运路线、天气条件、历史事件和货物规格等不同来源收集数据进行分析已成为一项具有挑战性的任务。这一挑战可能导致产品出现故障，而传统的测试技术又无法实现最佳性能。为解决这一问题，我们提出了一种基于迁移学习和区块链技术的新型去中心化软件系统，命名为 BETL（区块链迁移学习）。我们提出的系统旨在通过利用迁移学习和智能合约驱动的区块链的力量，自动检测和预防海事业务数据中的漏洞。漏洞检测过程是自动化的，不依赖于人工编写的规则。我们引入了非漏洞得分范围图，以便对操作因素进行有效分类。此外，为了确保区块链上的高效存储，我们还集成了一个跨行星文件系统（IPFS）。为了证明迁移学习和区块链集成在安全物流管理中的有效性，我们进行了基于试验平台的实验。结果表明，该方法可实现较高的精确度（98.00%）、检测率（98.98%）、准确度（97.90%）和 F 分数（98.98），这凸显了其在提高海上物流流程安全性和可靠性方面的优势。此外，与标准迁移学习相比，BETL（拟议方法）的计算时间缩短了 18.9%。
{"title":"Blockchain-Enabled Transfer Learning for Vulnerability Detection and Mitigation in Maritime Logistics","authors":"J. C. Priya, K. Rudzki, Xuan Huong Nguyen, Hoang Phuong Nguyen, Naruphun Chotechuang, Nguyen Dang Khoa Pham","doi":"10.2478/pomr-2024-0014","DOIUrl":"https://doi.org/10.2478/pomr-2024-0014","url":null,"abstract":"\u0000 With the increasing demand for efficient maritime logistic management, industries are striving to develop automation software. However, collecting data for analytics from diverse sources like shipping routes, weather conditions, historical incidents, and cargo specifications has become a challenging task in the distribution environment. This challenge gives rise to the possibility of faulty products and traditional testing techniques fall short of achieving optimal performance. To address this issue, we propose a novel decentralised software system based on Transfer Learning and blockchain technology named as BETL (Blockchain -Enabled Transfer Learning). Our proposed system aims to automatically detect and prevent vulnerabilities in maritime operational data by harnessing the power of transfer learning and smart contract-driven blockchain. The vulnerability detection process is automated and does not rely on manually written rules. We introduce a non-vulnerability score range map for the effective classification of operational factors. Additionally, to ensure efficient storage over the blockchain, we integrate an InterPlanetary File System (IPFS). To demonstrate the effectiveness of transfer learning and blockchain integration for secure logistic management, we conduct a testbed-based experiment. The results show that this approach can achieve high precision (98.00%), detection rate (98.98%), accuracy (97.90%), and F-score (98.98), which highlights its benefits in enhancing the safety and reliability of maritime logistics processes. Additionally, the computational time of BETL (the proposed approach) was improved by 18.9% compared to standard transfer learning.","PeriodicalId":49681,"journal":{"name":"Polish Maritime Research","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140402250","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The prediction of fuel consumption and resulting transportation costs is a crucial stage in ship design, particularly for conditions involving motion in waves. This study investigates the real-time fuel consumption of a container ship when sailing in waves. The overall ship performance is evaluated using a novel non-linear coupled hull-engine-propeller interaction model. A series of towing tank experiments for hull resistance in waves and propeller performance are conducted. The ship engine is mathematically modelled by a quasi-steady-state model equipped with a linear Proportional-Integrator (PI) governor. Various scenarios of shipping transportation are studied, and the resulting instantaneous fuel consumptions and their correlation to other dynamic particulars are demonstrated. Additionally, daily fuel consumption and fuel cost per voyage distance are presented. It is also shown that the controller can effectively adjust the fuel rate, resulting in minimum fuel consumption. The study concludes that there is no correlation between fuel consumption and the frequency of fuel rates. The present framework and mathematical model can also be employed for ship design and existing ships to predict the total required energy per voyage.
{"title":"Investigating Fuel Injection Strategies to Enhance Ship Energy Efficiency in Wave Conditions","authors":"Hossein Ghaemi, H. Zeraatgar, Mojtaba Barjasteh","doi":"10.2478/pomr-2024-0009","DOIUrl":"https://doi.org/10.2478/pomr-2024-0009","url":null,"abstract":"\u0000 The prediction of fuel consumption and resulting transportation costs is a crucial stage in ship design, particularly for conditions involving motion in waves. This study investigates the real-time fuel consumption of a container ship when sailing in waves. The overall ship performance is evaluated using a novel non-linear coupled hull-engine-propeller interaction model. A series of towing tank experiments for hull resistance in waves and propeller performance are conducted. The ship engine is mathematically modelled by a quasi-steady-state model equipped with a linear Proportional-Integrator (PI) governor. Various scenarios of shipping transportation are studied, and the resulting instantaneous fuel consumptions and their correlation to other dynamic particulars are demonstrated. Additionally, daily fuel consumption and fuel cost per voyage distance are presented. It is also shown that the controller can effectively adjust the fuel rate, resulting in minimum fuel consumption. The study concludes that there is no correlation between fuel consumption and the frequency of fuel rates. The present framework and mathematical model can also be employed for ship design and existing ships to predict the total required energy per voyage.","PeriodicalId":49681,"journal":{"name":"Polish Maritime Research","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140399343","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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