An Uncertainty-aware Hybrid Approach for Sea State Estimation Using Ship Motion Responses

Situation awareness is essential for autonomous ships. One key aspect is to estimate the sea state in a real-time manner. Considering the ship as a large wave buoy, the sea state can be estimated from motion responses without extra sensors installed. This task is challenging since the relationship between the wave and the ship motion is hard to model. Existing methods include a wave buoyanalogy (WBA) method, which assumes linearity between wave and ship motion, and a machine learning (ML) approach. Since the data collected from a vessel in the real world is typically limited to a small range of sea states, the ML method might suffer from catastrophic failure when the encountered sea state is not in the training dataset. This paper proposes a hybrid approach that combined the two methods above. The ML method is compensated by the WBA method based on the uncertainty of estimation results and, thus, the catastrophic failure can be avoided. Real-world historical data from the Research Vessel (RV) Gunnerus are applied to validate the approach. Results show that the hybrid approach improves estimation accuracy.acceptedVersio

Data-driven sea state estimation for vessels using multi-domain features from motion responses

Situation awareness is of great importance for autonomous ships. One key aspect is to estimate the sea state in a real-time manner. Considering the ship as a large wave buoy, the sea state can be estimated from motion responses without extra sensors installed. However, it is difficult to associate waves with ship motion through an explicit model since the hydrodynamic effect is hard to model. In this paper, a data-driven model is developed to estimate the sea state based on ship motion data. The ship motion response is analyzed through statistical, temporal, spectral, and wavelet analysis. Features from multi-domain are constructed and an ensemble machine learning model is established. Real-world data is collected from a research vessel operating on the west coast of Norway. Through the validation with the real-world data, the model shows promising performance in terms of significant wave height and peak period.acceptedVersio

Insertion of a TRIM-like sequence in MdFLS2-1 promoter is associated with its allele-specific expression in response to Alternaria alternata in apple

Alternaria blotch disease, caused by Alternaria alternata apple pathotype (AAAP), is one of the major fungal diseases in apple. Early field observations revealed, the anther-derived homozygote Hanfu line (HFTH1) was highly susceptible to AAAP, whereas Hanfu (HF) exhibited resistance to AAAP. To understand the molecular mechanisms underlying the difference in sensitivity of HF and HFTH1 to AAAP, we performed allele-specific expression (ASE) analysis and comparative transcriptomic analysis before and after AAAP inoculation. We reported an important immune gene, namely, MdFLS2, which displayed strong ASE in HF with much lower expression levels of HFTH1-derived alleles. Transient overexpression of the dominant allele of MdFLS2-1 from HF in GL-3 apple leaves could enhance resistance to AAAP and induce expression of genes related to salicylic acid pathway. In addition, MdFLS2-1 was identified with an insertion of an 85-bp terminal-repeat retrotransposon in miniature (TRIM) element-like sequence in the upstream region of the nonreference allele. In contrast, only one terminal direct repeat (TDR) from TRIM-like sequence was present in the upstream region of the HFTH1-derived allele MdFLS2-2. Furthermore, the results of luciferase and β-glucuronidase reporter assays demonstrated that the intact TRIM-like sequence has enhancer activity. This suggested that insertion of the TRIM-like sequence regulates the expression level of the allele of MdFLS2, in turn, affecting the sensitivity of HF and HFTH1 to AAAP

Excess Deaths of Gastrointestinal, Liver, and Pancreatic Diseases During the COVID-19 Pandemic in the United States

Objectives: To evaluate excess deaths of gastrointestinal, liver, and pancreatic diseases in the United States during the COVID-19 pandemic.Methods: We retrieved weekly death counts from National Vital Statistics System and fitted them with a quasi-Poisson regression model. Cause-specific excess deaths were calculated by the difference between observed and expected deaths with adjustment for temporal trend and seasonality. Demographic disparities and temporal-spatial patterns were evaluated for different diseases.Results: From March 2020 to September 2022, the increased mortality (measured by excess risks) for Clostridium difficile colitis, gastrointestinal hemorrhage, and acute pancreatitis were 35.9%; 24.8%; and 20.6% higher than the expected. For alcoholic liver disease, fibrosis/cirrhosis, and hepatic failure, the excess risks were 1.4–2.8 times higher among younger inhabitants than older inhabitants. The excess deaths of selected diseases were persistently observed across multiple epidemic waves with fluctuating trends for gastrointestinal hemorrhage and fibrosis/cirrhosis and an increasing trend for C. difficile colitis.Conclusion: The persistently observed excess deaths of digestive diseases highlights the importance for healthcare authorities to develop sustainable strategies in response to the long-term circulating of SARS-CoV-2 in the community

Data-driven Methods for Decision Support in Smart Ship Operations

Vessels operating on the surface of the ocean today are now increasingly equipped with sensors. This includes GPS, MRU, IMU that monitor the vessel’s motion behavior, and power, RPM, temperature sensors that monitor the status of components such as engines and thrusters, and anemometers that provide information about the surrounding environment. These sensor measurements are obtained in real-time and historical data is saved in cloud storage. The increased digital capabilities motivate the industry to increase the automation of the vessel by developing decision support systems, digital twin, or autonomous ships, which might potentially lead to safer and more efficient ship operations. How to use the massive data on ships to gain better insight into ship operations has always been a key issue, and the data-driven approach is a promising solution. Datadriven methods, or machine learning methods, have been used broadly across a range of industries concerned with data-intensive issues. As the ship is gradually turned into a colossal sensor hub, the massive volume of data can be used with supervised learning to generate models to support efficient ship operations, or unsupervised learning to provide key insights about ships. To provide information to the human ship operator or autonomous ship operating system, two aspects can be identified: (1) a better understanding of the current status, such as component’s status, environmental conditions, or operating conditions. (2) a better forecast on what will happen if a specific action is taken, which can be referred to as what-if analysis. In such a context, many elements can be involved (localization, trajectory prediction, etc.). In this dissertation, two important applications are highlighted: fault diagnostics and prognostics of components, and the estimation of the sea state. Fault diagnostics and prognostics aim to detect and isolate faults on components or systems, and then predict how the fault will progress and how long it will be until complete failure. Through these actions, recommendations for maintenance can be provided. In other words, an ideal maintenance schedule can be devised and failure can be eliminated. In this way, the vessel can operate safely and efficiently. The sea state information is of key importance for ship operations, such as motion control, pipeline laying, and path planning. Wave radar may be an ideal solution for obtaining information about surrounding waves, but most ships today do not equip with one. However, it is also possible to estimate the sea state from the vessel motion responses (especially motions that are not affected by the controller: roll, pitch, heave, etc.). Thus another focus of this dissertation is to develop sea state estimation models that use ship motion responses as inputs. Since both fault diagnostics and prognostics and sea state estimation can be achieved with machine learning, the main objective is to develop data-driven models for these two applications. Three case studies are conducted to validate the dei veloped data-driven models for these two applications, where the first two concerns fault diagnostics and prognostics (use thruster and engine as an example, respectively), the last one concerns sea state estimation. Experiments are carried out with data collected in simulations, in the laboratory, and on the vessel RV Gunnerus operating in the real world. The results demonstrate the advantages of developing data-driven models to support ship operations. Additionally, data-driven models can outperform traditional models in certain scenarios

Directional wave spectrum estimation with ship motion responses using adversarial networks

The external environmental conditions around a vessel are essential for efficient and safe ship operation, among which the sea state is of key importance. Considering the ship as a large wave buoy, the sea state can be estimated from motion responses without extra sensors installed. This is a challenging task since the relationships between the waves and the ship motions are hard to describe accurately. Machine learning approaches can learn these mapping without an explicit model, which is promising for sea state estimation. Current machine learning approaches represent the sea state as a set of categories or a number of wave parameters while neglecting the 2D wave spectrum. This paper proposes a sea state estimation network that estimates the 2D wave spectrum along with a discrimination network. The discrimination network can detect and correct high-order inconsistencies of the spectrum. Simulation studies are performed to show that the proposed method can provide wave spectrum estimation with high accuracy.publishedVersio

Directional wave spectrum estimation with ship motion responses using adversarial networks

The external environmental conditions around a vessel are essential for efficient and safe ship operation, among which the sea state is of key importance. Considering the ship as a large wave buoy, the sea state can be estimated from motion responses without extra sensors installed. This is a challenging task since the relationships between the waves and the ship motions are hard to describe accurately. Machine learning approaches can learn these mapping without an explicit model, which is promising for sea state estimation. Current machine learning approaches represent the sea state as a set of categories or a number of wave parameters while neglecting the 2D wave spectrum. This paper proposes a sea state estimation network that estimates the 2D wave spectrum along with a discrimination network. The discrimination network can detect and correct high-order inconsistencies of the spectrum. Simulation studies are performed to show that the proposed method can provide wave spectrum estimation with high accuracy

Directional wave spectrum estimation with ship motion responses using adversarial networks

The external environmental conditions around a vessel are essential for efficient and safe ship operation, among which the sea state is of key importance. Considering the ship as a large wave buoy, the sea state can be estimated from motion responses without extra sensors installed. This is a challenging task since the relationships between the waves and the ship motions are hard to describe accurately. Machine learning approaches can learn these mapping without an explicit model, which is promising for sea state estimation. Current machine learning approaches represent the sea state as a set of categories or a number of wave parameters while neglecting the 2D wave spectrum. This paper proposes a sea state estimation network that estimates the 2D wave spectrum along with a discrimination network. The discrimination network can detect and correct high-order inconsistencies of the spectrum. Simulation studies are performed to show that the proposed method can provide wave spectrum estimation with high accuracy

Data-driven sea state estimation for vessels using multi-domain features from motion responses

Situation awareness is of great importance for autonomous ships. One key aspect is to estimate the sea state in a real-time manner. Considering the ship as a large wave buoy, the sea state can be estimated from motion responses without extra sensors installed. However, it is difficult to associate waves with ship motion through an explicit model since the hydrodynamic effect is hard to model. In this paper, a data-driven model is developed to estimate the sea state based on ship motion data. The ship motion response is analyzed through statistical, temporal, spectral, and wavelet analysis. Features from multi-domain are constructed and an ensemble machine learning model is established. Real-world data is collected from a research vessel operating on the west coast of Norway. Through the validation with the real-world data, the model shows promising performance in terms of significant wave height and peak period

A Deep Learning Approach to Detect and Isolate Thruster Failures for Dynamically Positioned Vessels Using Motion Data

Vessels today are being fully monitored, thanks to the advance of sensor technology. The availability of data brings ship intelligence into great attention. As part of ship intelligence, the desire of using advanced data-driven methods to optimize operation also increases. Considering ship motion data reflects the dynamic positioning performance of the vessels and thruster failure might cause drift-offs, it is possible to detect and isolate potential thruster failure using motion data. In this article, thruster failure detection and isolation are considered as a time-series classification problem. A convolutional neural network (CNN) is introduced to learn the mapping from the logged motion sequence to the status of the thruster. CNN is expected to generate task-specific features from the original time series sensors data and then perform the classification. The data set is collected from a professional simulator in the Offshore Simulation Center AS. Experiments show that the proposed method can detect and isolate failed thrusters with up to 95% accuracy. The proposed model is further extended to deal with thruster failure in a real-time manner