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ABSTRACT. Pollution of aquatic environments is one of the biggest problems faced by mankind throughout history. The quality of ocean, sea and other stagnant waters that cover 70% of the world affects not only human life but also other living creatures in nature. Since these environments are large and difficult to access; observing, maintaining, and cleaning these environments are challenges that must be overcome in a reliable and safe manner. In this study, a deep learning algorithm has been trainned and implemented for real-time application in an unmanned surface vessel. This was designed to detect and track objects on the surface of the water, thus subsequently enabling the maintenance and cleaning operations. The device hardware developed and integrated with the real-time deep learning intelligence has been tested in both controlled and field environment. The real-time deep learning model has been retrained and validated using the public marine litter data set. As a result of the training, the model is able to detect objects on the water surface with a mean average precision of 85%, 94% recall, and precision of 78%. Moreover, processing time is less than 100 milliseconds for per frame. The implementation of the real-time YOLOv5 deep learning model will facilitate the operation of tracking objects on the sea surface and thus will reduce maintenance costs, shorten the time requirement for operation, and increase the efficiency of the detection process.
ABSTRACT. With increased ship traffic in the Arctic, the study of shipping risks in ice-infested waters has gained increased attention. Bayesian Networks (BN) appear to be a particularly popular tool among risk management frameworks. The Conditional Probability Table (CPT), which is used to quantify relationships between variables, is a critical component of the development of BNs. With an emphasis on Arctic navigation, the CPT is determined primarily through expert elicitation and is influenced by ice navigation experience. In this study, we elaborate on the designed questionnaire and Røed method for the determination of CPT and analyze the input data provided by five experts with varying backgrounds. The analysis was conducted using decision bias in variable weight assignment and outcome distribution index R of the sub-model. The preliminary findings indicate that a lack of experience with ice navigation may contribute to higher decision bias in factors such as ice conditions, ice channel, hydrometeorology, and ship maneuverability status. The smaller weight of the variable could result in a negligible change in the probability distribution of CPT. The results of this study demonstrate the importance of considering ice navigation experience when conducting expert elicitation for Arctic navigation, as well as the limitations of the Røed method for assessing the CPT for a BN model.
ABSTRACT. Specific features of climatic actions should be considered in structural design and reliability assessments of existing structures. Underlying physical processes, load durations, seasonality effects and mutual correlations between climatic actions make them largely different from other variable actions (such as imposed or traffic loads) and from other natural hazards (e.g. earthquakes). Climatic actions are all related to the physics of the atmosphere. Understanding the interdependencies between climate variables can be very important since a misrepresentation of the joint physical process may lead to a possible underestimation of hazards and structural risks. For example, understanding the interdependencies between wind, temperature and precipitation can improve the prediction of extreme events such as floods and droughts but also the assessment of climate change impacts on built infrastructures. Analyses of interactions of climatic actions revealed a weak positive correlation for atmospheric icing load and wind velocity at the investigated locations in Norway and Czech Republic. It appears that both climatic variables can be considered asymptotically independent. A negative correlation is observed for temperature and icing load as expected, an increase of icing load is obtained as temperatures decrease.Extremes of climatic actions, especially joint extremes, may lead to serious damages to the society, economy and environment. The combination of extreme environmental processes can be also particularly critical for structural design of lightweight structures. The technical subcommittee CEN/TC 250/SC1 responsible for actions on structures in Eurocodes recommended periodical revisions of the models for climatic actions at intervals of 15-20 years. Although the data from climate models provide information about future trends for climatic parameters, there is considerable dispersion in the data depending on relevant parameters and the emission scenario under consideration. The quantification of future extremes with low uncertainty required for reasonable estimates of design values (fractiles associated with long return periods) is unavailable at present. Regular re-examination of weather parameters considering uncertainties in extremes of climate actions should be used for verification and updating of partial factors and combination factors for climate actions.
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