Design and Control of the "TransBoat": A Transformable Unmanned Surface Vehicle for Overwater Construction

This paper presents the TransBoat, a novel omnidirectional unmanned surface vehicle (USV) with a magnetbased docking system for overwater construction with wave disturbances. This is the first such USV that can build overwater structures by transporting modules. The TransBoat incorporates two features designed to reject wave disturbances. First, the TransBoat's expandable body structure can actively transform from a mono-hull into a multi-hull for stabilization in turbulent environments by extending its four outrigger hulls. Second, a real-time nonlinear model predictive control (NMPC) scheme is proposed for all shapes of the TransBoat to enhance its maneuverability and resist disturbance to its movement, based on a nonlinear dynamic model. An experimental approach is proposed to identify the parameters of the dynamic model, and a subsequent trajectory tracking test validates the dynamics, NMPC controller and system mobility. Further, docking experiments identify improved performance in the expanded form of the TransBoat compared with the contracted form, including an increased success rate (of ~ 10%) and reduced docking time (of ~ 40 s on average). Finally, a bridge construction test verifies our system design and the NMPC control method

Monthly extended ocean predictions based on a convolutional neural network via the transfer learning method

Sea surface temperature anomalies (SSTAs) and sea surface height anomalies (SSHAs) are indispensable parts of scientific research, such as mesoscale eddy, current, ocean-atmosphere interaction and so on. Nowadays, extended-range predictions of ocean dynamics, especially in SSTA and SSHA, can provide daily prediction services in the range of 30 days, which bridges the gap between synoptic-scale weather forecasts and monthly average scale climate predictions. However, the forecast efficiency of extended range remains problematic. With the development of ocean reanalysis and satellite remote sensing products, large amounts datasets provide an unprecedented opportunity to use big data for the extended range prediction of ocean dynamics. In this study, a hybrid model, combing convolutional neural network (CNN) model with transfer learning (TL), was established to predict SSTA and SSHA at monthly scales, which makes full use of these data resources that arise from delayed gridding reanalysis products and real-time satellite remote sensing observations. The proposed model, where both ocean and atmosphere reanalysis datasets serve as the pretraining dataset and the satellite remote sensing observations are employed for fine-tuning based on the transfer learning (TL) method, can effectively capture the evolving spatial characteristics of SSTAs and SSHAs with low prediction errors over the 30 days range. When the forecast lead time is 30 days, the root means square errors for the SSTAs and SSHAs model results are 0.32°C and 0.027 m in the South China Sea, respectively, indicating that this model has not only satisfactory prediction performance but also offers great potential for practical operational applications in improving the skill of extended-range predictions

Evaluation of the Impact of Argo Data on Ocean Reanalysis in the Pacific Region

Observing System Simulation Experiments (OSSEs) have been conducted to evaluate the effect of Argo data assimilation on ocean reanalysis in the Pacific region. The “truth” is obtained from a 5-year model integration from 2003 to 2007 based on the MIT general circulation model with the truly varying atmospheric forcing. The “observations” are the projections of the truth onto the observational network including ocean station data, CTD, and various BTs and Argo, by adding white noise to simulate observational errors. The data assimilation method employed is a sequential three-dimensional variational (3D-Var) scheme within a multigrid framework. Results show the interannual variability of temperature, salinity, and current fields can be reconstructed fairly well. The spread of temperature anomalies in the tropical Pacific region is also able to be reflected accurately when Argo data is assimilated, which may provide a reliable initial field for the forecast of temperature and currents for the subsurface in the tropical Pacific region. The adjustment of salinity by using T-S relationship is vital in the tropical Pacific region. However, the adjustment of salinity is almost meaningless in the northwest Pacific if Argo data is included during the reanalysis

Bus Fleet Accident Prediction Based on Violation Data: Considering the Binding Nature of Safety Violations and Service Violations

The number and severity of bus traffic accidents are increasing annually. Therefore, this paper uses the historical data of Chongqing Liangjiang Public Transportation Co., Ltd. bus driver safety violations, service violations, and road traffic accidents from January to June 2022 and constructs road traffic accident prediction models using Extra Trees, BP Neural Network, Support Vector Machine, Gradient Boosting Tree, and XGBoost. The effects of safety and service violations on vehicular accidents are investigated. The quality of the prediction models is measured by five indicators: goodness of fit, mean square error, root mean square error, mean absolute error, and mean absolute percentage error. The results indicate that the XGBoost model provides the most accurate predictions. Additionally, simultaneously considering safety and service violations can improve the accuracy of the model’s predictions compared to a model that only considers safety violations. Bus safety violations, bus service violations, and bus safety operation violations significantly influence traffic accidents, which account for 27.9%, 20%, and 16.5%, respectively. In addition to safety violations, the service violation systems established by bus companies, such as bus service codes, can be an effective method of regulating the behavior of bus drivers and reducing accidents. They are improving both the safety and quality of public transportation

Diffusion Filters for Variational Data Assimilation of Sea Surface Temperature in an Intermediate Climate Model

Sequential, adaptive, and gradient diffusion filters are implemented into spatial multiscale three-dimensional variational data assimilation (3DVAR) as alternative schemes to model background error covariance matrix for the commonly used correction scale method, recursive filter method, and sequential 3DVAR. The gradient diffusion filter (GDF) is verified by a two-dimensional sea surface temperature (SST) assimilation experiment. Compared to the existing DF, the new GDF scheme shows a superior performance in the assimilation experiment due to its success in extracting the spatial multiscale information. The GDF can retrieve successfully the longwave information over the whole analysis domain and the shortwave information over data-dense regions. After that, a perfect twin data assimilation experiment framework is designed to study the effect of the GDF on the state estimation based on an intermediate coupled model. In this framework, the assimilation model is subject to “biased” initial fields from the “truth” model. While the GDF reduces the model bias in general, it can enhance the accuracy of the state estimation in the region that the observations are removed, especially in the South Ocean. In addition, the higher forecast skill can be obtained through the better initial state fields produced by the GDF

Upper Ocean Thermal Responses to Sea Spray Mediated Turbulent Fluxes during Typhoon Passage

A one-dimensional turbulent model is used to investigate the effect of sea spray mediated turbulent fluxes on upper ocean temperature during the passage of typhoon Yagi over the Kuroshio Extension area in 2006. Both a macroscopical sea spray momentum flux algorithm and a microphysical heat and moisture flux algorithm are included in this turbulent model. Numerical results show that the model can well reproduce the upper ocean temperature, which is consistent with the data from the Kuroshio Extension Observatory. Besides, the sea surface temperature is decreased by about 0.5°C during the typhoon passage, which also agrees with the sea surface temperature dataset derived from Advanced Microwave Scanning Radiometer for the Earth Observing and Reynolds. Diagnostic analysis indicates that sea spray acts as an additional source of the air-sea turbulent fluxes and plays a key role in increasing the turbulent kinetic energy in the upper ocean, which enhances the temperature diffusion there. Therefore, sea spray is also an important factor in determining the upper mixed layer depth during the typhoon passage

Study of the Performance of Deep Learning Methods Used to Predict Tidal Current Movement

To predict tidal current movement accurately is essential in the process of tidal energy development. However, the existing methods have limits to meet the need for accuracy. Recently, artificial intelligence technology has been widely applied to solve this problem. In this paper, a tidal current prediction model combining numerical simulation with deep learning methods is proposed. It adopts three deep learning algorithms for comparative investigations: multilayer perceptron (MLP), long-short term memory (LSTM) and attention-ResNet neural network (AR-ANN). The numerical simulation was carried out using ROMS, and the observation collected in the Zhoushan region were used to validate the results. Compared with the numerical simulations, deep learning methods can increase the original correlation coefficient from 0.4 to over 0.8. In comparison, the AR-ANN model shows excellent performance in both the meridional and zonal components. This advantage of deep learning algorithms is extended in the tidal energy resource assessment process, with MLP, LSTM and AR-ANN models reducing the root mean square error by 32.9%, 34.4% and 42%, respectively. The new method can be used to accurately predict the hydrodynamic of tidal flow in the process of tidal energy extraction, which contributes to determine the suitable location for energy generation and tidal turbine design

Significant Modules and Biological Processes between Active Components of Salvia miltiorrhiza Depside Salt and Aspirin

The aim of this study is to examine and compare the similarities and differences between active components of S. miltiorrhiza depside salt and aspirin using perspective of pharmacological molecular networks. Active components of S. miltiorrhiza depside salt and aspirin’s related genes were identified via the STITCH4.0 and GeneCards Database. A text search engine (Agilent Literature Search 2.71) and MCODE software were applied to construct network and divide modules, respectively. Finally, 32, 2, and 28 overlapping genes, modules, and pathways were identified between active components of S. miltiorrhiza depside salt and aspirin. A multidimensional framework of drug network showed that two networks reflected commonly in human aortic endothelial cells and atherosclerosis process. Aspirin plays a more important role in metabolism, such as the well-known AA metabolism pathway and other lipid or carbohydrate metabolism pathways. S. miltiorrhiza depside salt still plays a regulatory role in type II diabetes mellitus, insulin resistance, and adipocytokine signaling pathway. Therefore, this study suggests that aspirin combined with S. miltiorrhiza depside salt may be more efficient in treatment of CHD patients, especially those with diabetes mellitus or hyperlipidemia. Further clinical trials to confirm this hypothesis are still needed