Data Augmentation Methods of Parameter Identification of a Dynamic Model for Harbor Maneuvers

A dynamic model for an automatic berthing and unberthing controller has to estimate harbor maneuvers, which include berthing, unberthing, approach maneuvers to berths, and entering and leaving the port. When the dynamic model is estimated by the system identification, a large number of tests or trials are required to measure the various motions of harbor maneuvers. However, the amount of data that can be obtained is limited due to the high costs and time-consuming nature of full-scale ship trials. In this paper, we improve the generalization performance of the dynamic model for the automatic berthing and unberthing controller by introducing data augmentation. This study used slicing and jittering as data augmentation methods and confirmed their effectiveness by numerical experiments using the free-running model tests. The dynamic model is represented by a neural network-based model in numerical experiments. Results of numerical experiments demonstrated that slicing and jittering are effective data augmentation methods but could not improve generalization performance for extrapolation states of the original dataset.Comment: 12 pages, 11 figures, Submitted to Journal of Marine Science and Technolog

A study on the implementation of nonlinear Kalman filter applying MMG model

Many technologies need to be established to realize autonomous ships. In particular, accurate state estimation in real time is one of the most important technologies. In the ship and ocean engineering fields, there have been many studies on state estimation using nonlinear Kalman filters. Several methods have been proposed for nonlinear Kalman filters. However, there is insufficient verification on the selection of which filter should be applied among them. Therefore, this study aims to validate the filter selection to provide a guideline for filter selection. The effects of modeling error, observation noise, and type of maneuvers on the estimation accuracy of the unscented Kalman filter (UKF) and ensemble Kalman filter (EnKF) used in this study were investigated. In addition, it was verified whether filtering could be performed in real time. The results show that modeling error significantly impacts the estimation accuracy of the UKF and EnKF. However, the observation noise and types of maneuvers did not have an impact like the modeling error. Thus, we obtained the guideline that UKF and EnKF should be used differently depending on the required computation time. We also obtained that keeping the modeling error sufficiently small is essential to improving the estimation accuracy.The version of record of this article, first published in Journal of Marine Science and Technology (Japan), is available online at Publisher’s website: https://doi.org/10.1007/s00773-023-00953-

Covariance Matrix Adaptation Evolutionary Strategy with Worst-Case Ranking Approximation for Min--Max Optimization and its Application to Berthing Control Tasks

In this study, we consider a continuous min--max optimization problem $\min_{x \in \mathbb{X} \max_{y \in \mathbb{Y}}}f(x,y)$ whose objective function is a black-box. We propose a novel approach to minimize the worst-case objective function $F(x) = \max_{y} f(x,y)$ directly using a covariance matrix adaptation evolution strategy (CMA-ES) in which the rankings of solution candidates are approximated by our proposed worst-case ranking approximation (WRA) mechanism. We develop two variants of WRA combined with CMA-ES and approximate gradient ascent as numerical solvers for the inner maximization problem. Numerical experiments show that our proposed approach outperforms several existing approaches when the objective function is a smooth strongly convex--concave function and the interaction between $x$ and $y$ is strong. We investigate the advantages of the proposed approach for problems where the objective function is not limited to smooth strongly convex--concave functions. The effectiveness of the proposed approach is demonstrated in the robust berthing control problem with uncertainty.ngly convex--concave functions. The effectiveness of the proposed approach is demonstrated in the robust berthing control problem with uncertainty

Collision probability reduction method for tracking control in automatic docking/berthing using reinforcement learning

Automation of berthing maneuvers in shipping is a pressing issue as the berthing maneuver is one of the most stressful tasks seafarers undertake. Berthing control problems are often tackled by tracking a predefined trajectory or path. Maintaining a tracking error of zero under an uncertain environment is impossible; the tracking controller is nonetheless required to bring vessels close to desired berths. The tracking controller must prioritize the avoidance of tracking errors that may cause collisions with obstacles. This paper proposes a training method based on reinforcement learning for a trajectory tracking controller that reduces the probability of collisions with static obstacles. Via numerical simulations, we show that the proposed method reduces the probability of collisions during berthing maneuvers. Furthermore, this paper shows the tracking performance in a model experiment.The version of record of this article, first published in Journal of Marine Science and Technology (Japan), is available online at Publisher’s website: https://doi.org/10.1007/s00773-023-00962-

Collision probability reduction method for tracking control in automatic docking / berthing using reinforcement learning

Automation of berthing maneuvers in shipping is a pressing issue as the berthing maneuver is one of the most stressful tasks seafarers undertake. Berthing control problems are often tackled via tracking a predefined trajectory or path. Maintaining a tracking error of zero under an uncertain environment is impossible; the tracking controller is nonetheless required to bring vessels close to desired berths. The tracking controller must prioritize the avoidance of tracking errors that may cause collisions with obstacles. This paper proposes a training method based on reinforcement learning for a trajectory tracking controller that reduces the probability of collisions with static obstacles. Via numerical simulations, we show that the proposed method reduces the probability of collisions during berthing maneuvers. Furthermore, this paper shows the tracking performance in a model experiment.Comment: 14 pages, 15 figures, Submitted to Journal of Marine Science and Technolog

On Neural Network Identification for Low-Speed Ship Maneuvering Model

Several studies on ship maneuvering models have been conducted using captive model tests or computational fluid dynamics (CFD) and physical models, such as the maneuvering modeling group (MMG) model. A new system identification method for generating a low-speed maneuvering model using recurrent neural networks (RNNs) and free running model tests is proposed in this study. We especially focus on a low-speed maneuver such as the final phase in berthing to achieve automatic berthing control. Accurate dynamic modeling with minimum modeling error is highly desired to establish a model-based control system. We propose a new loss function that reduces the effect of the noise included in the training data. Besides, we revealed the following facts - an RNN that ignores the memory before a certain time improved the prediction accuracy compared with the "standard" RNN, and the random maneuver test was effective in obtaining an accurate berthing maneuver model. In addition, several low-speed free running model tests were performed for the scale model of the M.V. Esso Osaka. As a result, this paper showed that the proposed method using a neural network model could accurately represent low-speed maneuvering motions.Comment: 13 pages, 7 figures, submitted to Journal of Marine Science and Technology for peer-revie

Ameloblastin regulates osteogenic differentiation

Ameloblastin, the most abundant non-amelogenin enamel matrix protein, plays a role in ameloblast differentiation. Here we found that ameloblastin was expressed in osteosarcoma cells; to explore the potential functions of ameloblastin in osteoblasts, we investigated whether this protein is involved in osteogenic differentiation and bone formation on the premise that CD63, a member of the transmembrane-4 glycoprotein superfamily, interacts with integrins in the presence of ameloblastin. Ameloblastin bound to CD63 and promoted CD63 binding to integrin β1. The interaction between CD63 and integrin β1 induced Src kinase inactivation via the binding of CD63 to Src. The reduction of Src activity and osteogenic differentiation mediated by ameloblastin was abrogated by treatment with anti-CD63 antibody and overexpression of constitutive active Src, respectively. Moreover, amelobastin upregulated the formation of stress-fibre and focal adhesions and downregulated cell migration in association with RhoA regulation via Src activity. Therefore, our results suggest that ameloblastin is expressed in osteoblasts and functions as a promoting factor for osteogenic differentiation via a novel pathway through the interaction between CD63 and integrin β1

Odontogenic stem cells

Epithelial cell rests of Malassez (ERM) are quiescent epithelial remnants of the Hertwig’s epithelial root sheath (HERS) that are involved in the formation of tooth roots. ERM cells are unique epithelial cells that remain in periodontal tissues throughout adult life. They have a functional role in the repair/regeneration of cement or enamel. Here, we isolated odontogenic epithelial cells from ERM in the periodontal ligament, and the cells were spontaneously immortalized. Immortalized odontogenic epithelial (iOdE) cells had the ability to form spheroids and expressed stem cell-related genes. Interestingly, iOdE cells underwent osteogenic differentiation, as demonstrated by the mineralization activity in vitro in mineralization-inducing media and formation of calcification foci in iOdE cells transplanted into immunocompromised mice. These findings suggest that a cell population with features similar to stem cells exists in ERM and that this cell population has a differentiation capacity for producing calcifications in a particular microenvironment. In summary, iOdE cells will provide a convenient cell source for tissue engineering and experimental models to investigate tooth growth, differentiation, and tumorigenesis

Down-regulation of GATA1-dependent erythrocyte-related genes in the spleens of mice exposed to a space travel

Secondary lymphoid organs are critical for regulating acquired immune responses. The aim of this study was to characterize the impact of spaceflight on secondary lymphoid organs at the molecular level. We analysed the spleens and lymph nodes from mice flown aboard the International Space Station (ISS) in orbit for 35 days, as part of a Japan Aerospace Exploration Agency mission. During flight, half of the mice were exposed to 1 g by centrifuging in the ISS, to provide information regarding the effect of microgravity and 1 g exposure during spaceflight. Whole-transcript cDNA sequencing (RNA-Seq) analysis of the spleen suggested that erythrocyte-related genes regulated by the transcription factor GATA1 were significantly down-regulated in ISS-flown vs. ground control mice. GATA1 and Tal1 (regulators of erythropoiesis) mRNA expression was consistently reduced by approximately half. These reductions were not completely alleviated by 1 g exposure in the ISS, suggesting that the combined effect of space environments aside from microgravity could down-regulate gene expression in the spleen. Additionally, plasma immunoglobulin concentrations were slightly altered in ISS-flown mice. Overall, our data suggest that spaceflight might disturb the homeostatic gene expression of the spleen through a combination of microgravity and other environmental changes

Identification of mTEC precursor cells

Medullary thymic epithelial cells (mTECs) expressing autoimmune regulator (Aire) are critical for preventing the onset of autoimmunity. However, the differentiation program of Aire-expressing mTECs (Aire+ mTECs) is unclear. Here, we describe novel embryonic precursors of Aire+ mTECs. We found the candidate precursors of Aire+ mTECs (pMECs) by monitoring the expression of receptor activator of nuclear factor-κB (RANK), which is required for Aire+ mTEC differentiation. pMECs unexpectedly expressed cortical TEC molecules in addition to the mTEC markers UEA-1 ligand and RANK and differentiated into mTECs in reaggregation thymic organ culture. Introduction of pMECs in the embryonic thymus permitted long-term maintenance of Aire+ mTECs and efficiently suppressed the onset of autoimmunity induced by Aire+ mTEC deficiency. Mechanistically, pMECs differentiated into Aire+ mTECs by tumor necrosis factor receptor-associated factor 6-dependent RANK signaling. Moreover, nonclassical nuclear factor-κB activation triggered by RANK and lymphotoxin-β receptor signaling promoted pMEC induction from progenitors exhibiting lower RANK expression and higher CD24 expression. Thus, our findings identified two novel stages in the differentiation program of Aire+ mTECs