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

Kill Line Model Cross Flow Inline Coupled Vortex-Induced Vibration

Currents and waves cause flow-structure interaction problems in systems installed in the ocean. Particularly for bluff bodies, vortices form in the body wake, which can cause strong structural vibrations (Vortex-Induced Vibrations, VIV). The magnitude and frequency content of VIV is determined by the shape, material properties, and size of the bluff body, and the nature and velocity of the oncoming flow. Riser systems are extensively used in the ocean to drill for oil wells, or produce oil and gas from the bottom of the ocean. Risers of ten consist of a central pipe, surrounded by several smaller cylinders, including the kill and choke lines. We present a series of experiments involving forced in-line and cross flow motions of short rigid sections of a riser containing 6 symmetrically arranged kill and choke lines. The experiments were carried out at the MIT Towing Tank. We present a systematic database of the hydrodynamic coefficients, consisting of the forces in phase with velocity and the added mass coefficients that are also suitable to be used with semi-empirical VIV predicting codes

Incorporation of a hinge domain improves the expansion of chimeric antigen receptor T cells

© 2017 The Author(s). Background: Multiple iterations of chimeric antigen receptors (CARs) have been developed, mainly focusing on intracellular signaling modules. However, the effect of non-signaling extracellular modules on the expansion and therapeutic efficacy of CARs remains largely undefined. Methods: We generated two versions of CAR vectors, with or without a hinge domain, targeting CD19, mesothelin, PSCA, MUC1, and HER2, respectively. Then, we systematically compared the effect of the hinge domains on the growth kinetics, cytokine production, and cytotoxicity of CAR T cells in vitro and in vivo. Results: During in vitro culture period, the percentages and absolute numbers of T cells expressing the CARs containing a hinge domain continuously increased, mainly through the promotion of CD4+ CAR T cell expansion, regardless of the single-chain variable fragment (scFv). In vitro migration assay showed that the hinges enhanced CAR T cells migratory capacity. The T cells expressing anti-CD19 CARs with or without a hinge had similar antitumor capacities in vivo, whereas the T cells expressing anti-mesothelin CARs containing a hinge domain showed enhanced antitumor activities. Conclusions: Hence, our results demonstrate that a hinge contributes to CAR T cell expansion and is capable of increasing the antitumor efficacy of some specific CAR T cells. Our results suggest potential novel strategies in CAR vector design.Link_to_subscribed_fulltex

Quantitative evaluation of the immunodeficiency of a mouse strain by tumor engraftments

© 2015 Ye et al. Background: The mouse is an organism that is widely used as a mammalian model for studying human physiology or disease, and the development of immunodeficient mice has provided a valuable tool for basic and applied human disease research. Following the development of large-scale mouse knockout programs and genome-editing tools, it has become increasingly efficient to generate genetically modified mouse strains with immunodeficiency. However, due to the lack of a standardized system for evaluating the immuno-capacity that prevents tumor progression in mice, an objective choice of the appropriate immunodeficient mouse strains to be used for tumor engrafting experiments is difficult. Methods: In this study, we developed a tumor engraftment index (TEI) to quantify the immunodeficiency response to hematologic malignant cells and solid tumor cells of six immunodeficient mouse strains and C57BL/6 wild-type mouse (WT). Results: Mice with a more severely impaired immune system attained a higher TEI score. We then validated that the NOD-scid-IL2Rg-/- (NSI) mice, which had the highest TEI score, were more suitable for xenograft and allograft experiments using multiple functional assays. Conclusions: The TEI score was effectively able to reflect the immunodeficiency of a mouse strain.Link_to_subscribed_fulltex

Synthesis of Human-in-the-Loop Navigational Operations towards Maritime Autonomous Surface Ships

Maritime transportation is indispensable to the world economy as it dominates over 95 % of the trade volume, so stakeholders have been striving to promote maritime transportation efficiency and sailing security. Benefitted from the technical progress of advanced guidance, navigation, and control techniques, industrial digitalization, and artificial intelligence, the concept of maritime autonomous surface ships (MASS) has emerged. However, according to the regulatory scoping exercise by the International Maritime Organization, several critical phases with different degrees of autonomy are prerequisites to achieving full ship autonomy. In these phases, human practitioners participate the navigation operations loop at various levels in terms of intervention and operating venues, which means human navigators will stay in the loop until the final phase - fully autonomous ships - comes. Though, as professionals, human navigators have recorded numerous safe sailings and accumulated rich experience on the ship bridge, the major cause of most marine accidents is still attributed to human factors. In this regard, the research on human-in-the-loop (HITL) navigational operations will have impact on not only the enhancement of marine traffic safety, especially in the period when human navigators still play the dominant roles on the ship bridge; but also on the development of MASS, as humans contribute both expert knowledge and faulty cases onboard and in complex marine traffic systems as the input of the ship intelligence. The study of synthesis of HITL navigational operations is thus motivated and proposed to address the human-related issues towards the development of MASS by integration of maximizing expertise knowledge, monitoring on-bridge operations, summarizing human navigational logics and modeling the mechanism, and providing practical decision support tools. In establishing such a synthesis study framework, experimental facilities are based on different maritime ship-bridge simulators, while techniques involved in the route map can be categorized into three groups in terms of the aim of utilization: on-bridge monitoring & data collection (e.g., sensor fusion, computer vision, and motion/gesture/eye-movement tracking), analysis and learning of operational behaviors (e.g., statistics, pattern recognition, and expert system), and online surveillance & decision support (e.g., situation awareness, risk management, and collision avoidance algorithms). The experimental platforms incorporate the techniques in the route map and then become the rudiment of intelligent ship bridges on MASS. This dissertation explores the synthesis of HITL navigational operations towards MASS, especially in the experimental design & implementation and HITL applications. The techniques in the route map are adapted and applied in different scenarios based on various platforms. Three case studies are conducted to demonstrate how the synthesis study framework can be carried out to comprehend HITL navigational operations. The first relates to expertise-knowledge-aided path routing to increase sailing safety; the second conceptualizes navigating patterns analysis and illustrates a built-on decision support system for collision avoidance; the third discusses the navigational visual attention and improves the measurement solution. The case studies validate the applicability of the synthesis study framework

Parameter Identification of Ship Manoeuvring Model Under Disturbance Using Support Vector Machine Method

.Demanding marine operations increase the complexity of manoeuvring. A highly accurate ship model promotes predicting ship motions and advancing control safety. It is crucial to identify the unknown hydrodynamic coefficients under environmental disturbance to establish accurate mathematical models. In this paper, the identification procedure for a 3 degree of freedom hydrodynamic model under disturbance is completed based on the support vector machine with multiple manoeuvres datasets. The algorithm is validated on the clean ship model and the results present good fitness with the reference. Experiments in different sea states are conducted to investigate the effects of the turbulence on the identification performance. Generalisation results show that the models identified in the gentle and moderate environments have less than 10% deviations and are considered allowable. The higher perturbations, the lower fidelity the identified model has. Models identified under disturbance could provide different levels of reliable support for the operation decision system

A human-expertise based statistical method for analysis of log data from a commuter ferry

The proposed method in this paper aims to better understand the log data from the commuter ferry. By the method, the mechanism of how the human expertise operates the ferry can be found, and thus help to establish ship intelligence for the autonomous commuting sailing. The log data of sailings with the same departure and arrival ports is of interest in this respect. The method defines different phases of a sailing as different scenarios in terms of the features contained in the collected data. The features are reflected by the ship behavior/response and the ship machinery/actuators. Compared to the typical sailing phases which are distinct to each other, the features can be uncertain when the ferry transfers from the current phase to the sequential. The concept of the transition time window is thus raised to interpret the uncertainty between adjacent phases. Based on the collected data, the human expertise is involved to summarize features and generate empirical criteria for the decomposition. After the whole sailing being split into a sequential-scenario series, statistical heat maps are drawn to illustrate the likelihood site with respect to the collected log data. In practice, log data collected from a customized commuting route in Trondheim are analyzed by the proposed method

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

Interfacial Engineering of Attractive Pickering Emulsion Gel-Templated Porous Materials for Enhanced Solar Vapor Generation

Solar vapor generation is emerging as one of the most important sustainable techniques for harvesting clean water using abundant and green solar energy. The rational design of solar evaporators to realize high solar evaporation performances has become a great challenge. Here, a porous solar evaporator with integrative optimization of photothermal convention, water transport and thermal management is developed using attractive Pickering emulsions gels (APEG) as templated and followed by interfacial engineering on a molecular scale. The APEG-templated porous evaporators (APEG-TPEs) are intrinsically thermal insulation materials with a thermal conductivity = 0.039 W·m−1·K−1. After hydrolysis, t-butyl groups on the inner-surface are transformed to carboxylic acid groups, making the inner-surface hydrophilic and facilitating water transport through the inter-connected pores. The introduction of polypyrrole layer endows the porous materials with a high light absorption of ~97%, which could effectively convert solar irradiation to heat. Due to the versatility of the APEG systems, the composition, compressive modulus, porosity of APEG-TPEs could be well controlled and a high solar evaporation efficiency of 69% with an evaporation rate of 1.1 kg·m−2·h−1 is achieved under simulated solar irradiation. The interface-engineered APEG-TPEs are promising in clean water harvesting and could inspire the future development of solar evaporators