Saint-gobain Glass France

Wuhan University

Shanghai Maritime University

Vestlandets Innovasjonsselskap As

Polar Research Institute of China

Yang Sun

Yongsheng Yang

Xinqiang Chen

Huafeng Wu

Yu Wang

Bing Han

Jiangfeng Xian

Zhongdai Wu

Hao Wu

Bing Wu

Ce Shi

Yuchen Wang

Jingxue Guo

Qiuying Wang

Anna Finne Wistrand

Zichuang Wang

Xiaohui Guo

Chenxin Wei

Kamal Babikeir Eln Mustafa

Alexander Franciscus Van Tol

Chaofeng Li

Ahmad Rashad Saad Mohamed Elsebahy

Disclosed is a ship image track tracking and prediction method based on ship heading recognition, which includes the following steps: obtaining a ship image data set, preprocessing the data set to obtain a preprocessed data set; inputting the preprocessed data set into the rotating ship detection network for training, obtaining the trained rotating ship detection network, collecting the ship navigation video, and inputting the ship navigation video into the trained rotating ship detection network to obtain the ship detection result; inputting the ship detection result into the rotating ship tracking network and tracking the target ship to obtain the historical trajectory and the heading information of the target ship; inputting the historical trajectory and ship heading information of the target ship into the ship trajectory and ship heading prediction network, and predicting the navigation trajectory and ship heading at sea.

Ship image trajectory tracking and prediction method based on ship heading recognition

The present invention proposes an ensemble deep learning method for identifying unsafe behaviors of operators in maritime working environment. Firstly, extract features of maritime images with the You Only Look Once (YOLO) V3 model, and then enhance a multi-scale detection capability by introducing a feature pyramid structure. Secondly, obtain instance-level features and time memory features of the operators and devices in the maritime working environment with the Joint Learning of Detection and Embedding (JDE) paradigm. Thirdly, transfer spatial-temporal interaction information into a feature memory pool, and update the time memory features with the asynchronous memory updating algorithm. Finally, identify the interaction between the operators, the devices, and unsafe behaviors with an asynchronous interaction aggregation network. The proposed invention can accurately determine the unsafe behaviors of the operators, and thus provide operation decisions for maritime management relevant activities.

Ensemble deep learning method for identifying unsafe behaviors of operators in maritime working environment

The present disclosure provides a system and a method for measuring water capacity of polar lakes, and relates to the technical field of lake water capacity measurement. The method comprises the steps: controlling an unmanned aerial vehicle to move along with an unmanned underwater vehicle at a preset height right above the unmanned underwater vehicle by utilizing an ADRC algorithm; obtaining lake surface point cloud data, under-lake point cloud data and under-lake single-beam ranging data so as to construct a water three-dimensional topographic map, a lake coastal three-dimensional topographic map and a lake bottom three-dimensional topographic map under the same coordinate system, and then determining the lake water capacity. By arranging the unmanned underwater vehicle, an unmanned underwater vehicle-mounted module, the unmanned aerial vehicle and an unmanned aerial vehicle-mounted module, lake elevation and lake underwater terrain data are automatically measured, so that the observation precision of polar lakes is improved.

Systems and methods for measuring water capacity of polar lakes

The present invention provides a vessel traffic pattern identification method via data quality control and data compression, and includes the steps of assorting a collection of Automatic Identification System (AIS) data points according to Maritime Mobile Service Identity (MMSI) code; sorting each collection result by time ascending order; deleting duplicated vessel AIS data points considering time stamp, latitude, longitude and vessel speed over ground; segmenting vessel trajectories; obtaining high-quality AIS data with an AIS data anomaly detection; repairing and compressing each vessel trajectory with the Douglas-Peucker algorithm; clustering vessel trajectories with the Quick Bundles algorithm; and identifying a maritime traffic pattern. The invention can efficiently identify vessel traffic patterns and help maritime traffic management departments to accurately identify a traffic situation.

Method for vessel traffic pattern recognition via data quality control and data compression

A scaffold () for tissue engineering comprises an inner portion (), an outer portion (), and a base portion () connecting the inner portion and the outer portion. The inner portion () comprises a channel () surrounded by a first set of one or more walls. The outer portion () comprises a second set of one or more walls. The portions are arranged such that the second set of one or more walls substantially surrounds the first set of one or more walls with a spacing between the first and second sets of walls defining a cavity () between the inner portion () and the outer portion (). The inner portion () and the outer portion () may have different shapes; and/or the scaffold () may further comprise a filler material in the cavity () defined between the inner and outer portions.

Tissue engineering scaffolds

A method for forming an encapsulation assembly, and a vehicle window are provided. The method includes: providing a reinforcement component; forming a primer layer covering a surface of the reinforcement component; performing a heating treatment on the reinforcement component after the primer layer is formed, to elevate a temperature of the reinforcement component; and injecting a gasket material in a mold onto the surface of the reinforcement component with elevated temperature to form a gasket. The vehicle window includes a glass; and the encapsulation assembly formed by the above method. The gasket may bind with the primer layer more closely. That is to say, binding force between the reinforcement component and the gasket may be increased. In this way, the gasket may not easily detach from the reinforcement component.

Method for forming encapsulation assembly, and vehicle window

Growing community of inventors

Shanghai, China

Yang Sun