Sustainable Ship Management Post COVID-19 with In-Ship Positioning Services

COVID-19 is spreading out in the world now. Passenger ships such as cruise ships are very critical in this situation. Boats’ hazardous areas need to be identified in advance and managed carefully to prevent the virus. Therefore, this paper proposes for the first time that three technologies are required to support the sustainable management of ships in the post-COVID-19 era. They are ship indoor positioning, close contact identification, and risk area calculation. Ship environment-aware indoor positioning algorithms are proposed for the first time for the moving ship environment, followed by a clustering algorithm for close contact identification. Then, the risk area is calculated using the convex hull algorithm. Finally, a sustainable management approach for ships post COVID-19 is proposed.

Cross Product and Partitioned Filtering-Based Graham Convex Hull for Buoy Drifting Area Demarcating

An improved Graham scan convex hull algorithm is designed using the convex hull region shrinkage algorithm and the sample selection decision algorithm. In the sorting of Graham scan convex hull algorithm, the cross-multiplication method is used instead of the operation of finding the polar angle, which avoids the high computational complexity of finding the inverse trigonometric function. When the polar angles are the same, that is, the two points are collinear, the points close to each other are deleted directly. Select the maximal horizontal ordinate point, minimal horizontal ordinate point, maximal longitudinal coordinate point, and minimal longitudinal coordinate point. Connect these points and obtain lines. The whole plane is divided into different regions. The points that are not on the convex hull are deleted, and the redundant points are removed. This can speed up the calculation of approximate convex hull boundary and shorten the time of convex hull calculation. The proposed algorithm is used for buoy drifting area demarcating. The offsets of the geometric center of the high-frequency position point and the distance from geometric center of high-frequency position of buoy to sinking stone are calculated. The experimental results show that the new algorithm can effectively accelerate the convex hull calculation. We use the convex hull process to compute the area of the drifting buoy position and discover that the drift area of the port hand buoy is similar. The drift area of the port hand buoys is similar. The drift area of the port hand buoy is greater than that of the port hand buoy.

Cobb Angle Measurement Method of Scoliosis Based on U-net Network

The Cobb angle is an important indicator for judging the severity of scoliosis. However, the segmentation and corner marking methods based on deep learning have problems such as target area segmentation and corner detection blur in the X-ray Cobb angle measurement. In this paper, a new convex hull algorithm to detect the corners and a mask generation strategy are proposed to improve the accuracy of Cobb angle recognition. On this basis, the Cobb angle measurement method is presented to identify and segment the target area based on U-net network, and then combine the new convex hull algorithm to detect corners and mask generation strategies. A total of 68 corner points were marked on 17 vertebrae, and the corner points detected by the markers were used to calculate the Cobb angle. The experimental results have proved that the U-net based measurement method could effectively improve the corner detection accuracy on the basis of segmentation, thereby reducing the calculation error of the Cobb angle. The Cobb mean absolute error (AMAE) is 9.2832°, and the symmetric mean absolute percentage error (SMAPE) is 21.675%, which achieved a relatively good result compared with the measurement by professional orthopaedist in error.

Trajectory Based Market Models for Two Stocks

This paper studies the explicit calculation of the set of superhedging (and underhedging) portfolios where one asset is used to superhedge another in a discrete time setting. A general operational framework is proposed and trajectory models are defined based on a class of investors characterized by how they operate on financial data leading to potential portfolio rebalances. Trajectory market models will be specified by a trajectory set and a set of portfolios. Beginning with observing charts in an operationally prescribed manner, our trajectory sets will be constructed by moving forward recursively, while our superhedging portfolios are computed through a backwards recursion process involving a convex hull algorithm. The models proposed in this thesis allow for an arbitrary number of stocks and arbitrary choice of numeraire. Although price bounds, V 0 (X0, X2 ,M) ≤ V 0(X0, X2 ,M), will never yield a market misprice, our models will allow an investor to determine the amount of risk associated with an initial investment v.

Trajectory Based Market Models for Two Stocks

This paper studies the explicit calculation of the set of superhedging (and underhedging) portfolios where one asset is used to superhedge another in a discrete time setting. A general operational framework is proposed and trajectory models are defined based on a class of investors characterized by how they operate on financial data leading to potential portfolio rebalances. Trajectory market models will be specified by a trajectory set and a set of portfolios. Beginning with observing charts in an operationally prescribed manner, our trajectory sets will be constructed by moving forward recursively, while our superhedging portfolios are computed through a backwards recursion process involving a convex hull algorithm. The models proposed in this thesis allow for an arbitrary number of stocks and arbitrary choice of numeraire. Although price bounds, V 0 (X0, X2 ,M) ≤ V 0(X0, X2 ,M), will never yield a market misprice, our models will allow an investor to determine the amount of risk associated with an initial investment v.

UB-H: an unbalanced-hierarchical layer binary-wise construction method for high-dimensional data

Cloud computing, which is distributed, stored and managed, is drawing attention as data generation and storage volumes increase. In addition, research on green computing, which increases energy efficiency, is also widely studied. An index is constructed to retrieve huge dataset efficiently, and the layer-based indexing methods are widely used for efficient query processing. These methods construct a list of layers, so that only one layer is required for information retrieval instead of the entire dataset. The existing layer-based methods construct the layers using a convex hull algorithm. However, the execution time of this method is very high, especially in large, high-dimensional datasets. Furthermore, if the total number of layers increases, the query processing time also increases, resulting in efficient, but slow, query processing. In this paper, we propose an unbalanced-hierarchical layer method, which hierarchically divides the dimensions of input data to increase the total number of layers and reduce the index building time. We demonstrate that the proposed procedure significantly increases the total number of layers and reduces the index building time, compared to existing methods through the various experiments.