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The goal of maritime traffic management is to provide a safe and efficient maritime environment for different type of vessels facilitating port logistics and supply chain business. However, current maritime traffic management mainly relies on the massive individual vessel’s data for decision making. Lack of macro-level understanding of vessel crowd movement around port challenges maritime safety and traffic efficiency. In this paper, we describe a spatio-temporal data mining method to discover crowd movement patterns of vessels from their short-term history data. The method first captures vessels’ crowd movement features by building vessels’ tracklets with their speed and location. A movement vector clustering algorithm is developed to find different travel behaviors for different group of vessels. With nonparametric regression on the classified vessel movement vectors which represent the crowd travel behaviors, an overall vessel movement pattern can then be discovered. In this research, we tested real trajectory data of vessels near Singapore ports. Comparing with the actual massive vessel movement data, we found that this method was able to extract vessels’ crowd movement information. The hotspots on risk area in terms of vessel traffic and speed can be identified. The method can be used to provide decision-making support for maritime traffic management.
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Optimization techniques perform an important role in different domains of statistic. Examples of parameter estimation of different distributions, correlation analysis (parametric and nonparametric), regression analysis, optimal allocation of resources in partial research, exploration of response surfaces, design of experiments, efficiency tests, reliability theory, survival analysis are the most known methods of statistical analysis in which we find optimization techniques.
The paper contains a synthetic presentation of the main statistical methods using classical optimization techniques, numerical optimization methods, linear and nonlinear programming, variational calculus techniques. Also, an example of applying the “simplex” algorithm in making a decision to invest an amount on the stock exchange, using a prediction model..
Nonlinear Image Processing and Filtering: A Unified Approach Based on Vertically Weighted Regression
A class of nonparametric smoothing kernel methods for image processing and filtering that possess edge-preserving properties is examined. The proposed approach is a nonlinearly modified version of the classical nonparametric regression estimates utilizing the concept of vertical weighting. The method unifies a number of known nonlinear image filtering and denoising algorithms such as bilateral and steering kernel filters. It is shown that vertically weighted filters can be realized by a structure of three interconnected radial basis function (RBF) networks. We also assess the performance of the algorithm by studying industrial images.
The idea of worm tracking refers to the path analysis of Caenorhabditis elegans nematodes and is an important tool in neurobiology which helps to describe their behavior. Knowledge about nematode behavior can be applied as a model to study the physiological addiction process or other nervous system processes in animals and humans. Tracking is performed by using a special manipulator positioning a microscope with a camera over a dish with an observed individual. In the paper, the accuracy of a nematode’s trajectory reconstruction is investigated. Special attention is paid to analyzing errors that occurred during the microscope displacements. Two sources of errors in the trajectory reconstruction are shown. One is due to the difficulty in accurately measuring the microscope shift, the other is due to a nematode displacement during the microscope movement. A new method that increases path reconstruction accuracy based only on the registered sequence of images is proposed. The method Simultaneously Localizes And Tracks (SLAT) the nematodes, and is robust to the positioning system displacement errors. The proposed method predicts the nematode position by using NonParametric Regression (NPR). In addition, two other methods of the SLAT problem are implemented to evaluate the NPR method. The first consists in ignoring the nematode displacement during microscope movement, and the second is based on a Kalman filter. The results suggest that the SLAT method based on nonparametric regression gives the most promising results and decreases the error of trajectory reconstruction by 25% compared with reconstruction based on data from the positioning system
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References Eubank R. (1988): Spline Smoothing and NonparametricRegression. Marcel Dekker, New York. Afzal M., Yasin M., Sherawat S.M. (2002): Evaluation and Demonstration of Economic Threshold Level (ETL) for Chemical Control of Rice Stem Borers Scirpophagaincertulus Wlk . And S.innotata Wl. International Journal of Agriculture and Biology 3: 323-325. Simonoff J. (1995): Smoothing Methods of Statistics. Springer. New York. Thisted R.A. (1988): Elements of Statistical Computing. Chapman and Hall, New York. Weersink A., Deen W., Weaver S. (1991): Defining and
Network and Road Traffic?, Motorway Working Group Report, Brussels, Luxembourg. ISBN 92-826-4881-8 http://aei.pitt.edu/39803/1/A4167.pdf CNSP. (2018). Studiu de fundamentare Autostrada Târgu Neamț – Iași CNSP. (2018). Studiu de fundamentare Autostrada Ploieşti - Brașov Davis, G. A., & Nihan, N. L. (1991). Nonparametricregression and short-term freeway traffic forecasting. Journal of Transportation Engineering , 117 (2), 178-188. https://doi.org/10.1061/(ASCE)0733-947X(1991)117:2(178) Dolombyan A.V., Kocherga E.V., Semchugova E., Negrov N. (2017). Traffic