One of the most widely used multivariate control charts is the Hotelling T 2. In order to construct a Hotelling T 2 control chart, the mean vector (μ) and the variance-covariance matrix (Σ) must be first estimated. The classical estimators of μ and Σ are usually used to design Hotelling T 2 control chart. The classical estimators are sensitive to the presence of outliers. One way to deal with outliers is to use robust estimators. In this study, a robust T 2 control chart is proposed. The mean vector is obtained from the sample median. The median absolute deviation and the comedian are used as the estimates of the elements of the variance-covariance matrix. The proposed robust estimators of the mean vector and the variance-covariance matrix are compared with the sample mean vector and the sample variance-covariance matrix, and the M estimator of these parameters, through efficiency and robustness measures. The performances of the proposed robust T 2 control chart and the classical and the M estimators are also compared by means of average run length. Simulation results reveal that the proposed robust T 2 control chart has much better performance than the traditional Hotelling T 2 and similar performance to the M estimator in detecting shifts in process mean vector. Use of other robust estimators to estimate the process parameters is an area for further research.

In some statistical process control applications, the quality of a process is described by a linear relationship between the response variable(s) and the independent variable(s), which is called a linear profile. Process capability is a significant issue in statistical process control. The ability of a process to meet customer specifications or standards is measured by the process capability indices (PCIs). There are several attempts for studying the process capability in linear profiles. In this research, two robust PCIs for multiple linear profiles are proposed. In the suggested robust PCIs, the process capability is estimated using the M‐estimator and the Fast‐τ‐estimator. Performances of the proposed robust PCIs in comparison with the classical PCIs in the absence and presence of contamination are evaluated. The results show that the robust PCIs proposed in this research perform as well as the classical PCIs in the absence of contamination and much better in the presence of contamination. The proposed PCIs, using Fast‐τ‐estimator, perform better in small shifts, and the proposed PCIs, using M‐estimator, perform better in large shifts. Introduction of robust indices for multivariate multiple linear profiles is an area for further research.

Predicting the movement of the vessels can significantly improve the management of safety. While the movement can be a function of geographic contexts, the current systems and methods rarely incorporate contextual information into the analysis. This paper initially proposes a novel context-aware trajectories' simplification method to embed the effects of geographic context which guarantees the logical consistency of the compressed trajectories, and further suggests a hybrid method that is built upon a curvilinear model and deep neural networks. The proposed method employs contextual information to check the logical consistency of the curvilinear method and then, constructs a Context-aware Long Short-Term Memory (CLSTM) network that can take into account contextual variables, such as the vessel types. The proposed method can enhance the prediction accuracy while maintaining the logical consistency, through a recursive feedback loop. The implementations of the proposed approach on the Automatic Identification System (AIS) dataset, from the eastern coast of the United States of America which was collected, from November to December 2017, demonstrates the effectiveness and better compression, i.e. 80% compression ratio while maintaining the logical consistency. The estimated compressed trajectories are 23% more similar to their original trajectories compared to currently used simplification methods. Furthermore, the overall accuracy of the implemented hybrid method is 15.68% higher than the ordinary Long Short-Term Memory (LSTM) network which is currently used by various maritime systems and applications, including collision avoidance, vessel route planning, and anomaly detection systems.

Abstract. Oak decline is a complex phenomenon. The classification of oak decline potential could be a valuable tool for forest management. This paper identified seven factors that influence oak decline: height, slope, aspect, temperature, perception, soil type, and aerosol. Then, factor analysis is used to determine factors that should be included in oak decline potential classification and reduce data complexity.As a result, five components explaining 92.49% of total variance are selected. The first component explains 40.34% of the variance, and three factors, including perception with positive and temperature and aerosol with negative load, have contributed to its construction. The second component is composed of a positive load of aspect, and soil type explains 14.89% of the variance. By explaining 14.10% of the variance, the third component consists of soil type and aspect with positive and negative loads, respectively. Slop and height have a positive load in constructing the fourth and fifth components.Five extracted components are used as input sets of PNN, MLC and SVM methods. 80% of samples are used for training methods, and 20% are used for testing purposes. Results are compared based on the overall accuracy of the methods.These components are used as an input set of three classification methods, including Probabilistic Neural Network (PNN), Maximum Likelihood Classification (MLC) and a Support Vector Machine (SVM). Based on the results, the SVM, with an overall accuracy of 0.87%, has proved its capability in oak decline potential classification.

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