There is a collection of a large amount of automatic identification system (AIS) data that contains ship encounter information, but mining the collision avoidance knowledge from AIS big data and carrying out effective machine learning is a difficult problem in current maritime field. Herein, first the Douglas–Peucker (DP) algorithm was used to preprocess the AIS data. Then, based on the ship domain the risk of collision was identified. Finally, a double-gated recurrent unit neural network (GRU-RNN) was constructed to learn unmanned surface vehicle (USV) collision avoidance decision from the extracted data of successful encounters of ships. The double GRU-RNN was trained on the 2015 Tianjin Port AIS dataset to realize the effective learning of ship encounter data. The results indicated that the double GRU-RNN could effectively learn the collision avoidance pattern hidden in AIS big data, and generate corresponding ship collision-avoidance decisions for different maritime navigation states. This study contributes significantly to the increased efficiency and safety of sea operations. The proposed method could be potentially applied to USV technology and intelligence collision avoidance.
The pharmaceutical tablet manufacturing process (PTMP) via wet granulation holds a critical position in pharmaceutical industry. The interest in integrating mechanistic process modeling into the pharmaceutical development has been increased because simulation model is a prerequisite for process design, analysis, control, and optimization. So the simulation modeling for PTMP via wet granulation is very necessary and significant. This study aims at proposing a simulation modeling framework for PTMP via spray fluidized bed granulation (SFBG), which is one of the most widely used wet granulation techniques in pharmaceutical industry. For SFBG, a simulation model that simultaneously involves the influences of operating variables and material attributes on average particle size (APS) is firstly developed, and then a drying model to determine the particle moisture content is introduced to be coupled with the established model predicting APS. For PTMP, considering the important effect of porosity on tablet qualities, a model describing the changes in tablet porosity is developed based on a promoted form of the Heckel equation, and then several recognized models that are all related to porosity are introduced or constructed to calculate important tablet quality indexes. The feasibility and effectiveness of the developed simulation models are validated by performing a computational experimental study to explore the scientific understanding of process and process quality control.
The gold cyanidation leaching process (GCLP) is the central unit operation in hydrometallurgy, and satisfactory gold recovery is highly significant in practice. However, GCLP faces the challenge of an irregular slow time-varying feature (STVF), which seriously affects gold recovery, and blind treatment for STVF also has drawbacks, which results in the need for the recognition of STVF for purposeful, rather than blind, treatment. Meanwhile, it also faces the problem of change of working condition (COWC) due to the variability of mineral resources. Both STVF and COWC may cause degradation of the soft-measuring model, which presents the need for model correction. Therefore, a coping strategy is proposed to solve these existing problems. First, an improved model-based principal component analysis monitoring is proposed to detect model mismatch and monitor the change of process feature. Next, a support vector machine-based process feature change recognition method is presented to recognize change type, which not only provides guidance in treating STVF but also makes it possible to implement targeted model correction for STVF and COWC. Finally, an adaptive model correction strategy that combines case-based correction and just-intime learning-based correction is proposed. The simulation studies have verified the validity of the proposed coping strategy. INDEX TERMS Gold cyanidation leaching process, model-based principal component analysis, process feature change recognition, adaptive model correction.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.