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Collision-free automatic berthing of maritime autonomous surface ships via safety-certified active disturbance rejection control.

ISA Trans

May 2024

School of Marine Electrical Engineering, Dalian Maritime University, Dalian 116026, China; State Key Laboratory of Maritime Technology and Safety, Dalian, China; Dalian Key Laboratory of Swarm Control and Electrical Technology for Intelligent Ships, Dalian 116026, China.

This paper addresses the automatic berthing of a maritime autonomous surface ship operating in a confined water environment subject to static obstacles, dynamic obstacles, thruster constraints, and space constraints due to shorelines. A safety-certified active disturbance rejection control (ADRC) method is proposed for achieving the automatic berthing task of an MASS in the presence of model uncertainties and ocean disturbances. An extended state observer (ESO) based on a second-order robust exact differentiator (RED) is employed to estimate an extended state vector consisting of internal model uncertainties and external ocean disturbances.

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Purpose: Learner development and promotion rely heavily on narrative assessment comments, but narrative assessment quality is rarely evaluated in medical education. Educators have developed tools such as the Quality of Assessment for Learning (QuAL) tool to evaluate the quality of narrative assessment comments; however, scoring the comments generated in medical education assessment programs is time intensive. The authors developed a natural language processing (NLP) model for applying the QuAL score to narrative supervisor comments.

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Due to the complex and changeable train operation environment and the unstable and time-varying parameters, accurate modeling is limited. Therefore, a modified active disturbance rejection control algorithm based on feedforward compensation (FC-MADRC) is proposed targeting the speed control problem of trains under the circumstances of external disturbances, which reduces the dependence on the train model. Firstly, the state space equation is established based on the single-particle mathematical model of the train, and all the running resistances are regarded as disturbances.

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Objective: Chronic subdural hematoma (cSDH) has a reported 10%-24% rate of recurrence after surgery, and prognostic models for recurrence have produced equivocal results. The objective of this study was to leverage a data mining algorithm, chi-square automatic interaction detection (CHAID), which can incorporate continuous, nominal, and binary data into a decision tree, to identify the most robust predictors of repeat surgery for cSDH patients.

Methods: This was a retrospective cohort study of all patients with SDH from two level 1 trauma centers at a single institution.

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Objective: The goal of this study was to identify autonomic and gastric myoelectric biomarkers from throughout the day that differentiate patients with gastroparesis, diabetics without gastroparesis, and healthy controls, while providing insight into etiology.

Methods: We collected 19 24-hour recordings of electrocardiogram (ECG) and electrogastrogram (EGG) data from healthy controls and patients with diabetic or idiopathic gastroparesis. We used physiologically and statistically rigorous models to extract autonomic and gastric myoelectric information from the ECG and EGG data, respectively.

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