Design of Heading Fault-Tolerant System for Underwater Vehicles Based on Double-Criterion Fault Detection Method

This paper proposes a heading fault tolerance scheme for operation-level underwater robots subject to external interference.The scheme is based on a double-criterion fault detection method using a redundant structure of a dual electronic compass.First,two subexpansion Kalman filters are set up to fuse data with an inertial attitude measurement system.Then,fault detection can effectively identify the fault sensor and fault source.Finally,a fault-tolerant algorithm is used to isolate and alarm the faulty sensor.The program can effectively detect the constant magnetic field interference,change the magnetic field interference and small transient magnetic field interference,and conduct fault tolerance control in time to ensure the heading accuracy of the system.Test verification shows that the system is practical and effective.

Knowledge-enriched joint-learning model for implicit emotion cause extraction

Emotion cause extraction(ECE)task that aims at extracting potential trigger events of certain emotions has attracted extensive attention recently.However,current work neglects the implicit emotion expressed without any explicit emotional keywords,which appears more frequently in application scenarios.The lack of explicit emotion information makes it extremely hard to extract emotion causes only with the local context.Moreover,an entire event is usually across multiple clauses,while existing work merely extracts cause events at clause level and cannot effectively capture complete cause event information.To address these issues,the events are first redefined at the tuple level and a span-based tuple-level algorithm is proposed to extract events from different clauses.Based on it,a corpus for implicit emotion cause extraction that tries to extract causes of implicit emotions is constructed.The authors propose a knowledge-enriched jointlearning model of implicit emotion recognition and implicit emotion cause extraction tasks(KJ-IECE),which leverages commonsense knowledge from ConceptNet and NRC_VAD to better capture connections between emotion and corresponding cause events.Experiments on both implicit and explicit emotion cause extraction datasets demonstrate the effectiveness of the proposed model.

Space truss construction modeling based on on-orbit assembly motion feature

More space truss construction has been planned to develop and utilize space resources.These trusses are designed in the way of large-scale,complex,modular,and on-orbit assembly.To meet the upcoming challenge of large-scale space infrastructure construction,it is necessary to study space truss automation design and robotic construction.This paper proposes an ordinal finite screw adjacency matrix model(OFSAMM),focusing on the relationship between assembly motions,to express and compute a space truss structure.In this model,a space truss is abstracted as a set of ordered assembly motions,each of which is recorded as a finite screw as the basic element of the truss and its assembly.The operation of truss transformation is also derived under this model.Therefore,the truss configuration,the assembly sequence,the truss sub-assembly,the truss components,and the on-orbit assembly task can be expressed and calculated in a unified model,which is calculated and stores the truss topology and assembly with the minimum storage cost.At the end of this paper,we introduce how to synthesize and optimize space truss design through two cases.The study will help to improve design efficiency.Furthermore,it provides a theoretical basis for the automatic construction of space truss structures,especially in the next stage.