首页|Study Findings from University of Victoria Advance Knowledge in Robotics (Unveil ing the Black Box: A Unified XAI Framework for Signal-Based Deep Learning Models)
Study Findings from University of Victoria Advance Knowledge in Robotics (Unveil ing the Black Box: A Unified XAI Framework for Signal-Based Deep Learning Models)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators publish new report on ro botics. According to news originating from Victoria, Canada, by NewsRx correspon dents, research stated, “Condition monitoring (CM) is essential for maintaining operational reliability and safety in complex machinery, particularly in robotic systems.” Funders for this research include Ntwist Inc.. Our news editors obtained a quote from the research from University of Victoria: “Despite the potential of deep learning (DL) in CM, its ‘black box’ nature rest ricts its broader adoption, especially in missioncritical applications. Address ing this challenge, our research introduces a robust, four-phase framework expli citly designed for DL-based CM in robotic systems. (1) Feature extraction utiliz es advanced Fourier and wavelet transformations to enhance both the model’s accu racy and explainability. (2) Fault diagnosis employs a specialized Convolutional Long Short-Term Memory (CLSTM) model, trained on the features to classify signa ls effectively. (3) Model refinement uses SHAP (SHapley Additive exPlanation) va lues for pruning nonessential features, thereby simplifying the model and reduci ng data dimensionality. (4) CM interpretation develops a system offering insight ful explanations of the model’s decision-making process for operators.”
University of VictoriaVictoriaCanadaNorth and Central AmericaEmerging TechnologiesMachine LearningRoboticsRobots
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