Project: Computational Intelligence in Inverse Problem
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Damage Identification Using Tunable Circuitry Integration Enabled Measurement Enrichments
Experimental setup. (a) Schematic diagram of experimental setup, (b) Plate structure, (c) Agilent E3630A power supply for synthetic tunable inductor, (d) Dynamic Signal Analyzer (Agilent 35670A), and (e) Physical synthetic tunable inductor.
Flowchart for model updating process
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Learning Automata as Building Block for Reinforcement Learning in Multi-swarm Optimizer
Flowchart of Collaborative Learning Automata Guided MOPSO
Interactions between Learning Automata and Environment
Schematic Illustration of Structural Damage Identification
Probability Distribution of Learning Automata
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This algorithm due to its characteristics opens the avenues for more applications including :
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dynamic optimization,
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path planning,
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traffic prediction,
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neural network structure optimization,
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structural design, and beyond.
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Structural Damage Identification (Resilient extraterrestrial habitat, NASA), link
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Data-driven approach
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Model-based approach
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A demo for damage detection and repair
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The video is played at 1.5 speed
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Damage detection is conducted using data-driven based approach
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Meteorite is emulated using a mass block
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Intelligent Swarm Optimizer Improvement with Reinforcement Learning
Project: Investigation on Composite Bistable Structure and Optimization
Project: Mechanical Analysis and Data-driven Optimization for Deployable Structure
Project: Mechanical Analysis and Structural Design of Boiling Drying Granulator
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Fluid-structure interaction simulations
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HyperWorks, Fluent/CFD and EDEM.
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Iteratively analyzed results to recommend design enhancements, fostering an agile development cycle with continuous improvement
