Journal: Robotics

Article Title: A Novel Approach to Simulating Realistic Exoskeleton Behavior in Response to Human Motion

doi: 10.3390/robotics13020027

Figure Lengend Snippet: Figure 2. The exoskeleton Lucy (right) and its multibody model in MATLAB Simscape R2021b (left).

Article Snippet: The multibody exoskeleton model is built in MATLAB Simscape R2021b (MathWorks Inc., Natick, MA, USA), based on the CAD model of the exoskeleton Lucy built i SolidWorks 2022 SP 5.0 (SolidWorks Corporation, Concord, MA, USA).

Techniques:

Journal: Royal Society Open Science

Article Title: A simulated single ventilator/dual patient ventilation strategy for acute respiratory distress syndrome during the COVID-19 pandemic

doi: 10.1098/rsos.200585

Figure Lengend Snippet: Circuit diagram for the standard splitter. The control module contains a pulse-wave generator with a period appropriate to the respiration rate. It delivers three square signals to control the inspiration, expiration and voltage to the circuit. The R ETT resistors correspond to the endotracheal tubing. R Ex and R Ix correspond to the inflow and outflow tubing resistance. The patient module consists of a resistor (R Lx ) to model upper airway resistance, and a capacitor C Lx , to model the compliance of the lungs and chest wall. Grey boxes outline the splitter (i) and patient submodule (ii). The text ‘sensor’ on the patient blocks indicate that the respective signals are connected to appropriate Simscape sensor blocks, namely current and voltage sensors, to allow for these signals to be logged during simulation.

Article Snippet: We implemented the simulations using MathWork's Simscape (Simulink v4.8) Foundational Blocks and the tests were run via Matlab R2020a scripts and functions.

Techniques: Control

Journal: Data in Brief

Article Title: Dataset of noise signals generated by smart attackers for disrupting state of health and state of charge estimations of battery energy storage systems

doi: 10.1016/j.dib.2024.111200

Figure Lengend Snippet:

Article Snippet: Data collection , The data were collected using real-time simulations in MATLAB/Simscape (R2024a and R2024b). Deep Reinforcement Learning (DRL) agent known as Proximal Policy Optimization (PPO), was implemented to generate noise signals. These simulations focused on disrupting State of Charge (SoC) and State of Health (SoH) estimations using Unscented Kalman Filters (UKF) in Battery Energy Storage Systems (BESS). The dataset includes only those scenarios where noise signals successfully impacted the estimation accuracy..

Techniques: Battery, Biomarker Discovery