Continuous-time signal processing
In this paper, we propose a dual-loop control strategy to address the problems of the interference by the human-machine interaction of the lower limb exoskeleton movement. The outer ring adopts admittance control and the human-machine interaction torque is estimated by the generalized momentum observer based on Kalman filter. The inner ring adopts PID control based on DDPG.
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To achieve improved multi-node temperature estimation with limited training data in Permanent Magnet Synchronous Motors (PMSMs), a novel approach of a Lumped-Parameter Thermal Network (LPTN)-informed neural network is proposed in this paper. Firstly, the parameter and model uncertainties of third or higher-order LPTNs with global parameter identification for temperature estimation are systematically stated based on numerical analysis.
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441 Views
This dataset comprises data from six experimental participants, each undergoing nine walking trials. Each participant engaged in three trials of low-speed walking, three trials of medium-speed walking, and three trials of high-speed walking. The dataset includes multi-channel electromyography (EMG) data and center of pressure/ground reaction force (COP/GRF) data. Specifically, EMG data is utilized to extract muscle coordination activation time coefficients during human walking, and a deep learning model is established based on these coefficients to predict COP/GRF parameters.
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The uploaded .ZIP file contains the MATLAB codes used in Examples 1 and 2 of the following paper, which the authors have submitted to an IEEE Journal: Data-Driven Saturated State Feedback Design for Polynomial Systems Using Noisy Data. This is the abstract of the paper: "In this note, the problem of data-driven saturated state feedback design for polynomial nonlinear systems is solved by means of a sum-of-squares (SOS) approach. This new strategy combines recent results in dissipativity theory and data-driven feedback control using noisy input-state data.
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This is a PART of the dataset used in our paper titled "Detecting Anomalous Robot Motion in Collaborative Robotic Manufacturing Systems".
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This is a PART of the dataset used in our paper titled "Detecting Anomalous Robot Motion in Collaborative Robotic Manufacturing Systems".
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ARImulti-mic: real-world speech recordings on a humanoid robot (ARI)
This dataset includes “real-world” experiments. A recording campaign was held in the acoustic laboratory at Bar-Ilan University. This lab is a [6×6×2.4]m room with a reverberation time controlled by 60 interchangeable panels covering the room facets.
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336 Views
The "Queue Waiting Time Dataset" is a detailed collection of information that records the movement of waiting times in queues. This dataset contains important details such as the time of arrival, the start and finish times, the waiting time, and the length of the queue. The arrival time denotes the moment when customers enter the queue, while the start and finish times track the duration of the service process. The waiting time measures the time spent waiting in the queue, and the queue length shows the number of customers in the queue when a new customer arrives.
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2999 Views
<p>Ten individuals in good health were enlisted to execute 16 distinct movements involving the wrist and fingers in real-time. Before commencing the experimental procedure, explicit consent was obtained from each participant. Participants were informed that they had the option to withdraw from the study at any point during the experimental session. The experimental protocol adhered to the principles outlined in the Declaration of Helsinki and received approval from the local ethics committee at the National University of Sciences and Technology, Islamabad, Pakistan.
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The training data consists of data from various faults from five individual configurations, while the testing data is blind and is from one individual configuration of the rock drill. A final validation data set will be from two individual configurations from the rock drill and the labels are blind.
The training data set contains data from 11 different fault classification categories, in which 10 are different failure modes and one class is from the healthy/no fault condition.
Each file follows the naming convention of data_{sensor}{individual impact cycle number}.csv.
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