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1. Redefining Spectral Data Analysis with Immersive Analytics: Exploring Domain-Shifted Model Spaces for Optimal Model Selection

   https://doi.org/10.1177/00037028241280669  

   Peper, Jordan_MJ; Kalivas, John_H (September 2024, Applied Spectroscopy)

   Modern developments in autonomous chemometric machine learning technology strive to relinquish the need for human intervention. However, such algorithms developed and used in chemometric multivariate calibration and classification applications exclude crucial expert insight when difficult and safety-critical analysis situations arise, e.g., spectral-based medical decisions such as noninvasively determining if a biopsy is cancerous. The prediction accuracy and interpolation capabilities of autonomous methods for new samples depend on the quality and scope of their training (calibration) data. Specifically, analysis patterns within target data not captured by the training data will produce undesirable outcomes. Alternatively, using an immersive analytic approach allows insertion of human expert judgment at key machine learning algorithm junctures forming a sensemaking process performed in cooperation with a computer. The capacity of immersive virtual reality (IVR) environments to render human comprehensible three-dimensional space simulating real-world encounters, suggests its suitability as a hybrid immersive human–computer interface for data analysis tasks. Using IVR maximizes human senses to capitalize on our instinctual perception of the physical environment, thereby leveraging our innate ability to recognize patterns and visualize thresholds crucial to reducing erroneous outcomes. In this first use of IVR as an immersive analytic tool for spectral data, we examine an integrated IVR real-time model selection algorithm for a recent model updating method that adapts a model from the original calibration domain to predict samples from shifted target domains. Using near-infrared data, analyte prediction errors from IVR-selected models are reduced compared to errors using an established autonomous model selection approach. Results demonstrate the viability of IVR as a human data analysis interface for spectral data analysis including classification problems. 

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2. Eddy-Current Dynamic Model for Simultaneous Geometrical and Material Parameter Measurements of Magnetic Materials

   https://doi.org/10.1115/DSCC2018-9211  

   Hao, Bingjie; Lee, Kok-Meng; Bai, Kun (September 2018, Proceedings of the ASME 2018 Dynamic Systems and Control Conference)

   This paper presents a distributed current source (DCS) method for modeling the dynamic responses of eddy current density (ECD) induced in electrical conductors and its corresponding magnetic flux density (MFD); both nonmagnetic and weakly magnetized conductors are considered. Unlike conventional numerical methods such as finite element analysis (FEA), the DCS method, which accounts for the eddy-current and magnetization effects by means of equivalent volume and surface current-sources, derives closed-form solutions to the ECD and MFD fields in state-space representation. The model has been experimentally validated and verified by comparing results from FEA simulations with both harmonic and nonharmonic excitations. To gain physical insights to the measured MFD for simultaneous estimating the material/geometrical properties of a conductor, the static and dynamic responses to rectangular pulsed current excitations have been numerically investigated, confirming the feasibility and effectiveness of the measurement methods. 

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3. A Simple Controller for Omnidirectional Trotting of Quadrupedal Robots: Command Following and Waypoint Tracking

   https://doi.org/10.3390/robotics12020035  

   Bhounsule, Pranav A.; Yang, Chun-Ming (April 2023, Robotics)

   For autonomous legged robots to be deployed in practical scenarios, they need to perform perception, motion planning, and locomotion control. Since robots have limited computing capabilities, it is important to realize locomotion control with simple controllers that have modest calculations. The goal of this paper is to create computational simple controllers for locomotion control that can free up computational resources for more demanding computational tasks, such as perception and motion planning. The controller consists of a leg scheduler for sequencing a trot gait with a fixed step time; a reference trajectory generator for the feet in the Cartesian space, which is then mapped to the joint space using an analytical inverse; and a joint controller using a combination of feedforward torques based on static equilibrium and feedback torque. The resulting controller enables velocity command following in the forward, sideways, and turning directions. With these three velocity command following-modes, a waypoint tracking controller is developed that can track a curve in global coordinates using feedback linearization. The command following and waypoint tracking controllers are demonstrated in simulation and on hardware. 

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4. Adaptive Trajectory Tracking During Motorized and FES-Induced Biceps Curls via Integral Concurrent Learning

   https://doi.org/10.1115/DSCC2020-3125  

   Allen, Brendon C.; Stubbs, Kimberly J.; Dixon, Warren E. (October 2020, ASME Dynamic Systems and Control Conference)

   null (Ed.)

   A common rehabilitative technique for those with neuro-muscular disorders is functional electrical stimulation (FES) induced exercise such as FES-induced biceps curls. FES has been shown to have numerous health benefits, such as increased muscle mass and retraining of the nervous system. Closed-loop control of a motorized FES system presents numerous challenges since the system has nonlinear and uncertain dynamics and switching is required between motor and FES control, which is further complicated by the muscle having an uncertain control effectiveness. An additional complication of FES systems is that high gain feedback from traditional robust controllers can be uncomfortable to the participant. In this paper, data-based, opportunistic learning is achieved by implementing an integral concurrent learning (ICL) controller during a motorized and FES-induced biceps curl exercise. The ICL controller uses adaptive feedforward terms to augment the FES controller to reduce the required control input. A Lyapunov-based analysis is performed to ensure exponential trajectory tracking and opportunistic, exponential learning of the uncertain human and machine parameters. In addition to improved tracking performance and robustness, the potential of learning the specific dynamics of a person during a rehabilitative exercise could be clinically significant. Preliminary simulation results are provided and demonstrate an average position error of 0.14 ± 1.17 deg and an average velocity error of 0.004 ± 1.18 deg/s. 

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5. A fixed-switching-frequency Sliding Mode Current Controller for Mutually Coupled Switched Reluctance Machines using Asymmetric Bridge Converter

   Hu, Kun; Ye, Jin; Velni, Mohammadpour; Guo, Lulu; Yang, Bowen. (June 2019, IEEE Transportation Electrification Conference and Expo)

   In this paper, an integral sliding mode current controller (SMC) is proposed for mutually coupled switched reluctance motors (MCSRM) using asymmetric bridge converters aiming to achieve constant switching frequency and lower sampling rate. A generalized state-space model is built and then the design of a sliding mode controller along with the stability analysis of the closed-loop system are presented. The effectiveness of SMC is verified using simulation studies with a three-phase, sinusoidal excitation 12/8 MCSRM over a wide speed range. Compared to the hysteresis current control, the proposed SMC-based design approach demonstrates a comparable response in terms of currents ripples, the root-mean-square error of current and torque while achieving a constant switching frequency and lower sampling rate. 

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