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https://hdl.handle.net/10125/109222 

Laboratory experimentation of electromechanical systems can be challenging from a practical perspective and offers limited flexibility once built. Aiming at maximizing versatility and accelerating laboratory research, we propose a method of electric motor emulation via power electronics. This paper is focused on constant-frequency air conditioners based on single-phase induction machines and we show how to control a single-phase inverter to emulate the ac-terminal dynamics of such a system serving thermal loads. This approach offers a convenient method of high-bandwidth air conditioner load emulation without moving parts. Such a setup could be used to realize electrical experiments that mimic residential load dynamics with high fidelity. After outlining the system model, we propose a practical voltage-source inverter implementation and conclude with experiments on a 600 W converter. 

The need to create more viable soft sensors is increasing in tandem with the growing interest in soft robots. Several sensing methods, like capacitive stretch sensing and intrinsic capacitive self-sensing, have proven to be useful when controlling soft electro-hydraulic actuators, but are still problematic. This is due to challenges around high-voltage electronic interference or the inability to accurately sense the actuator at higher actuation frequencies. These issues are compounded when trying to sense and control the movement of a multiactuator system. To address these shortcomings, we describe a two-part magnetic sensing mechanism to measure the changes in displacement of an electro-hydraulic (HASEL) actuator. Our magnetic sensing mechanism can achieve high accuracy and precision for the HASEL actuator displacement range, and accurately tracks motion at actuation frequencies up to 30 Hz, while being robust to changes in ambient temperature and relative humidity. The high accuracy of the magnetic sensing mechanism is also further emphasized in the gripper demonstration. Using this sensing mechanism, we can detect submillimeter difference in the diameters of three tomatoes. Finally, we successfully perform closed-loop control of one folded HASEL actuator using the sensor, which is then scaled into a deformable tilting platform of six units (one HASEL actuator and one sensor) that control a desired end effector position in 3D space. This work demonstrates the first instance of sensing electro-hydraulic deformation using a magnetic sensing mechanism. The ability to more accurately and precisely sense and control HASEL actuators and similar soft actuators is necessary to improve the abilities of soft, robotic platforms. 

Abstract Meditation practices have been claimed to have a positive effect on the regulation of mood and emotions for quite some time by practitioners, and in recent times there has been a sustained effort to provide a more precise description of the influence of meditation on the human brain. Longitudinal studies have reported morphological changes in cortical thickness and volume in selected brain regions due to meditation practice, which is interpreted as an evidence its effectiveness beyond the subjective self reporting. Using magnetoencephalography (MEG) or electroencephalography to quantify the changes in brain activity during meditation practice represents a challenge, as no clear hypothesis about the spatial or temporal pattern of such changes is available to date. In this article we consider MEG data collected during meditation sessions of experienced Buddhist monks practicing focused attention (Samatha) and open monitoring (Vipassana) meditation, contrasted by resting state with eyes closed. The MEG data are first mapped to time series of brain activity averaged over brain regions corresponding to a standard Destrieux brain atlas. Next, by bootstrapping and spectral analysis, the data are mapped to matrices representing random samples of power spectral densities in$$\alpha$$ $\alpha$,$$\beta$$ $\beta$,$$\gamma$$ $\gamma$, and$$\theta$$ $\theta$frequency bands. We use linear discriminant analysis to demonstrate that the samples corresponding to different meditative or resting states contain enough fingerprints of the brain state to allow a separation between different states, and we identify the brain regions that appear to contribute to the separation. Our findings suggest that the cingulate cortex, insular cortex and some of the internal structures, most notably the accumbens, the caudate and the putamen nuclei, the thalamus and the amygdalae stand out as separating regions, which seems to correlate well with earlier findings based on longitudinal studies. 

We present a numerical methodology to estimate the transient fault currents and to simulate the remote sensing of transient fault information embedded in the magnetic field emissions caused by inter-turn shorts in 60 Hz air-core reactors, thru a magneto quasi-static (MQS) field approximation in the method of Finite-Difference Time-Domain (FDTD) in 2-dimensional (2D) space. The MQS 2D FDTD fields of reactor in normal operation are scaled by correlation against an equivalent circuit model that is derived from application of basic physics principles to parameters of the 3D air-core reactor. The proposed multi-scale quasi-static modeling methodology, based on the reduced c modification, provides fine-feature access down to the single-wire level and can efficiently estimate the transient fault fields and currents due to turn-to-turn short in a reactor with core height in several meters, core diameter in meters, wire diameter in millimeters, and number of turns in the thousands, at 60 Hz; this is accomplished by using computational resources of a typical laptop computer within seconds or minutes, as opposed to days that would be otherwise required without the reduced c modification.