The Impact of the Use of Large Non-Linear Lighting Loads in Low-Voltage Networks

The principal numerical and experimental results obtained by the authors on the harmonic power losses in low-voltage networks in the lighting area have been summarized in this review. Light-emitting diodes (LEDs) and compact fluorescent lamp (CFL) loads were considered. Four-core cables and four single-core cable arrangements were examined. The cables were modeled by using electromagnetic finite element analysis software. It was found that the cross section of the neutral conductor plays an important role in the derating of the cable ampacity due to the presence of a high level of triplen harmonics in the distorted current. In order to reduce the third-order harmonic currents in the neutral conductor, an experimental investigation of diversity factors for LED in combination with CFL and LED lamps was also performed. Attention was paid to the reduction of the third-order harmonic current, which is mainly responsible for the strong increase in power losses in the neutral conductor of low-voltage installations. The convenience of having LED lamps designed to operate as two-phase loads is suggested for certain applications

Multi-locus transcranial magnetic stimulation system for electronically targeted brain stimulation

Background: Transcranial magnetic stimulation (TMS) allows non-invasive stimulation of the cortex. In multi-locus TMS (mTMS), the stimulating electric field (E-field) is controlled electronically without coil movement by adjusting currents in the coils of a transducer. Objective: To develop an mTMS system that allows adjusting the location and orientation of the E-field maximum within a cortical region. Methods: We designed and manufactured a planar 5-coil mTMS transducer to allow controlling the maximum of the induced E-field within a cortical region approximately 30 mm in diameter. We developed electronics with a design consisting of independently controlled H-bridge circuits to drive up to six TMS coils. To control the hardware, we programmed software that runs on a field-programmable gate array and a computer. To induce the desired E-field in the cortex, we developed an optimization method to calculate the currents needed in the coils. We characterized the mTMS system and conducted a proof-of-concept motor-mapping experiment on a healthy volunteer. In the motor mapping, we kept the transducer placement fixed while electronically shifting the E-field maximum on the precentral gyrus and measuring electromyography from the contralateral hand. Results: The transducer consists of an oval coil, two figure-of-eight coils, and two four-leaf-clover coils stacked on top of each other. The technical characterization indicated that the mTMS system performs as designed. The measured motor evoked potential amplitudes varied consistently as a function of the location of the E-field maximum. Conclusion: The developed mTMS system enables electronically targeted brain stimulation within a cortical region. (c) 2021 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).Peer reviewe

DELMEP: a deep learning algorithm for automated annotation of motor evoked potential latencies

Abstract The analysis of motor evoked potentials (MEPs) generated by transcranial magnetic stimulation (TMS) is crucial in research and clinical medical practice. MEPs are characterized by their latency and the treatment of a single patient may require the characterization of thousands of MEPs. Given the difficulty of developing reliable and accurate algorithms, currently the assessment of MEPs is performed with visual inspection and manual annotation by a medical expert; making it a time-consuming, inaccurate, and error-prone process. In this study, we developed DELMEP, a deep learning-based algorithm to automate the estimation of MEP latency. Our algorithm resulted in a mean absolute error of about 0.5 ms and an accuracy that was practically independent of the MEP amplitude. The low computational cost of the DELMEP algorithm allows employing it in on-the-fly characterization of MEPs for brain-state-dependent and closed-loop brain stimulation protocols. Moreover, its learning ability makes it a particularly promising option for artificial-intelligence-based personalized clinical applications