A low frequency MEMS energy harvester scavenging energy from magnetic field surrounding an AC current-carrying wire

This paper reports on a low frequency piezoelectric energy harvester that scavenges energy from a wire carrying an AC current. The harvester is described, fabricated and characterized. The device consists of a silicon cantilever with integrated piezoelectric capacitor and proof-mass that incorporates a permanent magnet. When brought close to an AC current carrying wire, the magnet couples to the AC magnetic field from a wire, causing the cantilever to vibrate and generate power. The measured average power dissipated across an optimal resistive load was 1.5 μW. This was obtained by exciting the device into mechanical resonance using the electro-magnetic field from the 2 A source current. The measurements also reveal that the device has a nonlinear response that is due to a spring hardening mechanism

Broadening the Bandwidth of Piezoelectric Energy Harvesters Using Liquid Filled Mass

AbstractA narrow bandwidth is one of the most challenging issues that vibrational energy harvesters have to overcome. This paper demonstrates a novel method of broadening the bandwidth without significantly reducing the peak output voltage. The method uses a liquid filled mass to create a sliding mass effect in order to broaden the bandwidth. The fluid mass increased the full-width-half-maximum (FWHM) value from 1.6Hz to 4.45Hz with no significant decrease in peak-to-peak voltage when compared to an empty mass. The fluid filled mass has a non-linear mass distribution during low frequency, high acceleration applications

Location dependence of a MEMS electromagnetic transducer with respect to an AC power source

A MEMS, silicon based device with a cantilever oscillationsand an integrated magnet is presented for magnetic to electrical transduction. The cantilever structure can be configured either as an energy harvester to harvest power from an AC power line or as an AC current sensor. The positioning of the transducer with respect to the AC conductor is critical in both scenarios. For the energy scavenger, correct positioning is required to optimize the harvested power. For the current sensor, it is necessary to optimise the sensitivity of the sensor. This paper considers the effect of the relative position of the transducer with respect to the wire on the resulting electromagnetic forces and torques driving the device. It is shown here that the magnetic torque acting on a cantilever beam with an integrated magnet and in the vicinity of an alternating electromagnetic field is a very significant driver of the cantilever oscillations

Shock-induced aluminum nitride based MEMS energy harvester to power a leadless pacemaker

The next generation of implantable leadless pacemakers will require vibrational energy harvesters in order to increase the lifetime of the pacemaker. This paper reports for the first time the use of a piezoelectric MEMS linear energy harvester device that fits inside a pacemaker capsule. The silicon based MEMS cantilever device uses CMOS compatible Aluminum Nitride as the piezoelectric layer. The developed harvester operates based on a shock-induced vibration that is generated from the low frequency (60–240 beats per minute) high acceleration (>1 g) vibration of the heart. The off-resonance, high g impulses force the high-frequency harvester to oscillate at its resonant frequency. A power density of 97 and 454 μW cm−3 g−2 was achieved for a heart rate of 60 and 240 beats per minute respectively. The forced oscillation causes the linear harvester to dampen after 100–200 ms which reduces the average power compared to a typical sinusoidal excitation. A two and four cantilever system occupies 35% and 70% of the overall volume of the capsule while obtaining 2.98 and 5.96 μW respectively at a heart rate of 60 bpm respectively and 1 g acceleration. The results in this paper demonstrate that a shock-induced linear MEMS harvester can produce enough electrical energy from the vibration of a heart to power a leadless pacemaker while maintaining a small volume

A study on the spatial dependence of a MEMS electromagnetic transducer

A MEMS, silicon-based device with a piezoelectric layer and an integrated magnet is presented for magnetic to electrical transduction. The cantilever structure can be configured either as an energy harvester to harvest power from an AC power line or as an AC current sensor. The positioning of the transducer with respect to the conductor is critical in both scenarios. For the energy scavenger, correct positioning is required to optimize the harvested power. For the current sensor, it is necessary to optimize the sensitivity of the sensor. This paper considers the effect of the relative position of the transducer with respect to the wire on the resulting electromagnetic forces and torques driving the device. It is shown in this paper that it is the net equivalent torque at the anchor that must be considered and not just the vertical component of the magnetic force as has been widely assumed heretofore. We show that for single wire conductors, the commonly made assumption that there exists two symmetrical power peaks at 45° either side of the wire is untrue, but rather that the net driving torque on one side of the wire can be more than an order of magnitude greater than the other

Effect of plasma elongation on current dynamics during tokamak disruptions

Plasma terminating disruptions in tokamaks may result in relativistic runaway electron beams with potentially serious consequences for future devices with large plasma currents. In this paper, we investigate the effect of plasma elongation on the coupled dynamics of runaway generation and resistive diffusion of the electric field. We find that elongated plasmas are less likely to produce large runaway currents, partly due to the lower induced electric fields associated with larger plasmas, and partly due to direct shaping effects, which mainly lead to a reduction in the runaway avalanche gain. \ua9 Cambridge University Press 2020

Tuning MEMS cantilever devices using photoresponsive polymers

Microelectromechanical systems (MEMS) energy harvesting devices have had limited commercial success partly due to the frequency mismatch between the device and the vibration source. Tuning the cantilever device is one possible solution but developing a tunable MEMS device is difficult. This paper demonstrates a novel method of tuning a MEMS cantilever device post-fabrication by using light-responsive azobenzene liquid crystal polymers (LCP). Light exposure causes the photoresponsive polymers to change their elastic modulus, thus affecting the resonant frequency of the device. The photoresponsive polymer was integrated with three different MEMS cantilever substrates including LCP, parylene, and silicon. The three cantilever beams all demonstrated changes in resonant frequency when exposed to UV light of 10.4%, 8.13%, and 4.86%, respectively. The change in resonant frequency is dependent on the stiffness of the substrate, the thickness of the azo-LCP, the intensity and duration of the light exposure, and the wavelength of the light. The results in this paper validate that light responsive polymers can be used to reduce the frequency of MEMS cantilevers post-fabrication, which could lead to developing devices that can be precisely tuned for specific applications

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License

Inspeksi Kualitas Pengelasan Besi Menggunakan Teknik Segmentasi Citra Berbasis Convolutional Neural Network

Inspeksi pengelasan merupakan kebutuhan mutlak bagi dunia industri terutama yang bergerak dibidang otomotif untuk memastikan kualitas las. Namun demikian, sebagian besar industri masih menggunakan pemeriksaan manual yang bersifat subjektif dan penuh dengan bias yang dapat berakibat pada inkonsistensi dalam penilaian standar kualitas. Oleh karena itu, diperlukan suatu sistem cerdas yang dapat memeriksa kualitas pengelasan dengan konsisten. Penelitian ini bertujuan untuk membuat model kecerdasan buatan berbasis deep learning dan computer vision untuk mendeteksi area-area pengelasan dan mengklasifikasikannya kedalam kategori baik dan buruk. Model CNN dengan arsitektur UNet diadopsi untuk melakukan segmentasi citra pada gambar pengelasan besi. Studi penggunaan beberapa teknik ekstraksi fitur juga dilakukan untuk mendapatkan performa model terbaik berdasarkan skor IoU dan kecepatan konvergensi model. Berdasarkan hasil eksperimen, teknik CNN UNet terbukti mampu meningkatkan performa model dengan skor IoU sebesar 78,1% dan dengan kecepatan konvergensi dalam 144 epoch.--Welding inspection is an absolute necessity for the industrial world, especially those engaged in the automotive sector to ensure weld quality. However, most industries still use manual inspection which is subjective and full of bias which can result in inconsistencies in the assessment of quality standards. Therefore, intelligent system that can check the quality of welding consistently is needed. This study aims to create an artificial intelligence model based on deep learning and computer vision to detect welding spots and classify them into good and bad categories. CNN model with UNet architecture is adopted to perform image segmentation on iron welding images. Studies using several feature extraction techniques are also conducted to obtain the best model performance based on IoU scores and model convergence speed. Based on the experimental results, the UNet technique is proven to be able to improve the performance of the model with an IoU score of 78.1% and with a convergence speed of 144 epochs

Tuning MEMS cantilever devices using photoresponsive polymers

Microelectromechanical systems (MEMS) energy harvesting devices have had limited commercial success partly due to the frequency mismatch between the device and the vibration source. Tuning the cantilever device is one possible solution but developing a tunable MEMS device is difficult. This paper demonstrates a novel method of tuning a MEMS cantilever device post-fabrication by using light-responsive azobenzene liquid crystal polymers (LCP). Light exposure causes the photoresponsive polymers to change their elastic modulus, thus affecting the resonant frequency of the device. The photoresponsive polymer was integrated with three different MEMS cantilever substrates including LCP, parylene, and silicon. The three cantilever beams all demonstrated changes in resonant frequency when exposed to UV light of 10.4%, 8.13%, and 4.86%, respectively. The change in resonant frequency is dependent on the stiffness of the substrate, the thickness of the azo-LCP, the intensity and duration of the light exposure, and the wavelength of the light. The results in this paper validate that light responsive polymers can be used to reduce the frequency of MEMS cantilevers post-fabrication, which could lead to developing devices that can be precisely tuned for specific applications