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根据国家标准，镇流器的正常工作条件，电源电压的波动范围允许在220V的 6% 220V的-8%之间，即在198V 232V间。
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LED驱动电源把电源供应转换为特定的电压电流以驱动LED发光的电压转换器，通常情况下 LED驱动电源的输入包括高压工频交流 即市电 、低压直流、高压直流、低压高频交流 如电子变压器的输出 等。 1 恒流式恒流驱动电路输出的电流是恒定的，而输出的直流电压却随着负载阻值的大小不同在一定范围内变化，负载阻值小，输出电压就低，负载阻值越大，输出电压也就越高 恒流电路不怕负载短路，但严禁负载完全开路 恒流驱动电路驱动LED是较为理想的，但相对而言价格较高 应注意所使用最大承受电流及电压值，它限制了LED的使用数量。 2 稳压式当稳压电路中的各项参数确定以后，输出的电压是固定的，而输出的电流却随着负载的增减而变化 稳压电路不怕负载开路，但严禁负载完全短路 以稳压驱动电路驱动LED，每串需要加上合适的电阻方可使每串LED显示亮度平均 亮度会受整流而来的电压变化影响。
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Sina Honari's blog for Representation Learning | My blog on the Representation Learning Course
Sina Honari's blog for Representation Learning. My blog on the Representation Learning Course. Skip to primary content. Skip to secondary content. Fine tuning of unsupervised pretraining. May 11, 2013. Here are the results for the model with GCN preprocessing. In both experiments I had to set the learning rate to 1e-5. The training algorithm diverged using higher values of the learning rates . As the results demonstrate, sigmoid units work better with GCN. May 10, 2013. In the next attempt, I used unsupe...
ift6266speechsynthesisjt.wordpress.com
Experiments in deep learning for speech synthesis | Jessica Thompson's journal for IFT6266 – Representation Learning at Université de Montréal
Experiments in deep learning for speech synthesis. Jessica Thompson's journal for IFT6266 – Representation Learning at Université de Montréal. Skip to primary content. Skip to secondary content. January 24, 2014. This blog will document my progress on the final project for Dr. Yoshua Bengio’s course on Representation Learning. The task for the project is speech synthesis. April 30, 2014. Vincent published a blog post on h ow to use the Pylearn2 TIMIT class with multiple sources, specifically c. Monitor i...
Speech synthesis experiments | Smile! You’re at the best WordPress.com site ever
You’re at the best WordPress.com site ever. The plan, at this point, is to use a gammatone dictionary and sparse coding to train more efficiently on the TIMIT dataset. Gammatone functions are filters applied on frequencies. The goal is to keep frequencies processed by our ears. Joao explains this greatly in his blog. The idea is to use this sparse-coded version as input of a spike and slab RBM segmented by frames of 160 samples and with the previous, current and next phones. I kept the parameters use...
IFT6266 project | Experiments in speech synthesis with neural networks by Pierre-Luc Vaudry
Experiments in speech synthesis with neural networks by Pierre-Luc Vaudry. Predicting the standard deviation. Hubert Banville successfully implemented the kind of architecture I also had in mind. In this experiment, I wanted to see if having this standard deviation predicted by the neural network could improve the results. I based myself on Hubert’s code, which is described in his last post. To this loss function, the L1 and L2 norm penalties are also added as regulators, as in Hubert’s experiments...
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