波之间的转换，wave2wave
Translation Between Waves, wave2wave
摘要：
借助大数据的先进深度学习方法，对传感器数据的理解得到了极大的提升。然而，在现实世界中，可用的传感器数据仍然是有限的，这被称为机会传感器问题。本文提出了一种新的变种神经机器翻译seq2seq来处理连续信号波，通过引入基于窗口的（逆）表示，自适应地表示波的部分形状和迭代的高维数据的反向翻译模型。实验结果显示两个现实生活中的数据：地震和活动翻译。一维数据的性能改进约为46%的测试损失和高维数据的困惑是关于原始seq2seq的约1625%。
英文摘要：
The understanding of sensor data has been greatly improved by advanced deep learning methods with big data. However, available sensor data in the real world are still limited, which is called the opportunistic sensor problem. This paper proposes a new variant of neural machine translation seq2seq to deal with continuous signal waves by introducing the window-based (inverse-) representation to adaptively represent partial shapes of waves and the iterative back-translation model for high-dimensional data. Experimental results are shown for two real-life data: earthquake and activity translation. The performance improvements of one-dimensional data was about 46% in test loss and that of high-dimensional data was about 1625% in perplexity with regard to the original seq2seq.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
