实际生产响应时间将根据机器、内存量、网络负荷和速度、Web服务器负荷和其他应用程序消耗的处理时间而有所不同。
Actual production response times will vary by machine, amount of memory, network load and speed, Web server load, and the processing time consumed by other applications.
在神经网络负荷预测实际应用中，突出的问题是训练样本大、训练时间长、收敛速度慢。
In application of neural networks based short-term load forecasting model, the main problems are over many training samples, thus resulting long training time and slow convergence speed.
目的探讨应用定量组织速度成像(QTVI)技术结合小剂量多巴酚丁胺负荷超声心动图试验(LDDSE)诊断冠心病的价值。
Objective To clarify feasibility for diagnosing coronary artery disease by quantitative tissue velocity imaging (QTVI) technique during low-dose dobutamine stress echocardiography (LDDSE).
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