Land subsidence prediction using recurrent neural networks

Modeling and zoning of land subsidence in the southwest of Tehran using artificial neural networks

The earth's surface, due to its natural conditions and its structure is always changing and reshaping. One of the created deformations is the land subsidence. This is the most dangerous events which can be seen in most urban areas especially in the agricultural plains today. This study aims at zoning land subsidence and recognition of geometrical factors in southwest of Tehran. To estimate and ...

Using Recurrent Neural Networks in Trajectory Prediction

MIMO Channel Prediction Using Recurrent Neural Networks

Adaptive modulation is a communication technique capable of maximizing throughput while guaranteeing a fixed symbol error rate (SER). However, this technique requires instantaneous channel state information at the transmitter. This can be obtained by predicting channel states at the receiver and feeding them back to the trasnmitter. Existing algorithms used to predict single-input single-output...

rodbar dam slope stability analysis using neural networks

در این تحقیق شبکه عصبی مصنوعی برای پیش بینی مقادیر ضریب اطمینان و فاکتور ایمنی بحرانی سدهای خاکی ناهمگن ضمن در نظر گرفتن تاثیر نیروی اینرسی زلزله ارائه شده است. ورودی های مدل شامل ارتفاع سد و زاویه شیب بالا دست، ضریب زلزله، ارتفاع آب، پارامترهای مقاومتی هسته و پوسته و خروجی های آن شامل ضریب اطمینان می شود. مهمترین پارامتر مورد نظر در تحلیل پایداری شیب، بدست آوردن فاکتور ایمنی است. در این تحقیق ...

Logfile Failure Prediction using Recurrent and Quasi Recurrent Neural Networks

Inumerable man hours are spent waiting for running programs to finish and then debugging them when they crash by analyzing the logfile. Often times logfiles will contain a plethora of text extraneous to debugging, providing for a tedious undertaking. Therefore, we present an application of state-of-the-art natural language processing with neural networks to classify a run’s final outcome prior ...