We propose a quality control method based on machine learning neural networks to enhance the quality of high-frequency (HF) radar data. Unlike traditional quality control methods that rely on radar signals as indicators and involve extensive data manipulation in specialized software, our approach employs a Bi-LSTM neural network model. This method aims to improve data quality and streamline the quality control process. Through a series of analyses, we demonstrate the feasibility of using machine learning techniques to enhance radar data quality.
The Large Hadron Collider (LHC) is being prepared for an extensive upgrade to boost its particle discovery potential. The new phase, High Luminosity LHC, will operate at a factor-of-five-increased luminosity (the number proportional to the rate of collisions). Consequently, such…
This study aimed to investigate whether running kinematics can be accurately estimated through an artificial neural network (ANN) model containing GPS-based accelerometer variables and anthropometric data. Thirteen male participants with extensive running experience completed tre…
This investigation concentrates on refining the accuracy of earthquake parameters as reported by various Saudi seismic networks, addressing the significant challenges arising from data discrepancies in earthquake location, depth, and magnitude estimations. The application of soph…
To tackle the complex challenges inherent in gas turbine fault diagnosis, this study uses powerful machine learning (ML) tools. For this purpose, an advanced Temporal Convolutional Network (TCN)–Autoencoder model was presented to detect anomalies in vibration data. By synergizing…
Due to the high detection efficiency of the airborne time-domain electromagnetic method, it can quickly collect electromagnetic response data for large area-wide regions, but it also brings great challenges to the inversion interpretation of the data because there are numerous su…
Aiming at the imbalance of industrial control system data and the poor detection effect of industrial control intrusion detection systems on network attack traffic problems, we propose an ETM-TBD model based on hybrid machine learning and neural network models. Aiming at the prob…