# A Review of Acoustic Emission Source Localization Techniques in Different Dimensions
## 3 Sound Source Localization Methods for Two-Dimensional (2D) Structures
### Artificial Neural Networks  
An artificial neural network is an operational model composed of a large number of interconnected nodes (or neurons). Artificial neural networks abstract the neural network of the human brain from the perspective of information processing, establish a simple model and form different networks according to different connection methods. An artificial neural network (ANN) is a highly effective method for AE source localization. Its advantage is that it does not require knowledge of wave velocity and can be applied to anisotropic materials, but its disadvantage is that it requires a large amount of data and high computational requirements. Determining the position and size of the acoustic emission source from the displacement field is a reverse problem, and [PERSON] and [PERSON] [51] and [PERSON] et al. [52] used artificial neural networks and pattern recognition techniques to solve this inverse problem. This method can not only estimate its location, but also estimate the strength of the source, but it has high computational requirements and requires repeated training. [PERSON] et al. [53] attempted AE localization in anisotropic plates with nails and small holes and isotropic aluminum plate pairs using the least squares support vector machine. The AE source was generated by breaking a pencil lead. The dimensions of the tested aluminum plates with holes were 900 \(\times\) 900 mm\({}^{2}\), 600 \(\times\) 600 mm\({}^{2}\) and 600 \(\times\) 600 mm\({}^{2}\), while those of the anisotropic plate with a willow nail and holes were 600 \(\times\) 600 mm\({}^{2}\). The thickness was 11.88 mm. The errors varied between 9.8 mm and 1.9 mm. The advantages of the method were that it avoided local minima and could be used on small samples and in nonlinear conditions. The disadvantage was that the method required a large amount of data for training. Subsequently, [PERSON] et al. [54] utilized a Bayesian approach to sound source localization in clamped aluminum panels. They introduced a data fusion scheme combining the identification results obtained from arrival time difference data at different frequencies. They also improved the prediction accuracy, achieving a mean error of 2.74%. The disadvantages of this method were that the prediction accuracy was affected by frequency and it required a large amount of data, making it computationally intensive. [PERSON] et al. [55] used artificial neural networks to localize the sound source and compared the results to those of the time-based arrival difference-based localization method. They found that the localization accuracy was improved by a factor of six. By increasing the amount of data for training, the AE localization accuracy was improved. [PERSON] et al. [56] used a backpropagation artificial neural network for AE source localization from the data collected by a prismatically arranged sensor matrix on an anisotropic carbon fiber polymer plate (300 mm \(\times\) 300 mm \(\times\) 1.8 mm). They arranged a sensor array on an isotropic aluminum plate (300 mm \(\times\) 300 mm \(\times\) 4 mm) for a sound source localization test to verify the effectiveness of the algorithm, the localization accuracy of the method. Among a total of 21 test points, the error was large for only 5. The maximum error was 10.6 mm. For the anisotropic carbon fiber polymer plate, the maximum error was only 4.3 mm, and the test point of the maximum error was 6.4 mm. [PERSON] et al. [57] utilized convolutional neural networks and deep transfer learning to identify AE source types. A pencil core break and a steel ball impact were used to excite an aluminum plate and collect a large amount of data. An artificial neural network, Conv1D, Conv2D-TF and Conv2D-TF off were used and the AE source type recognition results were compared. [PERSON] et al. [19] firstly used a time-of-arrival-based in-plane source (IPS) localization algorithm for AE sources with rectangular, circular and sawtooth sensor arrangements.