Acoustic Emission (AE) technology faces challenges in concrete structures due to anisotropy and heterogeneity, causing wave velocity variations, reflection, refraction, and localization in-accuracies. To overcome these limitations, this study introduces a novel damage source localization approach that integrates AE technology with machine learning models. The proposed method utilizes a sensor array to capture AE signals, accurately determining their arrival times using the Akaike Information Criterion (AIC). Subsequently, the Time Difference of Arrival (TDOA) between sensors is computed as input features for the model. For linear localization, a linear regression model establishes a direct relationship between TDOA and damage locations. For planar localization, lightweight models based on deep neural networks (DNN), one-dimensional convolutional neural networks (1D-CNN), and long short-term memory networks (LSTM) are developed to balance computational efficiency and localization accuracy. Additionally, a fine-tuning strategy is implemented to adjust the models with a minimal amount of new data, enhancing their adaptability to the diverse characteristics of different concrete slabs. Compared to conventional localization techniques and recent deep learning-based methods, the proposed approach demonstrates significant advancements in adaptability, computational efficiency, and localization accuracy. These improvements highlight its superior generalization capabilities and potential for practical applications.

In order to improve the speech enhancement ability of FullSubNet model, an improved method FullSubNet-pMix is proposed. Specifically, pMix module is added to the structure of full-band frequency domain information processing, which realizes the information interaction between amplitude spectrum and phase spectrum. At the same time, the hyperparameters used in training are optimized so that the full-band and sub-band structure of the system can play a better role. Experiments are carried out on selected test sets. The experimental results show that the proposed method can independently improve the speech enhancement effect of the model, and the effect on the four evaluation indicators of WB-PESQ, NB-PESQ, STOI, and SI-SDR is better than the original model. Therefore, the FullSubNet-pMix method proposed in this paper can effectively enhance the ability of the model to extract and use voice information. The impact of different loss functions on the training performance was also verified.

Ultrasonic guided wave (UGW) inspection of small-diameter pipes is complicated by the narrow usable frequency band and by strong susceptibility to measurement noise, both of which degrade the reliability of damage identification. This study proposes a multi-channel attention Mamba-2 (MCA-Mamba-2) network that couples a convolutional front end with a bidirectional Mamba-2 state-space backbone and combines multi-head self-attention with channel attention for feature fusion. The network takes as input the four waveforms recorded simultaneously by a circumferential receiver array so that the input tensor has four physical channels that are subsequently expanded into learned feature channels. A dataset of ten damage states, spanning non-through-wall abrasions, through-wall holes and one multi-defect case, was acquired on a 20 mm outer-diameter steel pipe specimen. Averaged over three independent runs, MCA-Mamba-2 reaches 98.01 ± 0.85% accuracy on noise-free signals and 81.09 ± 1.74%, 83.65 ± 1.34%, 82.49 ± 0.20% and 84.15 ± 1.04% on 0 dB test sets contaminated with white, pink, Gaussian and Laplacian noise respectively, exceeding the Mamba-2 baseline in all sixteen tested noise-type and SNR combinations, by up to 17.87 percentage points. An ablation study attributes the improvement principally to the convolutional mixing block. A cross-noise evaluation reveals that accuracy drops by 59.80 percentage points, on average, at 0 dB when the training and testing noise distributions differ, indicating that the matched-noise accuracies are not indicative of robustness to unknown noise conditions; however, a single model trained on a mixture of all noise types and SNR levels recovers an average accuracy of 91.21% across the sixteen test conditions. Because each damage state was recorded in a single acquisition session, the dataset was partitioned at the level of individual records rather than by session, and the reported accuracies should be interpreted as an upper bound on the performance attainable on independently acquired data. The results were obtained on a single pipe geometry with fixed defect positions and synthetically added noise; extension to other geometries and to field measurements remains to be demonstrated.