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.

Significant advances have been made in emotional speech synthesis technology; however, existing models still face challenges in achieving fine-grained emotion style control and simple yet precise emotion intensity regulation. To address these issues, we propose Easy-Control Emotion Text-to-Speech (ECE-TTS), a zero-shot TTS model built upon the F5-TTS architecture, simplifying emotion modeling while maintaining accurate control. ECE-TTS leverages pretrained emotion recognizers to extract Valence, Arousal, and Dominance (VAD) values, transforming them into Emotion-Adaptive Spherical Vectors (EASV) for precise emotion style representation. Emotion intensity modulation is efficiently realized via simple arithmetic operations on emotion vectors without introducing additional complex modules or training extra regression networks. Emotion style control experiments demonstrate that ECE-TTS achieves a Word Error Rate (WER) of 13.91%, an Aro-Val-Domin SIM of 0.679, and an Emo SIM of 0.594, surpassing GenerSpeech (WER = 16.34%, Aro-Val-Domin SIM = 0.627, Emo SIM = 0.563) and EmoSphere++ (WER = 15.08%, Aro-Val-Domin SIM = 0.656, Emo SIM = 0.578). Subjective Mean Opinion Score (MOS) evaluations (1–5 scale) further confirm improvements in speaker similarity (3.93), naturalness (3.98), and emotional expressiveness (3.94). Additionally, emotion intensity control experiments demonstrate smooth and precise modulation across varying emotional strengths. These results validate ECE-TTS as a highly effective and practical solution for high-quality, emotion-controllable speech synthesis.