http://bura.brunel.ac.uk/handle/2438/8630 2026-04-17T09:41:57Z http://bura.brunel.ac.uk/handle/2438/33159 Title: Multi-Scale Decoupling of Industrial Dynamics Via Trend-Fluctuation Interaction-Aware Transformer for Quality Prediction Authors: Xiao, L; Wang, P; Fang, Y; Wang, Z Abstract: Accurate prediction of key quality variables is crucial for monitoring and optimizing modern industrial processes. However, most existing methods remain constrained by single-scale modeling, making it difficult to capture long-term global trends and short-term local fluctuations simultaneously. In addition, the dynamic couplings between these multi-scale components are often overlooked, leading to insufficient feature extraction. To address these limitations, a multi-scale trend-fluctuation interaction-aware transformer (MTI-Former) is proposed in this paper. First, a decoupling layer based on discrete wavelet transform (DWT) is designed to decompose industrial data into low-frequency trend and high-frequency fluctuation signals. Then, an adaptive high-pass enhancement filter is introduced to amplify critical high-frequency details and improve the perception of local disturbances. Cross-scale coupling is modeled through a trend-fluctuation interaction-aware attention module, which captures dynamic interactions between trends and fluctuations. Subsequently, a trend-fluctuation decoupling attention module applies a dual-path cross-attention mechanism to separately extract global dependencies and local variations. Finally, a gating mechanism fuses these representations to generate comprehensive multi-scale temporal predictions. The effectiveness of MTI-Former is verified on two real industrial datasets, and extensive results show that it outperforms several state-of-the-art methods in industrial quality prediction. 2026-04-13T00:00:00Z http://bura.brunel.ac.uk/handle/2438/33156 Title: Tldiag: a federated learning-based DB-mixer for privacy-preserving and fault diagnosis in transmission lines Authors: Mao, C; Wen, C; Wang, Z; Liu, W; Yang, J Abstract: As a critical component of smart grids where faults occur frequently, the rapid and accurate diagnosis of transmission line faults is essential for enhancing system reliability and response efficiency. However, existing centralized diagnostic methods face significant challenges, including data privacy concerns, limited feature representation capabilities, and class imbalance issues in real-world power grids. To address these issues, this paper proposes an adaptive diagnostic framework based on federated learning and a dual-branch mixer (TLDiag) to address the privacy-sensitive cross-domain fault diagnosis problem in dynamic grid environments. Specifically, a distributed federated learning architecture is designed to ensure data privacy by enabling localized model training through collaborative client–server interactions. Furthermore, a dual-branch mixer, i.e., DB-Mixer, guided by a dual-branch attention mechanism (DBAM), is developed to enhance feature representation by jointly modeling spatial and channel-wise information. Additionally, an adaptive dual-field loss (ADF loss) is introduced, incorporating dynamic task weighting and physical constraints to effectively mitigate class imbalance and improve diagnostic robustness. Extensive experiments conducted on the IEEE 5-bus system demonstrate that TLDiag achieves superior performance in both fault type classification (97.53%) and fault location identification (98.34%), while exhibiting stable convergence across varying client scales. Compared to baseline methods such as MDCNN and CNN-LSTM, TLDiag significantly outperforms in accuracy and robustness. By deeply integrating federated learning with the physical characteristics of power systems, this approach offers a high-accuracy, privacy-preserving solution for real-time fault diagnosis in smart grid scenarios. Description: Data availability: The data supporting the findings of this study are publicly available from the IEEE Data Port (https://ieee-dataport.org/documents/transmission-line-fault-using-line-voltages-and-currents-features). 2026-03-21T00:00:00Z http://bura.brunel.ac.uk/handle/2438/33151 Title: Extracting Meaningful Insights from User Research Videos Authors: Ghatoray, SK; Li, Y Abstract: Recognising and tracking user emotions in research videos is vital to understanding user needs and expectations. Limited research exists on automating emotion extraction from multimodal videos in user experience (UX). This study proposes a conceptual framework for automated extraction of actionable insights using facial, speech-to-text, and text-based emotion recognition to capture nuanced emotional data. The multimodal approach integrates visible and spoken cues through temporal alignment and fusion techniques, enabling robust behavioural pattern detection. An interactive AI analyst tool is used to query the integrated data in natural language, reduce manual workload, and improve the efficiency and scalability of UX evaluation. A case study of the implementation of the proposed framework is also provided with details of individual components, such as facial emotion recognition, speech-to-text, text-based emotion recognition, temporal alignment and fusion, and insight extraction via interactive AI. 2026-02-06T00:00:00Z http://bura.brunel.ac.uk/handle/2438/33138 Title: LLM-Assisted Optimisation of Multi-RIS Placement and Beamforming in Smart Warehouses Authors: Yuan, C; Hou, J; Yu, G; Qiu, K; Wang, K; Hu, H; Zhang, J Abstract: In this paper, we propose an optimisation framework for deployment of multiple reconfigurable intelligent surfaces (RISs) to meet the wireless coverage demands for smart warehouses. Specifically, we are the first to formulate a unified network optimisation task that jointly considers RIS placement and beamforming to maximize overall network coverage with a deterministic channel model to accurately describe the multipath effect for the warehouse. To address this problem, we design a hybrid optimisation framework composed of three synergistic modules. (1) A Large Language Model (LLM) acts as a semantic planner that generates physically feasible multi-RIS configurations, jointly determining the placement and beamforming directions guided by structured prompts and environment-aware embeddings. (2) A Genetic Algorithm (GA) module performs local numerical refinements to enhance the precision of LLMgenerated solutions under physical constraints. (3) A Diversity Reflection and Correction (DiRect) module evaluates structural similarity among candidate configurations and triggers additional semantic regeneration to maintain exploration diversity. These three modules form an alternating iterative process in which LLM reasoning, GA-based evolution, and DiRect-driven regeneration collectively guide the optimisation toward high-coverage configurations. Extensive simulations validate the effectiveness and robustness of the proposed framework. Compared with traditional heuristics, reinforcement learning methods, and LLMguided baselines, our hybrid framework achieves 10%-15% higher coverage within 10-20 iterations. The performance consistently scales with the number of RISs and element sizes, and remains stable under varying transmitter positions, demonstrating strong adaptability to complex smart warehouse layouts. Overall, the proposed hybrid optimisation framework provides a scalable and physically grounded solution for RIS-assisted network deployment optimisation in realistic in. 2026-03-16T00:00:00Z