Abstract Human Activity Recognition (HAR) allows for unobtrusive indoor monitoring, particularly in elderly care. However, existing HAR methods face significant challenges due to the variability in home layouts, sensor types, and activity labels across different datasets, which limits their generalization and scalability. Most approaches require extensive customization, making cross-environment HAR implementation challenging in real-world scenarios. To address these challenges, we propose a unified HAR framework that introduces Functional Areas, which abstract physical spaces into standardized activity zones, and Detector Units, which map heterogeneous sensor configurations into a common representation. We evaluate our framework using multiple publicly available HAR datasets based on ambient sensor data of smart homes, testing two model architectures: a Holistic Approach, which trains a single GRU-based neural network on the combined datasets, and a Reductionist Approach, which employs an ensemble bagging method. The Holistic Approach demonstrated superior generalisation, achieving 0.84 precision and 0.73 accuracy, outperforming the reductionist approach.

The efficacy of vibration-based fault diagnosis using CNN models could be improved through efficient image extraction from the vibration signals. This study proposed a time-frequency domain vibration image extraction framework based on spectral kurtogram to enhance the classification performance of a Bayesian-tuned CNN model in wind turbine gearbox fault diagnostics. Time-frequency domain kurtograms extracted from vibration signals of a wind turbine gearbox are employed to train a CNN model optimized by Bayesian optimization. Findings of the study show that the kurtogram-based CNN model performed better (recording 171 fewer alarms) than a comparable model trained on images extracted from the time domain vibration signals, highlighting the efficacy of the kurtogram method. In a similar fashion, the Bayesian-optimized CNN model with a classification accuracy of 99.1% recorded 79 fewer false alarms than its standalone counterpart which had an accuracy of 93.2%. It can be concluded that the optimized CNN model trained on Kurtograms is a potential tool for reliable vibration-based condition monitoring of the wind turbine gearbox, minimizing the incidence of false alarms.

This fourth issue of the tenth volume of the Journal of Reliable Intelligent Environments collects eleven articles in various technical areas.

Near-field tides prediction for tsunami detection in the coastal area is a significant problem of the cable-based tsunami meter system in north Sipora, Indonesia. The problem is caused by its shallow water condition and the unavailability of an applicable model or research for tsunami detection in this area. The problem foundation of shallow water area is its ambient noise level-dependent property that requires preprocessing to improve its feature representation. Moreover, because this shallow water is close to the land area, we must consider a model that can accommodate low prediction time for a Tsunami Early Warning System. Therefore, we propose a recurrent neural network (RNN) model because of its reliable performance for time series forecasting. Our report evaluates variants of the RNN model (the vanilla RNN, LSTM and GRU models) in tides prediction and z-score analysis for tsunami identification. The GRU model overwhelms the other two variants in error scores and time processed (training and prediction). It can achieve median error score distribution of 7.8×10-5\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$7.8 \ imes 10^{-5}$$\\end{document} on the L1000-P250 configuration with time prediction under 0.1 s. This lower-time prediction is necessary to ensure the early warning system is going well. Moreover, the GRU model can identify all synthetic tsunami tide spikes (compared with the ground truth result) from magnitude 7.2–8.2 by applying a z-score on the GRU’s prediction.

In the last years, there has been a growing interest in the emerging concept of digital twins (DTs) among software engineers and researchers. DTs not only represent a promising paradigm to improve product quality and optimize production processes, but they also may help enhance the predictability and resilience of cyber-physical systems operating in critical contexts. In this work, we investigate the adoption of DTs in the railway sector, focusing in particular on the role of artificial intelligence (AI) technologies as key enablers for building added-value services and applications related to smart decision-making. In this paper, in particular, we address predictive maintenance which represents one of the most promising services benefiting from the combination of DT and AI. To cope with the lack of mature DT development methodologies and standardized frameworks, we detail a workflow for DT design and development specifically tailored to a predictive maintenance scenario and propose a high-level architecture for AI-enabled DTs supporting such workflow.