Van Trung Nguyen

The rapid expansion of solar and wind energy systems has increased the demand for reliable monitoring solutions, particularly in regions where network infrastructure is limited or unstable. In Vietnam, most existing monitoring approaches primarily target single-source renewable installations and rely on WiFi or cellular networks, which are often unavailable in remote hybrid deployment scenarios. To address this limitation, this paper presents a low-cost IoT (Internet of Things)-based monitoring system for hybrid solar–wind energy applications, with an emphasis on reliable data acquisition under constrained connectivity conditions. The proposed system utilizes an ESP32 microcontroller integrated with a LoRa communication module to support long-range wireless transmission, while a local data buffering mechanism is employed to maintain data continuity during temporary network outages. Key electrical and environmental parameters, including voltage, current, temperature, and wind speed, are continuously monitored. Collected measurement data are stored in an InfluxDB time-series database and visualized using Grafana dashboards for real-time and historical analysis. Experimental validation demonstrates stable LoRa communication over distances of approximately 400 m in urban environments and up to 700 m in open areas, as well as consistent monitoring performance during WiFi interruptions and varying renewable input conditions. The proposed system provides a practical and reliable monitoring solution for hybrid renewable energy systems operating under limited connectivity and establishes a foundation for future extensions toward advanced data analytics and remote supervision.

Reliable fault diagnosis in rotating machinery is challenging due to the nonlinear and non-stationary nature of vibration signals. Although time–frequency analysis is widely used, it cannot capture the cross-scale coupling between amplitude-modulated (AM) and frequency-modulated (FM) components that carry essential diagnostic information. This study applies Holo-Hilbert Spectrum Analysis (HHSA) to extract amplitude–frequency modulation features and integrates them with six machine learning classifiers to identify four fault conditions. Random Forest, K-Nearest Neighbors, and Logistic Regression achieve accuracies of up to 99.95%, yielding higher accuracy than Fast Fourier Transform-based features. The proposed framework employs an HHSA-based feature extraction pipeline that effectively captures AM–FM coupling in nonlinear vibration signals. It also provides higher discriminative capability than traditional spectral approaches and maintains robustness across multiple classifiers. This method offers high diagnostic accuracy and strong potential for industrial predictive maintenance. Future work will focus on improving computational efficiency and evaluating the framework under more diverse and realistic operating conditions. Received: 10 September 2025 | Revised: 20 April 2026 | Accepted: 10 June 2026 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The VBL-VA001 datasets that support the findings of this study are openly available at https://doi.org/10.1007/s42417-023-00959-9, reference number [44]. Author Contribution Statement Van-Trung Nguyen: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Data curation, Writing - original draft, Writing - review & editing, Visualization. Ba-Tan Le: Investigation, Data curation, Writing - original draft, Writing - review & editing. Van-Phuong Dao: Methodology, Validation, Writing - review & editing.

This study introduces a robust approach for denoising pressure signals by integrating Improved Complete Ensemble Empirical Mode Decomposition (ICEEMDAN), Continuous Mean Square Error (CMSE) analysis, optimal wavelet selection, and wavelet thresholding techniques. ICEEMDAN decomposes noisy signals into Intrinsic Mode Functions (IMFs), which are classified via CMSE to distinguish noise-dominated IMFs from signal-dominated IMFs. A key innovation is the sparsity-based optimal wavelet selection strategy, which dynamically identifies the most effective wavelet basis and decomposition levels for each noise-dominated IMF. These IMFs undergo wavelet thresholding for noise suppression, while signal-dominated IMFs are preserved to ensure signal integrity during reconstruction. The proposed approach was validated on simulated signals and experimental data from lean syngas/air combustion at equivalence ratios. Results consistently demonstrate superior denoising performance across various metrics compared to traditional infinite impulse response (IIR) filters, empirical mode decomposition (EMD) and conventional ICEEMDAN-wavelet methods. By dynamically adapting wavelet selection and decomposition levels to varying signal and noise characteristics, this method achieves optimal noise suppression with high signal fidelity. Heursure and Minimaxi methods use hard thresholding and reveal consistent outperformance compared to Sqtwolog and Rigrsure in both simulated and real-world scenarios. This approach offers significant advancements in engine performance optimization, fuel efficiency improvement, combustion diagnostics, and industrial pressure monitoring. By bridging theoretical innovation with practical applications, the proposed method provides a scalable and precise solution for signal reconstruction in complex environments.

Response inhibition has been widely explored using the stop signal paradigm in the laboratory setting. However, the mechanism that demarcates attentional capture from the motor inhibition process is still unclear. Error monitoring is also involved in the stop signal task. Error responses that do not complete, i.e., partial errors, may require different error monitoring mechanisms relative to an overt error. Thus, in this study, we included a “continue go” (Cont_Go) condition to the stop signal task to investigate the inhibitory control process. To establish the finer difference in error processing (partial vs. full unsuccessful stop (USST)), a grip-force device was used in tandem with electroencephalographic (EEG), and the time-frequency characteristics were computed with Hilbert–Huang transform (HHT). Relative to Cont_Go, HHT results reveal (1) an increased beta and low gamma power for successful stop trials, indicating an electrophysiological index of inhibitory control, (2) an enhanced theta and alpha power for full USST trials that may mirror error processing. Additionally, the higher theta and alpha power observed in partial over full USST trials around 100 ms before the response onset, indicating the early detection of error and the corresponding correction process. Together, this study extends our understanding of the finer motor inhibition control and its dynamic electrophysiological mechanisms.