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The air quality monitoring system (AQMS) has attracted considerable attention due to its environmental significance and impact on human health. AQMS are critical for facilitating early-warning mechanisms to implement policies and protect urban communities. However, existing frameworks rely on physical sensors compromised by degradation, leading to unreliable decision-making. To overcome this limitation, this study introduces a region-wide soft sensor validation using a memory-integrated graph convolutional autoencoder (LSTM-GCN-AE). Results indicate that the relevance-embedded LSTM-GCN-AE outperforms the traditional GCN, achieving a 43.4 % improvement in reconstruction accuracy under precision faults and a 50.2 % enhancement in imputation performance for PM_2.5 sensor, identified through interpretability analysis of relevant nodes in the GCN. Moreover, the proposed framework successfully maintained consistency between predicted and actual environmental conditions, thereby enhancing the reliability of real-time AQMS data, health risk assessment, and early-warning mechanisms for urban air quality management.

In industrial process monitoring, factors such as production schedule changes, equipment aging, and environmental disturbances often lead to shifts in the underlying data distribution. These distributional changes tend to increase false alarm rates and undermine the reliability and adaptability of traditional fault detection methods, thereby compromising the safe and stable operation of industrial facilities. To address these critical challenges, a lifelong fault detection strategy that integrates a Kolmogorov-Arnold Network (KAN) with a novel test-time training (TTT) mechanism is proposed in this paper. Unlike conventional hybrid models, the proposed method introduces a new adaptation framework, in which reconstruction errors dynamically guide selective parameter updates. This allows the model to continuously adapt to distributional shifts without requiring retraining or labeled target data. During online test operation, the model automatically refines its hidden representations using trusted normal samples through a lightweight online update mechanism, thereby improving generalization and robustness under nonstationary conditions. Comprehensive experiments on a widely recognised benchmark dataset from the chemical industry demonstrate that the proposed method significantly outperforms existing state-of-the-art approaches, achieving 92.7 % correct monitoring rate with 0 % false alarm rate under nonstationary conditions.

This paper addresses the co-design problem of event-triggered control, feedback control law, and switching rules for discrete-time switched affine systems under unknown disturbances, from both model-based and data-driven perspectives. The main contribution is a unified framework that yields single LMI formulations applied to both model-based and data-driven settings and guarantees a tunable convergence region under the proposed switching strategies. For the model-based case, sufficient conditions are established for the co-designed control to ensure the practical exponential stabilization of the system. The convergence region can be tuned via parameters to actively compensate for disturbance effects. For the model-free scenario, a data-driven counterpart is developed, featuring a novel switching rule that eliminates the need for an explicit system model and improves computational efficiency. Within this setup, the event-triggered mechanism, switching rule, and control law are jointly synthesized by solving a single LMI, greatly simplifying the implementation. Simulations conducted on a DC-DC boost converter confirm the effectiveness of the proposed method in enhancing resource efficiency while maintaining system performance.

This work addresses the adaptive nonlinear control of a single-stage grid-connected photovoltaic (PV) system, focusing on harmonic current mitigation in highly distorted electrical grids. The proposed interfacing system employs a multilevel inverter topology, which improves power quality compared to its conventional two-level counterpart, particularly in single-stage PV applications. The control scheme employs a dual-loop structure to achieve the following three key objectives: (i) maximum power extraction from the PV arrays, (ii) reduced voltage stress on the switching devices, and (iii) power factor correction (PFC). To enhance performance under realistic grid conditions, the controller incorporates an adaptive observer for real-time estimation of grid voltage and impedance, which are often assumed to be known or directly measurable. The controller's design and analysis are carried out using Lyapunov stability theory. The theoretically predicted performances of the controller are confirmed by numerical simulations performed in Matlab/SimPowerSystems environment and in a Processor-In-the-Loop (PIL) implementation.

This study proposes a novel methodology to develop and manage data-driven models for ship machinery Prognostics and Health Management (PHM). A four-stroke marine engine is investigated considering exhaust valve wear degradation. Simulated datasets are generated using a physics-based digital twin integrated with stochastic degradation models. Health indicators (HI) construction and forecast sub-models are developed, based on Multi-Layer Perceptron and Bayesian Neural Networks, respectively. Data-driven model management employs error and uncertainty metrics for deciding re-training of HI forecast sub-models, resulting in R² increases from 0.24 to 0.89 and from 0.26 to 0.94 in Cases 1 and 2, respectively. This is the first study that integrates thermodynamic models with stochastic degradation models to develop marine engine digital twins, while also introducing data-driven model management, thus contributing to the PHM system adoption by the maritime industry.

As equipment ages, the distortion of relevant parameters leads to increasingly severe degradation in control performance. This paper proposes a novel aging-free estimationless sliding mode control strategy for interior permanent magnet synchronous motor (IPMSM) in low-carbon transportation systems. Firstly, a time-varying disturbance (TVD)-based aging model is proposed. The TVD integrates multi-stage dynamic disturbance transition models for various aging parameters of the IPMSM. Then, a multi-condition correction (MCC)-based sliding mode current control method is proposed. The MCC associates the sliding surface parameters with the temporal factor and integrates the correction functions in the reaching law. Finally, the stability conditions are derived based on the Lyapunov function. The proposed strategy is validated by the hardware-in-the-loop (HIL)-based platform. The test results show that the proposed strategy can reduce the current control error of the aging motor, thereby reducing system power losses and significantly enhancing the operational efficiency of the aging IPMSM for the low-carbon transportation system.

In industrial data stream environments, the acquisition of real quality variables is challenging and subject to delay, posing significant obstacles to effective updates of adaptive soft sensors. To this end, this paper proposes a novel framework, Adaptive Soft Sensor for Label Delay (ASSLD). An adaptive multilevel regression model, weighting and integrating outcomes from layers at different depths, is designed to enhance online adaptability. To efficiently reuse historical labeled data, a diverse database is maintained online, from which similar samples are selected and weighted. Moreover, unlabeled samples within the delay time are utilized to help accommodate recent data. The experimental results on the sulfur recovery unit dataset and polyester dataset show the effectiveness of ASSLD in handling label delay, with accuracy improvements of more than 12 % over baselines.

With the deep digital transformation of traditional manufacturing industry and the continuous automation level improvement of production lines, it is more important to predict the Key Performance Indicators (KPIs) of processes in a timely and accurate manner. The traditional laboratory destructive test method for obtaining KPIs consumes a large amount of time and incurs high costs, which not only fails to provide timely and effective guidance for production processes but also results in significant losses for manufacturing enterprises. To address these issues, an online prediction soft sensor model for KPIs based on a serial-parallel gated recurrent unit with self-attention mechanism (SPGRU-SA) soft sensor model is proposed. This model achieves accurate online prediction of KPIs by considering both the dynamic features of multi-unit processes and the static features of process setups. First, a serial-parallel gated recurrent unit model is designed to extract multi-unit dynamic features. Second, based on the self-attention mechanism, the attention weights of static features and dynamic features are calculated, which can reflect the correlation of the performance indicators. Then, the fully connected layers output the result. Finally, the comparative experimental results based on the hot rolling strip mill process and the Tennessee Eastman process show that SPGRU-SA can accurately predict the KPIs of complex multi-unit industrial processes.

High-power induction furnace (IF) is a highly complex thermoelectric system with strong nonlinear time-varying characteristics. The lack of direct online measurement methods complicates status awareness, leading to apparent "black-box" behavior and sensing difficulties. We propose a transferable layered physics-informed learning-based modeling approach to address the above challenges. At the underlying level, a series of linear physical models are established at multiple operating points to guide the data-driven models, thus comprehensively capturing the data characteristics and electrical dynamics of the IF. At the upper level, physical prior knowledge-based global nonlinear constraints are introduced to ensure the model accuracy and physical consistency. Each underlying model can be regarded as a single task, and the modeling problem is skillfully transformed into a multitask learning optimization with global nonlinear constraints. In addition, a transferable model training strategy with an architecture of cascaded shared layers and task layers is developed to facilitate knowledge transfer between adjacent melting batches and thereby optimize the training process. The feasibility and effectiveness of the scheme are validated by experiments using actual sampling data.

Soft sensors of quality indicators for industrial production processes compensate for the shortcomings of traditional measurement methods, which are essential for improving product quality. However, the complex mechanisms and time-varying delays in multi-unit, long-flow industrial processes pose significant challenges for multi-indicator soft sensing. Existing research on the fusion of mechanical and data-driven models is not sufficiently advanced. To address these challenges, a mechanism and data fusion driven multi-indicator soft sensor framework for industrial processes is proposed, using the hot strip rolling process (HSRP) as a case study. First, the mechanism of HSRP is analyzed and the unknown parameters in the mechanism model are identified. Second, the data derived from the mechanistic model are fused with the process data. Then Kolmogorov-Arnold Networks with an embedded time-series input layer (TS-KAN) are developed to address the challenge of time-varying delays caused by long production processes and production fluctuations. Finally, the proposed framework is validated using actual HSRP production data, achieving simultaneous accurate prediction of strip flatness and crown.