Shumei Zhang

Carbon market prices are jointly shaped by policy interventions, energy market fluctuations, and macroeconomic dynamics, and thus exhibit pronounced nonlinearity, non-stationarity, localized abrupt changes, and time-varying uncertainty. From an information-theoretic perspective, carbon price forecasting can be viewed as the extraction and fusion of effective information from a complex market system driven by heterogeneous endogenous and exogenous signals. To address the challenges of accurately characterizing local high-frequency fluctuations in carbon price series, effectively modeling the interactions between endogenous and exogenous variables, and mitigating the structural noise introduced by conventional serial forecasting frameworks, this study proposes ConvTimeXer, a hybrid model combining bidirectional temporal convolution and TimeXer for carbon market price forecasting. Specifically, the model first employs front-end bidirectional temporal convolutions to extract local multi-scale fluctuation features from the endogenous carbon price series. It then leverages the global token and cross-attention mechanism in TimeXer to achieve dynamic decoupling and deep interaction between endogenous and exogenous variables. Finally, residual fusion of shallow and deep features is introduced to enhance the preservation of local details. Experimental results based on data from China’s carbon market over the past three years demonstrate that the proposed framework delivers high predictive accuracy and strong robustness, effectively balancing responsiveness to local abrupt changes with global trend modeling. This study not only provides an effective approach for carbon price forecasting in complex and uncertain market environments, but also offers valuable insights into non-stationary time-series forecasting driven by multi-source heterogeneous information.

Transfer learning has been widely applied to intelligent fault diagnosis to address the challenge of insufficient labeled data. However, the efficacy of existing semi-supervised domain adaptation (SSDA) methods is often constrained by their critical dependence on pseudo-label quality and the adoption of indiscriminate global alignment strategies. To address the negative transfer induced by these limitations, a deep gated network (DGN) for targeted transfer learning is proposed in this article. First, an anchor-guided manifold clustering (AGMC) method is developed to generate high-quality pseudo-labels by exploiting both the local manifold structure and anchor supervision in the target domain. Subsequently, a feature extractor is constructed to learn discriminative representations from the source domain, integrate high-quality supervisory information from the target domain, and impose constraints on the feature space. Furthermore, a gated domain alignment strategy is designed to achieve precise class-level transfer. This strategy incorporates an integrated gating mechanism to selectively filter out domain-specific features while employing the local maximum mean discrepancy (LMMD) to align the conditional distributions across domains. Finally, the effectiveness and superiority of the proposed method are validated through transfer experiments on both cross-machine bearing and cross-condition two-phase flow datasets.

Oil–gas–water three-phase flow process exhibits randomness and transience. Under varying flow conditions and environmental factors, the process presents a dual challenge in state monitoring: data distribution discrepancies between source and target domains, along with the presence of unknown states in the target domain. Therefore, a cross-domain open-set state monitoring method based on multigrained adversarial network with hierarchical attribute causality (MANHAC) is presented in this work. A domain adversarial architecture is designed to distinguish unknown from known target flow states, in which the weighted thresholding method based on information entropy can adjust the decision boundary adaptively. Besides, to mitigate conditional distribution mismatch and negative transfer caused by forced global domain alignment, MANHAC introduces hierarchical causal attributes for describing different flow states, where attribute features are influenced by upstream cause attribute and optimized through competition with multiple discriminators. The proposed MANHAC employs a dual-alignment mechanism to achieve both global and fine-grained domain adaptation, which can effectively carry out cross-domain open set state identification. More importantly, it can describe unknown states by attribute vectors, providing more meaningful monitoring information. Dynamic experiments of three-phase flow demonstrate its effectiveness and superiority.

This article presents a decentralized dynamic event-sampled control (ESC) strategy for multi-unit power systems (MUPSs) subject to mismatched interconnections. Initially, with the introduction of a dynamic event-sampling mechanism, the decentralized ESC problem of MUPSs is converted into a set of event-sampled optimal control problems of auxiliary subsystems. It is demonstrated that all the solutions of the event-sampled Hamilton-Jacobi-Bellman equations (ES-HJBEs) arising in these optimal control problems together constitute the decentralized dynamic ESC law. Then, in order to solve the ES-HJBEs, the critic neural networks (CNNs) within the adaptive dynamic programming framework are constructed. The CNNs’ weights are updated via simultaneously using the gradient descent method and concurrent learning. The benefits of such a tuning rule are that it not only makes full use of state data (including historical and instantaneous state data) but also no longer requires the persistence of excitation condition. Moreover, uniform ultimate boundedness of the closed-loop auxiliary subsystem states and the CNNs’ weight estimation errors are proved via Lyapunov approach. Finally, simulations of a three-unit power system are given to validate the present decentralized dynamic ESC scheme.