Large group decision-making (LGDM) involves multiple decision-makers (DMs) and criteria, frequently resulting in conflicts and inconsistencies. This study proposes a novel conflict resolution model based on dynamic trust relationships to effectively identify and address potential disputes in LGDM. First, hesitant fuzzy 2-tuple linguistic sets (HF2TLSs) are utilized to accurately capture DMs’ preferences in uncertain environments. Then, a Markov trust state transition model is developed to capture the dynamic evolution of trust relationships. Next, an enhanced PageRank algorithm, built on trust networks, is employed to determine the weights of DMs and subgroups, thereby improving decision quality. Additionally, multidimensional conflict detection indicators are introduced to quantify cognitive and interest conflicts among subgroups. Finally, an adaptive conflict resolution mechanism is presented to balance heterogeneous interests and achieve agreement. An illustrative example validates the model, with comparative analyses demonstrating its rationality and superiority.

In recent years, deep learning has advanced the MIDI domain, solidifying music generation as a key application of artificial intelligence. However, most research focuses on Western music, facing challenges in generating Chinese traditional melodies, particularly in capturing modal characteristics and emotional expression. To address this, we propose the Dual-Feature Modeling Module, which integrates the long-range modeling of the Mamba Block with the global structure capturing of the Transformer Block. Additionally, we introduce the Bidirectional Mamba Fusion Layer, which integrates local details and global structures through bidirectional scanning, enhancing sequence modeling. Building on this, we propose the REMI-M representation to better capture and generate modal information in melodies. To support this, we developed FolkDB, a high-quality Chinese traditional music dataset covering over 11 hours of music. Experimental results show our architecture excels in generating melodies with Chinese traditional music characteristics, offering a new solution for music generation.

To mitigate the risk of data breaches, an increasing number of biometric recognition systems are introducing encryption biometric template protection methods and directly matching in the encrypted domain. Depending on the approach to key management, prevailing biometric template protection strategies can be categorized into declarative and distributive methods. The former are challenged by complexities and vulnerabilities linked to key loss, while the latter are compromised by fixed mapping rules that may expose personal information. We present a biometric template protection method that combines random-fixed factors to handle these challenges, thereby protecting the user’s biometric privacy. Firstly, we introduce a random activation factor generation module that extracts scaling and offset factors from the user’s biometric data. This module randomly binds factors to different positions in each authentication process, rendering distance-dependent bitwise cracking algorithms ineffective. Secondly, we propose a fixed multi-branch mapping module that enhances feature expression and minimizes information loss post-encryption. We also develop a trainable min-max hash method, optimized using an improved approximate contrastive loss. Employing palm veins as a case study, we conducted experiments across five datasets, where our method outperformed other encrypted domain methods and showed competitive advantages over mainstream non-encrypted methods. Moreover, we have demonstrated that our method ensures robust performance while meeting essential security requirements of irreversibility, unlinkability, and revocability.

Existing self-supervised multivariate time series anomaly detection methods struggle with interference among variables during reconstruction. They also tend to miss capturing critical anomaly information, resulting in unsatisfactory performance, especially in scenarios with strong mechanistic contexts. To this end, we propose a targeted anomaly detection algorithm called inference stacked recurrent autoencoder (ISRAE). Its key contribution lies in the design of a specific inference kernel, derived from specialist knowledge, which captures the strong mechanistic relationships among variables. This kernel is then fused with the multidimensional anomalies predicted by the SRAE, which mitigates interference among variables through the stacking technique. Furthermore, a novel differential constraint is introduced into the loss function, which not only highlights anomaly reconstruction errors, but also smooths the reconstructions, enhancing overall detection performance. Comprehensive comparison experiments and ablation studies show that ISRAE achieves superior anomaly detection performance under strong mechanistic contexts and highlight the importance of each key module in ISRAE.

Rapid developments of offshore wind industry offer a strong demand opportunity for offshore wind turbine remote diagnosis. As offshore wind turbines are often located in harsh and communication-constrained environments, the collection and transmission of data is severely restricted, which poses a serious challenge to the conventional centralized diagnostic paradigm that relies on data aggregation. To address this challenge, we propose a novel event-triggered federated learning framework for decentralized fault diagnosis of offshore wind turbines. Specifically, federated learning is first employed to learn decentralized local knowledge from geographically distributed offshore wind turbines, so that the communication objects are transformed from massive raw data into learned parameters, thereby relieving the communication burden. Then, we design an event-triggered communication mechanism and incorporate it into federated learning, the core of which is to modify the communication requirement from uploading all trained parameters periodically to communicating only when necessary. The proposed framework is verified by a real-world offshore wind turbine dataset from six large wind farms in China. An ablation study shows that the proposed framework can maintain high diagnostic performance while reducing communication costs. A comprehensive comparison based on three benchmark models demonstrates that the proposed framework can reduce the communication burden by up to 63% while obtaining better diagnostic performance.Note to Practitioners—This study was motivated by the problem of collaborative diagnosis of distributed offshore wind turbines under the constraints of data privacy and communication overhead. The method employs a federated learning-based fault diagnosis framework, which permits to obtain global fault diagnosis knowledge without aggregating raw data scattered in each end device, thus avoids the risk of data leakage. Moreover, a strategy integrating parameter variation and accuracy gain is designed to avoid communication redundancy for collaborative training. The practicability and superiority of our proposed framework is demonstrated using extensive experiments against actual industrial data collected from six offshore wind farms.

Multivariate time-series (MTS) collected from multiple sensors on industrial pumps often exhibit concept drift and noise contamination due to variable working conditions and complex environments. To detect anomalies in such MTS, we propose a novel model called spectral residual with self-attention variational autoencoder (SR-SAVAE). Specifically, the spectral residual operation is used to mitigate concept drift, while the variational inference combined with a total variation regularization is used to address the issue of noise contamination. Experimental results on three public datasets indicate that the SR-SAVAE model achieves good anomaly detection results for general MTS. More importantly, compared to other state-of-the-art models on a private dataset about pumps, the results illustrate the superiority of the SR-SAVAE model in anomaly detection for MTS with concept drift and noise contamination. Finally, ablation studies on the SR-SAVAE model detail the efficacy of each component.

In real industrial processes, machines usually run under variable working conditions, which impose challenges for anomaly detection. To complete anomaly detection for machines under variable working conditions, we develop a reconstruction-based autoencoder called clustering-based contrastive learning autoencoder (CBCL-AE). It integrates clustering-based contrastive learning (CBCL) to perform clustering in the feature space and enhance the differentiation of features from different working conditions, thereby achieving adaptive working condition recognition. Considering the crucial role of the clustering of CBCL, we theoretically and experimentally demonstrate its convergence property during the training process, which directly determines the effectiveness of CBCL-AE. CBCL-AE's superiority has been validated on three public datasets and two private datasets collected from an actual industrial process. These validations highlight its superiority over five state-of-the-art models in unsupervised anomaly detection.