Accurately predicting the remaining useful life (RUL) of lithium-ion batteries plays a crucial role in the sustainable development and efficient operation of fields such as new energy vehicles, numerous electronic products, and energy storage power stations. This study presents a dual-exponential model integrated with an Extended Kalman Filter (EKF) for RUL prediction, enhanced by Bayesian optimization to automatically adjust the model parameters for improved accuracy. A dynamic weight loss function is employed to adapt to battery characteristics at various degradation stages, enabling optimal model parameterization. The model is evaluated using four datasets under different starting prediction cycles (300, 400, and 500 cycles). Experimental results demonstrate that the model effectively tracks capacity degradation and predicts RUL with high accuracy, fitting well with actual degradation curves and showing strong generalization ability across various datasets. Based on the conducted experiment, the proposed model demonstrates improved accuracy in tracking capacity degradation and predicting RUL.

Millimeter wave (mmWave) communication and intelligent reflecting surface (IRS) are both promising solutions for the next generation of wireless communication technology. In this article, the near-field channel models based on the spherical wave assumption and parabolic wave assumption are proposed for double-IRS assisted mmWave communication systems, where the parabolic wave model serves as an approximation of the spherical wave model. Under the parabolic wave assumption, the directional-dependant rayleigh distances and near-field reflection phases are investigated, and explicit expressions for the normalized array gains and path power gains are obtained using the sine integral. Based on the obtained path power gains, the suboptimal IRS rotation angles are also explored. We first consider the range limits of the activation conditions for the rotation angles, and then take the derivative of the explicit expression for the amplitude of IRS-assisted link to obtain the suboptimal rotation angles for different links. Using the designed near-field reflection phases, the approximate achievable rate is obtained and verified by numerical results. From these results, an interesting finding emerges: under reasonable IRS dimensions, the performance gains of two IRSs working noncooperatively are significantly greater than those of two IRSs working together. In conclusion, this work highlights the importance of IRS rotation angles and the intrinsic nature of double-IRS assisted communications.

Digital twin (DT) technology, which creates virtual representations of physical systems to optimize their life-cycle, has drawn significant attention across various industries. The automotive and aviation industries have been pioneers in adopting DTs for enhanced efficiency, predictive maintenance, and real-time decision-making. However, the maritime industry, crucial to global trade and logistics, has lagged in DT implementation. This paper aims to bridge this gap by systematically surveying DT applications in the automotive and aviation industries and exploring how this knowledge can be transferred to the maritime industry. By analyzing existing literature, identifying key trends, and summarizing best practices, a comprehensive roadmap is provided for maritime industry adoption of DT technology. The surveyed papers are selected systematically following the PRISMA statement and categorized based on characteristics such as single vs. multiple systems, modeling methods (model-driven, data-driven, and hybrid), and life-cycle phases. We introduce DT models using a five-dimensional framework and analyze their characteristics in terms of research object, subsystem application, and modeling method. Additionally, DT applications from a product life-cycle perspective, covering design, manufacturing, operation, and maintenance phases are examined. Knowledge transfer from the automotive and aviation industries to the maritime industry is summarized. In the automotive industry, DTs enhance vehicle efficiency and safety, particularly for autonomous and electric vehicles. Aviation DT research focuses on predictive maintenance, pilot training, and real-time monitoring to improve operational efficiency and safety. The maritime industry faces data challenges and operational complexity but has significant potential for DTs to enhance ship performance, safety, and predictive maintenance.

In this work, a survey of DT-related research in the maritime industry is presented. A five-dimensional DT model for the maritime industry is presented and explained. Moreover, research object and characteristics of DT in maritime industry are categorized and discussed. The research objects of DT in the maritime industry are classified into ships, marine structures, underwater vehicles and marine engines. The characteristics of the maritime industry DT models are discussed in terms of target system, research contribution and simulation method. In addition, DT in the context of product life-cycle perspective in the maritime industry is analyzed and discussed in terms of design, manufacturing, operation, and maintenance phases. Based on our analysis and discussion of the research, we found that the current DT research in the maritime sector is mainly focused on relatively small modules, or small systems, or even only on individual components. In addition, only a small fraction of the reviewed DT-related papers focuses on the whole life-cycle in the maritime industry. The reason may come from that the models and sub-models are not yet flexible and adaptive enough at different life-cycle stages. Therefore, we believe that the development of DT technology is still in the developmental stage in the maritime industry.

Due to the diverse equipment and uneven load distribution in industrial environments, data regarding faults are often unbalanced. Moreover, data and models from clients may become contaminated or damaged, affecting diagnostic performance. To overcome these problems, this study proposes a stacking model for diagnosing interturn short circuit (ITSC) faults in permanent magnet synchronous motors (PMSMs). Federated learning (FL) is used to train the model to increase data security and overcome data islanding in distributed scenarios. Moreover, an improved verification strategy was adopted to select appropriate client models in each round to update the FL global model. We created a secondary server-side data set to validate the client weightings. The data set contains clean sample data for all ITSC fault categories. By calculating the fault diagnosis accuracy of the global model on the auxiliary data set, the model eliminates low-quality clients with uneven fault distributions. The improved particle swarm optimization (PSO) is used to optimize the weight coefficients of clients involved in aggregation, improving the robustness of the aggregation strategy under a joint learning system. In evaluation experiments, compared with the federated average (FedAvg) model, the proposed dynamic verification model exhibited the better diagnostic accuracy in situations of data imbalance, incurred lower communication costs, and prevented local oscillations in the model.

Smart manufacturing, which is increasingly popular worldwide, is aided by time-series forecasting. As the volume of historical data increases, powerful forecasting techniques that reveal unknown relationships between past and future values are required to provide accurate forecasts of production and sales. Thus, in this article, a composite gate recurrent unit (GRU)-Prophet model with an attention mechanism was constructed to predict sales volume. In this composite model, Prophet model and GRU model with attention mechanism were used to capture linear and nonlinear features, respectively. The composite model was experimentally determined to be more applicable and to provide more accurate predictions than did recurrent neural network, long short-term memory, gate recurrent unit, Prophet, and autoregressive integrated moving average models. This article's composite model is suited to rapid changes in market demand and helps enterprises be more competitive in the field of smart manufacturing.

A large amount of labeled data are important to enhance the performance of deep-learning-based methods in the area of fault diagnosis. Because it is difficult to obtain high-quality samples in real industrial applications, federated learning is an effective framework for solving the problem of sparse samples by using the distributed data. Its global model is updated by the local client without sharing data at each round. Considering computing resources and communication loss of multiple clients, an efficient method based on stacked sparse autoencoders (SSAEs) and Siamese networks is proposed to detect interturn short-circuit (ITSC) faults in permanent magnet synchronous motors. In this article, to achieve an accurate ITSC fault detection, an SSAE was employed to extract sparse features in a limited number of samples, and Siamese networks were used to determine the similarity between the given samples. The problem of fault diagnosis is transformed into a classification problem under few-shot learning. Furthermore, the proposed method is trained successfully in the frameworks of centralized learning and decentralized structure. The experimental results indicate that the proposed method achieved high fault diagnosis accuracy. Moreover, it is suitable for deployment in smart manufacturing systems.

High‐resolution millimetre wave images of contiguous targets often suffer from the influence of sidelobe artefacts, partial correlation between targets and so on. Owing to the characteristics of near‐field synthetic aperture imaging radiometers [such as the changing point spread function (PSF) and slender sideline], the existing CLEAN algorithms are unsuitable for near‐field synthetic aperture imaging. This study is devoted to establishing a novel CLEAN algorithm (named adaptive CLEAN) to clean the reconstructed millimetre wave images accurately. First, the characteristics of near‐field synthetic aperture imaging are studied. Then, the adaptive CLEAN algorithm is established based on these characteristics. In this study, the authors amend the amplitude of the targets and select the matching PSF for them according to their azimuths. Unlike other CLEAN algorithms, the parameters of this adaptive CLEAN algorithm are calculated by a formula, which is concluded from a lot of simulation experiments. Finally, the effectiveness of the proposed adaptive CLEAN algorithm is tested by several simulation experiments, and the superiority is also demonstrated by comparing it with the existing CLEAN algorithm. The results demonstrate that the proposed method is an efficient, feasible algorithm for cleaning the reconstructed images, irrespective of the point or contiguous targets.