In this work, we focus on solving the problem of timbre transfer in audio samples. The goal is to transfer the source audio’s timbre from one instrument to another while retaining as much of the other musical elements as possible, including loudness, pitch, and melody. While image-to-image style transfer has been used for timbre and style transfer in music recording, the current state of the findings is unsatisfactory. Current timbre transfer models frequently contain samples with unrelated waveforms that affect the quality of the generated audio. The diffusion model has excellent performance in the field of image generation and can generate high-quality images. Inspired by it, we propose a kind of timbre transfer technology based on the diffusion model. To be specific, we first convert the original audio waveform into the constant-Q transform (CQT) spectrogram and adopt image-to-image conversion technology to achieve timbre transfer. Lastly, we reconstruct the produced CQT spectrogram into an audio waveform using the DiffWave model. In both many-to-many and one-to-one timbre transfer tasks, we assessed our model. The experimental results show that compared with the baseline model, the proposed model has good performance in one-to-one and many-to-many timbre transfer tasks, which is an interesting technical progress.

Multi-robot coverage path planning (MCPP) has received increasing attention in agricultural automation. In practical agricultural applications, robots are critical resources, and optimizing their number to execute a specific coverage task with a deadline is an important topic. Existing MCPP methods typically assume a fixed number of robots and each robot covers the farmland with a constant speed, which may increase task costs, thereby causing a waste of robot resources. To address this challenge, this article proposes a resource-optimized coverage path planning method. First, as a base, we formulate a generalized Dubins model, containing variable speeds instead of a constant speed, to generate the (near-)fastest Dubins path between two poses instead of the shortest Dubins path, thus improving the efficiency of a single robot to perform tasks. Next, we propose a multi-objective evolutionary algorithm for MCPP called MEC. The MEC algorithm optimally minimizes the number of robots and the single-robot maximum coverage time, by reusing the optimization information through the computing process. We validate the performance of MEC through comparison experiments. The results demonstrate that MEC can reduce the robot usage by up to 25% without the generalized Dubins model, and MEC can further reduce robot usage by at least 33.3% when incorporating the generalized Dubins model.

The intelligent diagnosis of rotating machinery with big data has been widely studied. However, due to the variability of working conditions and difficulty in marking fault samples, it is difficult to obtain enough high-quality fault marking data for training bearing fault diagnosis models in practical industrial application scenarios. Aiming at the problem of training data imbalance caused by lack of fault samples, a novel metalearning fault diagnosis method (MOFD) is proposed to get the bearing fault diagnosis solution under data imbalance. Firstly, in order to enhance the variety of fault samples, a Feature Space Density Adaptive Synthetic Minority Oversampling Technique (FSDA-SMOTE) is proposed in this paper, which takes the density difference of minority samples in the spatial domain within the class as the constraint of local neighbor similarity to generate new fault samples for data augmentation. In addition, in order to strengthen the model’s learning ability and diagnosis performance under limited fault samples, a residual-attention convolutional neural network (RA-CNN) was constructed to identify the deep features of fault signals, and a metalearning strategy based on parameter gradient optimization was applied to RA-CNN for refining the learning process of the diagnosis model. Finally, the reliability of the proposed method is verified through experimental analysis of public bearing dataset.

The DFIG-based wind farm faces sub-synchronous oscillation (SSO) when it is integrated with a series-compensated transmission system. The equivalent SSO damping is influenced by both wind speed and compensation level. However, it is hard for the wind farm to obtain a compensation level in time to predict the SSO risk. In this paper, an SSO risk prediction method for a DFIG wind farm is proposed based on the characteristics identified from noise-like signals. First, SSO-related parameters are analyzed. Then, the potential SSO frequency and damping are identified from signals at normal working points by integration using variational mode decomposition and Prony analysis. Finally, a fuzzy inference system is established to predict the SSO risk of a DFIG wind farm. The effectiveness of the proposed method is verified by simulation. The proposed prediction method can predict SSO risks caused by the variation in wind speed, while the transmission line parameters are undetectable for the wind farm.

To improve the performance and reduce the vibration of the mistuned blisk, a novel approach combining hard-coating and multipackets is presented. First, the dynamical models of the blisk without hard-coating and multipackets, the hard-coated mistuned blisk without multipackets, and the hard-coated mistuned blisk with multipackets are established based on the lumped parameter model (LPM). Then, the solved results are compared with those previous literature to validate the feasibility and correctness of the proposed models. Furthermore, the characteristics of the natural frequencies and the vibration responses for the mistuned blisk is investigated by proposed LPMs. Finally, the effect of the hard-coating and multipackets on the vibration characteristics for the mistuned blisk are discussed. The obtained results manifest that the vibration response of the mistuned blisk can be further suppressed when the hard-coating and the multipackets are considered simultaneously compared with only the hard-coating or multipackets considered, which provides useful guidance on the vibration reduction for the mistuned blisk.