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High‐fidelity segmentation of blood vessels plays a pivotal role in numerous biomedical applications, such as injection assistance, cancer detection, various surgeries, and vein authentication. Near‐infrared (NIR) transillumination imaging is an effective and safe method to visualize the subcutaneous blood vessel network. However, such images are severely blurred because of the light scattering in body tissues. Inspired by the Vision Transformer model, this paper proposes a novel deep learning network known as transformer connection (TRC)‐Unet to capture global blurred and local clear correlations while using multi‐layer attention. Our method mainly consists of two blocks, thereby aiming to remap skip connection information flow and fuse different domain features. Specifically, the TRC extracts global blurred information from multiple layers and suppresses scattering to increase the clarity of vessel features. Transformer feature fusion eliminates the domain gap between the highly semantic feature maps of the convolutional neural network backbone and the adaptive self‐attention maps of TRCs. Benefiting from the long‐range dependencies of transformers, we achieved competitive results in relation to various competing methods on different data sets, including retinal vessel segmentation, simulated blur image segmentation, and real NIR blood vessel image segmentation. Moreover, our method remarkably improved the segmentation results of simulated blur image data sets and a real NIR vessel image data set. The quantitative results of ablation studies and visualizations are also reported to demonstrate the superiority of the TRC‐Unet design. © 2024 The Author(s). IEEJ Transactions on Electrical and Electronic Engineering published by Institute of Electrical Engineers of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Convolutional neural networks possess the capability of feature learning and nonlinear mapping, which has significant advantages in classifying and recognizing optical fiber vibration signals. In order to further enhance the recognition rate of vibration signals, this paper combines wavelet transform with convolutional neural networks and designs a convolutional layer based on parameterized wavelets. In this layer, the initial signal is convolved with parameterized Laplace wavelet dictionaries to complete the wavelet transform. Such customized filters make more sense than filters with randomly initialized parameters for traditional CNNs. Simultaneously, we introduce the channel attention mechanism to enhance the features of the filtered signals. Subsequently, standard CNNs are employed to extract and process signal features, ultimately utilizing a multi‐layer perceptron for recognition and classification. The experimental results show that the network model possesses better recognition refinement. © 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Photovoltaics (PV), which is one of variable renewable energies, have been installed largely. In the environment of largely installed PV, the output fluctuates of PV influences on the operation of power gird network adversely. Therefore, PV energy generation forecasting is valid for power grid operation and management and needs the reduction of large forecast error as well as average error. Due to a day‐ahead forecast of the total PV energy generation in the power grid area, we developed the forecasting method by using meteorological forecast data of many points in the target area. In order to forecast the regional PV energy generation, the proposed method uses auto‐encoder and convolutional neural network (CNN) for extracting requirements and valid data, and forecast with neural network (NN) by means of extracted information. In this paper, we improved the forecasting error by using multiple meteorological elements as input data. As a result, we indicated that using both of global horizontal irradiance and low cloud cover is required for the reginal PV energy generation forecasting, and large forecasting error can be reduced by adding either dew point depression, or cumulative precipitation as input data. © 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]