Aiming at the problem that the workload of human judgment of the power line tilt angle is large and prone to large errors, this paper proposes an improved algorithm Line-YOLO based on YOLOv8s-seg. Firstly, the problem of the variable shape of the power line is solved through the introduction of deformable convolutional DCNv4, and the detection accuracy is improved. The BiFPN structure is also introduced for the Neck layer, which shortens the time required for feature fusion and improves the detection efficiency. After that, the EMA attention mechanism module is added behind the second and third C2f modules of the original model, which improves the model’s ability to recognize the target, and effectively solves the problem of loss and error when power line targets overlap. Finally, a small target detection head is added after the first EMA attention mechanism module for detecting small or occluded targets in the image, which improves the model’s ability to detect small targets. In this paper, we conduct experiments by collecting relevant power line connection images and making our dataset. The experimental results show that the $mAP@0.5$ of Line-YOLO is improved by 6.2% compared to the benchmark model, the number of parameters is reduced by 28.2%, the floating-point operations per second is enhanced by 35.3%, and the number of detected frames per second is improved by 14 FPS. It is proved by the experiments that the enhanced model Line-YOLO detects the results better, and it can efficiently complete the power line angle detection task.

Fused deposition modeling (FDM) has the advantage of being able to process complex workpieces with relatively simple operations. However, when processing complex components in a suspended state, it is necessary to add support parts to be processed and formed, which indicates an excessive dependence on support. The stress intensity of the supported positions of the printing components can be modified by changing the supporting model of the parts, their density, and their distance in relation to the Z direction in the FDM printing settings. The focus of the present work was to study the influences of these three modified factors on the stress intensity of the supporting position of the printing components. In this study, 99 sets of compression tests were carried out using a position of an FDM-supported part, and the experimental results were observed and analyzed with a 3D topographic imager. A reference experiment on the anti-pressure abilities of the printing components without support was also conducted. The experimental results clarify how the above factors can affect the anti-pressure abilities of the supporting positions of the printing components. According to the results, when the supporting density is 30% and the supporting distance in the Z direction is Z = 0.14, the compressive strength of the printing component is lowest. When the supporting density of the printing component is ≤30% and the supporting distance in the Z direction is Z ≥ 0.10, the compressive strength of printing without support is greater than that of the linear support model. Under the same conditions, the grid-support method offers the highest compressive strength.

Cardiovascular disease (CVD) poses a significant challenge to global health, with cardiac arrhythmia representing one of its most prevalent manifestations. The timely and precise classification of arrhythmias is critical for the effective management of CVD. This study introduces an innovative approach to enhancing arrhythmia classification accuracy through advanced Electrocardiogram (ECG) signal processing. We propose a dual-channel feature fusion strategy designed to enhance the precision and objectivity of ECG analysis. Initially, we apply an Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) and enhanced wavelet thresholding for robust noise reduction. Subsequently, in the primary channel, region of interest features are emphasized using a ResNet-ICBAM network model for feature extraction. In parallel, the secondary channel transforms 1D ECG signals into Gram angular difference field (GADF), Markov transition field (MTF), and recurrence plot (RP) representations, which are then subjected to two-dimensional convolutional neural network (2D-CNN) feature extraction. Post-extraction, the features from both channels are fused and classified. When evaluated on the MIT-BIH database, our method achieves a classification accuracy of 97.80%. Compared to other methods, our approach of two-channel feature fusion has a significant improvement in overall performance by adding a 2D convolutional network. This methodology represents a substantial advancement in ECG signal processing, offering significant potential for clinical applications and improving patient care efficiency and accuracy.

Remote photoplethysmography (rPPG) has attracted growing attention due to its non-contact nature. However, existing non-contact heart rate detection methods are often affected by noise from motion artifacts and changes in lighting, which can lead to a decrease in detection accuracy. To solve this problem, this paper initially employs manual extraction to precisely define the facial Region of Interest (ROI), expanding the facial area while avoiding rigid regions such as the eyes and mouth to minimize the impact of motion artifacts. Additionally, during the training phase, illumination normalization is employed on video frames with uneven lighting to mitigate noise caused by lighting fluctuations. Finally, this paper introduces a 3D convolutional neural network (CNN) method incorporating an attention mechanism for heart rate detection from facial videos. We optimize the traditional 3D-CNN to capture global features in spatiotemporal data more effectively. The SimAM attention mechanism is introduced to enable the model to precisely focus on and enhance facial ROI feature representations. Following the extraction of rPPG signals, a heart rate estimation network using a bidirectional long short-term memory (BiLSTM) model is employed to derive the heart rate from the signals. The method introduced here is experimentally validated on two publicly available datasets, UBFC-rPPG and PURE. The mean absolute errors were 0.24 bpm and 0.65 bpm, the root mean square errors were 0.63 bpm and 1.30 bpm, and the Pearson correlation coefficients reached 0.99, confirming the method’s reliability. Comparisons of predicted signals with ground truth signals further validated its accuracy.