The combination of power intelligent inspection and edge computing plays a crucial role in the construction of power Internet of Things and transparent power grid. However, as a consequence of the low computing power of edge devices, the detection speed of the model running on edge devices tends to be slow. Accordingly, a fast and accurate method of power line edge intelligent inspection is proposed in this article. First, for key component detection of power lines, RepVGG, diverse branch block (DBB), efficient channel attention (ECA), and improved spatial pyramid pooling (SPP) are utilized to improve YOLOv5, giving rise to RepYOLO. Additionally, to solve the problem of low accuracy in pin defect detection, a two-stage cascaded method is proposed. The first stage localizes various connection fittings in the input image, and the second stage recognizes normal pins and missing pins in these connection fittings. Finally, C++ language combined with TensorRT is employed to optimize and accelerate the model on the NVIDIA Jetson Xavier NX embedded platform, fulfilling efficient power line edge intelligent inspection. Experimental results show that the TensorRT optimized RepYOLO algorithm is 4 times the inference speed of YOLOv5 with a 1.2% increase in accuracy. Moreover, TenosrRT optimized two-stage cascaded method can achieve accurate and real-time pin defect detection on edge devices.

Ensuring power transmission line reliability is crucial for power system stability. Mechanical joints, critical components of transmission lines, are susceptible to pin defects, leading to severe consequences. We propose an end-to-end lightweight defect detection method, Cross-Scale Spatial Attention Detector (CSSAdet), for accurately identifying pin defects in mechanical joints. CSSAdet integrates spatial and cross-scale attention mechanisms, enhancing feature representation and improving detection accuracy and recall. Our detection pipeline involves two stages: detecting mechanical joints in UAV-acquired images and identifying pin statuses within detected joints, using a single CSSAdet model. We evaluated CSSAdet on a large dataset of transmission line images, comparing it with existing methods. CSSAdet-2 achieves an average precision (AP) of 77.1% and a recall of 91.4% for joint detection, and an AP of 77.2% and a recall of 92.1% for pin status recognition, with a processing speed of 116.8 FPS. CSSAdet demonstrates superior performance in detecting mechanical joint defects with high efficiency, providing a valuable tool for maintaining and monitoring transmission line integrity and improving power system reliability and safety.

With photoplethysmograph (PPG) sensors showing increasing potential in wearable health monitoring, the challenging problem of motion artifact (MA) removal during intensive exercise has become a popular research topic. In this study, a novel method that combines heart rate frequency (HRF) estimation and notch filtering is proposed. The proposed method applies a cascaded adaptive noise cancellation (ANC) based on the least mean squares (LMS)-Newton algorithm for preliminary motion artifacts reduction, and further adopts special heart rate frequency tracking and correction schemes for accurate HRF estimation. Finally, notch filters are employed to restore the PPG signal with estimated HRF based on its quasi-periodicity. On an open source data set that features intensive running exercise, the proposed method achieves a competitive mean average absolute error (AAE) result of 0.92 bpm for HR estimation. The practical experiments are carried out with the PPG evaluation platform developed by ourselves. Under three different intensive motion patterns, a 0.89 bpm average AAE result is achieved with the average correlation coefficient between recovered PPG signal and reference PPG signal reaching 0.86. The experimental results demonstrate the effectiveness of the proposed method for accurate HR estimation and robust MA removal in PPG during intensive exercise.

Pattern recognition of DC partial discharge (PD) receives plenty of attention and recent researches mainly focus on the static characteristics of PD signals. In order to improve the recognition accuracy of DC cable and extract information from PD waveforms, a modified deep belief network (DBN) supervised fine-tuned by the adaptive moment estimation (ADAM) algorithm is proposed to recognize the four typical insulation defects of DC cable according to the PD pulse waveforms. Moreover, the effect of the training sample set size on recognition accuracy is analyzed. Compared with naive Bayes (NB), K-nearest neighbor (KNN), support vector machine (SVM), and back propagation neural networks (BPNN), the ADAM-DBN method has higher accuracy on four different defect types due to the excellent ability in terms of the feature extraction of PD pulse waveforms. Moreover, the increase of training sample set size would lead to the increase of recognition accuracy within a certain range.

Automatic detection of epileptic seizures is still a challenging problem due to the intolerance of EEG. Introducing ECG can help with EEG for detecting seizures. However, the existing methods depended on fusing either the extracted features or the classification results of EEG-only and ECG-only with ignoring the interaction between them, so the detection rate did not improve much. Also, all EEG channels were considered in a complex manner. Moreover, the detection of epilepsy firing location, which is an important issue for diagnosing epilepsy, is not considered before. Therefore, we propose a new method based on the brain-heart interaction (BHI) for detecting the seizure onset and its firing location in the brain with lower complexity and better performance. BHI allows us to study the nonlinear coupling and variation of phase-synchronization between brain regions and heart activity, which are effective for distinguishing seizures. In our method, the EEG channels are mapped into two surrogate channels to reduce the computational complexity. Moreover, the firing location detector is triggered only once the seizure is detected to save the system’s power. Evaluation using different proposed classification networks based on the TUSZ, the largest available EEG/ECG dataset with 315 subjects and 7 seizure types, showed that our BHI method improves the sensitivity by 48% with only 4 false alarms/24h compared to using only EEG. Moreover, it outperforms the performance of the average human detector based on the quantitative EEG tools by achieving a sensitivity of 68.2% with 11.9 false alarms/ 24h and a latency of 11.94 sec.