A convolutional neural network based deep learning methodology for recognition of partial discharge patterns from high voltage cables

It is a great challenge to differentiate partial discharge (PD) induced by different types of insulation defects in high-voltage cables. Some types of PD signals have very similar characteristics and are specifically difficult to differentiate, even for the most experienced specialists. To overcome the challenge, a convolutional neural network (CNN)-based deep learning methodology for PD pattern recognition is presented in this paper. First, PD testing for five types of artificial defects in ethylene-propylene-rubber cables is carried out in high voltage laboratory to generate signals containing PD data. Second, 3500 sets of PD transient pulses are extracted, and then 33 kinds of PD features are established. The third stage applies a CNN to the data; typical CNN architecture and the key factors which affect the CNN-based pattern recognition accuracy are described. Factors discussed include the number of the network layers, convolutional kernel size, activation function, and pooling method. This paper presents a flowchart of the CNN-based PD pattern recognition method and an evaluation with 3500 sets of PD samples. Finally, the CNN-based pattern recognition results are shown and the proposed method is compared with two more traditional analysis methods, i.e., support vector machine (SVM) and back propagation neural network (BPNN). The results show that the proposed CNN method has higher pattern recognition accuracy than SVM and BPNN, and that the novel method is especially effective for PD type recognition in cases of signals of high similarity, which is applicable for industrial applications

Random forest based optimal feature selection for partial discharge pattern recognition in HV cables

Optimal selection of features of Partial Discharge (PD) signals recorded from defects in High Voltage (HV) cables will contribute not only to the improvement of PD pattern recognition accuracy and efficiency but also to PD parameter visualization in HV cable condition monitoring and diagnostics. This paper presents a novel Random Forest (RF)-based feature selection algorithm for PD pattern recognition of HV cables. The algorithm is applied to feature selection of both PD signals and interference signals with the aim of obtaining the optimal features for data processing. Firstly, the experimental data acquisition and feature extraction processes are introduced. PD signals were captured from faults created in a cable to obtain the raw PD data, then a set of 3500 transient PD pulses and a set of 3500 typical interference pulses were extracted, based on which 34 PD features were extracted for further processing. Furthermore, 119 two-dimensional features and 1082 three-dimensional features were generated. The paper then discusses the basic principle of the RF algorithm. Finally, RF-based feature selection was implemented to determine the optimal features for PD pattern recognition. The results were obtained and evaluated with the Back Propagation Neural Network (BPNN) and Support Vector Machine (SVM). Results show that the proposed RF-based method is effective for PD feature selection of HV cables with the potential for application to additional HV power apparatus

Mediastinal parathyroid carcinoma: a case report and review of the literature

Abstract Background Parathyroid carcinoma (PC) is an uncommon cause of primary hyperparathyroidism (PHPT) and particularly rare in the mediastinum. Herein, we present a case of mediastinal PC and conduct a related literature review. Case presentation We described a case of a 50-year-old female patient with PHPT due to mediastinal PC. She was initially admitted to a local hospital in her hometown with hypercalcemia and high blood concentrations of PTH (parathyroid hormone). The patient underwent neck parathyroidectomy and pathological examination suggested parathyroid adenoma. Although the overproduction of serum calcium and PTH declined after the surgery, calcium and PTH increased again one month later, so the patient was transferred to our hospital. A 99mTc-sestamibi scan revealed an ectopic finding in the mediastinum, which was also indicated on the CT image. After removing the mediastinal mass, the metabolism of calcium and PTH quickly reverted to normal and the pathologic features of the mass were consistent with PC. By reviewing the related literature, we noticed that only scattered reports were published before 1982, and those were not included in the present review due to their differences with current radiological examination and treatment methods. After excluding outdated studies, we summarized and analyzed 20 reports of isolated mediastinal PC and concluded that. Parathyroidectomy remains the only curative treatment for the disease. Furthermore, the success of treatment directly depends on accurate preoperative localization. Conclusion With this study, we emphasize the importance of accurate preoperative diagnosis of mediastinal PC and improve clinicians’ understanding of the disease

Comparison between Three‐Dimensional Printed Titanium and PEEK Cages for Cervical and Lumbar Interbody Fusion: A Prospective Controlled Trial

Objectives The three‐dimensional printing titanium (3DPT) cage with excellent biomechanical properties and osseointegration capabilities has been initially used in spinal fusion, while the polyetheretherketone (PEEK) cage, a bioinert material device, has been a widely used for decades with relatively excellent clinical outcomes. This study was performed to investigate the early radiographic and clinical outcomes of 3DPT cage versus PEEK cage in patients undergoing anterior cervical discectomy and fusion (ACDF) and transforaminal lumbar interbody fusion (TLIF). Methods This prospective controlled trial, from December 2019 to June 2022, included patients undergoing ACDF and TLIF with 3DPT cages and compared them to patients using PEEK cages for treating spinal degenerative disorders. The outcome measures included radiographic parameters (intervertebral height [IH], subsidence, fusion status, and bone‐cage interface contact) and clinical outcomes (Japanese Orthopaedic Association [JOA], Neck Disability Index [NDI], Oswestry Disability Index [ODI], Short Form 12‐Item Survey [SF‐12], Visual Analog Scale [VAS], and Odom's criteria). Student's independent samples t test and Pearson's chi‐square test were used to compare the outcome measures between the two groups before surgery and at 1 week, 3 and 6 months after surgery. Results For the patients undergoing ACDF, the 3DPT (18 patients/[26 segments]) and PEEK groups (18 patients/[26 segments]) had similar fusion rates at 3 months and 6 months follow‐up (3 months: 96.2% vs. 83.3%, p = 0.182; 6 months: 100% vs. 91.7%, p = 0.225). The subsidence in the 3DPT group was significantly lower than that in the PEEK group (3 months: 0.4 ± 0.2 mm vs. 0.9 ± 0.7 mm p = 0.004; 6 months: 0.7 ± 0.3 mm vs. 1.5 ± 0.8 mm, p < 0.001). 3DPT and PEEK cage all achieved sufficient contact with the cervical endplates. For the patients undergoing TLIF, the 3DPT (20 patients/[26 segments]) and PEEK groups (20 patients/[24 segments]) had no statistical difference in fusion rate (3 months: 84.6% vs. 58.3%, p = 0.059; 6 months: 92.3% vs. 75%, p = 0.132). The subsidence was lower than that in the PEEK group without significantly difference (3 months: 0.9 ± 0.7 mm vs.1.2 ± 0.9 mm p = 0.136; 6 months: 1.6 ± 1.0 mm vs. 2.0 ± 1.0 mm, p = 0.200). At the 3‐month follow‐up, the bone‐cage interface contact of the 3DPT cage was significantly better than that of the PEEK cage (poor contact: 15.4% vs. 75%, p < 0.001). The values of UAR were higher in the 3DPT group than in the PEEK group during the follow‐up in cervical and lumbar fusion, there were more statistical differences in lumbar fusion. There were no significant differences in the clinical assessment between 3DPT or PEEK cage in spinal fusion. Conclusion The 3DPT cage and PEEK cage can achieve excellent clinical outcomes in cervical and lumbar fusion. The 3DPT cage has advantage in fusion quality, subsidence severity, and bone‐cage interface contact than PEEK cage

Detection of Citrus Psyllid Based on Improved YOLOX Model

【Objective】Yellow-shoot disease, known as the cancer of citrus, is a devastating disease, and psyllid is the main vector of yellow-shoot disease transmission, therefore, monitoring and precise disinfection and sterilization of psyllid is an effective way to prevent and control yellow-shoot disease and inhibit its transmission.【Method】The traditional way to eliminate the psyllid was mainly to spray drugs manually, and the control effect was not ideal due to high labor costs. In the study, we used an improved YOLOX based edge detection method for psyllid, added Convolutional Block Attention Module (CBAM) to the backbone network, and further extracted important features in the channel and space dimensions. The cross entropy loss in the target loss was changed to Focal Loss to further reduce the missed detection rate.【Result】The results showed that the algorithm described in the study fitted in with the detection platform of psyllid. The data set of psyllid was taken in Lianjiang Orange Garden, Zhanjiang City, Guangdong Province. It is deeply adapted to the actual needs of agricultural and rural development. Based on YOLOX model, the backbone network and loss function were improved to achieve a more excellent detection method of citrus psyllid. 85.66% of the AP value was obtained on the data set of citrus psyllid, which was 2.70 percentage points higher than that of the original model, and the detection accuracy was 8.61, 4.32 and 3.62 percentage points higher than that of YOLOv3, YOLOv4-Tiny and YOLOv5-s respectively, which has been greatly improved.【Conclusion】The improved YOLOX model can better identify citrus psyllid, and the accuracy rate has been improved, laying a foundation for the subsequent real-time detection platform