无线电工程

[1] YANG L,FAN J F,LIU Y H,et al.A Review on State-of-the-art Power Line Inspection Techniques[J].IEEE Transactions on Industrial Electronics,2020,69(12):9350-9365.

[2] STEFENON S F,BRUNSR,SARTORI A,et al.Analysis of the Ultrasonic Signal in Polymeric Contaminated Insulators Through Ensemble Learning Methods[J].IEEE Access,2022,10:33980-33991.

[3] SINGH G,YOW K C.These Do Not Look Like Those:An Interpretable Deep Learning Model for Image Recognition[J].IEEE Access,2021,9:41482-41493.

[4] SINGH G,YOW K C.An Interpretable Deep Learning Model for Covid-19 Detection with Chest X-ray Images[J].IEEE Access,2021,9:85198-85208.

[5] MEDEIROS A,SARTORI A,STEFENON S F,et al.Comparison of Artificial Intelligence Techniques to Failure Prediction in Contaminated Insulators Based on Leakage Current[J].Journal of Intelligent and Fuzzy Systems,2022,42(4):3285-3298.

[6] CORSO M P,PEREZ F L,STEFENON S F,et al.Classification of Contaminated Insulators Using k-Nearest Neighbors Based on Computer Vision[J].Computers,2021,10:112.

[7] IBRAHIM A,DALBAH A,ABUALSAUD A,et al.Application of Machine Learning to Evaluate Insulator Surface Erosion[J].IEEE Transactions on Instrumentation and Measurement,2020,69(2):314-316.

[8] PRATES R M,CRUZ R,MAROTTA A P,et al.Insulator Visual Non-conformity Detection in Overhead Power Distribution Lines Using Deep Learning[J].Computers & Electrical Engineering,2019,78:343-355.

[9] HAGMAR H,TONG L,ERIKSSON R,et al.Voltage Instability Prediction Using a Deep Recurrent Neural Network[J].Power Systemss & Security,2021,36(1):17-27.

[10] SAMPEDRO C,RODRIGUEZ-VAZQUEZ J,RODRIGUEZ-RAMOS A,et al.Deep Learning-based System for Automatic Recognition and Diagnosis of Electrical Insulator Strings[J].IEEE Access,2019,7:101283-101308.

[11] TAO X,ZHANG D P,WANG Z H,et al.Detection of Power Line Insulator Defects Using Aerial Images Analyzed with Convolutional Neural Networks[J].IEEE Transactions on Systems,Man,and Cybernetics:Systems,2020,50(4):1486-1498.

[12] MIAO X R,LIU X Y,CHEN J,et al.Insulator Detection in Aerial Images for Transmission Line Inspection Using Single Shot Multibox Detector[J].IEEE Access,2019,7:9945-9956.

[13] LI X X,SU H S,LIU G H.Insulator Defect Recognition Based on Global Detection and Local Segmentation[J].IEEE Access,2020,8:59934-59946.

[14] TEIMOURZADEH H,MORADZADEH A,SHOARAN M,et al.High Impedance Single-phase Faults Diagnosis in Transmission Lines via Deep Reinforcement Learning of Transfer Functions[J].IEEE Access,2021,9:15796-15809.

[15] ZHAO M H,ZHONG S S,FU X Y,et al.Deep Residual Networks with Adaptively Parametric Rectifier Linear Units for Fault Diagnosis[J].IEEE Transactions on Industrial Electronics,2021,68(3):2587-2597.

[16] ZHAO M H,KANG M,TANG B P,et al.Multiple Wavelet Coefficients Fusion in Deep Residual Networks for Fault Diagnosis[J].IEEE Transactions on Industrial Electronics,2019,66(6):4696-4706.

[17] SINIOSOGLOU I,RADOGLOU-GRAMMATIKIS P,EFSTATHOPOULOS G,et al.A Unified Deep Learning Anomaly Detection and Classification Approach for Smart Grid Environments[J].IEEE Transaction on Network and Service Management,2021,18(2):1137-1151.

[18] GUO Y P,PANG Z J,DU J,et al.An Improved AlexNet for Power Edge Transmission Line Anomaly Detection[J].IEEE Access,2020,8:97830-97838.

[19] LIANG H G,ZUO C,WEI W M.Detection and Evaluation Method of Transmission Line Defects Based on Deep Learning[J].IEEE Access,2020,8:38448-38458.

[20] JIANG H,QIU X J,CHEN J,et al.Insulator Fault Detection in Aerial Images Based on Ensemble Learning with Multi-level Perception[J].IEEE Access,2019,7:61797-61810.

[21] WEN Q D,LUO Z Q,CHEN R T,et al.Deep Learning Approaches on Defect Detection in High Resolution Aerial Images of Insulators[J].Sensors,2021,21(4):1033.

[22] SONG C H,XU W X,WANG Z F,et al.Analysis on the Impact of Data Augmentation on Target Recognition for UAV-based Transmission Line Inspection[J].Complexity,2020,2020(4):1-11.

[23] HABIB N,HASAN M M,REZA M M,et al.Ensemble of CheXNet and VGG-19 Feature Extractor with Random Forest Classifier for Pediatric Pneumonia Detection[J].SN Computer Science,2020,1(6):359.

[24] SANTOS G H D,SEMAN L O,BEZERRA E A,et al.Static Attitude Determination Using Convolutional Neural Networks[J].Sensors,2021,21(19):6419.

[25] GEORGESCU M I,IONESCU R T,POPESCU M.Local Learning with Deep and Handcrafted Features for Facial Expression Recognition[J].IEEE Access,2019,7:64827-64836.

[26] RATHNAYAKE N,RATHNAYAKE U,DANG T L,et al.An Efficient Automatic Fruit-360 Image Identification and Recognition Using a Novel Modified Cascaded-ANFIS Algorithm[J].Sensors,2022,22(12):4401.

[27] KUNDU S,MALAKAR S,GEEM Z W,et al.Hough Transform-based Angular Features for Learning-free Handwritten Keyword Spotting[J].Sensors,2021,21(14):4648.

[28] ZENG D,CHEN S J,CHEN B Y,et al.Improving Remote Sensing Scene Classification by Integrating Global-context and Local-object Features[J].Remote Sensing Images,2018,10(5):734.

[29] NASEER A,BARO E N,KHAN S D,et al.A Novel Detection Refinement Technique for Accurate Identification of Nephrops Norvegicus Burrows in Underwater Imagery[J].Sensors,2022,22(12):4441.

[30] ALRASHEDY H H N,ALMANSOUR A F,IBRAHIM D M,et al.BrainGAN:Brain MRI Image Generation and Classification Framework Using GAN Architectures and CNN Models[J].Sensors,2022,22(11):4297.

[31] SONG J M,KIM W,PARK K R.Finger-vein Recognition Based on Deep DenseNet Using Composite Image[J].IEEE Access,2019,7:66845-66863.

[32] POPESCU D,EL-KHATIB M,ICHIM L.Skin Lesion Classification Using Collective Intelligence of Multiple Neural Networks[J].Sensors,2022,22(12):4399.

[33] YANG J J,WU J Z,WANG X J.Convolutional Neural Network Based on Differential Privacy in Exponential Attenuation Mode for Image Classification[J].IET Image Process,2020,14(15):3676-3681.

[34] ZHANG Y,HUANG X B,JIA J Y,et al.A Recognition Technology of Transmission Lines Conductor Break and Surface Damage Based on Aerial Image[J].IEEE Access,2019,7:59022-59036.