ScholarWorks@Soongsil University: Yoon, Heonjun

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Yoon, Heonjun (윤헌준)

College of Engineering (School of Mechanical Engineering)

Research Area: 에너지 하베스팅, 탄성 메타물질, 인공지능 주도 설계, Energy Harvesting, Elastic Metamaterial, AI-driven Design

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Phononic crystal:6||Fault diagnosis:4||Energy localization:4||Piezoelectric energy harvesting:4||Band gap:2||fault diagnosis:2||Broadband:2||Boiler tube leakage:1||Broadband energy localization:1||Calibration metric:1||Contrastive learning:1||Convolutional neural network:1||Convolutionalneuralnetwork:1||Data augmentation:1||Decoupled double defects:1||Deeplearning:1||Defect band:1||Defect band splitting:1||Defect mode:1||Defectband formation:1||Defectband splitting:1||Double defect modes:1||Elastic metamaterial:1||Electroelastically coupled analytical model:1||Energy descriptor:1||Equifrequency contour:1||Evanescent wave:1||Fault relevance weighting:1||Faultdiagnosis:1||Feature extraction:1||Frequency response:1||Gearbox:1||Graded phononic crystal:1||Healthimage:1||Insufficient data condition:1||Intersensor relationship:1||Latent dimension optimization:1||Leak localization:1||Marginal probability and correlation residual (MPCR):1||Mechanical resonance:1||Modal superposition:1||Motor:1||Motorstatorcurrentsignal:1||Multisensor fault diagnosis:1||Multiple defects:1||Neumann expansion:1||Nonsymmetry:1||Optimizationbased statistical model calibration:1||Overhead hoist transport:1||Permanent magnet synchronous motor:1||Permanentmagnetsynchronousmotor:1||Phononic band gap:1||Piezoelectric defect:1||Powernormalized cepstral coefficient (PNCC):1||Preprocessing:1||Probabilistic modeling:1||Quarterwave stack (QWS):1||Repetitive operating condition:1||Resampling:1||Rescaling:1||Rolling element bearing:1||Rotating machinery:1||Sparameter method:1||Selfcollimation:1||Selfsupervised feature learning:1||Sensor information integration:1||Short time Fourier transform (STFT):1||Standing wave:1||Statistical correlation:1||Stator current signal:1||Steam turbine:1||Temporal relationship:1||Topology optimization:1||Transfer learning:1||Transfer matrix method:1||Uncertainty:1||Unsupervised learning:1||Variableoperatingcondition:1||Very low speed bearing:1||Vibration:1||Vibration signal:1||analytical model:1||bending moment reliability:1||convolutional neural network:1||cumulative damage rule:1||defect mode:1||double defects:1||effective mechanical boundary:1||fewshot learning:1||industrial robot:1||locally resonant elastic metamaterial:1||mechanical impedance:1||model interpretability:1||motor fault diagnosis:1||motor stator current:1||multiscale attention residual block:1||multiscale structure:1||new operating conditions:1||nonlinear creepfatigue interaction model:1||online trajectory planner:1||operationadaptive damage assessment:1||path attention module:1||phononic crystal:1||probabilistic design:1||random use condition:1||reflection coefficient:1||resonance band gap:1||rotating machines:1||semisupervised domain adaptation:1||systemlevel lifetime reliability:1||torque reliability:1||torque signal:1||ultrasonic transducer:1||Acoustic emission signal:1||unseen faults:1||Adaptive hybrid expansion:1||Anisotropy:1||Anomaly detection:1||Asymptotic analysis:1||Bearing fault diagnosis:1||Boiler tube:1||

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