ScholarWorks@Soongsil University: Kim, Cheol Hong

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Kim, Cheol Hong (김철홍)

College of Information Technology (School of Computer Science and Engineering)

Research Area: 컴퓨터시스템, 내장형시스템, 지능형IoT, Computer System, Embedded System, Intelligent IoT

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fault diagnosis:7||bearing fault diagnosis:5||convolutional neural network:5||Fault diagnosis:4||GPU:3||Performance:3||GPGPU:3||CNN:3||Feature extraction:3||Fault Diagnosis:2||MSHR:2||Blockage:2||machine health monitoring:2||Monitoring:2||고장 진단:2||DNN:2||딥러닝:2||베어링:2||Deep Learning:2||Acoustic emission:2||Bearing:2||Online learning:2||acoustic emission:2||acoustic emission signal:2||CTA:2||feature extraction:2||feature selection:2||Acoustic emission signals:2||Indoor:2||Cache:2||support vector machine:2||bearing:2||bearing fault:2||Machine health monitoring:1||Memory latency:1||Miss Rate:1||Mmwave:1||Multicore:1||Multiple Warp Schedulers:1||Multiple warp schedulers:1||Multitask learning:1||Neural networks:1||PID Control:1||PID 제어:1||Principal component analysis:1||Pumps:1||Reconfigurable intelligent surface:1||Resource Underutilization:1||SM(Streaming Multiprocessors):1||Secrecy rate:1||Swivel Seat:1||Task analysis:1||Timefrequency analysis:1||Torque Control:1||Training:1||Transforms:1||UAV:1||Utilization:1||Vibrations:1||Warp Scheduling:1||Warp scheduling:1||acoustic emission features:1||acoustic emission signals:1||adaptive noise canceling technique:1||adaptive noise reducer:1||autoregressive fuzzy GaussLaguerre:1||bearing fault condition monitoring:1||boruta:1||burst extraction:1||classification:1||concrete crack assessment indicator and classification:1||concrete structures:1||cosine linear discriminant analysis:1||crack detection:1||crack size identification:1||deep convolution neural network:1||deep learning:1||deep neural network (DNN):1||defect signature wavelet image:1||electric motor:1||embedded systems:1||envelope analysis:1||failure detection:1||fault alarm rate:1||fingerprint:1||fuzzy logic technique:1||gaussian window:1||gearbox fault diagnosis:1||gearbox fault identification:1||genetic algorithms:1||hard disk drives:1||hybrid beamforming:1||induction motor:1||knearest neighbor algorithm:1||long shortterm memory:1||long shortterm memory (LSTM):1||machine learning:1||mmWave:1||mmWave:1||multioutput classification:1||multiclass classification:1||multipleinput multipleoutput:1||neural network architecture search:1||orthogonal frequency diversity multiplexing:1||pipeline leakage detection:1||principal component analysis:1||remaining useful life (RUL):1||rotating machinery:1||signal decomposition:1||signal processing:1||smooth sliding digital twin:1||spectrogram:1||spherical tank:1||stacked autoencoder (SAE):1||stacked sparse autoencoderbased deep neural network:1||timefrequency domain:1||timefrequency representation:1||unbalance data:1||unscented Kalman filter:1||varying rotational speed:1||vibration signal:1||vibration signals:1||walsh transform:1||계산 복잡도:1||고급 암호화표준:1||고성능:1||고장 검출:1||그래픽 처리 장치:1||범용 그래픽 처리장치:1||복잡도:1||스위블 시트:1||스트리밍 멀티프로세서:1||임베디드 시스템:1||자율주행차:1||캐쉬:1||AE features:1||토크 제어:1||AES(Advanced Encryption Standard):1||Architecture:1||Autonomous Vehicle:1||Beam tracking:1||Bearing fault:1||BorutaMahalanobis system:1||Bypassing:1||CWT:1||Centrifugal pump:1||Complexity:1||Computation Complexity:1||Computer architecture:1||Continuous wavelet transform:1||Convolutional neural network:1||Convolutional neural networks:1||Coordinated beam selection:1||Deep learning:1||Deep neural network:1||EEMD:1||Embedded System:1||Extended Deep Convolutional Neural Networks:1||Fault Detection:1||GAN:1||GPGPU(GeneralPurpose computation on the GPU):1||GPU 상의 범용 계산:1||GPU(Graphics processing unit):1||Gaussian reference signal:1||Generative adversarial networks:1||Generators:1||High Performance:1||Leak detection:1||Linear discriminant analysis:1||

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