Nothing Special   »   [go: up one dir, main page]

Ma et al., 2017 - Google Patents

Deep learning on temporal-spectral data for anomaly detection

Ma et al., 2017

Document ID
9923582928255331590
Author
Ma K
Leung H
Jalilian E
Huang D
Publication year
Publication venue
Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR VIII

External Links

Snippet

Detecting anomalies is important for continuous monitoring of sensor systems. One significant challenge is to use sensor data and autonomously detect changes that cause different conditions to occur. Using deep learning methods, we are able to monitor and …
Continue reading at www.spiedigitallibrary.org (other versions)

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/14—Fourier, Walsh or analogous domain transformations, e.g. Laplace, Hilbert, Karhunen-Loeve, transforms
    • G06F17/141—Discrete Fourier transforms
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00—Subject matter not provided for in other groups of this subclass
    • G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30—Information retrieval; Database structures therefor; File system structures therefor

Similar Documents

Publication Publication Date Title
Ma et al. Deep learning on temporal-spectral data for anomaly detection
da Cruz et al. Machine learning and acoustic method applied to leak detection and location in low-pressure gas pipelines
Guo et al. Leakage detection in water distribution systems based on time–frequency convolutional neural network
Hu et al. Review of model-based and data-driven approaches for leak detection and location in water distribution systems
Ravichandran et al. Ensemble-based machine learning approach for improved leak detection in water mains
Sekhavati et al. Computational methods for pipeline leakage detection and localization: A review and comparative study
Cao et al. An enhance excavation equipments classification algorithm based on acoustic spectrum dynamic feature
US20230206119A1 (en) Event model training using in situ data
Liang et al. A wave change analysis (WCA) method for pipeline leak detection using Gaussian mixture model
Abdulla et al. Probabilistic multiple model neural network based leak detection system: Experimental study
Yang et al. A novel PPA method for fluid pipeline leak detection based on OPELM and bidirectional LSTM
Jiménez et al. Machine learning and neural network for maintenance management
Song et al. A novel noise reduction technique for underwater acoustic signals based on dual‐path recurrent neural network
Rajasekaran et al. A survey and study of signal and data-driven approaches for pipeline leak detection and localization
Yongkang et al. Detection and recognition of distributed optical fiber intrusion signal
Bencharif et al. Detection of acoustic signals from Distributed Acoustic Sensor data with Random Matrix Theory and their classification using Machine Learning
Lanjile et al. Exploiting multi-modal sensing for increased detection fidelity of pipeline leakage
Liu et al. Data-Driven Approaches for Vibroacoustic Localization of Leaks in Water Distribution Networks
Ma et al. Fiber-optic acoustic-based disturbance prediction in pipelines using deep learning
Yang et al. Vibration-based looseness identification of bolted structures via quasi-analytic wavelet packet and optimized large margin distribution machine
Glentis et al. Leak detection in the pipeline network of an oil refinery-a pattern classification paradigm
Liu et al. Research on residual life prediction of rolling bearings based on STFT-CNN
Zhuang et al. Fault detection filtering for uncertain Itô stochastic fuzzy systems with time-varying delays
Jia et al. Drone detection and classification model based on deep attention by using the RF signals
Martinez-Ríos et al. Water leakage classification with acceleration, pressure, and acoustic data: Leveraging the wavelet scattering transform, unimodal classifiers, and late fusion