Fault Diagnosis of High-Voltage Circuit Breakers via Hybrid Classifier …
Li, B., Liu, M., Guo, Z., Ji, Y.: Mechanical fault diagnosis of high voltage circuit breakers utilizing EWT-improved time frequency entropy and optimal GRNN classifier. Entropy 20, 448 (2018) Article Google Scholar Download references
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An ensemble approach using a frequency-based and stacking classifiers …
The stacking process combines multiple classifiers [22, 29] to create high-level classifiers and produce improved performance. In the first level, the features are fed into the various base classifiers which, outputs a new decision. ... (EfficientNet B0), combined with distinct classifiers using a frequency-based voting strategy, ...
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Environmentally adaptive automated recognition of
Classifiers are then adaptively retrained through active learning in these unfamiliar seabed types, resulting in improved mitigation of challenging environmental clutter as it is encountered. ... which combines three sonar images formed with different frequencies and bandwidths, 6 a dual-band SAS constructed with high-frequency (HF) …
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Quickly build a high-precision classifier for Φ-OTDR sensing …
Jiang et al. [11] proposed a method that uses Mel Frequency Cepstrum Coefficient to transform the signal and classifies 5 kinds of event through a CNN. Wu et al. [12] ... It is an easily generalized method to quickly construct a high-precision classifier with MCU and a few samples in a new field application.
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Automated High‐Frequency Geomagnetic Disturbance …
that contain high-frequency disturbances and the classification of the signals within. This list can be used to identify hour windows of data that are undesirable for space weather research as well as events that contain high-frequency geophysical disturbances that may provide insight to the small-scale features of space weather events.
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AUTOMATED DETECTOR AND CLASSIFIER OF HIGH FREQUENCY …
High frequency oscillations (HFOs) are automatically ... AUTOMATED DETECTOR AND CLASSIFIER OF HIGH FREQUENCY OSCILLATIONS AND INDICATOR SEIZURE ONSET . United States Patent Application 20160045127 . Kind Code: A1 . Abstract: High frequency oscillations (HFOs) are automatically detected in electroencephalogram …
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A time-frequency classifier for human gait recognition
This paper proposes a gait classifier based on subspace learning using principal components analysis(PCA) and shows that gait signature is captured effectively in feature vectors and is used in training a minimum distance classifiers based on Mahalanobis distance metric. Radar has established itself as an effective all-weather, …
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Data Set | Automated High-frequency Geomagnetic Disturbance Classifier
The data were used to study the high-frequency geomagnetic disturbances within the magnetic field data. Included in this repository are the python scripts that perform an identification and classification of high-frequency signals within the magnetometer data that is downloaded from the databases listed in the Methodology section.
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Automated High‐Frequency Geomagnetic Disturbance …
Abstract We present an automated method to identify high-frequency geomagnetic disturbances in ground magnetometer data and classify the events by the source of the perturbations. We developed an algorithm ... Automated high-frequency geomagnetic disturbance classifier: A machine learning approach to identifying noise while retaining …
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Automated detector and classifier of high frequency
WO-2016025724-A1 chemical patent summary. High frequency oscillations (HFOs) are automatically detected in electroencephalogram (EEG) signals and analyzed to assess whether they are predictive of the onset of a neurological dysfunction in a subject or an indication of nonneurological electrical activity or noise in the EEG signal.
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Entropy | Free Full-Text | Mechanical Fault Diagnosis of High …
The mechanical fault diagnosis results of the high voltage circuit breakers (HVCBs) are mainly determined by the feature vector and classifier used. ... Guo, and Yamin Ji. 2018. "Mechanical Fault Diagnosis of High Voltage Circuit Breakers Utilizing EWT-Improved Time Frequency Entropy and Optimal GRNN Classifier" Entropy 20, no. 6: 448. https ...
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Automated High‐Frequency Geomagnetic …
Automated High‐Frequency Geomagnetic Disturbance Classifier: A Machine Learning Approach to Identifying Noise While Retaining High‐Frequency …
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Multiclass Classifier based Cardiovascular Condition …
Changes in high-frequency QRS components are more sensitive than st-segment deviation for detecting acute coronary artery occlusion. Journal of the American College of Cardiology 36, 1827–1834 ...
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arXiv:2108.10257v1 [eess.IV] 23 Aug 2021
deep feature focus on recovering lost high-frequencies. With a long skip connection, SwinIR can transmit the low-frequency information directly to the reconstruction mod-ule, which can help deep feature extraction module focus on high-frequency information and stabilize training. For the implementation of reconstruction module, we use the
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Automated High‐Frequency Geomagnetic Disturbance …
Automated High‐Frequency Geomagnetic Disturbance Classifier: A Machine Learning Approach to Identifying Noise While Retaining High‐Frequency …
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Multiband entropy-based feature-extraction method for …
Hence, we are the first one to use high-frequency components (ripple and fast ripple) from interictal iEEG, the performances of localizing individual segments were observed in terms of sensitivity ...
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Automated sleep stage identification system based on time–frequency …
The REM stage shows low voltage, mixed frequency EEG, sawtooth wave-like pattern, low amplitude EMG, and high level EOG signal from both eyes. In stage N1, the EEG signal has the highest amplitude, a frequency range of 2–7 Hz, and the presence of Alpha waves in the EEG signal in less than half the epoch's duration.
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Robust dual-tone multi-frequency tone detection using k …
Findings. It is found that the model which is trained using the augmented data set and additionally includes the absolute DFT values of the second harmonic frequency values for the eight fundamental DTMF frequencies as the features, achieved the best performance with a macro classification F1 score of 0.980835, a five-fold stratified cross …
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ECG Signal Classification Using Various Machine Learning
Figure 10 shows the confusion matrix output of SVM classifier and here the accuracy of the classifier is 87.5%, sensitivity is 75%,specificity is .Figure 11 shows the Adaboost classifier output. To classify the ECG signal into normal or abnormal signal totally 16 ECG signal are taken from the database. The accuracy of the SVM classifier is …
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Classification of Low Frequency Signals Emitted by …
This paper proposes a method of automatically detecting and classifying low frequency noise generated by power transformers using sensors and dedicated …
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High-Frequency Trading with Machine Learning Algorithms …
Data Science in Finance and Economics. High-Frequency Trading with Machine Learning Algorithms and Limit Order Book Data. 1. 2. In this paper, we examine the usefulness of machine learning methods such as support vector machines, random forests and bagging for the extraction of information from the limit order book that can be …
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A New Model for Teaching High-Frequency Words
Linda Farrell, Michael Hunter, Tina Osenga. Integrating high-frequency words into phonics lessons allows students to make sense of spelling patterns for these words. To do this, …
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Time–frequency time–space LSTM for robust classification of
Time–frequency signal processing for feature extraction was reviewed as a useful approach for pattern recognition 17 that provided successful applications, including EEG seizure detection and ...
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High Frequency Trading and Price Discovery
We examine the role of high-frequency traders (HFTs) in price discovery and price efficiency. Overall HFTs facilitate price efficiency by trading in the direction of permanent price changes and in the opposite direction of transitory pricing errors, both on average and on the highest volatility days. This is done through their liquidity demanding …
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Automated High‐Frequency Geomagnetic Disturbance Classifier…
High-frequency (second-timescale) components of the surface geomagnetic field are not often included in studies on geomagnetically induced currents (GICs) because they do not pose a direct threat to technological infrastructure. ... The data used for this analysis as well as the fully automated geomagnetic disturbance classifier are available ...
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(PDF) Automated High‐Frequency Geomagnetic Disturbance Classifier…
In this paper, we present the full methodology for a GMD classifier that identifies occurrences of high-frequency (0.017–1 Hz) signals in magnetic field data and classifies whether t hey are a ...
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Recognition of emotional states using EEG signals based …
based on time-frequency analysis and SVM classifier. ... moving time windows are often used to calculate instantaneous high-frequency and low-frequency components [19].
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Forecasting trends of high-frequency KOSPI200 …
Forecasting trends of high-frequency KOSPI200 index data using learning classifiers. Youngdoo Son a., Dong-jin Noh b., Jaewook Lee a. Add to Mendeley. …
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Time–frequency time–space LSTM for robust …
Based on classification results obtained from two databases of sensor-induced physiological signals, the proposed approach has the potential for (1) achieving …
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A binary ensemble classifier for high-frequency trading
The aim of this study was to model and use machine learning techniques to maximize the chance of a market maker be executed successfully in a stock market, that is, when their bid and ask orders are filled at the desired prices. In this context, a
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