Kwoh Chee Keong

Appointment: Deputy Director, Centre for Professional and Continuing Education, School of Computer Science and Engineering
Associate Professor, School of Computer Science and Engineering

Research topics:

• Machine Learning and Statistical Inference
• Learning with Unlabeled Data
• Meta and Ensemble learning
• Ontology for Knowledge Representation

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1 to 10 of 32 Results

CWRU Domain Generalization Version Apr 3, 2023 - Conditional Contrastive Domain Generalization for Fault Diagnosis Ragab, Mohamed, 2023, "CWRU Domain Generalization Version", https://doi.org/10.21979/N9/8QPQHL, DR-NTU (Data), V1 The Case Western Reserve University (CWRU) is a widely adopted dataset for rolling bearing elements (Smith and Randall 2015). Accelerometer sensors were deployed at both the drive-end and fan-end of the housing motors. Vibration signals were collected with 12 KHz sampling rate un...
Machine Fault Diagnosis Dec 30, 2022 - ADATIME Ragab, Mohamed, 2022, "Machine Fault Diagnosis", https://doi.org/10.21979/N9/PU85XN, DR-NTU (Data), V1 The dataset consists of sensor readings from bearing machines under four different operating conditions. Each of these conditions has three classes: healthy, inner-bearing damage, and outer-bearing damage. The operating conditions refer to different values for rotational speed, l...
Paderborn Domain Generalization Version Oct 7, 2022 - Conditional Contrastive Domain Generalization for Fault Diagnosis Ragab, Mohamed, 2022, "Paderborn Domain Generalization Version", https://doi.org/10.21979/N9/UCIK2K, DR-NTU (Data), V1 This dataset is generated the KAT data center in Paderborn University with the sampling rate of 64 KHz (Lessmeier et al. 2016). The damages were generated using both artificial and natural ways. More specifically, an electric discharge machine (EDM), a drilling, and an electric e...
Conditional Contrastive Domain Generalization for Fault Diagnosis(Nanyang Technological University) Oct 7, 2022
Paderborn Dataset Processed for Domain Adaptation Scenarios Jun 16, 2022 - Self-Supervised Autoregressive Domain Adaptation for Time Series Data Ragab, Mohamed, 2022, "Paderborn Dataset Processed for Domain Adaptation Scenarios", https://doi.org/10.21979/N9/X6M827, DR-NTU (Data), V1 The dataset contains sensor readings of bearing machines under 4 different operating conditions, with each having 3 different classes, i.e., healthy, inner-bearing damage, and outer-bearing damage. Each operating condition refers to different operating parameters, including rotat...
Self-Supervised Autoregressive Domain Adaptation for Time Series Data(Nanyang Technological University) Jun 16, 2022
UCI HAR Dataset Processed May 27, 2022 - ADATIME Ragab, Mohamed; Eldele, Emadeldeen, 2022, "UCI HAR Dataset Processed", https://doi.org/10.21979/N9/0SYHTZ, DR-NTU (Data), V1 UCIHAR is one of the most widely used datasets to evaluate performance on time series data. It contains three different sensors namely, accelerometer, gyroscope, and body sensors. These sensors have been used to collect data from 30 different persons. In our experiments, we treat...
Subject-wise Sleep Stage Data May 27, 2022 - ADATIME Ragab, Mohamed; Eldele, Emadeldeen, 2022, "Subject-wise Sleep Stage Data", https://doi.org/10.21979/N9/UD1IM9, DR-NTU (Data), V1 Sleep stage classification (SSC) problem aims to classify the electroencephalography (EEG) signals into five stages i.e. Wake (W), Non-Rapid Eye Movement stages (N1, N2, N3), and Rapid Eye Movement (REM). We adopted Sleep-EDF dataset (Goldberger et al., 2000), which contains EEG...
WISDM Dataset Processed May 27, 2022 - ADATIME Ragab, Mohamed; Eldele, Emadeldeen, 2022, "WISDM Dataset Processed", https://doi.org/10.21979/N9/KJWE5B, DR-NTU (Data), V1, UNF:6:UNCqR5C0NzMZQm8xrjVq0Q== [fileUNF] WISDM is another popular activity recognition dataset for the evaluation of time series domain adaptation. In this dataset, accelerometer sensors were applied to collect data from 36 subjects. This data can be more challenging because of the class imbalance issue among different...
HHAR Processed Data May 27, 2022 - ADATIME Ragab, Mohamed; Eldele, Emadeldeen, 2022, "HHAR Processed Data", https://doi.org/10.21979/N9/OWDFXO, DR-NTU (Data), V1 The Heterogeneity Human Activity Recognition (HHAR) dataset has been collected from 9 different users using sensor readings from smartphones and smartwatches. In our experiments, we consider each user as a domain. We constructed 10 cross-domain scenarios from randomly selected us...

CWRU Domain Generalization Version

Apr 3, 2023 - Conditional Contrastive Domain Generalization for Fault Diagnosis

Ragab, Mohamed, 2023, "CWRU Domain Generalization Version", https://doi.org/10.21979/N9/8QPQHL, DR-NTU (Data), V1

The Case Western Reserve University (CWRU) is a widely adopted dataset for rolling bearing elements (Smith and Randall 2015). Accelerometer sensors were deployed at both the drive-end and fan-end of the housing motors. Vibration signals were collected with 12 KHz sampling rate un...

Machine Fault Diagnosis

Dec 30, 2022 - ADATIME

Ragab, Mohamed, 2022, "Machine Fault Diagnosis", https://doi.org/10.21979/N9/PU85XN, DR-NTU (Data), V1

The dataset consists of sensor readings from bearing machines under four different operating conditions. Each of these conditions has three classes: healthy, inner-bearing damage, and outer-bearing damage. The operating conditions refer to different values for rotational speed, l...

Paderborn Domain Generalization Version

Oct 7, 2022 - Conditional Contrastive Domain Generalization for Fault Diagnosis

Ragab, Mohamed, 2022, "Paderborn Domain Generalization Version", https://doi.org/10.21979/N9/UCIK2K, DR-NTU (Data), V1

This dataset is generated the KAT data center in Paderborn University with the sampling rate of 64 KHz (Lessmeier et al. 2016). The damages were generated using both artificial and natural ways. More specifically, an electric discharge machine (EDM), a drilling, and an electric e...

Conditional Contrastive Domain Generalization for Fault Diagnosis(Nanyang Technological University)

Oct 7, 2022

Paderborn Dataset Processed for Domain Adaptation Scenarios

Jun 16, 2022 - Self-Supervised Autoregressive Domain Adaptation for Time Series Data

Ragab, Mohamed, 2022, "Paderborn Dataset Processed for Domain Adaptation Scenarios", https://doi.org/10.21979/N9/X6M827, DR-NTU (Data), V1

The dataset contains sensor readings of bearing machines under 4 different operating conditions, with each having 3 different classes, i.e., healthy, inner-bearing damage, and outer-bearing damage. Each operating condition refers to different operating parameters, including rotat...

Self-Supervised Autoregressive Domain Adaptation for Time Series Data(Nanyang Technological University)

Jun 16, 2022

UCI HAR Dataset Processed

May 27, 2022 - ADATIME

Ragab, Mohamed; Eldele, Emadeldeen, 2022, "UCI HAR Dataset Processed", https://doi.org/10.21979/N9/0SYHTZ, DR-NTU (Data), V1

UCIHAR is one of the most widely used datasets to evaluate performance on time series data. It contains three different sensors namely, accelerometer, gyroscope, and body sensors. These sensors have been used to collect data from 30 different persons. In our experiments, we treat...

Subject-wise Sleep Stage Data

May 27, 2022 - ADATIME

Ragab, Mohamed; Eldele, Emadeldeen, 2022, "Subject-wise Sleep Stage Data", https://doi.org/10.21979/N9/UD1IM9, DR-NTU (Data), V1

Sleep stage classification (SSC) problem aims to classify the electroencephalography (EEG) signals into five stages i.e. Wake (W), Non-Rapid Eye Movement stages (N1, N2, N3), and Rapid Eye Movement (REM). We adopted Sleep-EDF dataset (Goldberger et al., 2000), which contains EEG...

WISDM Dataset Processed

May 27, 2022 - ADATIME

Ragab, Mohamed; Eldele, Emadeldeen, 2022, "WISDM Dataset Processed", https://doi.org/10.21979/N9/KJWE5B, DR-NTU (Data), V1, UNF:6:UNCqR5C0NzMZQm8xrjVq0Q== [fileUNF]

WISDM is another popular activity recognition dataset for the evaluation of time series domain adaptation. In this dataset, accelerometer sensors were applied to collect data from 36 subjects. This data can be more challenging because of the class imbalance issue among different...

HHAR Processed Data

May 27, 2022 - ADATIME

Ragab, Mohamed; Eldele, Emadeldeen, 2022, "HHAR Processed Data", https://doi.org/10.21979/N9/OWDFXO, DR-NTU (Data), V1

The Heterogeneity Human Activity Recognition (HHAR) dataset has been collected from 9 different users using sensor readings from smartphones and smartwatches. In our experiments, we consider each user as a domain. We constructed 10 cross-domain scenarios from randomly selected us...

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