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Dr Md Junayed Hasan


Automated analysis of sleep study parameters using signal processing and artificial intelligence. (2022)
Journal Article
SOHAIB, M., GHAFFAR, A., SHIN, J., HASAN. M.J. and SULEMAN, M.T. 2022. Automated analysis of sleep study parameters using signal processing and artificial intelligence. International journal of environmental research and public health [online], 19(20), article number 13256. Available from: https://doi.org/10.3390/ijerph192013256

An automated sleep stage categorization can readily face noise-contaminated EEG recordings, just as other signal processing applications. Therefore, the denoising of the contaminated signals is inevitable to ensure a reliable analysis of the EEG sign... Read More about Automated analysis of sleep study parameters using signal processing and artificial intelligence..

Transfer learning with 2D vibration images for fault diagnosis of bearings under variable speed. (2022)
Conference Proceeding
AHMAD, Z., HASAN, M.J. and KIM, J.-M. 2022. Transfer learning with 2D vibration images for fault diagnosis of bearings under variable speed. In Abraham, A., Gandhi, N., Hanne, T., Hong, T.-P., Rios, T.N. and Ding, W. (eds.) Intelligent systems design and applications: proceedings of 21st International conference on intelligent systems design and applications (ISDA 2021), 13-15 December 2021, [virtual event]. Lecture notes in networks and systems, 418. Cham: Springer [online], pages 154-164. Available from: https://doi.org/10.1007/978-3-030-96308-8_14

One of the most critical assignments in fault diagnosis is to decide the finest set of features by evaluating the statistical parameters of the time-domain signals. However, these parameters are vulnerable under variable speed conditions, i.e., diffe... Read More about Transfer learning with 2D vibration images for fault diagnosis of bearings under variable speed..

A fault diagnosis framework for centrifugal pumps by scalogram-based imaging and deep learning. (2021)
Journal Article
HASAN, M.J., RAI, A., AHMAD, Z. and KIM, J.-Y. 2021. A fault diagnosis framework for centrifugal pumps by scalogram-based imaging and deep learning. IEEE access [online], 9, pages 58052-58066. Available from: https://doi.org/10.1109/ACCESS.2021.3072854

Centrifugal pumps are the most vital part of any process industry. A fault in centrifugal pump can affect imperative industrial processes. To ensure reliable operation of the centrifugal pump, this paper proposes a novel automated health state diagno... Read More about A fault diagnosis framework for centrifugal pumps by scalogram-based imaging and deep learning..

Bearing fault diagnosis using multidomain fusion-based vibration imaging and multitask learning. (2021)
Journal Article
HASAN, M.J., ISLAM, M.M.M. and KIM, J.-M. 2022. Bearing fault diagnosis using multidomain fusion-based vibration imaging and multitask learning. Sensors [online], 22(1): sensing technologies for fault diagnostics and prognosis, article 56. Available from: https://doi.org/10.3390/s22010056

Statistical features extraction from bearing fault signals requires a substantial level of knowledge and domain expertise. Furthermore, existing feature extraction techniques are mostly confined to selective feature extraction methods namely, time-do... Read More about Bearing fault diagnosis using multidomain fusion-based vibration imaging and multitask learning..

A novel pipeline leak detection technique based on acoustic emission features and two-sample Kolmogorov–Smirnov test. (2021)
Journal Article
RAI, A., AHMAD, Z., HASAN, M.J. and KIM, J.-M. 2021. A novel pipeline leak detection technique based on acoustic emission features and two-sample Kolmogorov–Smirnov test. Sensors [online], 21(24): intelligent systems for fault diagnosis and prognosis, article 8247. Available from: https://doi.org/10.3390/s21248247

Pipeline leakage remains a challenge in various industries. Acoustic emission (AE) technology has recently shown great potential for leak diagnosis. Many AE features, such as root mean square (RMS), peak value, standard deviation, mean value, and ent... Read More about A novel pipeline leak detection technique based on acoustic emission features and two-sample Kolmogorov–Smirnov test..

A novel framework for centrifugal pump fault diagnosis by selecting fault characteristic coefficients of Walsh transform and cosine linear discriminant analysis. (2021)
Journal Article
AHMAD, Z., RAI, A., HASAN, M.J., KIM, C.H. and KIM, J.-M. 2021. A novel framework for centrifugal pump fault diagnosis by selecting fault characteristic coefficients of walsh transform and cosine linear discriminant analysis. IEEE access [online], 9, pages 150128-150141. Available from: https://doi.org/10.1109/ACCESS.2021.3124903

In this paper, we propose a three-stage lightweight framework for centrifugal pump fault diagnosis. First, the centrifugal pump vibration signatures are fast transformed using a Walsh transform, and Walsh spectra are obtained. To overcome the hefty n... Read More about A novel framework for centrifugal pump fault diagnosis by selecting fault characteristic coefficients of Walsh transform and cosine linear discriminant analysis..

A lightweight deep learning-based approach for concrete crack characterization using acoustic emission signals. (2021)
Journal Article
HABIB, M.A., HASAN, M.J. and KIM, J.-M. 2021. A lightweight deep learning-based approach for concrete crack characterization using acoustic emission signals. IEEE access [online], 9, pages 104029-104050. Available from: https://doi.org/10.1109/ACCESS.2021.3099124

This paper proposes an acoustic emission (AE) based automated crack characterization method for reinforced concrete (RC) beams using a memory efficient lightweight convolutional neural network named SqueezeNet. The proposed method also includes a sig... Read More about A lightweight deep learning-based approach for concrete crack characterization using acoustic emission signals..

An explainable AI-based fault diagnosis model for bearings. (2021)
Journal Article
HASAN, M.J., SOHAIB, M. and KIM, J.-M. 2021. An explainable AI-based fault diagnosis model for bearings. Sensors [online], 21(12): sensing technologies for fault diagnostics and prognosis, article 4070. Available from: https://doi.org/10.3390/s21124070

In this paper, an explainable AI-based fault diagnosis model for bearings is proposed with five stages, i.e., (1) a data preprocessing method based on the Stockwell Transformation Coefficient (STC) is proposed to analyze the vibration signals for var... Read More about An explainable AI-based fault diagnosis model for bearings..

A multitask-aided transfer learning-based diagnostic framework for bearings under inconsistent working conditions. (2020)
Journal Article
HASAN, M.J., SOHAIB, M. and KIM, J.-M. 2020. A multitask-aided transfer learning-based diagnostic framework for bearings under inconsistent working conditions. Sensors [online], 20(24): deep learning, artificial neural networks and sensors for fault diagnosis, article 7205. Available from: https://doi.org/10.3390/s20247205

Rolling element bearings are a vital part of rotating machines and their sudden failure can result in huge economic losses as well as physical causalities. Popular bearing fault diagnosis techniques include statistical feature analysis of time, frequ... Read More about A multitask-aided transfer learning-based diagnostic framework for bearings under inconsistent working conditions..

Sleep state classification using power spectral density and residual neural network with multichannel EEG signals. (2020)
Journal Article
HASAN, M.J., SHON, D., IM, K., CHOI, H.-K., YOO, D.-S. and KIM, J.-M. 2020. Sleep state classification using power spectral density and residual neural network with multichannel EEG signals. Applied sciences [online], 10(21): medical signal and image processing, article 7639. Available from: https://doi.org/10.3390/app10217639

This paper proposes a classification framework for automatic sleep stage detection in both male and female human subjects by analyzing the electroencephalogram (EEG) data of polysomnography (PSG) recorded for three regions of the human brain, i.e., t... Read More about Sleep state classification using power spectral density and residual neural network with multichannel EEG signals..