A hybrid feature pool-based emotional stress state detection algorithm using EEG signals.

Human stress analysis using electroencephalogram (EEG) signals requires a detailed and domain‐specific information pool to develop an effective machine learning model. In this study, a multi‐domain hybrid feature pool is designed to identify most of the important information from the signal. The hybrid feature pool contains features from two types of analysis: (a) statistical parametric analysis from the time domain, and (b) wavelet‐based bandwidth specific feature analysis from the time‐frequency domain. Then, a wrapper‐based feature selector, Boruta, is applied for ranking all the relevant features from that feature pool instead of considering only the nonredundant features. Finally, the k‐nearest neighbor (k‐NN) algorithm is used for final classification. The proposed model yields an overall accuracy of 73.38% for the total considered dataset. To validate the performance of the proposed model and highlight the necessity of designing a hybrid feature pool, the model was compared to non‐linear dimensionality reduction techniques, as well as those without feature ranking

Person recognition based on deep gait: a survey.

Gait recognition, also known as walking pattern recognition, has expressed deep interest in the computer vision and biometrics community due to its potential to identify individuals from a distance. It has attracted increasing attention due to its potential applications and non-invasive nature. Since 2014, deep learning approaches have shown promising results in gait recognition by automatically extracting features. However, recognizing gait accurately is challenging due to the covariate factors, complexity and variability of environments, and human body representations. This paper provides a comprehensive overview of the advancements made in this field along with the challenges and limitations associated with deep learning methods. For that, it initially examines the various gait datasets used in the literature review and analyzes the performance of state-of-the-art techniques. After that, a taxonomy of deep learning methods is presented to characterize and organize the research landscape in this field. Furthermore, the taxonomy highlights the basic limitations of deep learning methods in the context of gait recognition. The paper is concluded by focusing on the present challenges and suggesting several research directions to improve the performance of gait recognition in the future

A lightweight deep learning-based approach for concrete crack characterization using acoustic emission signals.

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 signal-to-image technique, which is continuous wavelet transformation (CWT) that decomposes the AE signals over time-frequency scales and extracts the crack/fracture information in both the time and frequency domains. First, AE signals for two types of cracks (minor and severe), along with the normal condition (no crack), are collected from the experimental test bed. Second, the previously mentioned CWT based signal-to-image technique is applied to generate two-dimensional time-frequency images that are then converted to gray scale images for faster computation. These images are supplied to the SqueezeNet for classification of the concrete crack types. We extensively modified the fire module of the SqueezeNet (SQN-MF) by introducing depth-wise convolutional kernels and channel shuffling operations. Not only does the proposed method utilize deep learning-based techniques for crack classification of concrete beams for the first time, but also the CWT-based imaging technique has not yet been explored in this field either. Additionally, this method does not follow the typical AE burst feature (features like AE counts, peak-amplitude, rise time, decay time, etc.) based methods, and as a result, we no longer require extensive human intervention and expertise to get deep understanding of the crack types. SQN-MF achieves AlexNet-level accuracy with fifty times fewer parameters and has an implementable memory size for the field programmable gate array boards. Overall, the method achieves 100% accuracy. It is 20.8% higher than the typical feature extraction and traditional machine learning based methods. We observed a 4% accuracy increase for the proposed SQN-MF compared to the typical SqueezeNet with bypass connections

Digital condition monitoring for wider blue economy.

In the process of decommissioning energy systems, condition monitoring is crucial. It can make the health status of offshore oil and gas installations, pipelines, wind farms etc. transparent to policymakers and stakeholders, and aid them in creating a better repurposing plan for the assets that will be decommissioned to create a sustainable ocean economy. In most cases, condition monitoring calls for experienced engineers to perform on-site testing, which raises labour costs as well as commuter carbon emissions (M.J. Hasan & Kim, 2019; Rai et al., 2021). A revolution in decarbonised and sustainable decommissioning may result from further digitalisation of condition monitoring to address this problem. We can gather and manipulte enormous amounts of real-time data, and create a simulated representation of physical assets. We can then quickly predict their health conditions by combining artificial intelligence, the Internet of Things, and augmented-, virtual- and mixed reality techniques (M.J. Hasan et al., 2019; Yan et al. 2018, 2020, 2021). Digital condition monitoring has social and economic benefits, including: 1) Delivering a plausible innovation that can be successfully used in other UK industries; 2) Opening a new high-tech talent demand market in the UK; 3) Reducing carbon emissions of decommissioning projects, especially for the marine environment; 4) Reshaping the offshore marine environment to benefit the blue economy; 5) Reducing costs across the decommissioning chain, from design and manufacturing to purchasing and maintenance

A New Approach of Iris Detection and Recognition

This paper proposes an IRIS recognition and detection model for measuring the e-security. This proposed model consists of the following blocks: segmentation and normalization, feature encoding and feature extraction, and classification. In first phase, histogram equalization and canny edge detection is used for object detection. And then, Hough Transformation is utilized for detecting the center of the pupil of an IRIS. In second phase, Daugmen’s Rubber Sheet model and Log Gabor filter is used for normalization and encoding and as a feature extraction method GNS (Global Neighborhood Structure) map is used, finally extracted feature of GNS is feed to the SVM (Support Vector Machine) for training and testing. For our tested dataset, experimental results demonstrate 92% accuracy in real portion and 86% accuracy in imaginary portion for both eyes. In addition, our proposed model outperforms than other two conventional methods exhibiting higher accuracy

A multitask-aided transfer learning-based diagnostic framework for bearings under inconsistent working conditions.

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, frequency, or time-frequency domain data. These engineered features are susceptible to variations under inconsistent machine operation due to the non-stationary, non-linear, and complex nature of the recorded vibration signals. To address these issues, numerous deep learning-based frameworks have been proposed in the literature. However, the logical reasoning behind crack severities and the longer training times needed to identify multiple health characteristics at the same time still pose challenges. Therefore, in this work, a diagnosis framework is proposed that uses higher-order spectral analysis and multitask learning (MTL), while also incorporating transfer learning (TL). The idea is to first preprocess the vibration signals recorded from a bearing to look for distinct patterns for a given fault type under inconsistent working conditions, e.g., variable motor speeds and loads, multiple crack severities, compound faults, and ample noise. Later, these bispectra are provided as an input to the proposed MTL-based convolutional neural network (CNN) to identify the speed and the health conditions, simultaneously. Finally, the TL-based approach is adopted to identify bearing faults in the presence of multiple crack severities. The proposed diagnostic framework is evaluated on several datasets and the experimental results are compared with several state-of-the-art diagnostic techniques to validate the superiority of the proposed model under inconsistent working conditions

A novel modified SFTA approach for feature extraction.

To increase the efficiency of conventional Segmentation Based Fractal Texture Analysis (SFTA), we propose a new approach on SFTA algorithm. We use an optimum multilevel thresholding hybrid method of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), called HGAPSO with the optimization technique for classification based on grey level range to get more accurate output. Experimental results show that proposed approach exhibits average 2% higher classification accuracy than conventional SFTA for our tested dataset

An explainable AI-based fault diagnosis model for bearings.

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 variable speed and load conditions, (2) a statistical feature extraction method is introduced to capture the significance from the invariant pattern of the analyzed data by STC, (3) an explainable feature selection process is proposed by introducing a wrapper-based feature selector—Boruta, (4) a feature filtration method is considered on the top of the feature selector to avoid the multicollinearity problem, and finally, (5) an additive Shapley ex-planation followed by k-NN is proposed to diagnose and to explain the individual decision of the k-NN classifier for debugging the performance of the diagnosis model. Thus, the idea of explaina-bility is introduced for the first time in the field of bearing fault diagnosis in two steps: (a) incorpo-rating explainability to the feature selection process, and (b) interpretation of the classifier performance with respect to the selected features. The effectiveness of the proposed model is demon-strated on two different datasets obtained from separate bearing testbeds. Lastly, an assessment of several state-of-the-art fault diagnosis algorithms in rotating machinery is included

Multi-sensor fusion-based time-frequency imaging and transfer learning for spherical tank crack diagnosis under variable pressure conditions.

In this paper, a crack diagnosis framework is proposed that combines a new signal-to-imaging technique and transfer learning-aided deep learning framework to automate the diagnostic process. The objective of the signal-to-imaging technique is to convert one-dimensional (1D) acoustic emission (AE) signals from multiple sensors into a two-dimensional (2D) image to capture information under variable operating conditions. In this process, a short-time Fourier transform (STFT) is first applied to the AE signal of each sensor, and the STFT results from the different sensors are then fused to obtain a condition-invariant 2D image of cracks; this scheme is denoted as Multi-Sensors Fusion-based Time-Frequency Imaging (MSFTFI). The MSFTFI images are subsequently fed to the fine-tuned transfer learning (FTL) model built on a convolutional neural network (CNN) framework for diagnosing crack types. The proposed diagnostic scheme (MSFTFI + FTL) is tested with a standard AE dataset collected from a self-designed spherical tank to validate the performance under variable pressure conditions. The results suggest that the proposed strategy significantly outperformed classical methods with average performance improvements of 2.36–20.26%

Acoustic spectral imaging and transfer learning for reliable bearing fault diagnosis under variable speed conditions.

Incipient fault diagnosis of a bearing requires robust feature representation for an accurate condition-based monitoring system. Existing fault diagnosis schemes are mostly confined to manual features and traditional machine learning approaches such as artificial neural networks (ANN) and support vector machines (SVM). These handcrafted features require substantial human expertise and domain knowledge. In addition, these feature characteristics vary with the bearing's rotational speed. Thus, such methods do not yield the best results under variable speed conditions. To address this issue, this paper presents a reliable fault diagnosis scheme based on acoustic spectral imaging (ASI) of acoustic emission (AE) signals as a precise health state. These health states are further utilized with transfer learning, which is a machine learning technique, which shares knowledge with convolutional neural networks (CNN) for accurate diagnosis under variable operating conditions. In ASI, the amplitudes of the spectral components of the windowed time-domain acoustic emission signal are transformed into spectrum imaging. ASI provides a visual representation of acoustic emission spectral features in images. This ensures enhanced spectral images for transfer learning (TL) testing and training, and thus provides a robust classifier technique with high diagnostic accuracy