Railway track surface faults dataset.

Railway infrastructure maintenance is critical for ensuring safe and efficient transportation networks. Railway track surface defects such as cracks, flakings, joints, spallings, shellings, squats, grooves pose substantial challenges to the integrity and longevity of the tracks. To address these challenges and facilitate further research, a novel dataset of railway track surface faults has been presented in this paper. It is collected using the EKENH9R cameras mounted on a railway inspection vehicle. This dataset represents a valuable resource for the railway maintenance and computer vision related scientific communities. This dataset includes a diverse range of real-world track surface faults under various environmental conditions and lighting scenarios. This makes it an important asset for the development and evaluation of Machine Learning (ML), Deep Learning (DL), and image processing algorithms. This paper also provides detailed annotations and metadata for each image class, enabling precise fault classification and severity assessment of the defects. Furthermore, this paper discusses the data collection process, highlights the significance of railway track maintenance, emphasizes the potential applications of this dataset in fault identification and predictive maintenance, and development of automated inspection systems. We encourage the research community to utilize this dataset for advancing the state-of-the-art research related to railway track surface condition monitoring.

Wheel Defect Detection Using a Hybrid Deep Learning Approach.

Defective wheels pose a significant challenge in railway transportation, impacting operational performance and safety. Excessive traction and braking forces give rise to deviations from the intended conical tread shape, resulting in amplified vibrations and noise. Moreover, these deviations contribute to the accelerated damage of track components. Detecting wheel defects at an early stage is crucial to ensure safe and comfortable operation, as well as to minimize maintenance costs. However, the presence of various vibrations, such as those induced by the track, traction motors, and other rolling stock subsystems, poses a significant challenge for onboard detection techniques. These vibrations create difficulties in accurately identifying wheel defects in real-time during operational activities, often resulting in false alarms. This research paper aims to address this issue by using a hybrid deep learning-based approach for the accurate detection of various types of wheel defects using accelerometer data. The proposed approach aims to enhance wheel defect detection accuracy while considering onboard techniques' cost-effectiveness and efficiency. A realistic simulation model of the railway wheelset is developed to generate a comprehensive dataset. To generate vibration data in various scenarios, the model is simulated for 20 s under different conditions, including one non-faulty scenario and six faulty scenarios. The simulations are conducted at different speeds and track conditions to capture a wide range of operating conditions. Within each simulation iteration, a total of 200,000 data points are generated, providing a comprehensive dataset for analysis and evaluation. The generated data are then utilized to train and evaluate a hybrid deep learning model, employing a multi-layer perceptron (MLP) as a feature extractor and multiple machine learning models (support vector machine, random forest, decision tree, and k-nearest neighbors) for performance comparison. The results demonstrate that the MLP-RF (multi-layer perceptron with random forest) model achieved an accuracy of 99%, while the MLP-DT (multi-layer perceptron with decision tree) model achieved an accuracy of 98%. These high accuracy values indicate the effectiveness of the models in accurately classifying and predicting the outcomes. The contributions of this research work include the development of a realistic simulation model, the evaluation of sensor layout effectiveness, and the application of deep learning techniques for improved wheel flat detections.

A Novel Low-Cost Compact High-Performance Flower-Shaped Radiator Design for Modern Smartphone Applications.

This manuscript examines the design principle and real-world validation of a new miniaturized high-performance flower-shaped radiator (FSR). The antenna prototype consists of an ultracompact square metallic patch of 0.116λ0 × 0.116λ0 (λ0 is the free space wavelength at 3.67 GHz), a rectangular microstrip feed network, and a partial metal ground plane. A novel, effective, and efficient approach based on open circuit loaded stubs is employed to achieve the antenna's optimal performance features. Rectangular, triangular, and circular disc stubs were added to the simple structure of the square radiator, and hence, the FSR configuration was formed. The proposed antenna was imprinted on a low-cost F4B laminate with low profile thickness of 0.018λ0, relative permittivity εr = 2.55, and dielectric loss tangent δ = 0.0018. The designed radiator has an overall small size of 0.256λ0 × 0.354λ0. The parameter study of multiple variables and their influence on the performance results has been extensively studied. Moreover, the impact of different substrate materials, impedance bandwidths, resonance tuning, and impedance matching has also been analyzed. The proposed antenna model has been designed, simulated, and fabricated. The designed antenna exhibits a wide bandwidth of 5.33 GHz ranging from 3.67 to 9.0 GHz at 10 dB return loss, which resulted in an 83.6% fractional impedance bandwidth; a maximum gain of 7.3 dBi at 8.625 GHz; optimal radiation efficiency of 89% at 4.5 GHz; strong intensity current flow across the radiator; and stable monopole-like far-field radiation patterns. Finally, a comparison between the scientific results and newly published research has been provided. The antenna's high-performance simulated and measured results are in a good agreement; hence, they make the proposed antenna an excellent choice for modern smartphones' connectivity with the sub-6 GHz frequency spectrum of modern fifth-generation (5G) mobile communication application.

Ethical Dilemmas and Privacy Issues in Emerging Technologies: A Review.

Industry 5.0 is projected to be an exemplary improvement in digital transformation allowing for mass customization and production efficiencies using emerging technologies such as universal machines, autonomous and self-driving robots, self-healing networks, cloud data analytics, etc., to supersede the limitations of Industry 4.0. To successfully pave the way for acceptance of these technologies, we must be bound and adhere to ethical and regulatory standards. Presently, with ethical standards still under development, and each region following a different set of standards and policies, the complexity of being compliant increases. Having vague and inconsistent ethical guidelines leaves potential gray areas leading to privacy, ethical, and data breaches that must be resolved. This paper examines the ethical dimensions and dilemmas associated with emerging technologies and provides potential methods to mitigate their legal/regulatory issues.

Novel Corrugated Long Period Grating Surface Balloon-Shaped Heterocore-Structured Plastic Optical Fibre Sensor for Microalgal Bioethanol Production.

A novel long period grating (LPG) inscribed balloon-shaped heterocore-structured plastic optical fibre (POF) sensor is described and experimentally demonstrated for real-time measurement of the ultra-low concentrations of ethanol in microalgal bioethanol production applications. The heterocore structure is established by coupling a 250 µm core diameter POF between two 1000 µm diameter POFs, thus representing a large core-small core-large core configuration. Before coupling as a heterocore structure, the sensing region or small core fibre (SCF; i.e., 250 µm POF) is modified by polishing, LPG inscription, and macro bending into a balloon shape to enhance the sensitivity of the sensor. The sensor was characterized for ethanol-water solutions in the ethanol concentration ranges of 20 to 80 %v/v, 1 to 10 %v/v, 0.1 to 1 %v/v, and 0.00633 to 0.0633 %v/v demonstrating a maximum sensitivity of 3 × 106 %/RIU, a resolution of 7.9 × 10-6 RIU, and a limit of detection (LOD) of 9.7 × 10-6 RIU. The experimental results are included for the intended application of bioethanol production using microalgae. The characterization was performed in the ultra-low-level ethanol concentration range, i.e., 0.00633 to 0.03165 %v/v, that is present in real culturing and production conditions, e.g., ethanol-producing blue-green microalgae mixtures. The sensor demonstrated a maximum sensitivity of 210,632.8 %T/%v/v (or 5 × 106 %/RIU as referenced from the RI values of ethanol-water solutions), resolution of 2 × 10-4%v/v (or 9.4 × 10-6 RIU), and LOD of 4.9 × 10-4%v/v (or 2.3 × 10-5 RIU). Additionally, the response and recovery times of the sensor were investigated in the case of measurement in the air and the ethanol-microalgae mixtures. The experimentally verified, extremely high sensitivity and resolution and very low LOD corresponding to the initial rate of bioethanol production using microalgae of this sensor design, combined with ease of fabrication, low cost, and wide measurement range, makes it a promising candidate to be incorporated into the bioethanol production industry as a real-time sensing solution as well as in other ethanol sensing and/or RI sensing applications.

Triaxial bearing vibration dataset of induction motor under varying load conditions.

Rotating machines as core component of an industry can effectively be monitored through vibration analysis. Considering the importance of vibration in industrial condition monitoring, this article reports and discusses triaxial vibration data for motor bearing faults detection and identification. The data is acquired using a MEMS based triaxial accelerometer and the National Instruments myRIO board. The bearing conditions include healthy bearing, bearings with inner race faults, and bearings with outer race faults. For each faulty bearing condition, the three-phase induction motor is operated under three different load conditions. The dataset can be used to assess performance of newly developed methods for effective bearing fault diagnosis. Mendeley Data. http://dx.doi.org/10.17632/fm6xzxnf36.2.

A Single-Channel Wireless EEG Headset Enabled Neural Activities Analysis for Mental Healthcare Applications.

Electroencephalography (EEG) is a technique of Electrophysiology used in a wide variety of scientific studies and applications. Inadequately, many commercial devices that are available and used worldwide for EEG monitoring are expensive that costs up to thousands of dollars. Over the past few years, because of advancements in technology, different cost-effective EEG recording devices have been made. One such device is a non-invasive single electrode commercial EEG headset called MindWave 002 (MW2), created by NeuroSky Inc that cost less than 100 USD. This work contributes in four distinct ways, first, how mental states such as a focused and relaxed can be identified based on EEG signals recorded by inexpensive MW2 is demonstrated for accurate information extraction. Second, MW2 is considered because apart from cost, the user's comfort level is enhanced due to non-invasive operation, low power consumption, portable small size, and a minimal number of detecting locations of MW2. Third, 2 situations were created to stimulate focus and relaxation states. Prior to analysis, the acquired brain signals were pre-processed to discard artefacts and noise, and band-pass filtering was performed for delta, theta, alpha, beta, and gamma wave extraction. Fourth, analysis of the shapes and nature of extracted waves was performed with power spectral density (PSD), mean amplitude values, and other parameters in LabVIEW. Finally, with comprehensive experiments, the mean values of the focused and relaxed signal EEG signals were found to be 30.23 µV and 15.330 µV respectively. Similarly, average PSD values showed an increase in theta wave value and a decrease in beta wave value related to the focus and relaxed state, respectively. We also analyzed the involuntary and intentional number of blinks recorded by the MW2 device. Our study can be used to check mental health wellness and could provide psychological treatment effects by training the mind to quickly enter a relaxed state and improve the person's ability to focus. In addition, this study can open new avenues for neurofeedback and brain control applications. Supplementary Information: The online version contains supplementary material available at 10.1007/s11277-022-09731-w.

A Review of Optical Fibre Ethanol Sensors: Current State and Future Prospects.

A range of optical fibre-based sensors for the measurement of ethanol, primarily in aqueous solution, have been developed and are reviewed here. The sensing approaches can be classified into four groups according to the measurement techniques used, namely absorption (or absorbance), external interferometric, internal fibre grating and plasmonic sensing. The sensors within these groupings can be compared in terms of their characteristic performance indicators, which include sensitivity, resolution and measurement range. Here, particular attention is paid to the potential application areas of these sensors as ethanol production is globally viewed as an important industrial activity. Potential industrial applications are highlighted in the context of the emergence of the internet of things (IoT), which is driving widespread utilization of these sensors in the commercially significant industrial and medical sectors. The review concludes with a summary of the current status and future prospects of optical fibre ethanol sensors for industrial use.

Artificial Neural Networks to Solve the Singular Model with Neumann-Robin, Dirichlet and Neumann Boundary Conditions.

The aim of this work is to solve the case study singular model involving the Neumann-Robin, Dirichlet, and Neumann boundary conditions using a novel computing framework that is based on the artificial neural network (ANN), global search genetic algorithm (GA), and local search sequential quadratic programming method (SQPM), i.e., ANN-GA-SQPM. The inspiration to present this numerical framework comes through the objective of introducing a reliable structure that associates the operative ANNs features using the optimization procedures of soft computing to deal with such stimulating systems. Four different problems that are based on the singular equations involving Neumann-Robin, Dirichlet, and Neumann boundary conditions have been occupied to scrutinize the robustness, stability, and proficiency of the designed ANN-GA-SQPM. The proposed results through ANN-GA-SQPM have been compared with the exact results to check the efficiency of the scheme through the statistical performances for taking fifty independent trials. Moreover, the study of the neuron analysis based on three and 15 neurons is also performed to check the authenticity of the proposed ANN-GA-SQPM.