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Bearings play a critical role in the reliable operation of induction machines, and their failure can lead to significant operational challenges and downtime. Detecting and diagnosing these defects is imperative to ensure the longevity of induction machines and preventing costly downtime. The purpose of this paper is to develop a novel approach for diagnosis of bearing faults in induction machine.

To identify the different fault states of the bearing with accurately and efficiently in this paper, the original bearing vibration signal is first decomposed into several intrinsic mode functions (IMFs) using variational mode decomposition (VMD). The IMFs that contain more noise information are selected using the Pearson correlation coefficient. Subsequently, discrete wavelet transform (DWT) is used to filter the noisy IMFs. Second, the composite multiscale weighted permutation entropy (CMWPE) of each component is calculated to form the features vector. Finally, the features vector is reduced using the locality-sensitive discriminant analysis algorithm, to be fed into the support vector machine model for training and classification.

The obtained results showed the ability of the VMD_DWT algorithm to reduce the noise of raw vibration signals. It also demonstrated that the proposed method can effectively extract different fault features from vibration signals.

This study suggested a new VMD_DWT method to reduce the noise of the bearing vibration signal. The proposed approach for bearing fault diagnosis of induction machine based on VMD-DWT and CMWPE is highly effective. Its effectiveness has been verified using experimental data.

Step length is a key factor for pedestrian dead reckoning (PDR), which affects positioning accuracy and reliability. Traditional methods are difficult to handle step length estimation of dynamic gait, which have larger error and are not adapted to real walking. This paper aims to propose a step length estimation method based on frequency domain feature analysis and gait recognition for PDR, which considers the effects of real-time gait.

The new step length estimation method transformed the acceleration of pedestrians from time domain to frequency domain, and gait characteristics of pedestrians were obtained and matched with different walking speeds.

Many experiments are conducted and compared with Weinberg and Kim models, and the results show that the average errors of the new method were improved by about 2 meters to 5 meters. It also shows that the proposed method has strong stability and device robustness and meets the accuracy requirements of positioning.

A sliding window strategy used in fast Fourier transform is proposed to implement frequency domain analysis of the acceleration, and a fast adaptive gait recognition mechanism is proposed to identify gait of pedestrians.

This study aims to propose a method for monitoring bearing health in the time–frequency domain, termed the Lock-in spectrum, to track the evolution of bearing faults over time and frequency.

The Lock-in spectrum uses vibration signals captured by vibration sensors and uses a lock-in process to analyze specified frequency bands. It calculates the distribution of signal amplitudes around fault characteristic frequencies over short time intervals.

Experimental results demonstrate that the Lock-in spectrum effectively captures the degradation process of bearings from fault inception to complete failure. It provides time-varying information on fault frequencies and amplitudes, enabling early detection of fault growth, even in the initial stages when fault signals are weak. Compared to the benchmark short-time Fourier transform method, the Lock-in spectrum exhibits superior expressive ability, allowing for higher-resolution, long-term monitoring of bearing condition.

The proposed Lock-in spectrum offers a novel approach to bearing health monitoring by capturing the dynamic evolution of fault frequencies over time. It surpasses traditional methods by providing enhanced frequency resolution and early fault detection capabilities.

This study aims to introduce a vision-based model to generate droplets with auto-tuned parameters. The model can auto-adjust the inherent uncertainties and errors involved with the fabrication and operating parameters in microfluidic platform, attaining precise size and frequency of droplet generation.

The photolithography method is utilized to prepare the microfluidic devices used in this study, and various experiments are conducted at various flow-rate and viscosity ratios. Data for droplet shape is collected to train the artificial intelligence (AI) models.

Growth phase of droplets demonstrated a unique spring back effect in droplet size. The fully developed droplet sizes in the microchannel were modeled using least absolute shrinkage and selection operators (LASSO) regression model, Gaussian support vector machine (SVM), long short term memory (LSTM) and deep neural network models. Mean absolute percentage error (MAPE) of 0.05 and R² = 0.93 were obtained with a deep neural network model on untrained flow data. The shape parameters of the droplets are affected by several uncontrolled parameters. These parameters are instinctively captured in the model.

Experimental data set is generated for varying viscosity values and flow rates. The variation of flow rate of continuous phase is observed here instead of dispersed phase. An automated computation routine is developed to read the droplet shape parameters considering the transient growth phase of droplets. The droplet size data is used to build and compare various AI models for predicting droplet sizes. A predictive model is developed, which is ready for automated closed loop control of the droplet generation.

Identifying the flow regime is a prerequisite for accurately modeling two-phase flow. This paper aims to introduce a comprehensive data-driven workflow for flow regime identification.

A numerical two-phase flow model was validated against experimental data and was used to generate dynamic pressure signals for three different flow regimes. First, four distinct methods were used for feature extraction: discrete wavelet transform (DWT), empirical mode decomposition, power spectral density and the time series analysis method. Kernel Fisher discriminant analysis (KFDA) was used to simultaneously perform dimensionality reduction and machine learning (ML) classification for each set of features. Finally, the Shapley additive explanations (SHAP) method was applied to make the workflow explainable.

The results highlighted that the DWT + KFDA method exhibited the highest testing and training accuracy at 95.2% and 88.8%, respectively. Results also include a virtual flow regime map to facilitate the visualization of features in two dimension. Finally, SHAP analysis showed that minimum and maximum values extracted at the fourth and second signal decomposition levels of DWT are the best flow-distinguishing features.

This workflow can be applied to opaque pipes fitted with pressure sensors to achieve flow assurance and automatic monitoring of two-phase flow occurring in many process industries.

This paper presents a novel flow regime identification method by fusing dynamic pressure measurements with ML techniques. The authors’ novel DWT + KFDA method demonstrates superior performance for flow regime identification with explainability.

The comfort of apparel is not only a feeling of perception but also a tangible measure. The fit and fabric of clothing can exert a perception of comfort for the wearer, whereas actual comfort largely depends on physiological and emotional soothing. However, there is still no solid work on connecting the bridge between physiological and emotional feelings to the comfort of clothing. In this study, we have conceptualized, formulated and proven the relation between physiological and emotional parameters with clothing fit and fabric to find the true comfort of the wearer.

A mixed-method research design using physiological and emotional parameters for different fabric and fit combinations were used for this study. The physiological comfort parameters (i.e. heart rate and respiration rate) are extracted from the subjects using gold-standard clinical devices for various fit and fabric combinations. For the emotional response, a survey was conducted for the same subjects wearing all the fit and fabric combinations. Statistical analysis and modeling were performed to obtain the results.

Physiological indicators such as heart rate are closely linked with user comfort. Due to the limitations in environmental control, the physiological changes obtained did not significantly vary for different fabric and fit combinations of the clothing. However, a significant change in emotional response indicated a definite relationship between different fabric and fit types. Based on the participants’ responses, weather conditions, size of the clothing item, types of fabrics and style also influence the participants’ choice of clothing.

The research was conducted to discover the relation between true comfort (physiological and emotional parameters) and clothing (fit and fabric), which is unique to the field. This study closes the gap and builds up the relationship, which can help introduce clothing comfort to users in the future. The findings of this study help us understand how fabric types (natural or synthetic) and clothing fit types (loose or fitted) can affect physiological and emotional responses, which can provide the consumer with satisfactory clothing with the suitable properties needed.

This study aims to present a new rapid enhancement digital elevation model (DEM) framework using Google Earth Engine (GEE), machine learning, weighted interpolation and spatial interpolation techniques with ground control points (GCPs), where high-resolution DEMs are crucial spatial data that find extensive use in many analyses and applications.

First, rapid-DEM imports Shuttle Radar Topography Mission (SRTM) data and Sentinel-2 multispectral imagery from a user-defined time and area of interest into GEE. Second, SRTM with the feature attributes from Sentinel-2 multispectral imagery is generated and used as input data in support vector machine classification algorithm. Third, the inverse probability weighted interpolation (IPWI) approach uses 12 fixed GCPs as additional input data to assign the probability to each pixel of the image and generate corrected SRTM elevations. Fourth, gridding the enhanced DEM consists of regular points (E, N and H), and the contour interval is 5 m. Finally, densification of enhanced DEM data with GCPs is obtained using global positioning system technique through spatial interpolations such as Kriging, inverse distance weighted, modified Shepard’s method and triangulation with linear interpolation techniques.

The results were compared to a 1-m vertically accurate reference DEM (RD) obtained by image matching with Worldview-1 stereo satellite images. The results of this study demonstrated that the root mean square error (RMSE) of the original SRTM DEM was 5.95 m. On the other hand, the RMSE of the estimated elevations by the IPWI approach has been improved to 2.01 m, and the generated DEM by Kriging technique was 1.85 m, with a reduction of 68.91%.

A comparison with the RD demonstrates significant SRTM improvements. The suggested method clearly reduces the elevation error of the original SRTM DEM.

This paper aims to investigate the effectiveness of immersive virtual reality (VR) media and the influence of user’s age in the context of destination marketing by exploring their impact on cognition (presence), affection (arousal), and behavioral (intention to visit and intention to recommend the destination) outcomes.

A laboratory experiment was conducted to compare the impact of using immersive VR (vs. 2D desktop) to experience a 360-degree virtual tour of Valencia on consumers’ behavior. The sample included 187 participants. Both self-reported and physiological measures were collected during the experimentation.

Results showed that participants in the immersive condition experienced a stronger sense of presence and higher physiological arousal than those exposed to nonimmersive content. Presence significantly mediated both the intention to visit and the intention to recommend the promoted venue. Physiological arousal mediated the relationship between media typology and the intention to recommend the destination. Upon introducing age as a moderating variable, the effect of physiological arousal on behavioral outcomes proves to be significant.

The study presents destination marketing organizations with a compelling use case for immersive technologies. It also offers design principles, potential applications and targeting strategies for VR marketing in hospitality management.

To the best of the authors’ knowledge, this study is the first to investigate the combined effect of physiological arousal and presence on behavioral intentions in VR destination marketing, while also examining the impact of age as an individual characteristic.

With the development of intelligent technology, deep learning has made significant progress and has been widely used in various fields. Deep learning is data-driven, and its training process requires a large amount of data to improve model performance. However, labeled data is expensive and not readily available.

To address the above problem, researchers have integrated semi-supervised and deep learning, using a limited number of labeled data and many unlabeled data to train models. In this paper, Generative Adversarial Networks (GANs) are analyzed as an entry point. Firstly, we discuss the current research on GANs in image super-resolution applications, including supervised, unsupervised, and semi-supervised learning approaches. Secondly, based on semi-supervised learning, different optimization methods are introduced as an example of image classification. Eventually, experimental comparisons and analyses of existing semi-supervised optimization methods based on GANs will be performed.

Following the analysis of the selected studies, we summarize the problems that existed during the research process and propose future research directions.

This paper reviews and analyzes research on generative adversarial networks for image super-resolution and classification from various learning approaches. The comparative analysis of experimental results on current semi-supervised GAN optimizations is performed to provide a reference for further research.

This study aimed to compare the performance of sustainable shoes made with bacterial cellulosic composite and commercial leather shoes using an experimental research design. The two specific research objectives were: (1) to examine the basic material properties of multi-layered bacterial cellulosic materials (MBC), which include green tea-based cellulosic (GBC) mats, hemp fabrics, and denim fabrics, in comparison with those of two-layered leathers (MCP) consisting of calf-skin and pig-skin – commonly used in shoe manufacturing; and (2) to explore wearers’ performance in the two types of shoes by assessing quantitative kinematic and kinetic parameters of lower body movements.

This study focused on assessing the basic materials testing and performance of sustainable shoes through a biomechanical approach, in contrast to commercially available leather shoes, through human wear trials. In this study, green tea-based cellulosic (GBC) mats were developed using the optimal combination of ingredients for cellulose growth. Subsequently, the GBC, denim fabric (100% cotton), and 100% hemp fabric were combined to create multi-layered bacterial cellulosic materials (MBC) as an alternative to leather. Additionally, calf-skin and pig-skin leathers were utilized to produce a commercially available two-layered leather (MCP), commonly employed in shoe manufacturing. 37 of the 42 human subjects who participated in wear testing were collected. A paired t-test was conducted to determine whether significant mean differences existed between the two shoe types, a paired t-test was conducted.

To develop a biodegradable and compostable material that could be used as a leather alternative for the footwear industry, we proposed MBC and examined its properties compared with those of MCP, a product often used when making shoes. These findings confirmed the similar properties of MBC and MCP from the material testing and the possibility of using a men’s sustainable shoe prototype as a leather alternative, in terms of kinematics and kinetics.

The new multi-layered bacterial cellulosic materials (MBC) could be an alternative to commercial leathers such as innovative sustainable material construction, advanced design, and advanced techniques to optimize the overall performance of sustainable footwear.

Investigating the integration of smart textile technologies, ergonomic design principles, and personalized customization will contribute to developing MBC and making sustainable shoes using MBC compared with commercial leather shoes. This study provides valuable insights into further refinement and innovation in the sustainable footwear industry.