This paper presents the effect of auditory feedback on tactile intensity perception, which may be of interest to haptic or audiotactile interaction engineers. An experimental setup consisted of a touchscreen, an electrodynamic shaker, and a closed-back headphone for a subject to interact with the touchscreen and to feel audiotactile feedback. In the experiment, participants were asked to judge perceived tactile intensity, using the magnitude estimation method, in the absence and presence of simultaneous auditory feedback. All data collected from the subjects were analyzed statistically, and then the effect of auditory feedback was investigated focusing on the following aspects: whether the presence of auditory feedback changes perceived tactile intensity, whether the frequency component of auditory feedback affects tactile intensity perception, and whether the coincidence of tactile and auditory frequencies influences on tactile intensity perception. Besides, changes in Stevens's exponent were analyzed to discuss how tactile intensity perception varies due to the auditory feedback. Finally, an equal intensity contour, in the domain of sensation level and frequency of tactile stimulation, was drawn. It can be applied to adjust the level of tactile stimuli for haptic feedback designers to provide a constant perceived tactile intensity considering the presence of auditory feedback.

Model-agnostic meta-learning (MAML), coupled with digital twins, is transformative for predictive maintenance (PdM), especially in robotic arms in assembly lines, where rapid and accurate fault classification of arms is essential. Despite gaining significant traction, the framework faces significant challenges, like hypersensitivity to learning parameters and limited generalization during meta-testing. To address these challenges, we proposed an ensemble-based meta-learning approach integrating majority voting with model-agnostic meta-learning (MAML), and operational grouping was implemented via Latin hypercube sampling (LHS) to enhance few-shot learning ability and generalization along with maintaining stable output. This approach demonstrates superior accuracy in classifying a significantly larger number of defective mechanical classes, particularly in cross-domain few-shot (CDFS) learning scenarios. The proposed methodology is validated using a synthetic vibration signal dataset of robotic arm faults generated via a digital twin. Comparative analysis with existing frameworks, including ANIL, Protonet, and Reptile, confirms that our approach achieves higher accuracy in the given scenario.

In the wake of COVID-19, the digital fitness market combining health equipment and ICT technologies is experiencing unexpected high growth. A smart trampoline fitness system is a new representative home exercise equipment for muscle strengthening and rehabilitation exercises. Recognizing the motions of the user and evaluating user activity is critical for implementing its self-guided exercising system. This study aimed to estimate the three-dimensional positions of the user’s foot using deep learning-based image processing algorithms for footprint shadow images acquired from the system. The proposed system comprises a jumping fitness trampoline; an upward-looking camera with a wide-angle and fish-eye lens; and an embedded board to process deep learning algorithms. Compared with our previous approach, which suffered from a geometric calibration process, a camera calibration method for highly distorted images, and algorithmic sensitivity to environmental changes such as illumination conditions, the proposed deep learning algorithm utilizes end-to-end learning without calibration. The network is configured with a modified Fast-RCNN based on ResNet-50, where the region proposal network is modified to process location regression different from box regression. To verify the effectiveness and accuracy of the proposed algorithm, a series of experiments are performed using a prototype system with a robotic manipulator to handle a foot mockup. The three root mean square errors corresponding to X, Y, and Z directions were revealed to be 8.32, 15.14, and 4.05 mm, respectively. Thus, the system can be utilized for motion recognition and performance evaluation of jumping exercises.

In this paper, a method using a semantic segmentation method based on edge-region focal loss was proposed to estimate the heading angle of resin in a catheter-extrusion process. The approach leveraged an improved semantic segmentation facilitated by this new loss function and principal component analysis. Accurate heading angle estimation was critical and depended on the precision of segmentation, demanding robust and precise segmentation even in the presence of external disturbances. The edge-region focal loss enhanceed segmentation by heavily weighting areas with ambiguous boundaries, which was particularly important in scenarios with various semantic elements in the background and foreground or near object boundaries. Image data were collected using RGB cameras to validate the effectiveness of this method. The method’s accuracy was affirmed by the mean intersection over union (mIoU) and mean absolute error measurements, achieving mean absolute errors of the angle and mIoU at 0.5002 and 0.8657, respectively. These results demonstrate the method’s suitability for monitoring the extrusion process. Furthermore, compared to traditional loss functions, the edge-region focal loss shows superior performance in segmenting adjacent boundary regions between the background and objects and maintains robustness in noisy environments.

Hydrogen refueling stations are pivotal for renewable energy and carbon neutrality; however, they encounter challenges owing to equipment malfunctions. This study addresses the use of time-series forecasting techniques to predict and diagnose critical equipment failure at these stations. An analysis of the station equipment was conducted to create scenarios for potential malfunctions in compression equipment. Techniques such as the Recurrent Neural Network(RNN), Long Short-Term Memory network (LSTM), and Gated Recurrent Unit(GRU) have been employed to forecast the conditions of high-pressure compression equipment. Deep neural networks were constructed to enhance prediction accuracy, typically achieving an error margin of 0.01. Multi-step predictions using autoregression were utilized to bolster equipment resilience against aging and progressive failures. Autoregressive prediction models, particularly those using LSTMs and GRUs, outperform RNNs. However, predictions may be subject to errors due to algorithmic limitations and environmental factors. This study introduces a stochastic forecasting approach that, utilizes Gaussian distributions to predict probability distributions, not single-point estimates. This method yielded a 95% prediction interval with a standard deviation of 1.96. The reliability of multi-time step forecasts is significantly improved by adopting stochastic autoregressive forecasting and establishing prediction intervals. The proposed model enhances not only the accuracy of equipment failure predictions but also proactive maintenance, thus reducing downtime and boosting the efficiency of the hydrogen fuel infrastructure, which contributes to the wider utilization of hydrogen as a clean energy source.

To evaluate the dynamic characteristics at all positions of the main spindle of a machine tool, an experimental point was selected using a full factorial design, and a vibration test was conducted. Based on the measurement position, the resonant frequency was distributed from approximately 236 to 242 Hz. The approximation model was evaluated based on its resonant frequencies and dynamic stiffness using regression and interpolation methods. The accuracy of the resonant frequency demonstrated by the kriging method was approximately 89%, whereas the highest accuracy of the dynamic stiffness demonstrated by the polynomial regression method was 81%. To further verify the approximation model, its dynamic characteristics were measured and verified at additional experimental points. The maximum errors yielded by the model, in terms of the resonant frequency and dynamic stiffness, were 1.6% and 7.1%, respectively.