Fault Diagnosis Method for Human Coexistence Robots Based on Convolutional Neural Networks Using Time-Series Data Generation and Image Encoding.

This paper proposes fault diagnosis methods aimed at proactively preventing potential safety issues in robot systems, particularly human coexistence robots (HCRs) used in industrial environments. The data were collected from durability tests of the driving module for HCRs, gathering time-series vibration data until the module failed. In this study, to apply classification methods in the absence of post-failure data, the initial 50% of the collected data were designated as the normal section, and the data from the 10 h immediately preceding the failure were selected as the fault section. To generate additional data for the limited fault dataset, the Wasserstein generative adversarial networks with gradient penalty (WGAN-GP) model was utilized and residual connections were added to the generator to maintain the basic structure while preventing the loss of key features of the data. Considering that the performance of image encoding techniques varies depending on the dataset type, this study applied and compared five image encoding methods and four CNN models to facilitate the selection of the most suitable algorithm. The time-series data were converted into image data using image encoding techniques including recurrence plot, Gramian angular field, Markov transition field, spectrogram, and scalogram. These images were then applied to CNN models, including VGGNet, GoogleNet, ResNet, and DenseNet, to calculate the accuracy of fault diagnosis and compare the performance of each model. The experimental results demonstrated significant improvements in diagnostic accuracy when employing the WGAN-GP model to generate fault data, and among the image encoding techniques and convolutional neural network models, spectrogram and DenseNet exhibited superior performance, respectively.

Trampoline Stiffness Estimation by Using Robotic System for Quantitative Evaluation of Jumping Exercises.

Trampolines are recognized as a valuable tool in exercise and rehabilitation due to their unique properties like elasticity, rebound force, low-impact exercise, and enhancement of posture, balance, and cardiopulmonary function. To quantitatively assess the effects of trampoline exercises, it is essential to estimate factors such as stiffness, elements influencing jump dynamics, and user safety. Previous studies assessing trampoline characteristics had limitations in performing repetitive experiments at various locations on the trampoline. Therefore, this research introduces a robotic system equipped with foot-shaped jigs to evaluate trampoline stiffness and quantitatively measure exercise effects. This system, through automated, repetitive movements at various locations on the trampoline, accurately measures the elastic coefficient and vertical forces. The robot maneuvers based on the coordinates of the trampoline, as determined by its torque and position sensors. The force sensor measures data related to the force exerted, along with the vertical force data at X, Y, and Z coordinates. The model's accuracy was evaluated using linear regression based on Hooke's Law, with Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Correlation Coefficient Squared (R-squared) metrics. In the analysis including only the distance between X and the foot-shaped jigs, the average MAE, RMSE, and R-squared values were 17.9702, 21.7226, and 0.9840, respectively. Notably, expanding the model to include distances in X, Y, and between the foot-shaped jigs resulted in a decrease in MAE to 15.7347, RMSE to 18.8226, and an increase in R-squared to 0.9854. The integrated model, including distances in X, Y, and between the foot-shaped jigs, showed improved predictive capability with lower MAE and RMSE and higher R-squared, indicating its effectiveness in more accurately predicting trampoline dynamics, vital in fitness and rehabilitation fields.

Three-Dimensional Foot Position Estimation Based on Footprint Shadow Image Processing and Deep Learning for Smart Trampoline Fitness System.

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.

Pd-Decorated Multi-Walled Carbon Nanotube Sensor for Hydrogen Detection.

As hydrogen (H2) gas is highly reactive and explosive in ambient atmosphere, its prompt detection in industrial areas is imperative to prevent serious accidents. In particular, high-performance H2 sensors that can promptly detect even low-concentrations of H2 gas are necessary for safety. Carbon nanotubes (CNTs) have a large surface area and a high surface-to-volume ratio, and therefore, they are suitable for use as sensing materials in gas sensors. Moreover, gold, platinum, and palladium are known to be excellent catalyst metals that increase reactivity with H2 gas through the catalytic effect referred to as spill-over mechanism. In this study, a CNT felt sensor with a palladium (Pd) layer was fabricated, and its reactivity with H2 was evaluated. The sensitivity of a CNT felt sensor to H2 gas at room temperature was found to improve when coated with Pd layer.

Carbon Nano Tube-Polymer Hybrid Nanocomposite Electrodes for Porous Polydimethylsiloxane Sponge-Based Flexible Triboelectric Nanogenerators.

Flexible triboelectric nanogenerators (TENGs) have attracted much attention because of its environmentally friendly, practical, and cost-producing advantages. In flexible TENGs, it is important to study the flexible electrodes in order to fabricate the fully flexible devices. Here, we compared electrical characteristics of the sponge porous polydimethylsiloxane (PDMS)-based flexible TENGs with two types of flexible electrodes, copper and carbon nanotube (CNT)-PDMS electrodes. The output voltage and maximum power density of sponge PDMS-based flexible TENGs with copper and CNTPDMS electrodes were compared. The voltage and power density of sponge PDMS-based flexible TENGs with CNT-PDMS electrodes were improved compare to those with copper electrodes. The output voltage and the maximum power density of sponge PDMS-based flexible TENGs with copper and CNT-PDMS electrodes increased 4 times and 7 times, respectively. It is attributed to higher electrical conductivity and stably flow electricity of CNT than those of copper.

3D Contact Position Estimation of Image-Based Areal Soft Tactile Sensor with Printed Array Markers and Image Sensors.

Tactile sensors have been widely used and researched in various fields of medical and industrial applications. Gradually, they will be used as new input devices and contact sensors for interactive robots. If a tactile sensor is to be applied to various forms of human-machine interactions, it needs to be soft to ensure comfort and safety, and it should be easily customizable and inexpensive. The purpose of this study is to estimate 3D contact position of a novel image-based areal soft tactile sensor (IASTS) using printed array markers and multiple cameras. First, we introduce the hardware structure of the prototype IASTS, which consists of a soft material with printed array markers and multiple cameras with LEDs. Second, an estimation algorithm for the contact position is proposed based on the image processing of the array markers and their Gaussian fittings. A series of basic experiments was conducted and their results were analyzed to verify the effectiveness of the proposed IASTS hardware and its estimation software. To ensure the stability of the estimated contact positions a Kalman filter was developed. Finally, it was shown that the contact positions on the IASTS were estimated with a reasonable error value for soft haptic applications.

Adaptive Hit-Quality Index for Raman Spectrum Identification.

The recognition capability of the identification system using Raman spectroscopy is increasing with the demands in the field. Among the various approaches that determine the identity of a target, signal correlation using a moving window is one of the most effective and intuitive methods. In this paper, we report a new correlation method that is robust to spectral intensity variations. Using the peak distribution of a given spectrum, this method adaptively determines meaningful spectral regions for the identification target. Three commercial Raman spectrometer and a 14 033 library were included in the study, which was used for a library-based chemical discrimination test and mixed material analysis experiments. According to the identification experimental results, the proposed method correctly identified all of the spectra and maintained a mean correlation score above 0.95 while maintaining the correlation score of nontarget materials as low as possible.

Comparative Study of Triboelectric Nanogenerators with Differently Woven Cotton Textiles for Wearable Electronics.

A comparative study of the electrical performance of triboelectric nanogenerators (TENGs) with plain- and 2/1 twill-woven cotton textiles was conducted. Furthermore, the microstructures of the cotton fiber surfaces were examined to understand the fundamental mechanical interaction among the cotton fibers in the TENGs. The TENG with 2/1 twill-woven cotton textiles exhibited higher output voltages compared to that with plain-woven cotton textiles. The difference in the output voltage between the two types of TENGs resulted from the difference in triboelectric charge generation between the constituent cotton textiles. The higher output voltage of the TENG with 2/1 twill-woven cotton textiles was attributed to the higher density in triboelectric interactions among the cotton fiber molecules.

Synthesis and Charge Transport of Polymer Nanocomposite of Polyaniline: Polystyrene Sulfonate.

We demonstrate the preparation of water-dispersible polyaniline:polystyrene sulfonate (PANI:PSS), which was doped with camphorsulfonic acid (CSA) and co-doped with poly (4-styrenesulfonic acid) (PSS). The proper formation of the PANI and PANI:PSS was verified by FTIR measurements. The synthesized samples were further characterized via UV-vis spectroscopy. The intensive study on the current density (J)-voltage (V) characteristics within the temperature range (143-303 K) of the synthesized sample was performed systematically. The electrical study shows that the doping of PANI with CSA as a dopant and PSS as a co-dopant significantly improves the overall semi-conducting property of PANI. The detailed analysis of the current density (J)-voltage (V) curve at various temperatures reveals the electrical conduction behavior, which follows the trap-dependent space-charge limited conduction (SCLC) mechanism.

Vision-based fluid-type tactile sensor for measurements on biological tissues.

Hardness, dimensions, and location of biological tissues are important parameters for electronic palpation protocols with standardized performance. This study presents a novel fluid-type tactile sensor able to measure size and depth of heterogeneous substances in elastic bodies. The new sensor is very simple and can be easily fabricated. It consists of an image sensor, LED lights, and a touchpad filled with translucent water. The intensity field of the light traveling in the touchpad is analyzed to estimate the touchpad shape which conforms to the shape of an object in contact. The use of the new sensor for measuring size and depth of heterogeneous substances inside elastic bodies as well as hardness of elastic bodies is illustrated. Results obtained for breast cancer dummies demonstrate the effectiveness of the proposed approach.