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The gait pattern of exoskeleton control conflicting with the human operator’s (the pilot) intention may cause awkward maneuvering or even injury. Therefore, it has been the focus of many studies to help decide the proper gait operation. However, the timing for the recognization plays a crucial role in the operation. The delayed detection of the pilot’s intent can be equally undesirable to the exoskeleton operation. Instead of recognizing the motion, this study examines the possibility of identifying the transition between gaits to achieve in-time detection. This study used the data from IMU sensors for future mobile applications. Furthermore, we tested using two machine learning networks: a linearfFeedforward neural network and a long short-term memory network. The gait data are from five subjects for training and testing. The study results show that: 1. The network can successfully separate the transition period from the motion periods. 2. The detection of gait change from walking to sitting can be as fast as 0.17 s, which is adequate for future control applications. However, detecting the transition from standing to walking can take as long as 1.2 s. 3. This study also find that the network trained for one person can also detect movement changes for different persons without deteriorating the performance.

Dynamic cell cultures simulate the in vivo cell environment for a regular loading system with curtain strains. However, it is difficult to obtain strains that are suitable for cells without conducting multiple trials. This study develops a device that increases the strain gradient by changing the tensile section, in order to determine the effect of various cyclic strains on cultured human keratinocytes (HK) cells. This device is used to determine the effect of 3% and 5% cyclic strain and shear strain on cell proliferation and arrangement at 1 Hz. The results show that compared with static and 3% strain, a 5% cyclic strain better inhibits the proliferation of HK cells. Compared to the initial cell attachment when there is no specific directionality, the cells are aligned in the vertical stretching direction after cyclic stretching. This equipment increases the efficiency of the experiment and more intuitively maps the cell behavior and shape to the strain field and the response to the shear strain.

In this paper, we propose a real-time model estimation framework using artificial intelligence techniques and apply it to a piezoelectric transducer (PZT) stage equipped with multiple switching controllers. Conventional fixed controllers often fail to satisfy diverse performance requirements: some achieve smooth but slow responses, while others deliver fast yet oscillatory behavior. To address this limitation, we developed a multi-controller switching mechanism that can select optimal control sequences based on predicted system responses, thereby enhancing overall performance. However, the existing mechanism relies on a nominal plant and neglects variations during operation. To address this problem, we employ the eXtreme Gradient Boosting (XGBoost) algorithm to construct a real-time model estimator, which continuously updates the system model during response prediction, thereby improving prediction accuracy. The corresponding controllers are then adjusted according to the updated models and integrated into the switching mechanism to further enhance performance. Finally, we validate the proposed approach through simulations and experiments.

Recent advances in the analysis of corneal biomechanical properties remain difficult to predict the structural stability before and after refractive surgery. In this regard, we applied the finite element method (FEM) to determine the roles of the Bowman’s membrane, stroma, and Descemet’s membrane in the hoop stresses of cornea, under tension (physiological) and bending (nonphysiological), for patients who undergo radial keratotomy (RK), photorefractive keratectomy (PRK), laser-assisted in situ keratomileusis (LASIK), or small incision lenticule extraction (SMILE). The stress concentration maps, potential creak zones, and potential errors in intraocular pressure (IOP) measurements were further determined. Our results confirmed that the Bowman’s membrane and Descemet’s membrane accounted for 20% of the bending rigidity of the cornea, and became the force pair dominating the bending behaviour of the cornea, the high stress in the distribution map, and a stretch to avoid structural failure. In addition, PRK broke the central linking of hoop stresses and concentrated stress on the edge of the Bowman’s membrane around ablation, which posed considerable risk of potential creaks. Compared with SMILE, LASIK had a higher risk of developing creaks around the ablation in the stroma layer. Our FEM models also predicted the postoperative IOPs precisely in a conditional manner.

This paper develops multiple control strategies for a precision long-travel stage, which comprises motor and piezoelectric transducer (PZT) stages. First, the PZT stage is equipped with control switching and model estimation mechanisms to achieve nm-level precision within 100 μm distances. The control switching mechanism selects the optimal control sequences by predicting system responses, while the model estimation algorithm updates the system model to improve the prediction accuracy. Second, the motor stage is equipped with gain-scheduling and feedforward control mechanisms to achieve a maximum displacement of 100 mm with a resolution of 0.1 μm. The gain scheduling control modifies the control gain in accordance with tracking errors, while the feedforward control can mitigate phase lags. We integrate the stages to achieve nm-level precision over long travels and conduct simulations and experiments to show the advantages of the control mechanisms. Finally, we apply the long-travel precision stage to fabricate micro-lenses using two-photon polymerization and evaluate the fabricated micro-lenses’ optical characteristics to illustrate the merits of the control strategies.

This paper proposes a multiple switching control method for a long-stroke precision stage to improve its performance. The stage consists of a motor stage with a travel of 10 cm and a piezoelectric transducer (PZT) stage with a resolution of 1 nm, which allows a simultaneously large travel length and precision positioning. The developed switching algorithms can select the optimal control sequence that minimizes the system cost by predicting of the future responses. We obtain the stage models through experiments. For the PZT stage, we design two robust loop-shaping controllers: the fast controller provides rapid transient responses and the smooth controller give smooth responses. We then derive corresponding robust proportional–integral-derivative controllers and develop a control switching mechanism that can simultaneously accomplish fast and smooth responses. The switching mechanism predicts future system responses by all possible control sequences and selects the optimal one to minimize specified costs. For the motor stage, we apply gain scheduling control, where the gain adjustment is larger than previously because the PZT stage can quickly compensate for the position errors of the combined stage. The designed control structures are then implemented for simulation and experimental verification. The results indicate that the proposed control is effective in improving system responses.

This note addresses the transient behavior of the different point-to-point motion control strategies. The transition between the high-speed velocity control and the high-precision position feedback often leads to undesirable overshoot and residual oscillation. This study first introduces the use of an integrated H ∞ -chain scattering description (CSD) synthesized controller for a unified point-to-point motion to a high-precision positioning control without switching. This study then compares the control effects with the s-curve trajectory control and an augmented Luenberger observer (ALO)-based control. The s-curve control is a position-dependent implementation and has to be converted into a time-domain trajectory for practical implementation. It is desirable to examine if the trajectory error would accumulate into a large overshoot. The ALO uses an auxiliary system to suppress the thrust ripple but is slower compared with the acceleration-based H ∞ -CSD and s-curve control. It is interesting to see that the H ∞ -CSD controller automatically reaches a similar performance as the s-curve trajectory. Both the s-curve and the H ∞ -CSD control algorithms achieve access time in the range of 200 ms for a 10-mm travel. It is also interesting to notice that the time-domain error for the s-curve trajectory does not accumulate, and the overshoot is < 0.08%. As a comparison, the access time for the integrated H ∞ -CSD controller is 195 ms with an overshoot of 0.097%, and the access time for the s-curve trajectory control is 212 ms with an overshoot of 0.016%.

Objective To investigate the correlation between corneal biomechanical properties and topographic parameters using machine learning networks for automatic severity diagnosis and reference benchmark construction. Methods This was a retrospective study involving 31 eyes from 31 patients with keratonus. Two clustering approaches were used (i.e., shape-based and feature-based). The shape-based method used a keratoconus benchmark validated for indicating the severity of keratoconus. The feature-based method extracted imperative features for clustering analysis. Results There were strong correlations between the symmetric modes and the keratoconus severity and between the asymmetric modes and the location of the weak centroid. The Pearson product-moment correlation coefficient (PPMC) between the symmetric mode and normality was 0.92 and between the asymmetric mode and the weak centroid value was 0.75. Conclusion This study confirmed that there is a relationship between the keratoconus signs obtained from topography and the corneal dynamic behaviour captured by the Corvis ST device. Further studies are required to gather more patient data to establish a more extensive database for validation.

Shape memory alloys (SMAs) offer a high power-to-weight ratio, large recovery strain, and low driving voltages, and have thus attracted considerable research attention. The difficulty of controlling SMA actuators arises from their highly nonlinear hysteresis and temperature dependence. This paper describes a combination of self-sensing and model-based control, where the model includes both the major and minor hysteresis loops as well as the thermodynamics effects. The self-sensing algorithm uses only the power width modulation (PWM) signal and requires no heavy equipment. The method can achieve high-accuracy servo control and is especially suitable for miniaturized applications.

As human–robot interaction becomes more prevalent in industrial and clinical settings, detecting changes in human posture has become increasingly crucial. While recognizing human actions has been extensively studied, the transition between different postures or movements has been largely overlooked. This study explores using two deep-learning methods, the linear Feedforward Neural Network (FNN) and Long Short-Term Memory (LSTM), to detect changes in human posture among three different movements: standing, walking, and sitting. To explore the possibility of rapid posture-change detection upon human intention, the authors introduced transition stages as distinct features for the identification. During the experiment, the subject wore an inertial measurement unit (IMU) on their right leg to measure joint parameters. The measurement data were used to train the two machine learning networks, and their performances were tested. This study also examined the effect of the sampling rates on the LSTM network. The results indicate that both methods achieved high detection accuracies. Still, the LSTM model outperformed the FNN in terms of speed and accuracy, achieving 91% and 95% accuracy for data sampled at 25 Hz and 100 Hz, respectively. Additionally, the network trained for one test subject was able to detect posture changes in other subjects, demonstrating the feasibility of personalized or generalized deep learning models for detecting human intentions. The accuracies for posture transition time and identification at a sampling rate of 100 Hz were 0.17 s and 94.44%, respectively. In summary, this study achieved some good outcomes and laid a crucial foundation for the engineering application of digital twins, exoskeletons, and human intention control.