To investigate the mechanism of aquatic pathogens in quorum sensing (QS) and decode the signal transmission of aquatic Gram-negative pathogens, this paper proposes a novel method for the intelligent matching identification of eight quorum signaling molecules (N-acyl-homoserine lactones, AHLs) with similar molecular structures, using terahertz (THz) spectroscopy combined with molecular dynamics simulation and spectral similarity calculation. The THz fingerprint absorption spectral peaks of the eight AHLs were identified, attributed, and resolved using the density functional theory (DFT) for molecular dynamics simulation. To reduce the computational complexity of matching recognition, spectra with high peak matching values with the target were preliminarily selected, based on the peak position features of AHL samples. A comprehensive similarity calculation (CSC) method using a weighted improved Jaccard similarity algorithm (IJS) and discrete Fréchet distance algorithm (DFD) is proposed to calculate the similarity between the selected spectra and the targets, as well as to return the matching result with the highest accuracy. The results show that all AHL molecular types can be correctly identified, and the average quantization accuracy of CSC is 98.48%. This study provides a theoretical and data-supported foundation for the identification of AHLs, based on THz spectroscopy, and offers a new method for the high-throughput and automatic identification of AHLs.

An analytical output force model is proposed for noncontact Lorentz force actuators (NLFAs), which are used for positioning and vibration isolation control of space sensitive loads that require an extremely quiet environment. A biaxial NLFA is presented. The actuator mainly consists of a closed magnetic circuit and a novel biaxial exciting circuit board developed by printed circuit board technology. The closed magnetic circuit consists of a C-type yoke and two permanent magnets and is simplified by the magnetic image method. A model of the spatial magnetic flux density in the air gap of the simplified closed magnetic circuit model is derived by the Ampere molecular current hypothesis and the Biot-Savart law. Finally, an analytical output force model of the actuator is obtained by calculating the magnetic flux densities and forces at selected finite points by considering the winding of the coils. The influences of the key dimensional parameters of the actuator on the output forces and their variations are studied using the derived analytical model. To verify the derived analytical model, a measurement setup is established based on an electronic scale. The experimental results show that the forces calculated by the proposed analytical model agree with the experimental values very well, and the deviation between the results is less than 4% when a rated force of 3 N is produced. Moreover, the relation between the number of selected points and computing time and the relation between the number of selected points and the extra calculation deviation, which are important for real-time control, are discussed. The proposed analytical model can help to improve the design efficiency and output force control of this kind of actuator.

Dynamic characteristics play a crucial role in evaluating the performance of weight sensors and are essential for achieving fast and accurate weight measurements. This study focuses on a weight sensor based on optical coherence displacement. Using finite element analysis, the sensor was numerically simulated. Frequency domain and time domain dynamic response characteristics were explored through harmonic response analysis and transient dynamic analysis. The superior dynamic performance and reduced conditioning time of the non-contact optical coherence-based displacement weight sensor were confirmed via a negative step response experiment that compared the proposed sensing method to strain sensing. Moreover, dynamic performance metrics for the optical coherence displacement-type weight sensor were determined. Ultimately, the sensor’s dynamic performance was enhanced using the pole-zero placement method, decreasing the overshoot to 4.72% and reducing the response time to 0.0132 s. These enhancements broaden the sensor’s operational bandwidth and amplify its dynamic response capabilities.

When a multi-component force sensor calibrated in the build-up type multi-component force calibration machine, its deformation caused by the load applied in one direction would induce the deflections in the series connection components in other directions, in which case the lateral forces would generate and applied to the reference sensor of the machine leading to deviations in the sensor readings. To address the above problems, this study adopted a two-axis rectangular flexure hinge as a series connection component and investigated how its geometric parameters influence the coupling effects in force sensors (reference sensors). The findings reveal a clear relationship between the hinge of each parameter and the coupling effects, providing valuable insights for optimizing the design of multi-component force sensor calibration machines and reducing inter-component coupling. Firstly, a mechanical model of the flexure hinges is derived based on material mechanics. Finite element method (FEM) simulations are then employed to investigate the influence of geometric sizes—width (b), thickness (h), and length (l)—on the reaction forces at the fixed end. Subsequently, 15 flexure hinges with varying geometric sizes are designed and mounted on the force sensor to conduct coupling experiments. The variations in the coupling rate are quantitatively analyzed, and the accuracy of the FEM results is verified. The results show that the greater the deflection applied at the loading end of the two-axis rectangular flexure hinges is, the larger the reaction force at the fixed end will be, thereby intensifying the coupling effect on the force sensor output. Flexure hinges with more minor b and h, as well as longer l, reduce the coupling effects introduced by the deflection at the loading end. Among the above elements, reducing h is the most effective approach of minimizing coupling. Furthermore, two-axis rectangular flexure hinges exhibit directional compliance around the radial direction, which results in different coupling rates in the y and z directions when deflections are the same.

In this study, the modeling of a six-degree-of-freedom (DOF) magnetic levitation platform actuated by noncontact Lorentz forces was analyzed. First, an analytic model of the actuator forces was studied, and the magnetic flux density in the working gap of the actuators was established using the Image Method, Ampere molecular hypothesis, and Biot–Savart law. The dynamics model of the floater actuated by the actuators was then built using the Newton–Euler method. Moreover, the double-pendulum chaotic nonlinear system of the hoisted floater was simplified as a single-DOF system, in which the interaction effects between two DOFs were considered. Furthermore, modeling of the platform control system was performed, which included an acceleration measuring unit with six uniaxial accelerometers, a position measuring unit with three two-dimensional PSDs, and a control unit that decouples the six-DOF control. Finally, an experimental setup was built to perform ground tests on the platform. The results verified the platform modeling; translational and rotational positioning accuracies were approximately 5 μm and 40 μrad at the six DOFs, respectively, and the vibrational suppression efficiencies were greater than 90% for low frequency disturbances. Moreover, Kalman estimators and disturbance observers were introduced into the controller to estimate the movement of the floater and observe the direct disturbance. Simulation testing demonstrated a significant improvement in the vibration-suppression performance of the platform.

The input-output relationship of inductive displacement sensors is typically nonlinear, which demands numerous computational resources for precise calculation. Therefore, the traditional analytical methods for the inductive displacement sensors cannot meet the real-time high-precision measurement requirements. In response to the aforementioned issues, this paper proposes an optimized Long Short-Term Memory (LSTM) neural networks based on one-dimensional convolutional neural networks (1DCNN) to modeling the input-output relationship of the inductive displacement sensor. First, the measurement principle of the inductive displacement sensors was analyzed, and the analytical model of the sensor output was derived. And the influences of the key parameters of sensors on the relationship between the induced voltage and the displacement were studied. Then, the 1DCNN-LSTM-AT network for modelling the input-output relationship of the inductive displacement sensor was studied. The spatial features of historical induced electromotive force (EMF) data generated by the induction coil were initially extracted using the 1DCNN network. And these spatial features were then utilized as input to the LSTM neural network to capture the temporal features of the historical induced EMF data. Subsequently, the spatiotemporal features of the induced EMF data were fed into the regression prediction layer to compute the displacement measurement results corresponding to the current input. Moreover, the attention mechanism was used for the 1DCNN-LSTM model to enhance the prediction accuracy and stability of the model. Finally, the experimental results demonstrate that the proposed 1DCNN-LSTM-AT model achieves an average absolute percentage error of 3.1%, significantly outperforming traditional models such as LSTM (29.3%), CNN (4.7%), and ANN (4.3%). This paper provides a new method for modelling the nonlinear relationships of the inductive displacement sensor, and presents a fresh perspective for research in the data processing of nonlinear sensors.