Minimum Value Point Research Articles

A wide range oxygen sensing strategy with the collaboration of multiple phosphorescence probes

A wide range oxygen measurement system with the relative measurement error of less than 1.5% across 4 orders of magnitude of oxygen dynamic range has been developed. We proposed a theoretical evaluation method of the measurement capability of oxygen probes, which points that the relative measurement error (δ) is equal to the reciprocal of the product of signal-to-noise ratio (SNR) and relative sensitivity (S r). A minimum value point in the curve of δ with oxygen concentration is existing due to the opposite trends of SNR and S r Based on the evaluation results, an oxygen probe group was selected scientifically for wide range oxygen measurement. The efficacy of the developed measurement system, featuring adaptive oxygen probe switching, was verified through testing, confirming its compliance with accuracy requirements for measurements.

Robot kinematics analysis and trajectory planning based on artificial potential field method

Abstract In order for a robot to complete a given task, it must first be made to autonomously reach a specified target location, so optimizing the robot path trajectory planning is a prerequisite for the use of robots. In this paper, for the two problems of the traditional artificial potential field method of target unreachable and local optimization, we first improve the repulsive potential field function so that the robot’s gravitational force and repulsive force are zero at the target position and then construct the robot kinematic analytical model by setting the virtual target point away from the local minimum value point. On this basis, the improved algorithm is used to compare simulation experiments in three environments with the real trajectory planning test in the showroom. In the climate “near the obstacle of the target point” and the pure U-shaped area environment, the robot of the traditional APF algorithm cannot reach the target point. However, the algorithm in this paper reaches the target point in all three environments, with a time taken of only 21, 33, and 42 seconds, respectively. In the real trajectory planning of the showroom, the improved algorithm in this paper reaches the target point quickly and accurately, with a total path trajectory length of 77.7835m, a time of 43 seconds, and a total turning angle of 793°. This paper provides an effective method for planning robot path trajectories in a complex and variable environment.

Optical quality with ocular aberrations in young myopic eyes with machine learning

Objective: The objective of this retrospective study was to employ machine learning techniques to examine age-related traits of ocular aberrations in a substantial population with myopia and myopic astigmatism. Methods: This was a cross-sectional study. Data from a population of myopic and myopic astigmatism patients who underwent wavefront aberration examinations at the Refractive Surgery Center of Tianjin Eye Hospital in Tianjin, China, were collected continuously from January 2013 to July 2017. The data from the right eye of each individual were collected for analysis. Each eye had 32 outcome data points, including age, best-corrected visual acuity (BCVA), lower-order aberrations (spherical diopter, cylindrical diopter, and astigmatic axis), and higher-order aberrations [Zernike coefficients and root mean square (RMS) of the third to sixth order aberrations] were analyzed. Higher-order aberrations were measured by Hartmann-Shack aberrometer. Results: The study included 1 507 subjects (1 507 eyes), comprising of 694 males and 813 females, with a mean age of (23.28±5.45) years. The findings demonstrated a decrease followed by an increase in most of the higher-order aberrations with age between 15-40 years. The minimum value points were observed in the age group of 25-30 years for RMS of total higher-order aberrations, 3rd RMS (with a confidence of 47.74% for range 1), 4th RMS (with a confidence of 86.01% for range 1), and trefoil aberrations (with a confidence of 56.38% for C33 and 73.25% for C3-3). The minimum value points were also observed in the age group of 30-35 years for primary spherical aberration (with a confidence of 56.10% for C40) and vertical coma-like aberration (with a confidence of 56.91% for C3-1). In contrast, astigmatism with the rule tended to decrease with age, while astigmatism against the rule and oblique astigmatism tended to increase (with a confidence of 88.66%, 84.71%, 81.07%, 79.67%, and 66.35% for astigmatism with the rule in different age groups). Conclusions: As age increases, the population with with-the-rule astigmatism decreases while the population with against-the-rule astigmatism increases. The high-order aberrations are the lowest in the 25-35 age group.

Maximum envelope-based Autogram and symplectic geometry mode decomposition based gear fault diagnosis method

Autogram is an effective optimal frequency band selection method, in which the signal spectrum is divided by the maximum overlapping discrete wavelet packet transform (MODWPT) and the position of maximum kurtosis value is used as the optimal frequency band. However, Autogram follows a binary tree structure in segmenting frequency domain and its segmentation position is fixed, this causes that its position cannot be adaptively determined according to the signal characteristics. To solve this issue, in this paper an improved frequency band selection method called maximum envelope based-Autogram (MEAutogram) is proposed. In MEAutogram method, the maximum value envelope method is used to process the signal spectrum and then the minimum value point closest to the middle position of adjacent maximum value points in envelope signal is used as the segmentation position. However, the segmentation accuracy of MEAutogram will decrease when the signal contains lots of irrelevant components. The recently proposed nonlinear time series analysis method termed symplectic geometry mode decomposition (SGMD) founded on the symplectic matrix similar transformation is used to remove irrelevant components. Based on this, a new SGMD and MEAutogram based fault diagnosis method for gear is proposed. The proposed fault diagnosis method of gear can reduce the calculation amount through overcoming the influence of irrelevant components on the segmentation position, which can be adaptively determined according to the characteristics of the raw signal. Finally, the analysis results of simulation and gear test data was used to verify that the appropriate demodulation frequency band can be accurately detected by the proposed method and the fault characteristics obtained by the proposed method are more obvious than that of the comparative methods.

The synthesis and modification of highly fluorescent carbon quantum dots for reversible detection of water-soluble phosphonate-1-hydroxyethane-1,1-diphosphonic acid by fluorescence spectroscopy.

Photoluminescent (PL) carbon quantum dots (CQDs) were prepared successfully using a facile and green procedure. They exhibited striking blue fluorescence and excellent optical properties, with a quantum yield as high as 61.44%. Due to the fluorescence quenching effect and the stronger complexing ability of the phosphoric acid group of 1-hydroxyethane-1,1-diphosphonic acid (HEDP) to Fe3+ , CQDs doped with Fe3+ were adequately constructed as an efficient and sensitive fluorescent probe for HEDP-specific sensing. The proposed fluorescent probe had a sensitive and rapid response in the range 5-70 μM. Furthermore, quantitative molecular surface (QMS) analysis based on the Multiwfn program was applied to explore the complexation mode of HEDP and metal ions. The distribution of electrostatic potential (ESP), average local ionization energy (ALIE), the minimum value points and the position of the lone pair electrons on the surface of molecular van der Waals were further determined. More strikingly, this experiment achieved the quantitative detection of water-soluble phosphonate-HEDP, for the first time using fluorescence spectrometry. It has been proved to be an effective and intuitive sensing method for the detection of HEDP in real samples.

A robust MoF method applicable to severely deformed polygonal mesh

The MoF (Moment of Fluid) method is an accurate approach for interface reconstruction in numerical simulation of multi-material fluid flow. So far, most works focus on improving its accuracy and efficiency, such as developing analytic reconstruction method and deducing the iteration schemes based on high order derivatives of the objective function. In this paper, we mainly concern on improving its robustness, especially for severely deformed polygonal meshes, in which case the objective function has multiple minimum value points. By using an efficient method for solving multiple roots of the nonlinear equation in large scope, a new algorithm is developed to enhance robustness of the MoF method. The main idea of this algorithm is as follows. The first derivative of the objective function is continuous, so the minimum value points of the objective function must be the zero points of the first derivative. Instead of finding the zero points of the first derivative directly, we turn to calculating the minimum value points (also zero points) of the square of the first derivative, which is a convex function on a neighborhood of each zero point. Applying the properties of convex function, the neighbor of each extreme minimum point of it can be obtained efficiently. Then each zero point of the square of the first derivative can be obtained using the iterative formula in its neighbor. Finally, by comparing the values of the objective function at these zero points of the first derivative, the global minimum value point of the objective function can be found and is the desired solution. The new algorithm only uses the first derivative of the objective function. It doesn't need an initial guess for the solution, which has to be carefully chosen in previous works. Numerical results are presented to demonstrate the accuracy and robustness of this new algorithm. The results show that it is applicable to severely deformed polygonal mesh, even with concave cells.

The Simulation of the UAV Collision Avoidance Based on the Artificial Potential Field Method

To deal with dynamic path planning of unmanned aerial vehicles(uav) in the complicated 3-D environment, a new method that combines the Lyapunov theorem with the artificial potential is proposed. The mission region is described as the artificial potential field. In this paper, it proves that the balance point is a saddle point, only when uav reaches the target, the balance point is stable, the rest of the balance point are divergent, so uav can escape the minimum point as soon as possible. The simulation results show that this proposed method can effectively make uav avoid collision, and escape well the local minimum value point. The optimization results are better than the simplex artificial potential field, and have better optimization precision and tracing speed.

Ground states of one-dimensional commensurate-incommensurate transition models with double-well interactions

Multiwell interparticle potentials are proposed as a mechanism for the occurrence of modulated phases. This is examined through two models with double-well interactions. For these systems, the effective potential method is reviewed and a certain process of calculation is emphasized. To characterize the modulated phases, the winding number omega, and the rotation number Omega are redefined. The method to recover the chain of particles and calculate the value of omega is given. We find that the phase diagrams strongly relate to the period of the external potential D. For each model, there is a threshold value in D, which equals to the distance between the two minimum value points. Within different interval of D, the phase diagrams exhibit different behavior. The periodicity of the phase diagram and the difference between modulated phases with the same Omega are also discussed.