Real-time prediction of the mechanical behavior of suction caisson during installation process using GA-BP neural network

Wu, Shengshen; Wu, Bisheng

doi:10.1016/j.engappai.2022.105475

Cited by 21 publications

(8 citation statements)

References 27 publications

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“…Sensors 2024, 24, 1221 3 of 17 random initialization of initial weights and thresholds, BP neural networks are prone to becoming trapped in local minima [30]. Some researchers have considered optimizing BPNN models using genetic algorithms (GAs) and sparrow search algorithms (SSAs), which have been successfully applied in nonlinear prediction domains such as transportation, industrial manufacturing, and cybersecurity [31][32][33]. Consequently, considering the impact of contact-based measurement devices on the natural piano keystroke actions of the hand, this study employs the Leap Motion sensor for non-contact measurement of the coordinated keystroke actions of the middle and ring fingers first.…”

Section: Coordinate System For Hand Jointsmentioning

confidence: 99%

“…However, due to the random initialization of initial weights and thresholds, BP neural networks are prone to becoming trapped in local minima [ 30 ]. Some researchers have considered optimizing BPNN models using genetic algorithms (GAs) and sparrow search algorithms (SSAs), which have been successfully applied in nonlinear prediction domains such as transportation, industrial manufacturing, and cybersecurity [ 31 , 32 , 33 ].…”

Section: Introductionmentioning

confidence: 99%

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A Model of Multi-Finger Coordination in Keystroke Movement

Lin,

Ding,

Song

et al. 2024

Sensors

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In multi-finger coordinated keystroke actions by professional pianists, movements are precisely regulated by multiple motor neural centers, exhibiting a certain degree of coordination in finger motions. This coordination enhances the flexibility and efficiency of professional pianists’ keystrokes. Research on the coordination of keystrokes in professional pianists is of great significance for guiding the movements of piano beginners and the motion planning of exoskeleton robots, among other fields. Currently, research on the coordination of multi-finger piano keystroke actions is still in its infancy. Scholars primarily focus on phenomenological analysis and theoretical description, which lack accurate and practical modeling methods. Considering that the tendon of the ring finger is closely connected to adjacent fingers, resulting in limited flexibility in its movement, this study concentrates on coordinated keystrokes involving the middle and ring fingers. A motion measurement platform is constructed, and Leap Motion is used to collect data from 12 professional pianists. A universal model applicable to multiple individuals for multi-finger coordination in keystroke actions based on the backpropagation (BP) neural network is proposed, which is optimized using a genetic algorithm (GA) and a sparrow search algorithm (SSA). The angular rotation of the ring finger’s MCP joint is selected as the model output, while the individual difference information and the angular data of the middle finger’s MCP joint serve as inputs. The individual difference information used in this study includes ring finger length, middle finger length, and years of piano training. The results indicate that the proposed SSA-BP neural network-based model demonstrates superior predictive accuracy, with a root mean square error of 4.8328°. Based on this model, the keystroke motion of the ring finger’s MCP joint can be accurately predicted from the middle finger’s keystroke motion information, offering an evaluative method and scientific guidance for the training of multi-finger coordinated keystrokes in piano learners.

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Section: Coordinate System For Hand Jointsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Model of Multi-Finger Coordination in Keystroke Movement

Lin,

Ding,

Song

et al. 2024

Sensors

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“…The blockiness level gradually becomes stable when the volume size of the rock masses reaches a certain condition. The coefficient of variation method is currently used to evaluate the stability of multiple stochastic simulations (Min and Jing, 2003;Wei et al, 2020;Wang et al, 2022c;Wu et al, 2022). The calculated data from random simulations are considered more reliable when the coefficient of variation of the multiple simulations keeps less than 10%, 5%, or 2%.…”

Section: Relationship Between the Distribution Dispersion Of Fracture...mentioning

confidence: 99%

The influence of normal distribution dispersion of fracture size on blockiness and REV of fractured rock masses

Xia²

2023

Front. Earth Sci.

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The fractures of different sizes in rock masses are important for describing rock fragmentation. The distribution dispersion of fracture size influences the blockiness level of the rock masses. Based on a normal statistical distribution, the volume ratio of blocks to rock (B) was obtained to describe the blockiness level. For exploring the effect of the dispersion of fracture size on blockiness level and the representative elementary volume (REV) of rock masses, the laboratory model and numerical simulation were established, and the theory of statistics and the method of analytical solution were applied. In addition, 4,525 practical rock models were established to qualitatively reproduce the behavior of B with changing domain size. The results show that by comparing the degree of convergence, the REV of a rock mass is determined by the fracture size rather than the degree of fracture dispersion. The value of B increases with the distribution dispersion of fracture size, indicating a higher blockiness level. From the experimental analysis of coin tossing, when the number of trials exceeds 69, the random results are nearly stable. In this study, 100 calculations were performed. A formula to calculate the blockiness by considering the dispersion degrees of fracture size was obtained. Moreover, a positive linear correlation between B and the coefficient of variation of fracture size was obtained. The rate of increase in B has a parabolic relationship with the ratio of fracture size to fracture spacing (L).

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“…Therefore, this paper takes the typical anti-dip rock slope as the research object. Based on field investigation, it takes the lithology, slope, and elevation of the rock slope as the characteristic geological factors and analyzes its spatial distribution characteristics Wu et al, 2022). Based on the discrete actual displacement monitoring data, the evolution nephogram of toppling displacement of rock slope is obtained by Inverse Distance Weight method, and the Spatiotemporal evolution nephogram of displacement is superimposed with geological and geometric characteristics.…”

Section: Figurementioning

confidence: 99%

Analysis of displacement evolution characteristics of reverse-dip layered rock slope based on geological geometric partition

Zhang

Teng

et al. 2023

Front. Earth Sci.

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To investigate the toppling displacement evolution characteristics of anti-dip rock slopes, the Xiaodongcao-Zhengjiadagou bank slope is taken as an engineering case, and firstly, the geological geometric distribution characteristics of the slope are obtained by superimposing the lithology, slope, and elevation raster layers of the slope through ArcGIS, and the geological partition with the largest area is the Lower Triassic Daye Formation, bottom elevation, and medium slope; based on the actual surface displacement monitoring data, the spatio-temporal evolution nephogram of toppling displacement of bank slope every half year is interpolated by Inverse Distance Weight method, and then the last displacement nephogram is assigned to the thousandth and superimposed with the geological geometric partition to obtain the displacement superposition characteristics. The results show that: the obvious zone of horizontal displacement deformation mainly occurs in the front and middle of the bank slope, mainly shear deformation, vertical displacement is primarily in the front and the back edge of the bank slope and the total displacement deformation is more similar to the horizontal displacement; the horizontal displacement value is larger than the vertical displacement value, the horizontal displacement deformation controls the overall deformation of the bank slope; through the analysis of the geometric superposition evolution of the anti-dip rock slope, the displacement superposition strong deformation zone is located at the boundary between the Triassic Jialingjiang Formation (T1j) and the Triassic Daye Formation (T1d).

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Real-time prediction of the mechanical behavior of suction caisson during installation process using GA-BP neural network

Cited by 21 publications

References 27 publications

A Model of Multi-Finger Coordination in Keystroke Movement

A Model of Multi-Finger Coordination in Keystroke Movement

The influence of normal distribution dispersion of fracture size on blockiness and REV of fractured rock masses

Analysis of displacement evolution characteristics of reverse-dip layered rock slope based on geological geometric partition

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