Characteristics Of Gaussian Distribution Research Articles

Optimization of robotic path planning and navigation point configuration based on convolutional neural networks.

This study introduces a novel approach for enhancing robotic path planning and navigation by optimizing point configuration through convolutional neural networks (CNNs). Faced with the challenge of precise area coverage and the inefficiency of traditional traversal and intelligent algorithms (e.g., genetic algorithms, particle swarm optimization) in point layout, we proposed a CNN-based optimization model. This model not only tackles the issues of speed and accuracy in point configuration with Gaussian distribution characteristics but also significantly improves the robot's capability to efficiently navigate and cover designated areas with high precision. Our methodology begins with defining a coverage index, followed by an optimization model that integrates polygon image features with the variability of Gaussian distribution. The proposed CNN model is trained with datasets generated from systematic point configurations, which then predicts optimal layouts for enhanced navigation. Our method achieves an experimental result error of <8% on the test dataset. The results validate effectiveness of the proposed model in achieving efficient and accurate path planning for robotic systems.

A Multi-Parameter-Coupled Mixed Lubrication Model for Rolling Interface Analysis: An Investigation on Interfacial Oil Pressure and Oil Film Thickness

Utilizing the average Reynolds equation, lubrication friction theory, metal rolling deformation theory, and temperature energy theory, a multi-parameter-coupling mixed lubrication model considering the influence of surface roughness with Gaussian distribution characteristics under rolling conditions was established. The results show that the stress distribution of the lubricating oil at the rolling interface conforms to the typical characteristics of line contact stress distribution. The oil film pressure does not increase significantly with the increase in the reduction ratio. A second peak pressure appears before the outlet. The second peak pressure reaches its maximum when ε = 0.2. The rolling speed has little effect on oil film pressure, but greatly affects oil film thickness. The influence of rolling speed mainly occurs in the outlet zone and at the second peak pressure location. The oil film thickness increases with rolling speed. During rolling, the temperature rise in the rolls exhibits two peak values. The first peak appears near the neutral point, and the second peak appears near the outlet, which is also where the second peak pressure is located. The influence of temperature will reduce the peak stress on the rolls. The overall stress on the rolls will also decrease.

Failure Mode and Effects Analysis on the Air System of an Aero Turbofan Engine Using the Gaussian Model and Evidence Theory.

Failure mode and effects analysis (FMEA) is a proactive risk management approach. Risk management under uncertainty with the FMEA method has attracted a lot of attention. The Dempster-Shafer (D-S) evidence theory is a popular approximate reasoning theory for addressing uncertain information and it can be adopted in FMEA for uncertain information processing because of its flexibility and superiority in coping with uncertain and subjective assessments. The assessments coming from FMEA experts may include highly conflicting evidence for information fusion in the framework of D-S evidence theory. Therefore, in this paper, we propose an improved FMEA method based on the Gaussian model and D-S evidence theory to handle the subjective assessments of FMEA experts and apply it to deal with FMEA in the air system of an aero turbofan engine. First, we define three kinds of generalized scaling by Gaussian distribution characteristics to deal with potential highly conflicting evidence in the assessments. Then, we fuse expert assessments with the Dempster combination rule. Finally, we obtain the risk priority number to rank the risk level of the FMEA items. The experimental results show that the method is effective and reasonable in dealing with risk analysis in the air system of an aero turbofan engine.

Experimental study on characteristic and mechanism of simulated lunar rock destruction under high energy laser irradiation

The large load and poor heat dissipation of moon rock core drilling leads to the difficulty of rock breaking and low drilling and sampling efficiency. As a new auxiliary rock breaking method, laser rock breaking is expected to be applied to perturbation sampling in lunar rock drilling. Revealing the fracture characteristics and mechanism of rock under laser irradiation is an important basis for realizing laser-assisted lunar rock sampling. Basalt was used as simulated lunar rock sample, and its mechanical response characteristics under laser irradiation were analyzed from macro to micro point of view, and the failure law under different laser power and different irradiation time was explored. The results show that the failure of the sample under laser irradiation is mainly characterized by local rock melting and dynamic crack propagation, and the surface temperature of the sample follows the characteristics of Gaussian distribution. The laser power has a greater influence on the degree of rock weakening than the irradiation time. Laser irradiation of rocks can significantly reduce rock strength, and has obvious effects on improving rock breaking efficiency and reducing in situ disturbance. It is expected to provide theoretical and technical support for assisting lunar rock drilling and sampling in the future.

Reliability analysis of dynamic accuracy for the heavy-duty machine tool segmented beam

The dynamic reliability of the heavy-duty CNC machine tool beam is directly related to the machining accuracy which is challenging to maintain because of the gantry machine structure. It is important to evaluate the reliability and accuracy of gantry machine components, especially the tool beam. This research investigated the dynamic accuracy of a segmented beam of gantry heavy machine tool by studying the surface morphology of the joint surface. The mechanical model of a single asperity is established based on the finite element method. The elastic and the plastic stage deformation boundary values are obtained, and the contact model of the full joint surface is established by using the Gaussian distribution characteristics. The finite element model of the sectional beam is developed by transforming the contact parameters of the joint surface. The accuracy of the model is verified by the field modal experiment. Additionally, the proposed model is used to analyze the impact of vibration on machining accuracy. The results revealed that the proposed model is highly reliable.

A Dynamic Thermal-Mechanical Coupling Numerical Model to Solve the Deformation and Thermal Diffusion of Plates.

Elastic materials include metal plates, rubber, foam, airbags and so on, which have a good buffer effect, toughness and strong recovery ability. In this paper, the deformation and thermal diffusion of and 3D thin plates are studied. Two models are established for the deformation of 2D thin plates. The bending deformation equation of rectangular and circular plates is derived, and the semi-analytical solution of the deflection function is found through the Fourier series approximation in the polar coordinate. The consistencies of the numerical solution and the theoretical solution are verified by numerical method. Then, we find that the factors affecting the deformation are related to the Young’s modulus, load, plate length and deformation factor of the material. In a separate temperature physics field, we establish a heat conduction model of graphene film. Three numerical schemes of the transient heat conduction equation of FDM-FEM are given. In contrast, this paper uses the implicit Euler method to discrete the time term. Furthermore, we compared the difference between the adiabatic condition and the convection condition by the graphical method and the curve trend. The results show that the temperature near the adiabatic boundary is higher. Finally, we proposed a 3D dynamic thermal–mechanical coupling model (3D-DTMCM) that has been established. A laser heating monocrystalline silicon sheet with periodic motion formula is given. The temperature radiation of the laser heat source has Gaussian distribution characteristics. Our proposed model can dynamically determine Young’s modulus with a variable temperature. The numerical results show that the higher the temperature is, the higher the strain energy density of the plate is. In addition, the deformation amplitude of the plates in the coupling field is larger than that in the single mechanical field. Finally, we also discussed the stress field distribution of mixed cracks under high temperature and high load. Our research provides theoretical support for the deformation of different plates, and also reflects the value of the coupled model in practical applications.

An enhancement method of waveform simulation in dual-color laser field imaging based on Internet of Things

AbstractIn view of the fact that the existing methods are easy to be affected by nonlinear factors in the incident process of the light source, resulting in the received shock wave inconsistent with the reality and large calculation error, a two-color laser field imaging waveform simulation and enhancement method based on the Internet of things is proposed. According to the automatic transformation characteristics of augmented reality technology, the waveform simulation control process of two-color laser field imaging is constructed. The Gaussian distribution characteristics are used to analyze the target, the optical grid and double oscillation device are used to control the results, the spectrum Fourier transform is used to adjust the phase of the waveform, and finally the simulated modulated laser waveform is output. Through the comparison of laser waveform spectrum and peak value, the results show that this method is closest to the actual laser waveform, saves calculation time, and has good practical application effect.

Level set framework based on local scalable Gaussian distribution and adaptive-scale operator for accurate image segmentation and correction

Active contour model is a popular image segmentation method, but its current development is still limited by the problem of uneven image intensity. This paper proposes a new active contour model based on level set framework using local scalable Gaussian distribution (LSGD) and adaptive-scale operator for accurate image segmentation and correction, and uses the split Bregman method to minimize the energy functional. We construct a fitting energy with local Gaussian distribution characteristics, and segment the image with the maximum posterior probability. Then we use an adaptive-scale operator to adjust the scale of the LSGD according to the degree of the intensity inhomogeneity. In addition, the bias field information is added to the model to achieve better robustness to unevenness and correction effects. Moreover, the energy functional is minimized by the split Bregman method to improve the efficiency of image segmentation. In order to meet the needs of more diverse image segmentation, we extend the two-phase energy functional to the multi-scale and the vector-valued energy functionals. To evaluate the effectiveness of the proposed methods, we select the composite images as well as brain MRI, melanoma and Berkeley nature images. We select the quantitative indexes to evaluate the image segmentation effect, including JS, Dice, CV, the Precision and Recall, correlation index, SSIM and MSE index. We compare our model with the classic traditional image segmentation models and U-net machine learning model. Experimental results show that the models can perform appropriate segmentation and bias field estimation, and the quantitative index values are better than other segmentation methods.

Real-time method for traffic sign detection and recognition based on YOLOv3-tiny with multiscale feature extraction

As a part of Intelligent Transportation System (ITS), the vehicle traffic sign detection and recognition system have been paid more attention by Intelligent transportation researchers, the traffic sign detection and recognition algorithm based on convolution neural network has great advantages in expansibility and robustness, but it still has great optimization space inaccuracy, computation and storage space. In this paper, we design a multiscale feature fusion algorithm for traffic sign detection and recognition. In order to improve the accuracy of the network, the gaussian distribution characteristics are used in the loss function. The training and analysis of two neural networks with different feature scales and YOLOv3-tiny were carried out on the Tsinghua-Tencent open traffic sign dataset. The experimental results show that the detection and recognition of the targets by networks with multiple feature scales have improved significantly, and the recall and accuracy are 95.32% and 93.13% respectively. Finally, the algorithm of traffic sign detection and recognition is verified on the NVIDIA Jetson Tx2 platform and delivers 28 fps outstanding performances.

Fault Detection for Suspension System of Maglev Trains Based on Historical Health Data

To overcome the influence of multiple operating conditions for fault detection, this paper proposes a method to detect fault for the suspension system of maglev trains. Firstly, the complex operating condition of the maglev train is divided into some simple conditions, and the operating samples are extracted through the time window in the same simple operating condition. Secondly, the features of the extracted samples are extracted by the Fast Walsh-Hadamard transform, and the noise is removed by the median filtering. Thirdly, after adopting principal component analysis to reduce the dimensionality of the feature, the mean of the feature is calculated and applied to calculate the Euclidean distance from the features of the new data obtained by the same processing in each time. Fourthly, the new Euclidean distance obeying the Gaussian distribution is obtained through the Box-Cox transformation. Finally, the fault threshold in each simple operating condition is established through the characteristics of Gaussian distribution. Taking a certain operating condition as an example, the method is compared with the three similar methods. The results reveal that the proposed method can detect the faults timely and has a low false alarm rate. Moreover, the proposed sub-healthy data are within an acceptable range. Based on these results, the proposed method can be applied in fault detection of the maglev train, providing a certain basis for fault diagnosis.

Mechanistic prediction for cutting force in rotary ultrasonic machining of BK7 glass based on probability statistics

Material removal in rotary ultrasonic machining (RUM) of hard-brittle material is an abnormal complicated process, which involves the combination effects of the numerous abrasive grains with the random distributions in the dimensions and penetration depths. These stochastic characteristics result in the evident differences in the extrusion loading between the material and each individual grain, and their aggregate effects serve to significantly affect the cutting force of the diamond tool. However, few mechanistic prediction models of the cutting force have incorporated in the random distribution features of the abrasive grains, restricting the current optimization methods for the reduction of the cutting force during the formal RUM process. Giving consideration to the abrasive processing kinematics and their distribution features on the tool end-face, the number of the effective grains together with their penetration depths was calculated utilizing the probability statistics. Subsequently, the novel theoretical model of the cutting force was established by incorporating the gaussian distribution characteristics of the grain size and their penetration depths. Afterward, the confirmatory experiments were performed for the validation of the proposed cutting force model, revealing that the predicted results were accordant well with the experimental measurements. Furthermore, it was found that the number of the active abrasive grains accounted for 2.972% of the total number on the tool end-face at the specific processing parameters. Additionally, the mechanistic predictions of the developed model represented that the cutting force depicting an irregular decreasing trend with the grain dimension increasing, which was attributed to the coupling effects between the grain size and their number.

Probability prediction model for bridging defects induced by combined influences from lithography and etch variations

Background: As semiconductor technologies continue to shrink, the growth in the number of process variables and combined effects tighten the overall process window, which leads to a more serious yield loss. Yield cannot be totally guaranteed by design rule check and verifications of optical proximity correction, due to complex process variations. The joint effects from unreasonable designs and unstable control of critical dimensions and overlay mainly contribute to the formation of bridging defects in critical interconnect layers. Aim: Our paper puts forward a model to detect the potential bridging region and predicts the corresponding failure probability under a litho-etch-litho-etch process. Approach: The proposed model is based on input error sources from variations of lithography and etch processes. In this scheme, bridging is expected when the minimum space of simulated postetch contours within a specific range is smaller than a user-defined bridging threshold. Gaussian distribution characteristics of line edge roughness (LER) and overlay are considered in the proposed model. Moreover, the proposed model provides meaningful guidelines for bridging prediction with the use of process variation bands. Results: The experiment results indicate consistency and validity of theoretical derivation of the proposed model. The concrete impacts of LER and overlay on the model have been quantitatively analyzed as well. Conclusions: According to the predicted probabilities, the model can early discover potential bridging defects quantitatively by considering the statistical properties of process variations with very few calculations and can give a ranking of failure severity as a decision foundation for design rule optimization.

Improved quantisation mechanisms in impulse radio ultra wideband systems based on compressed sensing

To reduce the impact of quantisation noise during low-rate sampling of Impulse Radio Ultra-Wideband (IR-UWB) under compressed sensing framework and also considering the equal information carrying of compressed measurements and characteristics of Gaussian distribution, three modified quantisation mechanisms are designed in this study: overload uniform quantisation, non-uniform quantisation, and overload non-uniform quantisation. Also, the influencing elements of overload factor in overload mechanism are mentioned to obtain the optimisation scheme close to the optimal overload by fitting. The simulation results show that all the three modified mechanisms, especially the overload non-uniform quantisation mechanism, have wrought great improvement in performance when compared to the uniform quantisation. What's more, the performance of the overload uniform quantisation mechanism featured with low complexity is better than that of the non-uniform quantisation mechanism with high complexity, thus providing a practical quantisation method for the IR-UWB system under the compressed sensing framework.

Improved quantisation mechanisms in impulse radio ultra wideband systems based on compressed sensing

To reduce the impact of quantisation noise during low-rate sampling of Impulse Radio Ultra-Wideband (IR-UWB) under compressed sensing framework and also considering the equal information carrying of compressed measurements and characteristics of Gaussian distribution, three modified quantisation mechanisms are designed in this study: overload uniform quantisation, non-uniform quantisation, and overload non-uniform quantisation. Also, the influencing elements of overload factor in overload mechanism are mentioned to obtain the optimisation scheme close to the optimal overload by fitting. The simulation results show that all the three modified mechanisms, especially the overload non-uniform quantisation mechanism, have wrought great improvement in performance when compared to the uniform quantisation. What's more, the performance of the overload uniform quantisation mechanism featured with low complexity is better than that of the non-uniform quantisation mechanism with high complexity, thus providing a practical quantisation method for the IR-UWB system under the compressed sensing framework.

Molecular dynamics simulations on scattering of Ar molecules on smooth and rough surfaces

Molecular dynamics method is used to investigate the scattering characteristics of Ar molecule on smooth and rough Pt(100) surface. In this paper, a velocity sampling method is proposed to obtain the tangential momentum accommodation coefficients (TMACs) and the sticking probabilities of gas molecules on smooth and rough surface under different temperature conditions. The results show that the TMAC and the sticking probability decrease with increasing temperature under smooth surface condition. The results of our work are in excellent agreement with the results of the reference for a three-dimensional gas flow in a nanochannel. Unlike the scenario of smooth surfaces, the roughness of rough surfaces greatly promotes the accommodation of tangential momentum between the gas molecules and surfaces. When the roughness becoming larger, the TMAC approaches to 1.0 and the sensitivity to temperature decreases gradually. Unlike the relationship between TMAC and roughness, although the sticking probability of gas molecules increases with roughness increasing, its dependence on temperature does not change. Furthermore, the beam method where the incident velocity and angle are determined is used to quantitatively analyze the scattering characteristics of gas molecules on different surfaces. According to the number of collisions between gas molecule and the surface, we classify the scattering of gas molecules on a smooth surface into two types: single collision scattering and multiple collision scattering. For those gas molecules that experience one collision, their average tangential momentum decreases to a certain extent, however, the gas molecules scattered after multiple collisions tend to maintain the original tangential momentum. For gas molecules reflected from the smooth surface, their velocity distribution exhibits a typical bimodal distribution. The position of the first peak appears at the incident velocity value, and the position of the second peak appears at a velocity value of zero. Regarding rough surfaces, the existence of roughness changes the mode of exchange of momentum and energy between gas molecules and walls, resulting in a significant decrease in the average tangential momentum of gas molecules scattered on rough surfaces. Besides, the more the gas molecules colliding on the surface, the more severe the energy loss after scattering will be. For gas molecules reflected from the rough surfaces, their velocity distribution conforms to the characteristics of Gaussian distribution.

Impact factor analysis of mixture spectra unmixing based on independent component analysis

Based on spectral independence of different materials, independent component analysis (ICA), a blind source separation technique, can be applied to separate mixed hyperspectral signals. For the purpose of detecting objects on the sea and improving the precision of target recognition, an original ICA method is applied by analyzing the influence exerted by spectral features of different materials and mixture materials on spectral unmixing results. Due to the complexity of targets on the sea, several measured spectra of different materials have been mixed with water spectra to simulate mixed spectra for mixture spectra decomposition. Synthetic mixed spectra are generated by linear combinations of different materials and water spectra to obtain separated results. We then compared the separated results with the measured spectra of each endmember by coefficient of determination. We conclude that these factors that will change the original spectral characteristics of Gaussian distribution have significant influence on the separated results and selecting a proper initial matrix, and processing spectral data with lower noise can help improve the ICA method for more accurate separated results from hyperspectral data.

Predictive reliability of summer precipitation in China based on error distribution of numerical model

In this paper, we made some statistical analyses on precipitation prediction errors of coupled global climate model of National Climate Center based on CPC merged analysis of precipitation. Through the statistics of the forecast results of summer precipitation in many years and the errors, we find that the distribution of the model prediction errors satisfies the Gaussian distribution. Based on the Gaussian distribution characteristics, the ability to predict the mode for summer precipitation in China can be analyzed and compared. The forecast performance of the dynamic-statistics scheme is significantly better than that of system error correction scheme of the model. The dynamic-statistics optimal combination of factor revise is corrected, relative to error distribution pattern of the system revised forecast, and there are obtained two kinds of error distribution characteristics: 1) improvement of the amplitude; 2) improvement of the displacement. Based on the statistical characteristics of the prediction error, measure of the credibility of model forecast results is proposed to quantitatively assess the credibility of the numerical model forecast results in different regions. Taking the summer precipitations in 2012 and 2013 for example, we analyze the credibility of actual observations, prediction and drought or flood level. Comparing actual observations and prediction, we find that there is a good consistency where the credibility of them is larger, while there are many bad predictions in the region where the credibility of them is smaller. What is more, compared with other reliability test method, the credibility of the abnormal precipitation forecast by this method is more accurate, showing the effectiveness of the method.

A Novel Dynamic Bandwidth Allocation Algorithm with Correction-based the Multiple Traffic Prediction in EPON

According to the upstream TDM in the system of Ethernet passive optical network (EPON), this paper proposes a novel dynamic bandwidth allocation algorithm which supports the mechanism with correction-based the multiple services estimation. To improve the real-time performance of the bandwidth allocation, this algorithm forecasts the traffic of high priority services, and then pre-allocate bandwidth for various priority services is corrected according to Gaussian distribution characteristics, which will make traffic prediction closer to the real traffic. The simulation results show that proposed algorithm is better than the existing DBA algorithm. Not only can it meet the delay requirement of high priority services, but also control the delay abnormity of low priority services. In addition, with rectification scheme, it obviously improves the bandwidth utilization.