Estimation of Hourly and Daily Global Solar Radiation at Clear Days Using an Approach Based on Modified Version of Gaussian Distribution

The performance of two models,Jam and Baig,based on the modified version of Gaussian distribution function in estimating the daily total of global solar radiation and its distribution through the hours of the day from sunrise to sunset al any clear day is evaluated with our own measured data in the period from June 1992 to May 1993 in Qena Egypt The results show a high relative deviation of calculated values from measured ones,especially for Jain model,in the most hours of the day,except for those near to local noon.This misfit behavior is quite obvious in the early morning and late afternoon A new approach has been proposed in this paper to estimate the daily and hourly global solar radiation This model performs with very high accuracy on the recorded data in our region.The validity of this approach was verified with new measurements in some clear days in June and August 1994.The resultant very low relative deviation of the calculated values of global solar radiation from the measured ones confirms the high performance of the approach proposed in this work

Derivation of Gaussian Probability Distribution: A New Approach

The famous de Moivre’s Laplace limit theorem proved the probability density function of Gaussian distribution from binomial probability mass function under specified conditions. De Moivre’s Laplace approach is cumbersome as it relies heavily on many lemmas and theorems. This paper invented an alternative and less rigorous method of deriving Gaussian distribution from basic random experiment conditional on some assumptions.

Mechanical properties of double-stranded DNA biofilm with Gaussian distribution

In microcantilever-based label-free biodetection technologies, deflection changes induced by adsorptions of double-stranded DNA （dsDNA） molecules on Au-layer surface are greatly affected by the mechanical, thermal and electrical properties of DNA biofilm. In this paper, the elastic properties of dsDNA biofilm are studied. First, the Parsegian＇s empirical potential based on a mesoscopic liq- uid crystal theory is employed to describe the interaction energy among coarse-grained DNA cylinders. Then, con- sidering a Gaussian distribution of DNA interaxial distance, the thought experiment method is used to derive an analyti- cal expression for Young＇s modulus of DNA biofilm with a stochastic packing pattern for the first time. Results show that Young＇s modulus of DNA biofilm is on the order of 10 MPa. These findings could provide a simple and effective method to evaluate the mechanical properties of soft biofilm on snbstrate.

The application of Gaussian distribution deconvolution method to separate the overlapping signals in the 2D NMR map

The 2D NMR(T_(1)-T_(2))mapping technique,which can be used to separate different proton populations from various sources(hydroxyls,solid organic matter,free water,and free HC)has gained attention in petroleum industry.To separate proton contributions,a fixed straight line is commonly employed to separate different regions representing proton sources on the map.However,some of these regions(Region 1 and 2)might overlap which makes extracting the NMR signal amplitude from these regions inaccurate.In order to solve this issue,in this study,we applied the Gaussian distribution deconvolution method to separate the T_(1)and T_(2)relaxation distributions and then derived the signal amplitude of each region instead of following the common fixed line approach.Next,we employed this method to analyze several shale samples from the literature and compared the results following both methods to verify our methodology.Finally,samples from the Bakken Shale were studied to separate signals from Region 1 and Region 2 and corelated the results with geochemical properties that were obtained from programmed(Rock Eval)pyrolysis.Results demonstrated an improvement in their relation when our approach is employed compared to the fixed line technique to differentiate signal from overlapping regions.This means the Gaussian distribution deconvolution method can be used with confidence to provide us with more accurate petrophysical and geochemical understanding of complex formations.

Investigation of the inhomogeneous barrier height of an Au/Bi_4Ti_3O_(12)/n-Si structure through Gaussian distribution of barrier height

A Au/Bi4Ti3O12/n-Si structure is fabricated in order to investigate its current voltage （IV） characteristics in a temperature range of 300 K-400 K. Obtained I-V data are evaluated by the thermionic emission （TE） theory. Zero-bias barrier height （Ф0） and ideality factor （n） calculated from I-V characteristics, are found to be temperature-dependent such that ФB0 increases with temperature increasing, whereas n decreases. The obtained temperature dependence of ФB0 and linearity in ФB0 versus the n plot, together with a lower barrier height and Richardson constant values obtained from the Richardson plot, indicate that the barrier height of the structure is inhomogeneous in nature. Therefore, I-V characteristics are explained on the basis of Caussian distribution of barrier height.

Research on Gaussian distribution preprocess method of infrared multispectral image background clutter

This paper introduces a sliding-window mean removal high pass filter by which background clutter of infrared multispectral image is obtained. The method of selecting the optimum size of the sliding-window is based on the skewness-kurtosis test. In the end, a multivariate Gaussian distribution mathematical expression of background clutter image is given.

A Gaussian Noise-Based Algorithm for Enhancing Backdoor Attacks

Deep Neural Networks(DNNs)are integral to various aspects of modern life,enhancing work efficiency.Nonethe-less,their susceptibility to diverse attack methods,including backdoor attacks,raises security concerns.We aim to investigate backdoor attack methods for image categorization tasks,to promote the development of DNN towards higher security.Research on backdoor attacks currently faces significant challenges due to the distinct and abnormal data patterns of malicious samples,and the meticulous data screening by developers,hindering practical attack implementation.To overcome these challenges,this study proposes a Gaussian Noise-Targeted Universal Adversarial Perturbation(GN-TUAP)algorithm.This approach restricts the direction of perturbations and normalizes abnormal pixel values,ensuring that perturbations progress as much as possible in a direction perpendicular to the decision hyperplane in linear problems.This limits anomalies within the perturbations improves their visual stealthiness,and makes them more challenging for defense methods to detect.To verify the effectiveness,stealthiness,and robustness of GN-TUAP,we proposed a comprehensive threat model.Based on this model,extensive experiments were conducted using the CIFAR-10,CIFAR-100,GTSRB,and MNIST datasets,comparing our method with existing state-of-the-art attack methods.We also tested our perturbation triggers using various defense methods and further experimented on the robustness of the triggers against noise filtering techniques.The experimental outcomes demonstrate that backdoor attacks leveraging perturbations generated via our algorithm exhibit cross-model attack effectiveness and superior stealthiness.Furthermore,they possess robust anti-detection capabilities and maintain commendable performance when subjected to noise-filtering methods.

Cognitive-affective regulation process for micro-expressions based on Gaussian cloud distribution

In this paper, we explore the process of emotional state transition. And the process is impacted by emotional state of interaction objects. First of all, the cognitive reasoning process and the micro-expressions recognition is the basis of affective computing adjustment process. Secondly, the threshold function and attenuation function are proposed to quantify the emotional changes. In the actual environment, the emotional state of the robot and external stimulus are also quantified as the transferring probability. Finally, the Gaussian cloud distribution is introduced to the Gross model to calculate the emotional transitional probabilities. The experimental results show that the model in human-computer interaction can effectively regulate the emotional states, and can significantly improve the humanoid and intelligent ability of the robot. This model is consistent with experimental and emulational significance of the psychology, and allows the robot to get rid of the mechanical emotional transfer process.

Error Thresholds in Single-Peak Gaussian Distributed Fitness Landscapes

Based on the Eigen and Crow-Kimura models with a single-peak fitness landscape, we propose the fitness values of all sequence types to be Gausslan distributed random variables to incorporate the effects of the fluctuations of the fitness landscapes （noise of environments） and investigate the concentration distribution and error threshold of quasispecies by performing an ensemble average within this theoretical framework. We find that a small fluctuation of the fitness landscape causes only a slight change in the concentration distribution and error threshold, which implies that the error threshold is stable against small perturbations. However, for a sizable fluctuation, quite different from the previous deterministic models, our statistical results show that the transition from quasi-species to error catastrophe is not so sharp, indicating that the error threshold is located within a certain range and has a shift toward a larger value. Our results are qualitatively in agreement with the experimental data and provide a new implication for antiviral strategies.

Spatial batch optimal design based on self-learning Gaussian process models for LPCVD processes

Low pressure chemical vapor deposition(LPCVD) is one of the most important processes during semiconductor manufacturing.However,the spatial distribution of internal temperature and extremely few samples makes it hard to build a good-quality model of this batch process.Besides,due to the properties of this process,the reliability of the model must be taken into consideration when optimizing the MVs.In this work,an optimal design strategy based on the self-learning Gaussian process model(GPM) is proposed to control this kind of spatial batch process.The GPM is utilized as the internal model to predict the thicknesses of thin films on all spatial-distributed wafers using the limited data.Unlike the conventional model based design,the uncertainties of predictions provided by GPM are taken into consideration to guide the optimal design of manipulated variables so that the designing can be more prudent Besides,the GPM is also actively enhanced using as little data as possible based on the predictive uncertainties.The effectiveness of the proposed strategy is successfully demonstrated in an LPCVD process.

Critical Behavior of the Gaussian Model with Periodic Interactions on Diamond—Type Hierarchical Lattices in External Magnetic Fields

The Gaussian spin model with periodic interactions on the diamond-type hierarchical lattices is constructed by generalizing that with uniform interactions on translationally invariant lattices according to a class of substitution sequences. The Gaussian distribution constants and imposed external magnetic fields are also periodic depending on the periodic characteristic of the interaction bonds. The critical behaviors of this generalized Gaussian model in external magnetic fields are studied by the exact renormalization-group approach and spin rescaling method. The critical points and all the critical exponents are obtained. The critical behaviors are found to be determined by the Gaussian distribution constants and the fractal dimensions of the lattices. When all the Gaussian distribution constants are the same, the dependence of the critical exponents on the dimensions of the lattices is the same as that of the Gaussian model with uniform interactions on translationally invariant lattices.

Technique for Multi-Pass Turning Optimization Based on Gaussian Quantum-Behaved Bat Algorithm

The multi-pass turning operation is one of the most commonly used machining methods in manufacturing field.The main objective of this operation is to minimize the unit production cost.This paper proposes a Gaussian quantum-behaved bat algorithm(GQBA)to solve the problem of multi-pass turning operation.The proposed algorithm mainly includes the following two improvements.The first improvement is to incorporate the current optimal positions of quantum bats and the global best position into the stochastic attractor to facilitate population diversification.The second improvement is to use a Gaussian distribution instead of the uniform distribution to update the positions of the quantum-behaved bats,thus performing a more accurate search and avoiding premature convergence.The performance of the presented GQBA is demonstrated through numerical benchmark functions and amulti-pass turning operation problem.Thirteen classical benchmark functions are utilized in the comparison experiments,and the experimental results for accuracy and convergence speed demonstrate that,in most cases,the GQBA can provide a better search capability than other algorithms.Furthermore,GQBA is applied to an optimization problem formulti-pass turning,which is designed tominimize the production cost while considering many practical machining constraints in the machining process.The experimental results indicate that the GQBA outperforms other comparison algorithms in terms of cost reduction,which proves the effectiveness of the GQBA.

Nonlinear industrial process fault diagnosis with latent label consistency and sparse Gaussian feature learning

With the increasing complexity of industrial processes, the high-dimensional industrial data exhibit a strong nonlinearity, bringing considerable challenges to the fault diagnosis of industrial processes. To efficiently extract deep meaningful features that are crucial for fault diagnosis, a sparse Gaussian feature extractor(SGFE) is designed to learn a nonlinear mapping that projects the raw data into the feature space with the fault label dimension. The feature space is described by the one-hot encoding of the fault category label as an orthogonal basis. In this way, the deep sparse Gaussian features related to fault categories can be gradually learned from the raw data by SGFE. In the feature space,the sparse Gaussian(SG) loss function is designed to constrain the distribution of features to multiple sparse multivariate Gaussian distributions. The sparse Gaussian features are linearly separable in the feature space, which is conducive to improving the accuracy of the downstream fault classification task. The feasibility and practical utility of the proposed SGFE are verified by the handwritten digits MNIST benchmark and Tennessee-Eastman(TE) benchmark process,respectively.

Characteristics of Fallen Seed Distribution by Domestic and Imported Aerial Seeding Equipment

In order to explore the seed drop characteristics by aerial seeding equipment,taking aerial seeding for Pinus tabulaeformis as an example,the Gaussian curve fitting and chi-square goodness-of-fit test were carried out on the data of fallen seed distribution,and the seed distribution models of domestic FB-85 and imported PZLM-18 equipment were established.The seeding performance indexes of the two kinds of equipment were calculated and compared by using the model,the existing problems of domestic equipment and their causes are analyzed,and finally,some suggestions for equipment optimization were put forward.The results indicated that the seed drop of the two kinds of equipment showed the characteristics of dense distribution in the middle and sparse distribution on both sides,and followed the Gaussian distribution as a whole;compared with PZLM-18,FB-85 had better seeding performance,but it also had the problem of uneven seed distribution;in addition to the influence of aircraft flow field,the fishtail structure design of diffuser is another important reason for the uneven seed distribution of domestic equipment;without changing the fishtail structure design,it is suggested that the principle of cross-superposition of two seeding belts should be used to replace a single large-size diffuser with two small-size diffusers,which can reduce the number of seeds in the middle and increase the number of seeds on both sides,so as to improve the uniformity of seed distribution.

Plasma current tomography for HL-2A based on Bayesian inference

An accurate plasma current profile has irreplaceable value for the steady-state operation of the plasma.In this study,plasma current tomography based on Bayesian inference is applied to an HL-2A device and used to reconstruct the plasma current profile.Two different Bayesian probability priors are tried,namely the Conditional Auto Regressive(CAR)prior and the Advanced Squared Exponential(ASE)kernel prior.Compared to the CAR prior,the ASE kernel prior adopts nonstationary hyperparameters and introduces the current profile of the reference discharge into the hyperparameters,which can make the shape of the current profile more flexible in space.The results indicate that the ASE prior couples more information,reduces the probability of unreasonable solutions,and achieves higher reconstruction accuracy.

Hexagon-Islands Density and Size Distribution on Growing Single-Walled Carbon Nanotubes in the Kinetic 5-Vertex Model

A kinetic 5-vertex model is used to investigate hexagon-islands formation on growing single-walled carbon nanotubes (SWCNT). In the model, carbon atoms adsorption and migration processes on the SWCNT edge are considered. These two dynamic processes are assumed to be mutually independent as well as mutually dependent as far as the whole growth of the nanotube is concerned. Key physical parameters of the model are the growth time t, the diffusion length Γ defined as the ratio of the diffusion rate D to the carbon atomic flux F and the SWCNT chiral angle. The kinetic equation that describes the nanotube edge dynamics is solved using kinetic Monte Carlo simulations with the Bortz, Kalos and Lebowitz update algorithm. The behaviors of islands density and size distribution are investigated within the growth parameters’ space. Our study revealed key mechanisms that enable the formation of a new ring of hexagons at the SWCNT edge. The growth occurs either by pre-existing steps propagation or by hexagon-islands growth and coalescence on terraces located between dislocation steps, depending on values of model parameters. This should offer a road map for edge design in nanotubes production. We also found that in appropriate growth conditions, the islands density follows Gaussian and generalized Wigner distributions whereas their size distribution at a given growth time shows a decreasing exponential trend.

Numerical simulation of 3D-microstructures in solidification processes based on the CAFE method

It was analyzed that the finite element-cellular automaton （CAFE） method was used to simulate 3D-microstructures in solidification processes. Based on this method, the 3D-microstructure of 9SMn28 free-cutting steel was simulated in solidification processes and the simulation results are consistent with the experimental ones. In addition, the effects of Gaussian distribution parameters were also studied. The simulation results show that the higher the mean undercooling, the larger the columnar dendrite zones, and the larger the maximum nucleation density, the smaller the size of grains. The larger the standard deviation, the less the number of minimum grains is. However, the uniformity degree decreases first, and then increases gradually.

Genetic Data Clustering Based on Minimum Coding Length

[Objective] This paper aimed to provide a new method for genetic data clustering by analyzing the clustering effect of genetic data clustering algorithm based on the minimum coding length. [Method] The genetic data clustering was regarded as high dimensional mixed data clustering. After preprocessing genetic data, the dimensions of the genetic data were reduced by principal component analysis, when genetic data presented Gaussian-like distribution. This distribution of genetic data could be clustered effectively through lossy data compression, which clustered the genes based on a simple clustering algorithm. This algorithm could achieve its best clustering result when the length of the codes of encoding clustered genes reached its minimum value. This algorithm and the traditional clustering algorithms were used to do the genetic data clustering of yeast and Arabidopsis, and the effectiveness of the algorithm was verified through genetic clustering internal evaluation and function evaluation. [Result] The clustering effect of the new algorithm in this study was superior to traditional clustering algorithms, and it also avoided the problems of subjective determination of clustering data and sensitiveness to initial clustering center. [Conclusion] This study provides a new clustering method for the genetic data clustering.

Coverage of communication-based sensor nodes deployed location and energy efficient clustering algorithm in WSN

An effective algorithm based on signal coverage of effective communication and local energy-consumption saving strategy is proposed for the application in wireless sensor networks.This algorithm consists of two sub-algorithms.One is the multi-hop partition subspaces clustering algorithm for ensuring local energybalanced consumption ascribed to the deployment from another algorithm of distributed locating deployment based on efficient communication coverage probability（DLD-ECCP）.DLD-ECCP makes use of the characteristics of Markov chain and probabilistic optimization to obtain the optimum topology and number of sensor nodes.Through simulation,the relative data demonstrate the advantages of the proposed approaches on saving hardware resources and energy consumption of networks.

COMPARISONS AMONG THE SOLUTIONS OF FIBER TEXTURE VECTOR

The texture vectors, as the fundatnental elements of the vector method (VM), are usually determined by the Durand's iterative method. In present paper, the texture vector is derived by two kinds of the maximum entropy method (MEM), which choose pole figure data (MEM(I)) and the series coefficients of pole figures (MEM(II)), respectively, as a constrained condition. The detailed comparisons, including the texture vector and residual vector in the pole figure and ODF, among the results obtained by different methods are given through the ideal fiber texture simulation with Gaussian distribution. It is demonstrated that, although both methods the good results in the ideal texture simulation, the solution on assumption of maximum entropy displays more attractive results. In order to compare the sensitivity of the different methods to the experimental errors, the stochastical errors in pole figures are introduced by the computer random processes (Monte-Carlo simulation). The Monte-Carlo simulation shows that the MEM with the series coefficients as a constrained condition is rather sensitive to the 'experimental' errors, however, inversely the conventional VM and MEM with pole figure data as a constrained condition.