Magnesium(Mg)alloys face a critical challenge in balancing performance optimization and unintended density increases caused by high-density secondary phases.To address this,machine learning was employed to predict the...Magnesium(Mg)alloys face a critical challenge in balancing performance optimization and unintended density increases caused by high-density secondary phases.To address this,machine learning was employed to predict the density and volume of Mg-containing binary phases,aiming to guide lightweight alloy design.Using 211 experimentally observed data points,five machine learning(ML)algorithms—Random Forest(RF),Support Vector Machine(SVM),Artificial Neural Network(ANN),K-Nearest Neighbors(KNN),and Bayesian Ridge(Bayes)—were trained and tested.Quantitative results showed that RF achieved exceptional performance in volume prediction,with a testing coefficient of determination(R^(2))exceeding 0.96 and a mean absolute error(MAE)of 41.0Å^(3),while SVM outperformed others in density prediction with a testing R^(2) of 0.885 and MAE of 0.421 g/cm^(3).Feature importance analysis revealed that atomic count is the primary determinant of phase volume,whereas density prediction depends on the synergistic interaction of relative atomic mass and stoichiometric ratio,as further validated by SHapley Additive exPlanations(SHAP)analysis.This work establishes a physics-informed predictive model that accelerates the development of lightweight Mg alloys by mitigating high-density secondary phases,and can be extended to other alloy systems.展开更多
Crowd density estimation in wide areas is a challenging problem for visual surveillance. Because of the high risk of degeneration, the safety of public events involving large crowds has always been a major concern. In...Crowd density estimation in wide areas is a challenging problem for visual surveillance. Because of the high risk of degeneration, the safety of public events involving large crowds has always been a major concern. In this paper, we propose a video-based crowd density analysis and prediction system for wide-area surveillance applications. In monocular image sequences, the Accumulated Mosaic Image Difference (AMID) method is applied to extract crowd areas having irregular motion. The specific number of persons and velocity of a crowd can be adequately estimated by our system from the density of crowded areas. Using a multi-camera network, we can obtain predictions of a crowd's density several minutes in advance. The system has been used in real applications, and numerous experiments conducted in real scenes (station, park, plaza) demonstrate the effectiveness and robustness of the proposed method.展开更多
An improved method using kernel density estimation （KDE） and confidence level is presented for model validation with small samples. Decision making is a challenging problem because of input uncertainty and only smal...An improved method using kernel density estimation （KDE） and confidence level is presented for model validation with small samples. Decision making is a challenging problem because of input uncertainty and only small samples can be used due to the high costs of experimental measurements. However, model validation provides more confidence for decision makers when improving prediction accuracy at the same time. The confidence level method is introduced and the optimum sample variance is determined using a new method in kernel density estimation to increase the credibility of model validation. As a numerical example, the static frame model validation challenge problem presented by Sandia National Laboratories has been chosen. The optimum bandwidth is selected in kernel density estimation in order to build the probability model based on the calibration data. The model assessment is achieved using validation and accreditation experimental data respectively based on the probability model. Finally, the target structure prediction is performed using validated model, which are consistent with the results obtained by other researchers. The results demonstrate that the method using the improved confidence level and kernel density estimation is an effective approach to solve the model validation problem with small samples.展开更多
Based on the octadecahedron of eleven-vertex closo-borane, the eleven-vertex closo-heteroborane was suggested with nonmetallic atoms instead of the different nonequivalent boron, and the stabilities were predicted at ...Based on the octadecahedron of eleven-vertex closo-borane, the eleven-vertex closo-heteroborane was suggested with nonmetallic atoms instead of the different nonequivalent boron, and the stabilities were predicted at G96PW91/6-31＋G（3d,2p） level. The small heteroatoms, C, N, O, preferentially occupy vertex 2 with the absolutely lowest relative energy to form the high stabilization closo-heteroboranes. They cap four-membered rings to satisfy the geometrical demand of short B--Z bonds. The electron attractions from the vicinal boron atoms make the frameworks shrink. Differently, Si and Ge preferentially substitute for boron at vertex 1 with six tight B--Z bonds and form stabilized molecules. P, As, S, and Se tend to occupy vertex 4 and the optimized structures belong to the nido configura- tions. In contrast to high electronegative heteroatoms, S and Se transfer less negative charges to framework and the electropositive heteroatoms, Si and Ge transfer more negative charges to framework to form the delocalization structures. The HOMO-LUMO gaps show that most of predicted clusters possess chemical stabilities. The substitutions of heteroatoms for boron atoms in eleven-vertex closo-heteroboranes are consistent with the topological charge stabilization rule proposed by Gimarc.展开更多
The admittance measurements of a hetero-junction can be used to derive the density of the interfacial state in the hetero-junction. Hence, prediction conductance via frequency is very useful for comprehension of the a...The admittance measurements of a hetero-junction can be used to derive the density of the interfacial state in the hetero-junction. Hence, prediction conductance via frequency is very useful for comprehension of the admittance of a hetero-junction using a mathematical strategy. From the observations on the curve of the frequencydependent conductance of the hetero-junction an analytic model with four-parameters was developed that relates conductance to frequency; the theoretical results agree quite well with the experimental data. The model shows potential for a variety of applications including different electronic devices. The model is a practical tool that can be readily used for assessing the electronic behaviors of a hetero-junction and is scientifically justifiable. In addition, the mathematical bridge to link the density of the interfacial state of the（pyronine-B）/p-Si structure to energy implies a good route to discuses the density of the interfacial state of interfaces.展开更多
基金supported by Jinhua City Science and Technology Plan Project(2024-1-106)the National Natural Science Foundation of China(U24A2035).
文摘Magnesium(Mg)alloys face a critical challenge in balancing performance optimization and unintended density increases caused by high-density secondary phases.To address this,machine learning was employed to predict the density and volume of Mg-containing binary phases,aiming to guide lightweight alloy design.Using 211 experimentally observed data points,five machine learning(ML)algorithms—Random Forest(RF),Support Vector Machine(SVM),Artificial Neural Network(ANN),K-Nearest Neighbors(KNN),and Bayesian Ridge(Bayes)—were trained and tested.Quantitative results showed that RF achieved exceptional performance in volume prediction,with a testing coefficient of determination(R^(2))exceeding 0.96 and a mean absolute error(MAE)of 41.0Å^(3),while SVM outperformed others in density prediction with a testing R^(2) of 0.885 and MAE of 0.421 g/cm^(3).Feature importance analysis revealed that atomic count is the primary determinant of phase volume,whereas density prediction depends on the synergistic interaction of relative atomic mass and stoichiometric ratio,as further validated by SHapley Additive exPlanations(SHAP)analysis.This work establishes a physics-informed predictive model that accelerates the development of lightweight Mg alloys by mitigating high-density secondary phases,and can be extended to other alloy systems.
基金supported by the National Natural Science Foundation of China under Grant No. 61175007the National Key Technologies R&D Program under Grant No. 2012BAH07B01the National Key Basic Research Program of China (973 Program) under Grant No. 2012CB316302
文摘Crowd density estimation in wide areas is a challenging problem for visual surveillance. Because of the high risk of degeneration, the safety of public events involving large crowds has always been a major concern. In this paper, we propose a video-based crowd density analysis and prediction system for wide-area surveillance applications. In monocular image sequences, the Accumulated Mosaic Image Difference (AMID) method is applied to extract crowd areas having irregular motion. The specific number of persons and velocity of a crowd can be adequately estimated by our system from the density of crowded areas. Using a multi-camera network, we can obtain predictions of a crowd's density several minutes in advance. The system has been used in real applications, and numerous experiments conducted in real scenes (station, park, plaza) demonstrate the effectiveness and robustness of the proposed method.
基金Funding of Jiangsu Innovation Program for Graduate Education (CXZZ11_0193)NUAA Research Funding (NJ2010009)
文摘An improved method using kernel density estimation （KDE） and confidence level is presented for model validation with small samples. Decision making is a challenging problem because of input uncertainty and only small samples can be used due to the high costs of experimental measurements. However, model validation provides more confidence for decision makers when improving prediction accuracy at the same time. The confidence level method is introduced and the optimum sample variance is determined using a new method in kernel density estimation to increase the credibility of model validation. As a numerical example, the static frame model validation challenge problem presented by Sandia National Laboratories has been chosen. The optimum bandwidth is selected in kernel density estimation in order to build the probability model based on the calibration data. The model assessment is achieved using validation and accreditation experimental data respectively based on the probability model. Finally, the target structure prediction is performed using validated model, which are consistent with the results obtained by other researchers. The results demonstrate that the method using the improved confidence level and kernel density estimation is an effective approach to solve the model validation problem with small samples.
文摘Based on the octadecahedron of eleven-vertex closo-borane, the eleven-vertex closo-heteroborane was suggested with nonmetallic atoms instead of the different nonequivalent boron, and the stabilities were predicted at G96PW91/6-31＋G（3d,2p） level. The small heteroatoms, C, N, O, preferentially occupy vertex 2 with the absolutely lowest relative energy to form the high stabilization closo-heteroboranes. They cap four-membered rings to satisfy the geometrical demand of short B--Z bonds. The electron attractions from the vicinal boron atoms make the frameworks shrink. Differently, Si and Ge preferentially substitute for boron at vertex 1 with six tight B--Z bonds and form stabilized molecules. P, As, S, and Se tend to occupy vertex 4 and the optimized structures belong to the nido configura- tions. In contrast to high electronegative heteroatoms, S and Se transfer less negative charges to framework and the electropositive heteroatoms, Si and Ge transfer more negative charges to framework to form the delocalization structures. The HOMO-LUMO gaps show that most of predicted clusters possess chemical stabilities. The substitutions of heteroatoms for boron atoms in eleven-vertex closo-heteroboranes are consistent with the topological charge stabilization rule proposed by Gimarc.
文摘The admittance measurements of a hetero-junction can be used to derive the density of the interfacial state in the hetero-junction. Hence, prediction conductance via frequency is very useful for comprehension of the admittance of a hetero-junction using a mathematical strategy. From the observations on the curve of the frequencydependent conductance of the hetero-junction an analytic model with four-parameters was developed that relates conductance to frequency; the theoretical results agree quite well with the experimental data. The model shows potential for a variety of applications including different electronic devices. The model is a practical tool that can be readily used for assessing the electronic behaviors of a hetero-junction and is scientifically justifiable. In addition, the mathematical bridge to link the density of the interfacial state of the（pyronine-B）/p-Si structure to energy implies a good route to discuses the density of the interfacial state of interfaces.
