This paper studies the geometric boundary representations for Inverse Lax-Wendroff(ILW)method,aiming to develop a practical computer-aided engineering method without body-fitted meshes.We propose the signed distance f...This paper studies the geometric boundary representations for Inverse Lax-Wendroff(ILW)method,aiming to develop a practical computer-aided engineering method without body-fitted meshes.We propose the signed distance function(SDF)representation of the geometric boundary and design an extremely efficient algorithm for foot point calculation,which is particularly in line with the needs of ILW.Theoretical and numerical analyses demonstrate that the SDF representation of geometric boundary can satisfy ILW’s needs better than others.The effectiveness and robustness of our proposed method are verified by simulating initial boundary value computational physical problems of Euler equation for compressible fluids.展开更多
The issues of seepage in calcareous sand foundations and backfillshave a potentially detrimental effect on the stability and safety of superstructures.Simplifying calcareous sand grains as spheres or ellipsoids in num...The issues of seepage in calcareous sand foundations and backfillshave a potentially detrimental effect on the stability and safety of superstructures.Simplifying calcareous sand grains as spheres or ellipsoids in numerical simulations may lead to significantinaccuracies.In this paper,we present a novel intelligence framework based on a deep convolutional generative adversarial network(DCGAN).A DCGAN model was trained using a training dataset comprising 11,625 real particles for the random generation of three-dimensional calcareous sand particles.Subsequently,3800 realistic calcareous sand particles with intra-particle voids were generated.Generative fidelityand validity of the DCGAN model were well verifiedby the consistency of the statistical values of nine morphological parameters of both the training dataset and the generated dataset.Digital calcareous sand columns were obtained through gravitational deposition simulation of the generated particles.Directional seepage simulations were conducted,and the vertical permeability values of the sand columns were found to be in accordance with the objective law.The results demonstrate the potential of the proposed framework for stochastic modeling and multi-scale simulation of the seepage behaviors in calcareous sand foundations and backfills.展开更多
Three-dimensional(3D)modeling is an important topic in computer graphics and computer vision.In recent years,the introduction of consumer-grade depth cameras has resulted in profound advances in 3D modeling.Starting w...Three-dimensional(3D)modeling is an important topic in computer graphics and computer vision.In recent years,the introduction of consumer-grade depth cameras has resulted in profound advances in 3D modeling.Starting with the basic data structure,this survey reviews the latest developments of 3D modeling based on depth cameras,including research works on camera tracking,3D object and scene reconstruction,and high-quality texture reconstruction.We also discuss the future work and possible solutions for 3D modeling based on the depth camera.展开更多
Short time existence and uniqueness for the classical motion are studied by the function of the principal curvatures of a smooth surface and the Evans and Spruck's results are generalized.
This paper presents a handheld 3D vision-based scanner for small objects by using Kinect. It is different from the previous color-glove-based approaches which require segmenting the target object. First, we eliminate ...This paper presents a handheld 3D vision-based scanner for small objects by using Kinect. It is different from the previous color-glove-based approaches which require segmenting the target object. First, we eliminate the noises and the outliers caused by holding hands. Second, we apply Kinect-fusion algorithm and truncated signed distance function （TSDF） to represent 3D surfaces. Third, we propose a modified integration strategy to eliminate the hand effect. Fourth, we take advantage of the parallel computation of GPUs for real-time operation. The major contributions of this paper are （1） the registration precision is improved, （2） the oflline amendment and loop closure operation are not required, and （3） concave 3D object reconstruction is feasible.展开更多
This paper establishes the fuzzy discounted cash flow model to settle the uncertainties of the cash flow and discount rate in two-stage DCF model, to take the imprecise of the time period of the supernormal growth pha...This paper establishes the fuzzy discounted cash flow model to settle the uncertainties of the cash flow and discount rate in two-stage DCF model, to take the imprecise of the time period of the supernormal growth phase with considering the investor＇s attitude to risk. Firstly, the discount rate and the growth rate are fuzzified as triangular fuzzy numbers in this fuzzy discounted cash flow model. Then the intrinsic value of an asset can be evaluated by the arithmetic operation on interval and λ- signed distance method under the framework of DCF approach. Finally, a listed company at Shanghai Stock Exchange is analyzed as the case to demonstrate the process of stock value calculation by the fuzzy discounted cash flow model.展开更多
This paper presents an integrated inventory model that examines imperfect production processes in the context of intuitionistic fuzzy demand.Defective products identified during the inspection process undergo rework b...This paper presents an integrated inventory model that examines imperfect production processes in the context of intuitionistic fuzzy demand.Defective products identified during the inspection process undergo rework before being released for sale.The dynamic production system allows for adaptability to the environment,with the production cost tied to the variability in production rates.Acknowledging the substantial carbon dioxide emissions from industries and transportation,the manufacturer aligns item production with carbon emission levels and incurs a corresponding carbon emission tax.Market demand for the item is represented as a triangular intuitionistic fuzzy number due to inherent uncertainty.The primary objective of this study is to minimize the integrated cost function of the proposed model through multivariable calculus.Numerical experiments are conducted to assess the model’s performance,and sensitivity analysis is employed to validate the effectiveness of the optimal parameters.展开更多
Intelligent manufacturing design of a complex production-inventory system becomes a key issue for the organization of responsiveness to uncertainties.This paper addresses a two-echelon production-inventory model for a...Intelligent manufacturing design of a complex production-inventory system becomes a key issue for the organization of responsiveness to uncertainties.This paper addresses a two-echelon production-inventory model for a non-repairable product where the system consists of single manufacturer and single retailer.The manufacturer procures raw material(which also contains imperfect raw materials)from an outside supplier then proceeds to convert perfect-quality raw material as a finished product,and finally delivers to the retailer.In this study we assume that the demand is sensitive to promotional efforts/sales teams’initiatives and the production rate is uncertain but possible to describe with a triangular fuzzy number.Then we use the signed distance method to defuzzify the fuzzy joint total cost and an analytical method is employed to achieve the optimal solutions so that the total costs of both manufacturer and retailer are minimized.An efficient algorithm is developed to design an intelligent manufacturing strategy such as optimal production lot-size,backlogging and the initiatives of sales teams.A numerical example and sensitivity analysis are given to demonstrate the application of the proposed model.展开更多
Machine learning methods are increasingly used for spatially predicting a categorical target variable when spatially exhaustive predictor variables are available within the study region.Even though these methods exhib...Machine learning methods are increasingly used for spatially predicting a categorical target variable when spatially exhaustive predictor variables are available within the study region.Even though these methods exhibit competitive spatial prediction performance,they do not exactly honor the categorical target variable's observed values at sampling locations by construction.On the other side,competitor geostatistical methods perfectly match the categorical target variable's observed values at sampling locations by essence.In many geoscience applications,it is often desirable to perfectly match the observed values of the categorical target variable at sampling locations,especially when the categorical target variable's measurements can be reasonably considered error-free.This paper addresses the problem of exact conditioning of machine learning methods for the spatial prediction of categorical variables.It introduces a classification random forest-based approach in which the categorical target variable is exactly conditioned to the data,thus having the exact conditioning property like competitor geostatistical methods.The proposed method extends a previous work dedicated to continuous target variables by using an implicit representation of the categorical target variable.The basic idea consists of transforming the ensemble of classification tree predictors'(categorical)resulting from the traditional classification random forest into an ensemble of signed distances(continuous)associated with each category of the categorical target variable.Then,an orthogonal representation of the ensemble of signed distances is created through the principal component analysis,thus allowing to reformulate the exact conditioning problem as a system of linear inequalities on principal component scores.Then,the sampling of new principal component scores ensuring the data's exact conditioning is performed via randomized quadratic programming.The resulting conditional signed distances are turned out into an ensemble of categorical outputs,which perfectly honor the categorical target variable's observed values at sampling locations.Then,the majority vote is used to aggregate the ensemble of categorical outputs.The effectiveness of the proposed method is illustrated on a simulated dataset for which ground-truth is available and showcased on a real-world dataset,including geochemical data.A comparison with geostatistical and traditional machine learning methods show that the proposed technique can perfectly match the categorical target variable's observed values at sampling locations while maintaining competitive out-of-sample predictive performance.展开更多
In this article, we consider a two level supply chain to evaluate the impact ofpostponement strategy on the retailer. Here the cost parameters are fuzzified.Signed distance method is used to defuzzify and to obtain th...In this article, we consider a two level supply chain to evaluate the impact ofpostponement strategy on the retailer. Here the cost parameters are fuzzified.Signed distance method is used to defuzzify and to obtain the estimation of thetotal cost in the fuzzy sense. The common variable production cost, commonfixed cost and the common unit holding cost per unit time are assumed to befuzzy in nature. Inventory models are formulated for postponement system andindependent system such that the total average inventory cost function per unittime is minimized. Algorithms are given to derive the optimal solutions of theproposed model. Theoretical analysis and the computational procedure helps tostudy the impact of deterioration rate on the optimal inventory policies. Acomparative study between the postponement system and independent systemconsidering fuzzy costs is also made.展开更多
While recent Gaussian-based SLAM methods achieve photorealistic reconstruction from RGB-D data, their computational performance remains a critical bottleneck. State-of-the-art techniques operate at less than 20 fps, s...While recent Gaussian-based SLAM methods achieve photorealistic reconstruction from RGB-D data, their computational performance remains a critical bottleneck. State-of-the-art techniques operate at less than 20 fps, significantly lagging behind geometry-based approaches like KinectFusion (hundreds of fps). This limitation stems from the heavy computational burden: modeling scenes requires numerous Gaussians and complex iterative optimization to fit RGB-D data;insufficient Gaussian counts or optimization iterations cause severe quality degradation. To address this, we propose a Gaussian-SDF hybrid representation, combining a colorized signed distance field (SDF) for smooth geometry and appearance with 3D Gaussians to capture underrepresented details. The SDF is efficiently constructed via RGB-D fusion (as in geometry-based methods), while Gaussians undergo iterative optimization. Our representation enables significant Gaussian reduction (50% fewer) by avoiding full-scene Gaussian modeling, and efficient Gaussian optimization (75% fewer iterations) through targeted appearance refinement. Building upon this representation, we develop GPS-SLAM (Gaussian-plus-SDF SLAM), a real-time 3D reconstruction system achieving over 150 fps on real-world Azure Kinect sequences, faster by an order-of-magnitude than state-of-the-art techniques while maintaining comparable reconstruction quality. The source code and data are available at https://gapszju.github.io/GPS-SLAM.展开更多
文摘This paper studies the geometric boundary representations for Inverse Lax-Wendroff(ILW)method,aiming to develop a practical computer-aided engineering method without body-fitted meshes.We propose the signed distance function(SDF)representation of the geometric boundary and design an extremely efficient algorithm for foot point calculation,which is particularly in line with the needs of ILW.Theoretical and numerical analyses demonstrate that the SDF representation of geometric boundary can satisfy ILW’s needs better than others.The effectiveness and robustness of our proposed method are verified by simulating initial boundary value computational physical problems of Euler equation for compressible fluids.
基金financially supported by the National Natural Science Foundation of China(Grant No.42077232)the National Natural Science Foundation for Excellent Young Scholars of China(Grant No.52222110)the Fundamental Research Funds for the Central Universities(Grant No.14380229).
文摘The issues of seepage in calcareous sand foundations and backfillshave a potentially detrimental effect on the stability and safety of superstructures.Simplifying calcareous sand grains as spheres or ellipsoids in numerical simulations may lead to significantinaccuracies.In this paper,we present a novel intelligence framework based on a deep convolutional generative adversarial network(DCGAN).A DCGAN model was trained using a training dataset comprising 11,625 real particles for the random generation of three-dimensional calcareous sand particles.Subsequently,3800 realistic calcareous sand particles with intra-particle voids were generated.Generative fidelityand validity of the DCGAN model were well verifiedby the consistency of the statistical values of nine morphological parameters of both the training dataset and the generated dataset.Digital calcareous sand columns were obtained through gravitational deposition simulation of the generated particles.Directional seepage simulations were conducted,and the vertical permeability values of the sand columns were found to be in accordance with the objective law.The results demonstrate the potential of the proposed framework for stochastic modeling and multi-scale simulation of the seepage behaviors in calcareous sand foundations and backfills.
基金National Natural Science Foundation of China(61732016).
文摘Three-dimensional(3D)modeling is an important topic in computer graphics and computer vision.In recent years,the introduction of consumer-grade depth cameras has resulted in profound advances in 3D modeling.Starting with the basic data structure,this survey reviews the latest developments of 3D modeling based on depth cameras,including research works on camera tracking,3D object and scene reconstruction,and high-quality texture reconstruction.We also discuss the future work and possible solutions for 3D modeling based on the depth camera.
文摘Short time existence and uniqueness for the classical motion are studied by the function of the principal curvatures of a smooth surface and the Evans and Spruck's results are generalized.
基金supported by the Ministry of Science and Technology of Taiwan under Grant No.MOST103-2221-E-468-006–MY1
文摘This paper presents a handheld 3D vision-based scanner for small objects by using Kinect. It is different from the previous color-glove-based approaches which require segmenting the target object. First, we eliminate the noises and the outliers caused by holding hands. Second, we apply Kinect-fusion algorithm and truncated signed distance function （TSDF） to represent 3D surfaces. Third, we propose a modified integration strategy to eliminate the hand effect. Fourth, we take advantage of the parallel computation of GPUs for real-time operation. The major contributions of this paper are （1） the registration precision is improved, （2） the oflline amendment and loop closure operation are not required, and （3） concave 3D object reconstruction is feasible.
基金Supported by the Natural Science Foundation of Anhui Province （Item No： 070416276X）.
文摘This paper establishes the fuzzy discounted cash flow model to settle the uncertainties of the cash flow and discount rate in two-stage DCF model, to take the imprecise of the time period of the supernormal growth phase with considering the investor＇s attitude to risk. Firstly, the discount rate and the growth rate are fuzzified as triangular fuzzy numbers in this fuzzy discounted cash flow model. Then the intrinsic value of an asset can be evaluated by the arithmetic operation on interval and λ- signed distance method under the framework of DCF approach. Finally, a listed company at Shanghai Stock Exchange is analyzed as the case to demonstrate the process of stock value calculation by the fuzzy discounted cash flow model.
文摘This paper presents an integrated inventory model that examines imperfect production processes in the context of intuitionistic fuzzy demand.Defective products identified during the inspection process undergo rework before being released for sale.The dynamic production system allows for adaptability to the environment,with the production cost tied to the variability in production rates.Acknowledging the substantial carbon dioxide emissions from industries and transportation,the manufacturer aligns item production with carbon emission levels and incurs a corresponding carbon emission tax.Market demand for the item is represented as a triangular intuitionistic fuzzy number due to inherent uncertainty.The primary objective of this study is to minimize the integrated cost function of the proposed model through multivariable calculus.Numerical experiments are conducted to assess the model’s performance,and sensitivity analysis is employed to validate the effectiveness of the optimal parameters.
基金The second author’s research work is supported by DST INSPIRE,Ministry of Science and Technology,Government of India under the grant no.DST/INSPIRE Fellowship/2011/413B dated 15.01.2014.
文摘Intelligent manufacturing design of a complex production-inventory system becomes a key issue for the organization of responsiveness to uncertainties.This paper addresses a two-echelon production-inventory model for a non-repairable product where the system consists of single manufacturer and single retailer.The manufacturer procures raw material(which also contains imperfect raw materials)from an outside supplier then proceeds to convert perfect-quality raw material as a finished product,and finally delivers to the retailer.In this study we assume that the demand is sensitive to promotional efforts/sales teams’initiatives and the production rate is uncertain but possible to describe with a triangular fuzzy number.Then we use the signed distance method to defuzzify the fuzzy joint total cost and an analytical method is employed to achieve the optimal solutions so that the total costs of both manufacturer and retailer are minimized.An efficient algorithm is developed to design an intelligent manufacturing strategy such as optimal production lot-size,backlogging and the initiatives of sales teams.A numerical example and sensitivity analysis are given to demonstrate the application of the proposed model.
文摘Machine learning methods are increasingly used for spatially predicting a categorical target variable when spatially exhaustive predictor variables are available within the study region.Even though these methods exhibit competitive spatial prediction performance,they do not exactly honor the categorical target variable's observed values at sampling locations by construction.On the other side,competitor geostatistical methods perfectly match the categorical target variable's observed values at sampling locations by essence.In many geoscience applications,it is often desirable to perfectly match the observed values of the categorical target variable at sampling locations,especially when the categorical target variable's measurements can be reasonably considered error-free.This paper addresses the problem of exact conditioning of machine learning methods for the spatial prediction of categorical variables.It introduces a classification random forest-based approach in which the categorical target variable is exactly conditioned to the data,thus having the exact conditioning property like competitor geostatistical methods.The proposed method extends a previous work dedicated to continuous target variables by using an implicit representation of the categorical target variable.The basic idea consists of transforming the ensemble of classification tree predictors'(categorical)resulting from the traditional classification random forest into an ensemble of signed distances(continuous)associated with each category of the categorical target variable.Then,an orthogonal representation of the ensemble of signed distances is created through the principal component analysis,thus allowing to reformulate the exact conditioning problem as a system of linear inequalities on principal component scores.Then,the sampling of new principal component scores ensuring the data's exact conditioning is performed via randomized quadratic programming.The resulting conditional signed distances are turned out into an ensemble of categorical outputs,which perfectly honor the categorical target variable's observed values at sampling locations.Then,the majority vote is used to aggregate the ensemble of categorical outputs.The effectiveness of the proposed method is illustrated on a simulated dataset for which ground-truth is available and showcased on a real-world dataset,including geochemical data.A comparison with geostatistical and traditional machine learning methods show that the proposed technique can perfectly match the categorical target variable's observed values at sampling locations while maintaining competitive out-of-sample predictive performance.
文摘In this article, we consider a two level supply chain to evaluate the impact ofpostponement strategy on the retailer. Here the cost parameters are fuzzified.Signed distance method is used to defuzzify and to obtain the estimation of thetotal cost in the fuzzy sense. The common variable production cost, commonfixed cost and the common unit holding cost per unit time are assumed to befuzzy in nature. Inventory models are formulated for postponement system andindependent system such that the total average inventory cost function per unittime is minimized. Algorithms are given to derive the optimal solutions of theproposed model. Theoretical analysis and the computational procedure helps tostudy the impact of deterioration rate on the optimal inventory policies. Acomparative study between the postponement system and independent systemconsidering fuzzy costs is also made.
基金supported by the National Natural Science Foundation of China(U23A20311,62421003).
文摘While recent Gaussian-based SLAM methods achieve photorealistic reconstruction from RGB-D data, their computational performance remains a critical bottleneck. State-of-the-art techniques operate at less than 20 fps, significantly lagging behind geometry-based approaches like KinectFusion (hundreds of fps). This limitation stems from the heavy computational burden: modeling scenes requires numerous Gaussians and complex iterative optimization to fit RGB-D data;insufficient Gaussian counts or optimization iterations cause severe quality degradation. To address this, we propose a Gaussian-SDF hybrid representation, combining a colorized signed distance field (SDF) for smooth geometry and appearance with 3D Gaussians to capture underrepresented details. The SDF is efficiently constructed via RGB-D fusion (as in geometry-based methods), while Gaussians undergo iterative optimization. Our representation enables significant Gaussian reduction (50% fewer) by avoiding full-scene Gaussian modeling, and efficient Gaussian optimization (75% fewer iterations) through targeted appearance refinement. Building upon this representation, we develop GPS-SLAM (Gaussian-plus-SDF SLAM), a real-time 3D reconstruction system achieving over 150 fps on real-world Azure Kinect sequences, faster by an order-of-magnitude than state-of-the-art techniques while maintaining comparable reconstruction quality. The source code and data are available at https://gapszju.github.io/GPS-SLAM.
