A spectrum-domain instance segmentation model for casting defects

2021 ◽  
pp. 1-20
Author(s):  
Jinhua Lin ◽  
Lin Ma ◽  
Yu Yao
Keyword(s):  
Interpolation Method ◽  
Solidification Process ◽  
Linear Interpolation ◽  
Hot Tearing ◽  
Generation Model ◽  
Casting Defects ◽  
Full Spectrum ◽  
Positive Role ◽  
Proposed Model ◽  
Instance Segmentation

Accurate segmentation of casting defects plays a positive role in the quality control of casting products, and is of great significance for accurate extraction of the mechanical properties of defects in the casting solidification process. However, as the shape of casting defects is complex and irregular, it is challenging to segment casting defects by existing segmentation methods. To address this, a spectrum domain instance segmentation model (SISN) is proposed for segmenting five types of casting defects with complex shapes accurately. The five defects are inclusion, shrinkage, hot tearing, cold tearing and micro pore. The proposed model consists of three sub-models: the spectrum domain region proposal model (SRPN), spectrum domain region of interest alignment model (SRoIAlign) and spectrum domain instance generation model (SIGN). SRPN uses a multi-scale anchoring mechanism to detect defects of various sizes, where the SSReLU and SCPool functions are used to solve the spectrum domain gradient explosion problem and the spectrum domain over-fitting problem. SRoIAlign uses the floating-point quantization operation and the tri-linear interpolation method to quantize the 3D proposals to the feature values in an accurate manner. SIGN is a full-spectrum domain neural network applied to 3D proposals, generating a segmentation instance of defects in a point-wise manner. In the experiments, we test the effectiveness of the proposed model from three aspects: segmentation accuracy, time performance and mechanical property extraction accuracy.

Numerical Simulation of Filling and Solidification in Sand Casting by Procast

2013 ◽  
Vol 791-793 ◽  
pp. 550-553 ◽  
Author(s):  
Dong Dong Han ◽  
Cheng Jun Wang ◽  
Juan Chang ◽  
Lei Chen ◽  
Huai Bei Xie
Keyword(s):  
Numerical Simulation ◽  
Heat Dissipation ◽  
Solidification Process ◽  
Simulation Software ◽  
Sand Casting ◽  
Production Costs ◽  
Raw Material ◽  
Casting Defects ◽  
Three Dimension ◽  
Filling And Solidification

At present, pulley produced in China has been able to meet the demand of domestic and international markets. But there are many problem of the pulley industry in our country, such as too many production enterprises and the low level of export products. And as components of drive system are light weight and raw material price of pulley casting are rising, manufacturing requirements of the pulley are also more and more high. Aiming at the casting defects of pulley that enterprise current product, pulley casting blank model of common material HT250 be made by three-dimension software, numerical simulation of filling and solidification process for pulley sand casting by the casting simulation software Procast, the size and location of the various casting defects were forecasted and analyzed, reflecting the pulley filling and solidification process of the actual situation, due to the thicker pulley rim and less heat dissipation, position of shrinkage is close to the middle of rim [, a method of eliminating defects is proposed to realize sequential solidification, and thus to minimize porosity shrinkage and improve casting performance and reduce casting time and reduce production costs.

An experimental study on optimal spark timing control for improved performance of a flex fuel vehicle engine

2019 ◽  
Vol 234 (5) ◽  
pp. 1294-1303
Author(s):  
Junsang Yoo ◽  
Taeyong Lee ◽  
Pyungsik Go ◽  
Yongseok Cho ◽  
Kwangsoon Choi ◽  
...  
Keyword(s):  
Empirical Equation ◽  
Interpolation Method ◽  
Linear Interpolation ◽  
Combustion Characteristics ◽  
Combustion Stability ◽  
Timing Control ◽  
Vehicle Engine ◽  
Spark Timing ◽  
Linear Interpolation Method ◽  
Blended Fuels

In the American continent, the most frequently used alternative fuel is ethanol. Especially in Brazil, various blends of gasoline–ethanol fuels are widely spread. The vehicle using blended fuel is called flexible fuel vehicle. Because of several selections for the blending ratios in gas stations, the fuel properties may vary after refueling depending on a driver’s selection. Also, the combustion characteristics of the flexible fuel vehicle engine may change. In order to respond to the flexible fuel vehicle market in Brazil, a study on blended fuels is performed. The main purpose of this study is to enhance performance of the flexible fuel vehicle engine to target Brazilian market. Therefore, we investigated combustion characteristics and optimal spark timings of the blended fuels with various blending ratios to improve the performance of the flexible fuel vehicle engine. As a tool for prediction of the optimal spark timing for the 1.6L flexible fuel vehicle engine, the empirical equation was suggested. The validity of the equation was investigated by comparing the predicted optimal spark timings with the stock spark timings through engine tests. When the stock spark timings of E0 and E100 were optimal, the empirical equation predicted the actual optimal spark timings for blended fuels with a good accuracy. In all conditions, by optimizing spark timing control, performance was improved. Especially, torque improvements of E30 and E50 fuels were 5.4% and 1.8%, respectively, without affecting combustion stability. From these results, it was concluded that the linear interpolation method is not suitable for flexible fuel vehicle engine control. Instead of linear interpolation method, optimal spark timing which reflects specific octane numbers of gasoline–ethanol blended fuels should be applied to maximize performance of the flexible fuel vehicle engine. The results of this study are expected to save the effort required for engine calibration when developing new flexible fuel vehicle engines and to be used as a basic strategy to improve the performance of other flexible fuel vehicle engines.

scholarly journals Mean Dynamic Topography Modeling Based on Optimal Interpolation from Satellite Gravimetry and Altimetry Data

2021 ◽  
Vol 11 (11) ◽  
pp. 5286
Author(s):  
Yihao Wu ◽  
Jia Huang ◽  
Hongkai Shi ◽  
Xiufeng He
Keyword(s):  
Ocean Circulation ◽  
Interpolation Method ◽  
Optimal Interpolation ◽  
Surface Model ◽  
Geopotential Model ◽  
Generation Model ◽  
Dynamic Topography ◽  
Altimetry Data ◽  
Mean Dynamic Topography ◽  
Gaussian Filtering

Mean dynamic topography (MDT) is crucial for research in oceanography and climatology. The optimal interpolation method (OIM) is applied to MDT modeling, where the error variance–covariance information of the observations is established. The global geopotential model (GGM) derived from GOCE (Gravity Field and Steady-State Ocean Circulation Explorer) gravity data and the mean sea surface model derived from satellite altimetry data are combined to construct MDT. Numerical experiments in the Kuroshio over Japan show that the use of recently released GOCE-derived GGM derives a better MDT compared to the previous models. The MDT solution computed based on the sixth-generation model illustrates a lower level of root mean square error (77.0 mm) compared with the ocean reanalysis data, which is 2.4 mm (5.4 mm) smaller than that derived from the fifth-generation (fourth-generation) model. This illustrates that the accumulation of GOCE data and updated data preprocessing methods can be beneficial for MDT recovery. Moreover, the results show that the OIM outperforms the Gaussian filtering approach, where the geostrophic velocity derived from the OIM method has a smaller misfit against the buoy data, by a magnitude of 10 mm/s (17 mm/s) when the zonal (meridional) component is validated. This is mainly due to the error information of input data being used in the optimal interpolation method, which may obtain more reasonable weights of observations than the Gaussian filtering method.

Improved linear interpolation method for the estimation of snow-covered area from optical data

2002 ◽  
Vol 82 (1) ◽  
pp. 64-78 ◽  
Author(s):  
Sari Metsämäki ◽  
Jenni Vepsäläinen ◽  
Jouni Pulliainen ◽  
Yrjö Sucksdorff
Keyword(s):  
Interpolation Method ◽  
Linear Interpolation ◽  
Optical Data ◽  
Snow Covered Area ◽  
Covered Area ◽  
Linear Interpolation Method

Study on Lidar Data Interpolation Method Based on GA-BP

2012 ◽  
Vol 588-589 ◽  
pp. 1312-1315
Author(s):  
Yi Kun Zhang ◽  
Ming Hui Zhang ◽  
Xin Hong Hei ◽  
Deng Xin Hua ◽  
Hao Chen
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Interpolation Method ◽  
Linear Interpolation ◽  
Lidar Data ◽  
Data Interpolation ◽  
Interpolation Model ◽  
Genetic Method ◽  
Interpolation Accuracy ◽  
Linear Interpolation Method

Aiming at building a Lidar data interpolation model, this paper designs and implements a GA-BP interpolation method. The proposed method uses genetic method to optimize BP neural network, which greatly improves the calculation accuracy and convergence rate of BP neural network. Experimental results show that the proposed method has a higher interpolation accuracy compared with BP neural network as well as linear interpolation method.

Comparison of two gap-filling techniques for nitrous oxide fluxes from agricultural soil

2019 ◽  
Vol 99 (1) ◽  
pp. 12-24 ◽  
Author(s):  
Rezvan Taki ◽  
Claudia Wagner-Riddle ◽  
Gary Parkin ◽  
Rob Gordon ◽  
Andrew VanderZaag
Keyword(s):  
Missing Values ◽  
Interpolation Method ◽  
Weather Conditions ◽  
Linear Interpolation ◽  
Bias Error ◽  
Research Station ◽  
Ann Model ◽  
Gap Filling ◽  
Adverse Weather ◽  
The Impact

Micrometeorological methods are ideally suited for continuous measurements of N2O fluxes, but gaps in the time series occur due to low-turbulence conditions, power failures, and adverse weather conditions. Two gap-filling methods including linear interpolation and artificial neural networks (ANN) were utilized to reconstruct missing N2O flux data from a corn–soybean–wheat rotation and evaluate the impact on annual N2O emissions from 2001 to 2006 at the Elora Research Station, ON, Canada. The single-year ANN method is recommended because this method captured flux variability better than the linear interpolation method (average R2 of 0.41 vs. 0.34). Annual N2O emission and annual bias resulting from linear and single-year ANN were compatible with each other when there were few and short gaps (i.e., percentage of missing values <30%). However, with longer gaps (>20 d), the bias error in annual fluxes varied between 0.082 and 0.344 kg N2O-N ha−1 for linear and 0.069 and 0.109 kg N2O-N ha−1 for single-year ANN. Hence, the single-year ANN with lower annual bias and stable approach over various years is recommended, if the appropriate driving inputs (i.e., soil temperature, soil water content, precipitation, N mineral content, and snow depth) needed for the ANN model are available.

scholarly journals Seismic tomography of the Gulf of Corinth: a comparison of methods

1997 ◽  
Vol 40 (1) ◽  
Author(s):  
E. Le Meur ◽  
J. Virieux ◽  
P. Podvin
Keyword(s):  
Linear System ◽  
Travel Time ◽  
Velocity Structure ◽  
Interpolation Method ◽  
Spline Interpolation ◽  
Linear Interpolation ◽  
Regular Grid ◽  
Partial Derivatives ◽  
Simultaneous Inversion ◽  
Gulf Of Corinth

At a local scale, travel-time tomography requires a simultaneous inversion of earthquake positions and velocity structure. We applied a joint iterative inversion scheme where medium parameters and hypocenter parameters were inverted simultaneously. At each step of the inversion, rays between hypocenters and stations were traced, new partial derivatives of travel-time were estimated and scaling between parameters was performed as well. The large sparse linear system modified by the scaling was solved by the LSQR method at each iteration. We compared performances of two different forward techniques. Our first approach was a fast ray tracing based on a paraxial method to solve the two-point boundary value problem. The rays connect sources and stations in a velocity structure described by a 3D B-spline interpolation over a regular grid. The second approach is the finite-difference solution of the eikonal equation with a 3D linear interpolation over a regular grid. The partial derivatives are estimated differently depending on the interpolation method. The reconstructed images are sensitive to the spatial variation of the partial derivatives shown by synthetic examples. We aldo found that a scaling between velocity and hypocenter parameters involved in the linear system to be solved is important in recovering accurate amplitudes of anomalies. This scaling was estimated to be five through synthetic examples with the real configuration of stations and sources. We also found it necessary to scale Pand S velocities in order to recover better amplitudes of S velocity anomaly. The crustal velocity structure of a 50X50X20 km domain near Patras in the Gulf of Corinth (Greece) was recovered using microearthquake data. These data were recorded during a field experiment in 1991 where a dense network of 60 digital stations was deployed. These microearthquakes were widely distributed under the Gulf of Corinth and enabled us to perform a reliable tomography of first arrival P and S travel-times. The obtained images of this seismically active zone show a south/north asymmetry in agreement with the tectonic context. The transition to high velocity lies between 6 km and 9 km indicating a very thin crust related to the active extension regime.At a local scale, travel-time tomography requires a simultaneous inversion of earthquake positions and velocity structure. We applied a joint iterative inversion scheme where medium parameters and hypocenter parameters were inverted simultaneously. At each step of the inversion, rays between hypocenters and stations were traced, new partial derivatives of travel-time were estimated and scaling between parameters was performed as well. The large sparse linear system modified by the scaling was solved by the LSQR method at each iteration. We compared performances of two different forward techniques. Our first approach was a fast ray tracing based on a paraxial method to solve the two-point boundary value problem. The rays connect sources and stations in a velocity structure described by a 3D B-spline interpolation over a regular grid. The second approach is the finite-difference solution of the eikonal equation with a 3D linear interpolation over a regular grid. The partial derivatives are estimated differently depending on the interpolation method. The reconstructed images are sensitive to the spatial variation of the partial derivatives shown by synthetic examples. We aldo found that a scaling between velocity and hypocenter parameters involved in the linear system to be solved is important in recovering accurate amplitudes of anomalies. This scaling was estimated to be five through synthetic examples with the real configuration of stations and sources. We also found it necessary to scale Pand S velocities in order to recover better amplitudes of S velocity anomaly. The crustal velocity structure of a 50X50X20 km domain near Patras in the Gulf of Corinth (Greece) was recovered using microearthquake data. These data were recorded during a field experiment in 1991 where a dense network of 60 digital stations was deployed. These microearthquakes were widely distributed under the Gulf of Corinth and enabled us to perform a reliable tomography of first arrival P and S travel-times. The obtained images of this seismically active zone show a south/north asymmetry in agreement with the tectonic context. The transition to high velocity lies between 6 km and 9 km indicating a very thin crust related to the active extension regime.

scholarly journals PROCESSING JUMP POINT OF LIDAR DETECTION DATA AND INVERSING THE AEROSOL EXTINCTION COEFFICIENT

2019 ◽  
Vol XLII-3/W9 ◽  
pp. 227-232
Author(s):  
H. L. Zhang ◽  
H. Zhao ◽  
Y. P. Liu ◽  
X. K. Wang ◽  
C. Shu
Keyword(s):  
Atmospheric Aerosols ◽  
Extinction Coefficient ◽  
Interpolation Method ◽  
Linear Interpolation ◽  
Mie Scattering ◽  
Inversion Method ◽  
Aerosol Extinction ◽  
Detection Range ◽  
Long Time ◽  
Aerosol Extinction Coefficient

Abstract. For a long time, the research of the optical properties of atmospheric aerosols has aroused a wide concern in the field of atmospheric and environmental. Many scholars commonly use the Klett method to invert the lidar return signal of Mie scattering. However, there are always some negative values in the detection data of lidar, which have no actual meaning，and which are jump points. The jump points are also called wild value points and abnormal points. The jump points are refered to the detecting points that are significantly different from the surrounding detection points, and which are not consistent with the actual situation. As a result, when the far end point is selected as the boundary value, the inversion error is too large to successfully invert the extinction coefficient profile. These negative points are jump points, which must be removed in the inversion process. In order to solve the problem, a method of processing jump points of detection data of lidar and the inversion method of aerosol extinction coefficient is proposed in this paper. In this method, when there are few jump points, the linear interpolation method is used to process the jump points. When the number of continuous jump points is large, the function fitting method is used to process the jump points. The feasibility and reliability of this method are verified by using actual lidar data. The results show that the extinction coefficient profile can be successfully inverted when different remote boundary values are chosen. The extinction coefficient profile inverted by this method is more continuous and smoother. The effective detection range of lidar is greatly increased using this method. The extinction coefficient profile is more realistic. The extinction coefficient profile inverted by this method is more favorable to further analysis of the properties of atmospheric aerosol. Therefore, this method has great practical application and popularization value.

scholarly journals Interpolation uncertainty of atmospheric temperature radiosoundings

2020 ◽  
Author(s):  
Alessandro Fassò ◽  
Michael Sommer ◽  
Christoph von Rohden
Keyword(s):  
Missing Data ◽  
Standard Error ◽  
Cross Validation ◽  
Interpolation Method ◽  
Average Length ◽  
Linear Interpolation ◽  
Atmospheric Temperature ◽  
Radiosonde Data ◽  
Published Data ◽  
Set Up

Abstract. This paper is motivated by the fact that, although temperature readings made by Vaisala RS41 radiosondes at GRUAN sites (http://www.gruan.org) are given at 1 s resolution, for various reasons, missing data are spread along the atmospheric profile. Such a problem is quite common in radiosonde data and other profile data. Hence, (linear) interpolation is often used to fill the gaps in published data products. In this perspective, the present paper considers interpolation uncertainty. To do this, a statistical approach is introduced giving some understanding of the consequences of substituting missing data by interpolated ones. In particular, a general frame for the computation of interpolation uncertainty based on a Gaussian process (GP) set-up is developed. Using the GP characteristics, a simple formula for computing the linear interpolation standard error is given. Moreover, the GP interpolation is proposed as it provides an alternative interpolation method with its standard error. For the Vaisala RS41, the two approaches are shown to give similar interpolation performances using an extensive cross-validation approach based on the block-bootstrap technique. Statistical results about interpolation uncertainties at various GRUAN sites and for various missing gap lengths are provided. Since both provide an underestimation of the cross-validation interpolation uncertainty, a bootstrap-based correction formula is proposed. Using the root mean square error, it is found that, for short gaps, with an average length of 5 s, the average uncertainty is smaller than 0.10 K. For larger gaps, it increases up to 0.35 K for an average gap length of 30 s, and up to 0.58 K for a gap of 60 s.

The Offshore Wave Simulation Based on the Improved P-M Spectrum and Multiple Fractal Interpolation

2014 ◽  
Vol 1030-1032 ◽  
pp. 1832-1836
Author(s):  
Ying Li ◽  
Rui Zhou ◽  
Hao Kuan Li ◽  
Ming Wang
Keyword(s):  
Shallow Water ◽  
Interpolation Method ◽  
Fractal Interpolation ◽  
Fractal Method ◽  
Growth State ◽  
Water Factor ◽  
Proposed Model ◽  
Simulation Based ◽  
Offshore Wave ◽  
The Waves

The Pierson - Moskowitz model is only applicable to full growth state of the waves, and it has low authenticity and hopping phenomenon under the condition of offshore shallow water. This paper proposes a simulation model of offshore wave based on the improved P-M spectrum and multiple fractal interpolation methods. In order to calculate the sea wave with shallow water, a spectrum peak regulation factor and a depth of the water factor are introduced to the P - M spectrum model. Based on this model, the wavelength and wave speed are used as the initial values of wave height. Then, the amplitude and the number of iterations in diamond square fractal method are controlled to obtain the fractal static sea. In order to reduce the influence of the hopping phenomenon to the simulation authenticity, meanwhile, a multiple dynamic non-uniform interpolation method is proposed. The experimental results show that the proposed model can simulate offshore wave with better effect and in real time.

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