According to the factors that confirm the shape of surface, it is classified into two categories： arc surface and curve surface The method to confirm the category of surfaces and the plotting methods are discussed in...According to the factors that confirm the shape of surface, it is classified into two categories： arc surface and curve surface The method to confirm the category of surfaces and the plotting methods are discussed in this paper, which provide guidance for parts plotting.展开更多
With rigorous dynamic performance of mechanical products,it is important to identify dynamic parameters exactly.In this paper,a response surface plotting method is proposed and it can be applied to identify the dynami...With rigorous dynamic performance of mechanical products,it is important to identify dynamic parameters exactly.In this paper,a response surface plotting method is proposed and it can be applied to identify the dynamic parameters of some nonlinear systems.The method is based on the principle of harmonic balance method(HBM).The nonlinear vibration system behaves linearly under the steady-state response amplitude,which presents the equivalent stiffness and damping coefficient.The response surface plot is over two-dimensional space,which utilizes excitation as the vertical axis and the frequency as the horizontal axis.It can be applied to observe the output vibration response data.The modal parameters are identified by the response surface plot as linearity for different excitation levels,and they are converted into equivalent stiffness and damping coefficient for each resonant response.Finally,the HBM with first-order expansion is utilized for identification of stiffness and damping coefficient of nonlinear systems.The classical nonlinear systems are applied in the numerical simulation as the example,which is used to verify its effectiveness and accuracy.An application of this technique for nonlinearity identification by experimental setup is also illustrated.展开更多
We contrast a new continuous approach(CA)for estimating plot-level above-ground biomass(AGB)in forest inventories with the current approach of estimating AGB exclusively from the tree-level AGB predicted for each tree...We contrast a new continuous approach(CA)for estimating plot-level above-ground biomass(AGB)in forest inventories with the current approach of estimating AGB exclusively from the tree-level AGB predicted for each tree in a plot,henceforth called DA(discrete approach).With the CA,the AGB in a forest is modelled as a continuous surface and the AGB estimate for a fixed-area plot is computed as the integral of the AGB surface taken over the plot area.Hence with the CA,the portion of the biomass of in-plot trees that extends across the plot perimeter is ignored while the biomass from trees outside of the plot reaching inside the plot is added.We use a sampling simulation with data from a fully mapped two hectare area to illustrate that important differences in plot-level AGB estimates can emerge.Ideally CA-based estimates of mean AGB should be less variable than those derived from the DA.If realized,this difference translates to a higher precision from field sampling,or a lower required sample size.In our case study with a target precision of 5%(i.e.relative standard error of the estimated mean AGB),the CA required a 27.1%lower sample size for small plots of 100 m2 and a 10.4%lower sample size for larger plots of 1700 m2.We examined sampling induced errors only and did not yet consider model errors.We discuss practical issues in implementing the CA in field inventories and the potential in applications that model biomass with remote sensing data.The CA is a variation on a plot design for above-ground forest biomass;as such it can be applied in combination with any forest inventory sampling design.展开更多
The analysis of environmental daily evaporation plays a vital role in the field of agriculture. It is very essential to know the daily evaporation rate of a particular area for proper cultivation. So, we need a standa...The analysis of environmental daily evaporation plays a vital role in the field of agriculture. It is very essential to know the daily evaporation rate of a particular area for proper cultivation. So, we need a standard prediction model which can predict the daily evaporation. In this paper, we use subtractive clustering and Fuzzy logic to predict daily evaporation of a particular area. The input data used in the paper are: maximum soil temperature, average soil temperature, average air temperature, minimum relative humidity, average relative humidity and total wind, which are related to the daily evaporation of a particular area as the output. The accuracy of output of the paper is compared with the previous model of Artificial Neural Network (ANN) and we get better result towards the target value. The finding of the paper is applicable in environmental science, geological science and agriculture.展开更多
Data clustering plays a vital role in object identification. In real life we mainly use the concept in biometric identification and object detection. In this paper we use Fuzzy Weighted Rules, Fuzzy Inference System (...Data clustering plays a vital role in object identification. In real life we mainly use the concept in biometric identification and object detection. In this paper we use Fuzzy Weighted Rules, Fuzzy Inference System (FIS), Fuzzy C-Mean clustering (FCM), Support Vector Machine (SVM) and Artificial Neural Network (ANN) to distinguish three types of Iris data called Iris-Setosa, Iris-Versicolor and Iris-Virginica. Each class in the data table is identified by four-dimensional vector, where vectors are used as the input variable called: Sepal Length (SL), Sepal Width (SW), Petal Length (PL) and Petal Width (PW). The combination of five machine learning methods provides above 98% accuracy of class identification.展开更多
文摘According to the factors that confirm the shape of surface, it is classified into two categories： arc surface and curve surface The method to confirm the category of surfaces and the plotting methods are discussed in this paper, which provide guidance for parts plotting.
文摘With rigorous dynamic performance of mechanical products,it is important to identify dynamic parameters exactly.In this paper,a response surface plotting method is proposed and it can be applied to identify the dynamic parameters of some nonlinear systems.The method is based on the principle of harmonic balance method(HBM).The nonlinear vibration system behaves linearly under the steady-state response amplitude,which presents the equivalent stiffness and damping coefficient.The response surface plot is over two-dimensional space,which utilizes excitation as the vertical axis and the frequency as the horizontal axis.It can be applied to observe the output vibration response data.The modal parameters are identified by the response surface plot as linearity for different excitation levels,and they are converted into equivalent stiffness and damping coefficient for each resonant response.Finally,the HBM with first-order expansion is utilized for identification of stiffness and damping coefficient of nonlinear systems.The classical nonlinear systems are applied in the numerical simulation as the example,which is used to verify its effectiveness and accuracy.An application of this technique for nonlinearity identification by experimental setup is also illustrated.
文摘We contrast a new continuous approach(CA)for estimating plot-level above-ground biomass(AGB)in forest inventories with the current approach of estimating AGB exclusively from the tree-level AGB predicted for each tree in a plot,henceforth called DA(discrete approach).With the CA,the AGB in a forest is modelled as a continuous surface and the AGB estimate for a fixed-area plot is computed as the integral of the AGB surface taken over the plot area.Hence with the CA,the portion of the biomass of in-plot trees that extends across the plot perimeter is ignored while the biomass from trees outside of the plot reaching inside the plot is added.We use a sampling simulation with data from a fully mapped two hectare area to illustrate that important differences in plot-level AGB estimates can emerge.Ideally CA-based estimates of mean AGB should be less variable than those derived from the DA.If realized,this difference translates to a higher precision from field sampling,or a lower required sample size.In our case study with a target precision of 5%(i.e.relative standard error of the estimated mean AGB),the CA required a 27.1%lower sample size for small plots of 100 m2 and a 10.4%lower sample size for larger plots of 1700 m2.We examined sampling induced errors only and did not yet consider model errors.We discuss practical issues in implementing the CA in field inventories and the potential in applications that model biomass with remote sensing data.The CA is a variation on a plot design for above-ground forest biomass;as such it can be applied in combination with any forest inventory sampling design.
文摘The analysis of environmental daily evaporation plays a vital role in the field of agriculture. It is very essential to know the daily evaporation rate of a particular area for proper cultivation. So, we need a standard prediction model which can predict the daily evaporation. In this paper, we use subtractive clustering and Fuzzy logic to predict daily evaporation of a particular area. The input data used in the paper are: maximum soil temperature, average soil temperature, average air temperature, minimum relative humidity, average relative humidity and total wind, which are related to the daily evaporation of a particular area as the output. The accuracy of output of the paper is compared with the previous model of Artificial Neural Network (ANN) and we get better result towards the target value. The finding of the paper is applicable in environmental science, geological science and agriculture.
文摘Data clustering plays a vital role in object identification. In real life we mainly use the concept in biometric identification and object detection. In this paper we use Fuzzy Weighted Rules, Fuzzy Inference System (FIS), Fuzzy C-Mean clustering (FCM), Support Vector Machine (SVM) and Artificial Neural Network (ANN) to distinguish three types of Iris data called Iris-Setosa, Iris-Versicolor and Iris-Virginica. Each class in the data table is identified by four-dimensional vector, where vectors are used as the input variable called: Sepal Length (SL), Sepal Width (SW), Petal Length (PL) and Petal Width (PW). The combination of five machine learning methods provides above 98% accuracy of class identification.
