Abstract: Crop yield prediction is a paramount issue in
agriculture. The main idea of this paper is to find out efficient
way to predict the yield of corn based meteorological records.
The prediction models used in this paper can be classified into
model-driven approaches and data-driven approaches, according to
the different modeling methodologies. The model-driven approaches are based on crop mechanistic
modeling. They describe crop growth in interaction with their
environment as dynamical systems. But the calibration process of
the dynamic system comes up with much difficulty, because it
turns out to be a multidimensional non-convex optimization problem.
An original contribution of this paper is to propose a statistical
methodology, Multi-Scenarios Parameters Estimation (MSPE), for the
parametrization of potentially complex mechanistic models from a
new type of datasets (climatic data, final yield in many situations).
It is tested with CORNFLO, a crop model for maize growth. On the other hand, the data-driven approach for yield prediction
is free of the complex biophysical process. But it has some strict
requirements about the dataset.
A second contribution of the paper is the comparison of these
model-driven methods with classical data-driven methods. For this
purpose, we consider two classes of regression methods, methods
derived from linear regression (Ridge and Lasso Regression, Principal
Components Regression or Partial Least Squares Regression) and
machine learning methods (Random Forest, k-Nearest Neighbor,
Artificial Neural Network and SVM regression).
The dataset consists of 720 records of corn yield at county scale
provided by the United States Department of Agriculture (USDA) and
the associated climatic data. A 5-folds cross-validation process and
two accuracy metrics: root mean square error of prediction(RMSEP),
mean absolute error of prediction(MAEP) were used to evaluate the
crop prediction capacity.
The results show that among the data-driven approaches, Random
Forest is the most robust and generally achieves the best prediction
error (MAEP 4.27%). It also outperforms our model-driven approach
(MAEP 6.11%). However, the method to calibrate the mechanistic
model from dataset easy to access offers several side-perspectives.
The mechanistic model can potentially help to underline the stresses
suffered by the crop or to identify the biological parameters of interest
for breeding purposes. For this reason, an interesting perspective is
to combine these two types of approaches.
Abstract: In this paper, we consider the parametrization of the
discrete-time systems without the unit-delay element within the
framework of the factorization approach. In the parametrization,
we investigate the number of required parameters. We consider
single-input single-output systems in this paper. By the investigation,
we find, on the discrete-time systems without the unit-delay element,
three cases that are (1) there exist plants which require only one
parameter and (2) two parameters, and (3) the number of parameters
is at most three.
Abstract: Matching an embedded electronic application with a
cantilever vibration energy harvester remains a difficult endeavour
due to the large number of factors influencing the output power.
In the presented work, complementary balanced energy harvester
parametrization is used as a methodology for simplification of
harvester integration in electronic applications. This is achieved
by a dual approach consisting of an adaptation of the general
parametrization methodology in conjunction with a straight forward
harvester benchmarking strategy. For this purpose, the design and
implementation of a suitable user friendly cantilever energy harvester
benchmarking platform is discussed. Its effectiveness is demonstrated
by applying the methodology to a commercially available Mide
V21BL vibration energy harvester, with excitation amplitude and
frequency as variables.
Abstract: This paper presents a technique for compact three
dimensional (3D) object model reconstruction using wavelet
networks. It consists to transform an input surface vertices
into signals,and uses wavelet network parameters for signal
approximations. To prove this, we use a wavelet network architecture
founded on several mother wavelet families. POLYnomials
WindOwed with Gaussians (POLYWOG) wavelet families are used
to maximize the probability to select the best wavelets which
ensure the good generalization of the network. To achieve a better
reconstruction, the network is trained several iterations to optimize the
wavelet network parameters until the error criterion is small enough.
Experimental results will shown that our proposed technique can
effectively reconstruct an irregular 3D object models when using
the optimized wavelet network parameters. We will prove that an
accurateness reconstruction depends on the best choice of the mother
wavelets.
Abstract: In this paper, we consider the two-stage compensator
designs of SISO plants. As an investigation of the characteristics of
the two-stage compensator designs, which is not well investigated
yet, of SISO plants, we implement three dimensional visualization
systems of output signals and optimization system for SISO plants by
the parametrization of stabilizing controllers based on the two-stage
compensator design. The system runs on Mathematica by using
“Three Dimensional Surface Plots,” so that the visualization can
be interactively manipulated by users. In this paper, we use the
discrete-time LTI system model. Even so, our approach is the
factorization approach, so that the result can be applied to many
linear models.
Abstract: The length of a given rational B'ezier curve is
efficiently estimated. Since a rational B'ezier function is nonlinear,
it is usually impossible to evaluate its length exactly. The
length is approximated by using subdivision and the accuracy
of the approximation n is investigated. In order to improve
the efficiency, adaptivity is used with some length estimator.
A rigorous theoretical analysis of the rate of convergence of
n to is given. The required number of subdivisions to
attain a prescribed accuracy is also analyzed. An application
to CAD parametrization is briefly described. Numerical results
are reported to supplement the theory.
Abstract: In this paper, we first show a relationship between two
stabilizing controllers, which presents an extended feedback system
using two stabilizing controllers. Then, we apply this relationship to
the two-stage compensator design. In this paper, we consider singleinput
single-output plants. On the other hand, we do not assume the
coprime factorizability of the model. Thus, the results of this paper
are based on the factorization approach only, so that they can be
applied to numerous linear systems.
Abstract: The excellent suitability of the externally excited synchronous
machine (EESM) in automotive traction drive applications
is justified by its high efficiency over the whole operation range and
the high availability of materials. Usually, maximum efficiency is
obtained by modelling each single loss and minimizing the sum of all
losses. As a result, the quality of the optimization highly depends on
the precision of the model. Moreover, it requires accurate knowledge
of the saturation dependent machine inductances. Therefore, the
present contribution proposes a method to minimize the overall losses
of a salient pole EESM and its inverter in steady state operation based
on measurement data only. Since this method does not require any
manufacturer data, it is well suited for an automated measurement
data evaluation and inverter parametrization. The field oriented control
(FOC) of an EESM provides three current components resp. three
degrees of freedom (DOF). An analytic minimization of the copper
losses in the stator and the rotor (assuming constant inductances) is
performed and serves as a first approximation of how to choose the
optimal current reference values. After a numeric offline minimization
of the overall losses based on measurement data the results are
compared to a control strategy that satisfies cos (ϕ) = 1.
Abstract: The two-stage compensator designs of linear system are
investigated in the framework of the factorization approach. First, we
give “full feedback" two-stage compensator design. Based on this
result, various types of the two-stage compensator designs with partial
feedbacks are derived.