Experiments have proven that traditional prediction research methods limitations in practice. Proposing countermeasures for environmental changes is the key to optimal control of cold chain environment and reducing lag effects. In this paper, a transportation method, combining k-means++ long short-term memory (LSTM) neural network, proposed according characteristics agricultural products. The m...

Abstract In this study, data fusion algorithm is used to classify the soil species and calibrate humidity sensor, by using edge computing a wireless sensor network, farmland environment monitoring system with two-stage calibration function of frequency domain reflectometer (FDR) established. Edge in nodes, including saturation value calculated hardness, calculation process neural model classifi...

This paper develops and explores applications of a linear shaping transformation that minimizes the mean squared error (MSE) between the original and shaped data, i.e., that results in an output vector with the desired covariance that is as close as possible to the input, in an MSE sense. Three applications of minimum MSE shaping are considered, specifically matched filter detection, multiuser ...

The traditional procedure of predicting the settling velocity a spherical particle is inconvenient as it involves iterations, complex correlations, and an unpredictable degree uncertainty. limitations can be addressed efficiently with artificial intelligence-based machine-learning algorithms (MLAs). limited number isolated studies conducted to date were constricted specific fluid rheology, part...

Let observations y1, · · · , yn be generated from a first-order autoregressive (AR) model with positive errors. In both the stationary and unit root cases, we derive moment bounds and limiting distributions of an extreme value estimator, ρ̂n, of the AR coefficient. These results enable us to provide asymptotic expressions for the mean squared error (MSE) of ρ̂n and the mean squared prediction err...

Mean Squared Error (MSE) has been the most widely used tool to solve the linear filter estimation or system identification problem. However, MSE gives biased results when the input signals are noisy. This paper presents a novel Error Whitening Criterion (EWC) to tackle the problem of linear system identification in the presence of additive white disturbances. We will motivate the theory behind ...

The problem of accurately estimating the Mean Squared Error (MSE) of Small Area Estimators (SAE’s) within a Fay-Herriot (1979) normal-error model is studied theoretically in the common setting where the model is fitted to a logarithmically transformed response variable. For bias-corrected EBLUP small-area point estimators, MSE formulas and estimators are provided, with biases of smaller order t...