A Conceptual Artificial Neural Network Model in Warehouse Receiving Management

Abstract

The purpose of this research is to explore a suitable Artificial Neural Network (ANN) method applying to warehouse receiving management. A conceptual ANN model is proposed to perform identification and counting of components. The proposed model consists of a standard image library, an ANN system to present objects for identification from the real-time images and to count the number of objects in the image. The authors adopted four basic mechanical design shapes as the attributes of images for shape analysis and pre-defined features; the joint probability from Bayes theorem and image pixel values for object counting is applied in this research. Compared to other ANNs, the proposed conceptual model is straightforward to perform classification and counting. The model is tested by employing a mini image dataset which is industrial enterprise relevant. The initial result shows that the proposed model has achieved an accuracy rate of 80% in classification and a 97% accuracy rate in counting. The development of the model is associated with a few challenges, including exploring algorithms to enhance the accuracy rate for component identification and testing the model in a larger dataset.

Neural Network Model for Control of Operating Modes of Crushing and Grinding Complex

“…Artificial neural networks are used to build mathematical models of various processes, pattern recognition and signal prediction. Examples of using ANN to solve problems related to automated control are: the estimation of the spectral composition of the information signal and classification of signals for decision-making (Malisuwan et al 2016, Faek et al 2009, classification of non-stationary data to create automatic control systems (Venkatesan et al 2018), operational control of technological processes (Yang et al 2021). Thus, neural networks make it possible to effectively determine the mode of operation of the complex and highlight the influence of individual factors on the target function, reflecting only the existence between the input and output values of the objective relationships.…”