In order to forecast quickly the operating condition of the loom, optimize the parameters of loom production, so that the production efficiency of loom will be improved. This paper studies the prediction of the operating condition of the loom based on the neural networks. The neural networks technology is applied to forecast the operating condition of the loom production, establishes corresponding prediction model of loom production. With the help of neural networks samples are trained and checked, then are applied to forecast the operating condition of the loom production, the results are compared with the Bayesian theorem. The study indicates that network model based on the neural networks has reliability and high accuracy.
How to quickly and accurately identify the quality of grain seed, grain type and other basic indicators and improve the efficiency of breeding quality, wheat production and quality in the work of selection or breeding have become a serious problem. The project developed the necessary software and hardware detection device platform, carried out research on the segmentation and counting algorithms of particle images for the system, and designed and implemented image processing techniques based on particle quality grain seed of the determination system. The result of the research has broad application prospects.
The cloud computing platforms are being deployed nowadays for resource scheduling of real time data intensive applications. Cloud computing still deals with the challenge of time oriented effective scheduling for resource allocation, while striving to provide the efficient quality of service. This article proposes a time prioritization-based ensemble resource management and Ant Colony based optimization (ERM-ACO) algorithm in order to aid effective resource allocation and scheduling mechanism which specifically deals with the task group feasibility, assessing and selecting the computing and the storage resources required to perform specific tasks. The research outcomes are obtained in terms of time-effective demand fulfillment rate, average response time as well as resource utilization time considering various grouping mechanisms based on data arrival intensity consideration. The proposed framework when compared to the present state-of-the-art methods, optimal fitness percentage of 98% is observed signifying the feasible outcomes for real-time scenarios.
For the shortage of artificial methods in classification of apple level, a method of classification of apple level based on BP artificial neural networks is presented. Artificial neural network provides technical means for research on apple classification, because it can provide nonlinear mapping of the input vector and output vector with arbitrary dimension, and also can reach any nonlinear continuous system. The BP neural network model is established for the classification by taking feature parameters as input vectors and apple level as output vectors. The results has shown that the classification precision of model is very high, and which has good application in realizing automatic identification of apple level.
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