Design of an Artificial Neural Network (BPNN) to Predict the Content of Silicon Oxide (SiO2) based on the Values of the Rock Main Oxides: Glass Factory Feed Case Study

Artificial neural network (ANN) is one of the practical methods for prediction in various sciences. In this study, which was carried out on Glass and Crystal Factory in Isfahan, the amount of silica purification used in industry has been investigated according to its analyses. In this discussion, according to the artificial neural network algorithm back propagation neural network (BPNN), the amount of silica (SiO2) was predicted according to rock main oxides in chemical analysis. These studies can be used as a criterion for estimating the purity for use in the factory due to the high accuracy obtained.

A Design Method of the Transmitting Waveform of Electromagnetic Sounding Based on Selective Harmonic Elimination

The exploration target at different depths through the ground-airborne frequency domain electromagnetic method (GAFDEM) is detected by transmitting waveforms at different frequencies. When taking these different depths into detail, arbitrarily distributed frequencies are needed. However, the current transmitting waveforms are mostly in a fixed frequency ratio or frequency difference, which fails to meet the requirements of exploration accuracy and efficiency at the same time. Therefore, as a solution to this problem, this paper proposes a transmitting waveform design method based on selective harmonic eliminated pulse width modulation (SHEPWM) technology. In the SHEPWM method, three transmitting waveforms with the desired spectrum are obtained by directly controlling the switching angles of a binary sequence with an artificial neural network algorithm. Firstly, our study puts forward the basic theories and principles of the full-periodic asymmetric SHEPWM waveform. Secondly, the study, through simulation, realizes the pseudorandom, depth-focused, and layer-identification waveform with different detection depths. Finally, the application of the proposed SHEPWM waveform to the geological survey in Kaili City, Guizhou Province, proves the correctness and feasibility of this proposed method.

Cancer Cell Detection in Bone Using ANN

A vigorous disease is bone cancer results in deaths of many people. The identification and classification system must be done at its early stage to diagnose. The early detection plays an important role to safe guard the patient from death. And also cancer categorization is one of the toughest tasks in clinical analysis. This paper deals with MR images of various patients used to identify the tumor and classify cancer using Artificial Neural Network algorithm. The proposed methodology uses filtering as preprocessing techniques followed by gray conversion and other image processing methods like edge detection, morphological operation, segmentation, feature extraction and classification are prepared for the identification of bone cancer. By this method time required is reduced for identification and classification of bone cancer.

RESEARCH ON OPTIMIZATION OF AGRICULTURAL MACHINERY FAULT MONITORING SYSTEM BASED ON ARTIFICIAL NEURAL NETWORK ALGORITHM

Aiming at the demand of mileage statistics, work area statistics, fault site return and related data automatic retention in the current agricultural machinery reliability appraisal process, the optimization of agricultural machinery video monitoring system based on artificial neural network algorithm was studied. Together with the new video monitoring technology, the agricultural machinery GPS, GSM and fuel consumption recorder technology are combined to realize the functions of real-time data transmission, monitoring, analysis and statistics. Aiming at intelligent fault analysis, a real-time online detection mechanism is proposed, and a cloud collaborative detection mechanism is proposed to solve the problem of inaccurate offline model detection. Use plane map or satellite map to browse. Thus, an online monitoring and visual testing platform for agricultural machinery faults without real-time monitoring records is established. Finally, the test platform is tested and applied. Test results show that the algorithm can greatly shorten the training time and improve the accuracy of training model detection. With the increase of online training iterations, it is helpful to improve the detection accuracy of the generated model. In a word, the system service platform can provide scientific and transparent data for agricultural machinery fault identification, ensure the scientific, open and fair principles of agricultural machinery fault identification, and greatly improve the efficiency of agricultural machinery management.