# Deep Neural Network for Predicting Ore Production by Truck-Haulage Systems in Open-Fit Mines
## 3 Methods
### Data Preparation for DNN Model  
Training data for the proposed DNN model were generated by processing approximately 35.7 million data packets obtained from the mine-safety-management system. The processing was performed over two months--December 2018 and January 2019. The results of packet-data analysis revealed that the morning operation progressed from 8:30 a.m. (relative time: 0 min) to 12:00 p.m. (relative time: 210 min), whereas afternoon operations lasted from 1:00 p.m. (relative time: 0 min) to 4:30 p.m. (relative time: 210 min). The data-generation process employed in this study is explained as follows (refer to Figure 6):  
1. First, all incident packet data were preprocessed. Subsequently, packet data sent from the third wireless AP (AP3) were extracted along with truck-tag recognition data. All hexadecimal values were converted to decimal. 2. Packet data recorded during valid operation time intervals were subsequently sampled. Operation time intervals were set to 30, 60, 90, 120, 150, 180, and 210 min with incremental shifts of 2-min each to consider probable cases covering the period from 0 to 210 min. For example, if the operation time interval equaled 30 min, 91 probable cases, such as 0-30, 2-32, 4-34, and 180-210 min, can be considered during the morning session. 3. Extracted packet data were classified according to the truck-tag ID, and dumping-zone utilization of the trucks was calculated. The dumping-zone utilization of zone A by 45-ton trucks was calculated as the ratio of the number of dumping-zone-A visits to the sum of dumping-zone-A and dumping-zone-B visits (refer to Equation (4)). If trucks remained inside a dumping zone for more than 1 min (can be calculated by comparing the latitude and longitude coordinates of a truck recorded as packet data with dumping-zone coordinates), the number of dumping-zone visits made by a truck was increased by one. In Equation (4), \(\mathrm{U(DA)_{45}}\) and \(\mathrm{U(DB)_{45}}\) denote dumping zone A and B utilizations, respectively, by the 45-ton trucks, whereas \(\mathrm{NV(DA)_{45}}\) and \(\mathrm{NV(DB)_{45}}\) denote the number of visits made by 45-ton trucks to dumping zones A and B, respectively. 4. The average stay and travel times of trucks inside and outside the dumping zone, respectively, were also calculated. Equation (5) calculates the average stay time (AST(DA)) of all trucks inside dumping zone A. Here, \(\mathrm{TST(DA)_{45}}\), \(\mathrm{TST(DA)_{60}}\), and \(\mathrm{TST(DA)_{84}}\) represent the sum of the dumping zone A stay times corresponding to the 45-, 60-, and 84-ton trucks, respectively. Equation (6) calculates the average travel time (ATT(DA)) of all trucks using dumping zone \begin{table}
\begin{tabular}{c c c} \hline \hline
**ID** & **Type** & **Unit** \\ \hline  
ravel time of trucks using dumping zone A & Min \\
19 & Average travel time of trucks using dumping zone B & Min \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Description of input nodes in the proposed DNN model. A. In this equation, \(\mathrm{TTT(DA)_{45}}\), \(\mathrm{TTT(DA)_{60}}\), and \(\mathrm{TTT(DA)_{84}}\) represent the sum of travel times corresponding to the 45-, 60-, and 84-ton trucks, respectively, using dumping zone A. 5. The amount of ore produced during a given operation time was calculated by multiplying the loading capacity of each truck with the number of visits it made to the dumping zone (refer to Equation (7)). 6. Finally, all calculated values were saved in a training-data format, the next operation-time interval was set, and the above process was repeated 2-5 times.