# A Model for Working Environment Monitoring in Smart Manufacturing
## 3 Model of a System for Monitoring Working Environments in Smart Manufacturing
### Indoor Dust Control Simulation
In [39] an assessment of the envelope air permeability of the room was obtained by the infiltration rate calculated by an equation. In both [37; 38] the same linear interpolation formula was used in order to the calculate index value of pollutant. This formula included rounded pollutant concentration and pre-defined breakpoint values as standardized values. This suggests that it is possible to simulate pollution levels using a formula, which however does not have to include standardized pre-defined breakpoint values; which, are not strictly standardized, as these values vary in different countries. Furthermore, it is necessary to choose the exact method of simulating the levels of pollution and dispersion of particles (dust particles in this research). Particle propagation models or physical simulation of particle dispersion are not suitable for this research because of their complexity, but bilinear interpolation on the unit square [40; 41] is suitable in order to calculate dust concentration in any point (x,y) inside the square. Given four corner values (a,b,c,d), the dust-concentration value f(x,y) is calculated by:  
\[\text{f(x,y)}=\text{a(1}-\text{x)(1}-\text{y)}+\text{bx(1}-\text{y)}+\text{ dy(1}-\text{x)}+\text{cxy} \tag{1}\]  
where:  
* a is the dust-concentration value at (0,0), the lower left corner
* b is the dust-concentration value at (1,0), the lower right corner
* c is the dust-concentration value at (0,1), the upper left corner
* d is the dust-concentration value at (1,1), the upper right corner  
Now, by applying Equation (1) it is possible to simulate dust propagation in a plane (approximately 1.5 m above floor, this is the height of the sensor placement). The simulation was performed in three steps. The first step is to generate a large number of images representing various cases of dust particle distribution and classify them into four classes: none: neither vent is turned on, both: both vents are turned on, top-left: vent V1 is turned on, and bottom-down: vent V2 turns on. The second step involves determining the convolutional neural net (CNN) architecture, as well as its training. This step was performed using a tensorflow/keras environment (Jupyter Notebook). The third step involves the testing of the CNN on a test set. The situation when the sensors are placed only in the room corners corresponds to a case when there is no movement in the room (or it is negligible). This situation is shown in Figure 4a. The presence of dust particles is shown in shades of gray (dark: more particles, light: fewer particles). It has been established that this situation does not correspond to reality, due to the swirling of the air caused by the movement of employees who operate the machines and the possible opening of the door when entering and leaving the room. In reality, this room is mostly isolated from the rest of the plant to prevent the spread of dust particles. Figure 4b shows a case of interpolation using multiple sensors, as shown in Figure 4. This way of generating images representing dust particles is more in line with the real situation; it is now possible to represent pollution in separate sectors. Figure 4: Bilinear interpolation, (**a**) sensors in corners, (**b**) sensors on walls and corners. Synthetic data in the form of images were generated in the first step. A total of 616 images were generated, which were classified into four classes according to which went should be turned on: V1, V2, both, or none. First, the dust particle emitters values were randomly initialized, and then the readings on the sensors were selected so that they depend on the distances of the dust emitters, but some randomness was also included. In total, 1200 test images were synthesized in this step. Examples of synthesized training images for each class are shown in Figure 5.  
In the next step, a suitable artificial neural network architecture was chosen: a convolutional neural network (CNN) was chosen because this architecture recognizes the patterns that exist in the images very well [42; 43].