## 3 PLC Production Line Signal and IoT Connection
### Implementation of Smart-Production Configuration in the Manufacturing Plant

Table 1: IoT method for production line rearrangement. trained using back-propagation algorithms. Thus, for other methods, feed-forward neural networks and CNNs are necessary to consider certain parameters. The CNN yielded an attractive deep-learning structure in this study. Some related structures are listed below. * Convolutional layer. By using a small kernel, the entire image and the intermediate feature map can be convolved. This step allows the kernel to learn the functions in the image. The convolutional layer is a set of parallel feature maps that are composed of different sliding convolution kernels on the input image and perform certain operations. In addition, an element-corresponding product and summation operation at each sliding position are performed between the convolution kernel and the input image, which sets the project information in the receptive field to an element in the feature map. * Pooling layer. This layer typically follows one or several convolutional layers to reduce the size of the feature map and the risk of overfitting. The pooling layer is another important concept in CNNs, which is a nonlinear form of down sampling. There are many different forms of nonlinear pooling functions, and max pooling is the most common. It divides the input image into several rectangular regions and outputs the maximum value for each sub region. * Fully connected layer. The final connected layer uses one or several convolution and pooling layers, and the 2D feature maps are converted into 1D vectors for classification or other processes. After several convolution and max-pooling layers, advanced reasoning in the neural network is completed by the fully connected layers. This is like a conventional non-convolutional artificial neural network, and the neurons in the fully connected layer are connected to all activations in the previous layer. Therefore, their activation could be calculated as an affine transformation. The result was first calculated by multiplying it by a matrix. Subsequently, a bias offset was added to shift the values. * Total connection layer. For our application concept, CNN (machine learning) is widely used as a common image-recognition system. The image-recognition problem is much more difficult in video analysis than in still images. CNNs are often used to solve such problems. There is also natural language processing, for which CNNs are often used. The CNN model has been proven to be able to effectively deal with various natural-language-processing problems, such as semantic analysis, search result extraction, and sentence modelling. * CNN algorithm. Figure 4: The proposed architecture for CNN algorithm. Image recognition is highly challenging in smart factories. A high recognition performance is proposed, which is faced with the effects of changes in non-linear parameters, such as changing illumination, posture, facial expressions, and occlusion. This study proposes a four-layer convolutional neural network architecture. The CNN is proposed as a solution to this problem which includes face and morphological recognition of production artefacts of a smart factory. This study establishes an applicable method which has more than 1000 image recognition problems. A partial connection was introduced between the first two layers to ensure that different functions were learned during the training process. The results of the proposed CNN method. The general method requires two stages to perform convolution and subsampling, whereas the fusion method requires only one stage. The artificial neural network training process was as follows. The artificial neural network model is a multi-layer perceptron (MLP), in which a feedforward neural network is used to map the input dataset to a set of appropriate outputs. MLP is characterized by the LC2 layer of neurons (input layer, hidden layer L, and output layer), which has a non-linear activation function at the hidden layer unit.