## 2 Related Works
### A Design of an Optimized Deep Learning Module (ODLM)
#### 3.3.2 A Design of TDS  
The TDS is a Lightweight Neural Network Module that diagnoses a vehicle's total condition by entering each diagnosed result of the autonomous vehicle parts computed by the VPDS. The TDS of Figure 8 shows the Multiple Output Perceptron that sets the weights of the input nodes differently. That is, it shows a Lightweight Neural Network with a Step Function that has a critical value 0 as an Activation Function. For example, Figure 8 shows that the TDS judges total condition by integratingeach analysis result of an engine, a tire, a headlight, and a generator etc. If the analysis result of the TDS is (1, 1, 0), it judges the total diagnosis result as \"Check\". A Learning Method of the TDS  
The TDS should set the learning method. The following Algorithm 1 is the Learning Algorithm of the TDS and works as follows. ```
TDS_Learning(VPDS Output, W[], learning rate, Training Data Set)| \(k<-1\) while(true) { new-W < W} for 1 to the number of the Training Data Sets{  NET <- VPDS Output * new-W  TDS-Output <- Step Function(NET) if (TDS-Output == Training Data.out) { } else {  new-W <- learning rate * VPDS Output * (Training Data.out-Output) } } if (W == new-W) then End else { \(k<-k+1\) \(\mathcal{T}\mathcal{W}\) <- new-W } }
```  
**Algorithm 1**TDS learning algorithm  
* First, the TDS uses the VPDS output, the initial value of weight W[], the learning rate and the Training Data. * Second, if the learning gets started, an existing weight W is assigned to the new-W.
* Third, the fourth and fifth phases are repeated as large as the number of the Training Data Sets is. * Fourth the VPDS Output value multiplied by new-W is stored in the NET. Then, the result of a Step Function for NET is stored in the TDS-Output  
Figure 8: The example of the Total Diagnosis Sub-module (TDS) perceptron operation. * Fifth, if the TDS-Output is the same as the output of the learning data, the weight is not modified. If the TDS-Output is different from the output of the learning data, TDS modifies weight and stores it in the new-W.
* Sixth, if to repeat the learning is over, the existing weight W compares the new-W. If the W is the same as the new-W, the learning is completed. If the existing W is different from the new-W, the learning cycle is increased by 1 and is repeated. The Operational Process of TDS  
A vehicle's total condition is judged as follows by using the TDS. * The 1 st Phase: The TDS uses as input data a vehicle's part analysis result of the VPDS. * The 2 nd Phase: The TDS computes a Output Node value by learning the input data. At this time, the Output Nodes consists of y1, y2, and y3. If the Output node y1 is 1, it becomes \"Normality\". If the Output Node y2 is 1, it becomes \"Check\". If the Output node y3 is 1, it becomes \"Danger\". * The 3 rd Phase : The TDS judges a vehicle's total condition according to an Output Node value as shown in the Table 2. At this time, a vehicle's total condition is classified into \"Normality\", \"Check\", \"Danger\", and \"Error\". \"Normality\" means that a vehicle has no problem at all in the total condition. \"Check\" means that abnormality happened in some parts with a low possibility of an accident. \"Danger\" means that vehicle breakdown can happen in some parts with a high possibility of an accident. \"Error\" means that the condition of a vehicle cannot be judged. The TDS decides that the total condition of a vehicle is \"Normality\" if y1 is \"1\", y2 is \"0\", and y3 is \"0\". The TDS decides that the total condition of a vehicle is \"Check\" if y1 is \"1\", y2 is \"1\" and y3 is \"0\" and that the total condition of a vehicle is \"Danger\" if y1 is \"0\", y2 is \"1\" and y3 is \"1\". The TDS decides that the total condition of a vehicle is \"Error\" if y1, y2, and y3 are all \"1\" and that the total condition of a vehicle is \"Error\" if y1 is \"1\", y2 is \"0\" and y3 is \"1\". * The 4 th Phase : The TDS informs a vehicle manager of it in case a total condition of a vehicle is \"Check\" or \"Danger\".