## 2 Existing Research
### Data Analysis on Mating Connector  
According to [PERSON] [13], the probabilistic approach for the mating process indicates the connector's analysis at different states and what a connector encounters during the process of mating. At the preliminary stage, it utilized the method from [PERSON] [14] that all the connectors produced visual, audible, and force signals during mating and used them for the analysis by using logistic regression. [PERSON] [15] chose a fuzzy pattern match to detect and diagnose failures during the mating process. The speed also affects the mating operation, as the insertion rate directly influences the peak of the force in the mating process [16]. Approximately all research utilizes a plug-in connector with PINs varying between 4 and 8 pins.  
Presently, reinforcement learning (RL) is also extensively discussed in the assembly system, especially the peg-in-hole (PIH) method. The PIH approach generally differs in search and insertion [17]. [PERSON] [18] proposed fuzzy logic-driven variable time scale prediction-based reinforcement learning to improve the efficiency of a reinforcement learning algorithm in the PIH assembly process, which successfully decreases the assembly time by about 44% compared to the Deep-Q Learning approach [19]. Then, [PERSON] [20] proposed meta-reinforcement learning to solve the PIH approach in five experiments, namely (1) straight downwards; (2) random search; (3) spiral search; (4) RL-PEARL from scratch; and (5) PEARL Sim2 Real. The result indicated that their method could solve the PIH approach in less than 20 trials. Similarly, [PERSON] [21] initiated research on the PIH approach using generative adversarial imitation learning (GAIL) to help the robot learn within 20 episodes of human expert demonstration. The approach could improve the insertion time by five seconds compared to the standard operation.  
In linear research about reinforcement learning, the study discusses the assembly process by imitating human behavior, still a hot topic in this field. [PERSON] [22] solved the peg-in-hole task by human demonstration using exception strategies. Moreover, [PERSON] and [PERSON] [23; 24] proposed learning compliance for assembly motion demonstration. The key idea of these methods is to learn from human demonstration and exploration for the distribution in their dynamic model, as [PERSON] mentioned in his research [25].  
As a result, the inspiration for using the encoding method of our data was inspired by [PERSON] and [PERSON] [26; 27; 28], which applied Gramian angular field (GAF) and Markov transition field (MTF) on the time series data. Furthermore, [PERSON] [29] tried to classify the time-series images using a deep convolutional neural network. However, in this study, we propose a data-driven approach method based on a fine pitch 0.4 mm connector with a 20 pin connector to predict the best location within a small area of insertion. Figure 2 illustrates a plug-in connector whose size is significantly more prominent with a simplified design than the standard BtB connector. The fine pitch BtB connector is an optimized interconnect solution for a smaller and thinner electronics product [30]. It originates for compact and high-density packaging electronics devices. The following section will explain each fundamental theory related to our research, including the various deep learning techniques utilized on our dataset for the prediction to assemble the board-to-board connector.