## 1 Introduction  
Continuous casting is one of the most common methods of producing metal products. This technique involves pouring liquid metal into a mold and continuously withdrawing the solidified product from the other end. The product can be a billet, a bloom, or a slab, depending on its shape and size. Continuous casting reduces the need for intermediate steps and saves energy and material costs [1]. It is the most frequently used process to cast steel, aluminum, and copper alloys. Continuous casting involves several stages [2]: ladle treatments, tundish operation, mold filling and solidification, secondary cooling, strand support, cutting, and straightening. Each step requires careful monitoring and control of various parameters, such as temperature [3], pressure, flow rate, composition, liquid level, drawing speed, etc. Moreover, continuous casting is influenced by many factors that are difficult to measure or model accurately, for example, turbulence and mixing phenomena in the molten metal, heat transfer across different interfaces (metal-mold-water-air), phase transformations, microstructural evolution during solidification, thermal stresses, strains induced by temperature gradients, and metallurgical reactions between metal and slag or refractory materials, etc. The current continuous casting process also involves two phases, i.e., manual control and automatic control. Once the molten steel in the mold reaches a certain level, the casting machine will be activated, and the continuous casting goes into the automatic control phase from the manual control phase. But during the manual control phase, the casting operator must adjust the stopper position by manually observing the liquid level in the mold. The anomalous detection of liquid level in molds is a crucial technique for improving the quality of steel products in continuous casting. The fluctuation of the liquid level in the mold is closely related to the casting speed, depth, condition of the Submerged Entry Nozzle (SEN), and argon gas injection [4]. Excessive fluctuations in the liquid level in the mold can cause slag entrainment, which leads to inclusions and surface defects on the cast billet. Therefore, monitoring and controlling the liquid level in the mold during casting is vital to ensuring a stable and uniform solidification process [5]. However, conventional methods such as eddy current sensors have limitations in detecting local fluctuations or capturing dynamic changes of the liquid level in mold, and current deep-learning-based techniques are heavily focused on the automatic control phase. The temporal features of the liquid level in the manual and automatic control phases are different. The steel liquid enters the mold during the manual control phase and does not leave. Once the continuous casting machine is activated, the drawing machine starts drawing solidified steel out of the mold. During the manual control phase, if an anomaly occurs, such as an open error stopper, it may cause the liquid level in the mold to be higher or lower than it is supposed to be. To eliminate the anomaly, the casting operator must manually identify the liquid level anomaly and operate the stopper to eliminate the error. Due to the limitations of manual control, the error in liquid level during manual control lasts longer. Due to different casting requirements, the abnormal stopper sequence can be correctly applied under another requirement or in different casting periods. Therefore, the temporal features between anomaly and normal sequences can share similar features that AEs can mistake. Therefore, advanced techniques based on deep learning are proposed to detect various types of anomalies in time-dependent process parameters and provide timely feedback for quality control. Figure 1 shows the required components in a continuous casting process. The liquid level in the mold is controlled by both the stopper and the withdrawal unit. Figure 1: Continuous casting process. 1: Ladle. 2: Stopper. 3: Tundish. 4: Shroud. 5: Mold. 6: Roll support. 7: Turning zone.