Manufacturing line (ML) designers have understood that simulation studies can help to form a more reliable ML than conventional methods that for the most based upon engineering experiences. However, the use of simulation has not been applied much in designing the ML, and a confident method of designing a new ML or modifying the capacity of a current manufacturing line (CML) has remained a task in Japanese automobile manufacturing plants.The main purpose of this research is to propose a new perspective of a simulation study in Japanese automobile industry from an empirical point of view to implement the framework for designing a ML. The second purpose is to introduce the method and analytical procedures of modifying a CML utilized by a linear programming (LP) model for selecting alternatives for simulation study. The proposed method was applied in an actual design project to confirm the feasibility of the framework.
Facial behavior analysis is a broad topic with various categories such as facial emotion recognition, age and gender recognition, . . . . Many studies focus on individual tasks while the multi-task learning approach is still open and requires more research. In this paper, we present our solution and experiment result for the Multi-Task Learning challenge of the Affective Behavior Analysis in-the-wild competition. The challenge is a combination of three tasks: action unit detection, facial expression recognition and valance-arousal estimation. To address this challenge, we introduce a cross-attentive module to improve multi-task learning performance. Additionally, a facial graph is applied to capture the association among action units. As a result, we achieve the evaluation measure of 1.24 on the validation data provided by the organizers, which is better than the baseline result of 0.30.
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