Many human movements can be aided by exo-suit. One of them is that humans put a lot of strain on their knee and waist joints while lifting large objects, but using an exo-suit can lessen the risk. However, since the weight of the exo-suit itself acts as an additional burden on body, an appropriate torque distribution strategy considering the entire system is necessary. To solve this problem, this paper proposed an assistive technique based on whole-body control. Meanwhile, when the legs are fully extended during torque control, the system has a singularity problem and the required torque will be highly increased. Singularity is serious problem because it is essential to fully straighten the legs during the lifting operation. In this paper, this problem was solved by adding a straight-leg term to the whole-body control cost function. The feasibility of the proposed method was verified through simulation, and it was shown that the exo-suit could stably perform lifting motions due to the method.

Recently, in-depth studies on sensors of autonomous vehicles have been conducted. In particular, the trend to pursue only camera-based autonomous driving is progressing. Studies on object detection using IR (Infrared) cameras is essential in overcoming the limitations of the VIS (Visible) camera environment. Deep learning-based object detection technology requires sufficient data, and data augmentation can make the object detection network more robust and improve performance. In this paper, a method to increase the performance of object detection by generating and learning a high-resolution image of an infrared dataset, based on a data augmentation method based on a Generative Adversarial Network (GAN) was studied. We collected data from VIS and IR cameras under severe conditions such as snowfall, fog, and heavy rain. The infrared data images from KAIST were used for data learning and verification. We confirmed that the proposed data augmentation method improved the object detection performance, by applying generated dataset to various object detection networks. Based on the study results, we plan on developing object detection technology using only cameras, by creating IR datasets from numerous VIS camera data to be secured in the future and fusion with VIS cameras.

In this paper, chip-on-glass(COG) interconnection with anisotropic conductive film(ACF) using lateral thermosonic bonding technology is considered. In general, thermo-compression bonding which is used in practice for flip-chip bonding suffers from the low productivity due to the long bonding time. It will be shown that the bonding time can be improved by using lateral thermosonic bonding in which lateral ultrasonic vibration together with thermo-compression is utilized. By measuring the internal temperature of ACF, the fast curing of ACF thanks to lateral ultrasonic vibration will be verified. Moreover, to prove the reliability of the lateral thermosonic bonding, observation of pressured mark by conductive particles, shear test, and water absorption test will be conducted.

Generally a plastic injection molding is a manufacturing process used to produce the various parts of complicated shape at low cost. The objective of this study is to implement a new plastic injection molding process with inserted Aluminum sheet, which is highly durable, light and luminous. Moldflow analysis and simulation of plastic injection molding process with inserted Aluminum sheet were carried out in order to predict optimal molding operation conditions. The experimental results in the AI-inserted plastic injection molding process were compared with the simulation results by Moldflow. Durability and reliability test results for trial products were satisfied to adopt the AI-inserted plastic injection molding process developed as manufacturing of automobile interior parts.