Chun‐Chi Wang

Po-Heng Chou, Chun-Chi Wang, Wei-Lung Mao

In this paper, we propose a YOLO-based deep learning (DL) model for automatic defect detection to solve the time-consuming and labor-intensive tasks in industrial manufacturing. In our experiments, the images of metal sheets are used as the dataset for training the YOLO model to detect the defects on the surfaces and in the holes of metal sheets. However, the lack of metal sheet images significantly degrades the performance of detection accuracy. To address this issue, the ConSinGAN is used to generate a considerable amount of data. Four versions of the YOLO model (i.e., YOLOv3, v4, v7, and v9) are combined with the ConSinGAN for data augmentation. The proposed YOLOv9 model with ConSinGAN outperforms the other YOLO models with an accuracy of 91.3%, and a detection time of 146 ms. The proposed YOLOv9 model is integrated into manufacturing hardware and a supervisory control and data acquisition (SCADA) system to establish a practical automated optical inspection (AOI) system. Additionally, the proposed automated defect detection is easily applied to other components in industrial manufacturing.

Wei-Lung Mao, Chun-Chi Wang, Po-Heng Chou et al.

The rising aging population has increased the importance of fall detection (FD) systems as an assistive technology, where deep learning techniques are widely applied to enhance accuracy. FD systems typically use edge devices (EDs) worn by individuals to collect real-time data, which are transmitted to a cloud center (CC) or processed locally. However, this architecture faces challenges such as a limited ED model size and data transmission latency to the CC. Mobile edge computing (MEC), which allows computations at MEC servers deployed between EDs and CC, has been explored to address these challenges. We propose a multilayer MEC (MLMEC) framework to balance accuracy and latency. The MLMEC splits the architecture into stations, each with a neural network model. If front-end equipment cannot detect falls reliably, data are transmitted to a station with more robust back-end computing. The knowledge distillation (KD) approach was employed to improve front-end detection accuracy by allowing high-power back-end stations to provide additional learning experiences, enhancing precision while reducing latency and processing loads. Simulation results demonstrate that the KD approach improved accuracy by 11.65% on the SisFall dataset and 2.78% on the FallAllD dataset. The MLMEC with KD also reduced the data latency rate by 54.15% on the FallAllD dataset and 46.67% on the SisFall dataset compared to the MLMEC without KD. In summary, the MLMEC FD system exhibits improved accuracy and reduced latency.

Chun-Chi Wang, He-Zhi Liu, Rui-Yuan Lin et al.

In this paper, we describe features of two new robot prototypes that are actuated by an actively controlled gyro (flywheel, symmetric rotor) inside a hollow sphere that is located in the middle of the robots. No external actuators are used. The outside structure of the robots and the gyro are connected by a gimbal, which is similar in structure to a control moment gyroscope in spacecrafts. The joints of the gimbal can be actuated. In this way, the orientation of axis for the gyro in relation to the egg can be changed. Since the inertia of the fast rotating gyro is large in relation to the outside structure, a relative rotation of the axis against the outside structure results in a motion of the egg by inertia principle. In this way, we can use this principle for controlling the robot to move forward and turn around. The robots are shaped as spheroidal ellipsoids so they resemble eggs. So far, we have built and tested two robot prototypes.