Ting-Hsuan Chien

In recent years, bilingual education has become a critical index, and with its growth, internationalization has been significantly improved in the Republic of China (Taiwan). Therefore, countries worldwide are promoting all-English teaching, taking English as a medium of instruction and indicator of university education while moving toward an internationalized curriculum and teaching excellence. Finding the required number of bilingual teachers is also one of the keys to increasing the index. If information technology can be adopted to reduce the time and cost of bilingual teacher training, it will significantly improve the effectiveness of bilingual education promotion. However, the current traditional online training system only focuses on the delivery of one-way training content, and it cannot assist in judging the training effectiveness of the trainees. In this paper, we integrate the pose estimation technology into the online teacher training system to analyze the interactive content of the trainees during the training. We can assist in recording and interpreting the teaching demonstration process in classroom observation. We also verify the result by comparing our method’s efficiency and judgment accuracy with the traditional way. The results show that our approach is more convenient and cost-efficient.

At present, the global COVID-19 epidemic has not slowed down. To reduce the contact between people during the epidemic and prevent the epidemic from expanding, we have developed a robot to assist medical staff in patient guidance and communication services. The robot can provide an emergency contact so that users can immediately contact the counter for help. The user does not have face-face contact with the medical staff. When the robot encounters obstacles in the path of travel, the detected event and the time of occurrence are sent back to the back-end system. It also provides security personnel with real-time images and robot control rights to understand the situation and deal with it in real-time.

As the growth of the Internet has become very rapid, telemedicine can be performed efficiently. One important issue of telemedicine is nutrition recommendations for patients who live in long-distance areas far away from hospitals. Diet imbalance of people has become a very serious issue that the occurrences of obesity, metabolic syndrome, diabetes, and even cancer have raised. However, according to the data of the Health and Welfare Department, there are only 1663 dietitians in all hospitals in Taiwan in 2015. Undoubtedly it is a significant loading burden for these few dietitians. Therefore, this proposal aims to design and develop a virtual nutritionist and provide effective diet. Our data has established a complete database based on the recommendations of nutritionists. The database contains most of the food types. The data is trained using the model framework we have established based on the past analysis results of nutritionists, and the items and quantities that users must eat in each time period are accurately recommended and it include breakfast, lunch, and dinner. In the final stage, the training process can clearly show that the model is accurately trained, and the generated menu can be compared with the nutritionist to have good results. Based on the results of the nutritional assessment, the virtual nutritionists will provide each patient with good dietary advice and dietary guardians. The goal is to (1) assist nutritionists in nutritional screening, thereby saving time and energy; (2) inquiring unlimited nutritional information; (3) allowing people to experience advanced medical services and quality.

During the COVID-19 epidemic, most programming courses were revised to distance learning. However, many problems occurred, such as students pretending to be actively learning while actually being absent and students engaging in plagiarism. In most existing systems, obtaining status updates on the progress of a student’s learning is hard. In this paper, we first define the term “class loyalty”, which means that a student studies hard and is willing to learn without using any tricks. Then, we propose a novel method combined with the parsing trees of program codes and the fuzzy membership function to detect plagiarism. Additionally, the fuzzy membership functions combined with a convolution neural network (CNN) are used to predict which students obtain high scores and high class loyalty. Two hundred and twenty-six students were involved in the experiments. The dataset was randomly separated into the training datasets and the test datasets for twenty runs. The average accuracies of the experiment in predicting which students obtain high scores using the fuzzy membership function combined with a CNN and using the duration and number of actions are 93.34% and 92.62%. The average accuracies of the experiment in predicting which students have high class loyalty are 95.00% and 92.74%. Both experiments show that our proposed method not only can detect plagiarism but also can be used to detect which students are diligent.

This paper describes a real-time simulator that allows the user in the factories to simulate arbitrary interaction between machinery and equipment. We discussed in details not only the general technique for developing such a real-time simulator but also the implementation of the simulator in its actual use. As such, people on the production line could benefit from observing and controlling robots in factories for preventing or reducing the severity of a collision, using the proposed simulator and its related technique. For that purpose, we divided the simulator into two main models: the real-time communication model and the simulation model. For the communication model, we present the distributed messaging server structure and the database semaphore technique for handling the real-time peer-to-peer (P2P) connections and control. For the simulation model, we used parallel programming and the general-purpose GPU (GPGPU) technique to speed up the processing time of the simulation. In the paper, we exhaustively explain how the simulator’s techniques are designed and implemented and verify two real world models with robots and robot-operating systems (ROS). Moreover, we compare the results between our technique and the traditional method.

Since the rapid growth of the Fourth Industrial Revolution (or Industry 4.0), robots have been widely used in many applications. In the catering industry, robots are used to replace people to do routine jobs. Because meal is an important part of the catering industry, we aim to design and develop a robot to deliver meals for saving cost and improving a restaurant’s performance in this paper. However, for the existing meal delivery system, the guests must make their meals by themselves. To let the food delivery system become more user-friendly, we integrate an automatic guided vehicle (AGV) and a robotic arm to deliver a meal and put it on a table efficiently. The system consists of four phases. In the first stage, since most food feeding environments are indoors, we must build a map of an environment for a robot and use a laser sensor to construct a surrounding object’s real science. The second stage is global planning. The map is used for indoor navigation by computing the shortest path from the starting point to the map’s end point. The third phase is regional planning for obstacle avoidance, immediate avoidance, and re-planning of obstacles on the path. The last stage is how to control a robot arm to grab and put a meal on a table. We use an icon as the hint of the height of a table and operate the arm rotation angle according to this icon’s position. Our system provides the concept of an AGV combined with a robotic arm and provides a service for the food delivery system. In this way, waiters do not spend time dealing with meals and guests can automatically obtain their meals. The result shows that the proposed system will be helpful for the robot industry and the catering industry.

With the rapid growth of the Fourth Industrial Revolution (or Industry 4.0), five-axis machining has played an important role nowadays. Due to the expensive cost of five-axis machining, how to solve the collision detection for five-axis machining in real-time is very critical. In this paper, we present a parallel method to detect collision for five-axis machining. Moreover, we apply the bounding volume hierarchy technique with two-level bounding volume represent the surface or solid of the object to reduce triangle meshes inside each axis of the five-axis machine tool, and then matching the operating range limit of the five-axis machine tool itself, delete the no colliding triangle mesh. Additionally, we also propose some optimization with loop unrolling and prefetching techniques to improve performance of collision detection. Our approach can reduce the execution time significantly by computing six separating axes in plan and eleven separating axis in non-plan between two triangle meshes based on the characteristic of GPUs (Graphics Processing Units) for program acceleration. Our proposed work consists of kinematic analysis and interpolation for axes to save the numerous collision detection for five-axis machining computations. In this experiment, the result shows that using the proposed approach above can achieve approximately 37.1 times speedup than that of CPU.