SUMMARYUp to now, most people are still cooking in the kitchen, which makes them feel fatigued and also makes the air polluted. With the development of the numerical control technology, it becomes more and more urgent to apply the related technology to the automatic cooking field. In this paper, the cooking technique for the Chinese dishes is introduced and China's first cooking robot, named AI Cooking Robot (AIC), is presented. The robot mainly consists of four parts: the wok mechanism, the stirring-fry and dispersing mechanism, the feeding mechanism, and the mechanism of leaving the material in the middle process. In order to adjust the temperature, the fire control system is also given in this paper. Experiments show that the new robot will be a milestone in the cooking automation science because of its cooking technique.
Deep learning has been greatly improved recently, and natural image processing based on deep learning has also been greatly improved. However, there are still great differences between natural images and remote sensing images, among which the biggest is that the size of the target in remote sensing images is greatly different, which requires the model to have a strong multi-scale processing ability. In order to meet this goal, we use HRNet with full multi-scale fusion capability to replace ResNet to process remote sensing images. HRNet fully integrates low-level detail features, middle-level structure features and high-level semantic features, which is very suitable for remote sensing images. The experimental results show that our method has been greatly improved.
Students have different levels of motivation, approaches to learning, and intellectual levels. The better that instructors understand these differences, the better the chances they have of improving their quality of teaching. To explore differences thoroughly, we focuses on three crucial factors in student learning features – i.e., personality, learning style and multiple intelligences – and propose an approach effective in classifying students for the purpose of instructing instructors while optimizing their teaching process. We collected data on learning features from a class of 58 college students and analyzed these data by using principal component analysis (PCA) and then classified them using Ward clustering. Results of experiments indicate that our proposal effectively classifies students based on their learning features and that classification results facilitate instructors in creating personalized teaching strategies.
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