Search

In recent years, the technology of artificial intelligence (AI) and robots is rapidly spreading to countries around the world. More and more scholars and industry experts have proposed AI deep learning models and methods to solve human life problems and improve work efficiency. Modern people's lives are very busy, which led us to investigate whether the demand for Bento buffet cafeterias has gradually increased in Taiwan. However, when eating at a buffet in a cafeteria, people often encounter two problems. The first problem is that customers need to queue up to check out after they have selected and filled their dishes from the buffet. However, it always takes too much time waiting, especially at lunch or dinner time. The second problem is sometimes customers question the charges calculated by cafeteria staff, claiming they are too expensive at the checkout counter. Therefore, it is necessary to develop an AI-enabled checkout system. The AI-enabled self-checkout system will help the Bento buffet cafeterias reduce long lineups without the need to add additional workers. In this paper, we used computer vision and deep-learning technology to design and implement an AI-enabled checkout system for Bento buffet cafeterias. The prototype contains an angle steel shelf, a Kinect camera, a light source, and a desktop computer. Six baseline convolutional neural networks were applied for comparison on food recognition. In our experiments, there were 22 different food categories in a Bento buffet cafeteria employed. Experimental results show that the inception_v4 model can achieve the highest average validation accuracy of 99.11% on food recognition, but it requires the most training and recognition time. AlexNet model achieves a 94.5% accuracy and requires the least training time and recognition time. We propose a hierarchical approach with two stages to achieve good performance in both the recognition accuracy rate and the required training and recognition time. The approach is designed to perform the first step of identification and the second step of recognizing similar food images, respectively. Experimental results show that the proposed approach can achieve a 96.3% accuracy rate on our test dataset and required very little recognition time for input images. In addition, food volumes could be estimated using the depth images captured by the Kinect camera, and a framework of visual checkout system was successfully built. [ABSTRACT FROM AUTHOR]

The main cause of urban flooding is heavy rain. When the heavy rain exceeds the load of the river, drainage and sewer systems, the risk of urban flooding is relatively increased. The heavy rains and inundation in urban areas are often a few cases. In addition, there are uneven rainfalls in space and time. It is difficult to effectively control the inundation areas, and the disaster prevention unit often has insufficient early warning preparation time. Therefore, in urban flooding and disaster prevention, it is necessary to be able to obtain more time for early warning of disaster prevention, and to accurately understand the flooded area. Artificial intelligence methods can provide new options and utilization. In this paper, using the prepared hydrological, hydraulic, and inundation models, combined with the 6000 hydrological uncertainty analysis of the two-dimensional spatial rainfall data, to carry out the numerical simulation and storage of the urban inundation results data to form a big data database of heavy rain and urban flooding scenarios. After the big data database is used, the AI-CNNs deep learning method is used to analyze and store the rainfall characteristic parameters in the two-dimensional space to the AI-CNNs analysis results through the training and learning processes such as parameter training, pooling and ReLU. In the database, fast image calculation, query and feature comparison are provided. Finally, the actual case description of Taiwan in Taoyuan City and Nantunxi River Basin shows that after receiving the two-dimensional real-time and forecast rainfall data through the flood early warning system (FEWS) platform, the AI-CNNs urban flooding forecast is carried out, and the flooding result is converted into KML file. Display and use on the map platform, and then through the IoT (internet of things) equipment and CCTV real-time image data such as the surface water level sensor and image to predict the area where the city may be flooded, and provide early warning use. [ABSTRACT FROM AUTHOR]

In Taiwan, mushrooms are an agricultural product with high nutritional value and economic benefit. However, global warming and climate change have affected plant quality. As a result, technological greenhouses are replacing traditional tin houses as locations for mushroom planting. These greenhouses feature several complex parameters. If we can reduce the complexity such greenhouses and improve the efficiency of their production management using intelligent schemes, technological greenhouses could become the expert assistants of farmers. In this paper, the main goal of the developed system is to measure the mushroom size and to count the amount of mushrooms. According to the results of each measurement, the growth rate of the mushrooms can be estimated. The proposed system also records the data of the mushrooms and broadcasts them to the mobile phone of the farmer. This improves the effectiveness of the production management. The proposed system is based on the convolutional neural network of deep learning, which is used to localize the mushrooms in the image. A positioning correction method is also proposed to modify the localization result. The experiments show that the proposed system has a good performance concerning the image measurement of mushrooms. [ABSTRACT FROM AUTHOR]