In aquaculture, leaving dead fish that have sunk to the bottom of the net for a long time causes harmful components from the dead fish body. This risk making other fish in the aquaculture net sick. The early collection of dead fishes is required, but when divers work in the sea, high water pressure and low water temperatures make it difficult for them to work for a long time, and the work efficiency of the entire aquaculture facility is reduced due to the need for manpower. To protect aquaculture fishes from disease, it is useful to install devices and robots to detect and collect the dead fish at an early stage. If the target is small fishes, there is a pumping method. But if the target is medium to large fishes, it is not reasonable. Therefore, we devised a system for collecting dead fishes using AUV(autonomous underwater vehicle) and wire. This report describes the configuration and operation of this system, and conducts simple experiments to confirm its feasibility.

The Tiger Pufferfish (Takifugu rubripes) is a staple in Japanese cuisine, with over ten species of the Takifugu genus found in the surrounding seas. Given that certain parts of the pufferfish are toxic, they are predominantly prepared by trained professionals. Furthermore, species within the Takifugu genus are susceptible to hybridization, leading to an increase in hybrid numbers. However, identifying these hybrids is a challenging and time-consuming task, even for experts. To address this, we developed a transfer learning model using the pretrained VGG16 model to differentiate between pufferfish species. The VGG16 model, commonly used in image recognition, is built on convolutional neural networks. We also implemented Gradient-weighted Class Activation Mapping (Grad-CAM) for visual interpretation of the model. Grad-CAM generates a heat map that highlights the areas focused on by the AI model in the image, allowing us to identify factors contributing to misjudgment and make further improvements. We used seven species from the Takifugu genus (excluding hybrids), and approximately 15 colored images of each species were prepared for machine learning. The results showed that our model was able to distinguish between pufferfish species with relatively high accuracy, although some misclassification occurred among species with similar body patterns. The Grad-CAM results revealed that the model was able to distinguish body patterns, but some misclassifications occurred due to gravel and background objects being recognized as patterns.

Since the construction of class timetables in the university requires a great deal of effort and time, many studies have been conducted in Japan and abroad on the automatic construction of class timetables. Similarly, at the National Fisheries University, labor and time are devoted to the creation of class timetables. Therefore, author aims to conduct basic research on automatic timetable construction with the simulated annealing method in this study. This paper describes the proposed method and algorithm for generating timetables using the simulated annealing method. The generated timetables by the proposed method are also reported.