This paper treats the development of radiation therapy operation training systems. For the operation training systems of various types of radiation therapy equipment, their 3D models should be prepared. The authors investigated how easily create such 3D models using Generative AI tools, and performed 3D model generation experiments using several Generative AI tools. As a result, the authors developed radiation therapy operation training prototype systems using the 3D models actually generated by PartPacker, one of the Generative AI tools for 3D models. In this paper, the authors report the experimental results of 3D model generation and introduce how radiation therapy operation training prototype systems can be developed using the generated 3D models.
Subjects manipulate objects according to the access rights. Here, illegal information flow occurs in the read operation. In our previous studies, a read rejection method is considered where the illegal read operations are rejected. The illegal read operation is a read operation occurring illegal information flow. In this paper, we propose a write rejection method. In the write rejection method, a read operation is not rejected even if the read operation occurs illegal information flow. However, the illegal write operations are rejected. The illegal write operation is a write operation to write the data read in an illegal read operation to an object. Therefore, it is prevented to flow the data read in illegal read operations to objects. Suppose data of objects are text. The LLM (Large Language Model) is used to check the data read in an illegal read operation are written to an object in a write operation. By fine tuning the LLM, the similarity between the data read in an illegal read operation from an object and the data written to another object is made clear. If the similarity is larger than a threshold, the data read in an illegal read operation flow to another object in the write operation. Therefore, the illegal write operation is rejected. Otherwise, the write operation is not rejected. Generally, most operations issued by subjects are read. The smaller number of read operations are rejected in the write rejection method than the read rejection method.
Battery Swapping Stations (BSSs), which have a lot of batteries primarily for Battery Swapping Electric Vehicles (BSEVs), can operate as an emergency power supply with proper allocation of electricity. BSEVs can redistribute the stored energy between two BSSs through battery migrations, then, the difference of stored electricity among BSSs also can be minimized. Electricity is transferred from a BSS with a higher State Of Charge (SOC) to the lower one by BSEV. By the way, the effects of battery migrations appear with delays. In other words, it needs a time to appear the effects accordance with the distance between BSSs. Therefore, BSEVs perform migration consisting of loading/unloading a battery with an easy decision as of only start time, they put energy into destination BSS after equalization. This phenomenon incurs both of the unneeded inversions of high-low relationship and it makes wider the SOC gaps among BSSs. We propose two methods to decide migration considering transportation delay to suppress improper electricity transports. The proposed methods effectively narrow the gap of SOC between BSSs and restrain the fluctuation of SOC. The latter method also cannot take effect for proper judgement of migration with decreased estimation accuracy.
This paper proposes a Digital Twin (DT)-driven framework to proactively combat ransomware in IoT ecosystems by simulating ransomware propagation, detecting anomalies via federated learning, and triggering automated responses such as device isolation and firmware rollbacks. A smart city case study demonstrates the framework’s effectiveness, achieving a 78
A biodiversity learning support system was developed based on immersive virtual reality (IVR). The aim of this system was to deepen users’ understanding of and interest in the importance of preserving biodiversity by allowing them to visualize endangered and extinct species in a realistic VR space. Mammals, birds, reptiles, and amphibians were selected from the Ministry of the Environment of Japan’s Red List of endangered and extinct species. These animals were modeled in three-dimensional (3D) form and placed in the virtual space. Users wear a head-mounted display and move freely within the VR space from a first-person perspective while viewing detailed information alongside the 3D animal models. Quizzes are provided to enhance the learning experience. These features aim to give users with a visual and physical experience that cannot be conveyed through conventional passive learning methods and to increase their general interest on biodiversity preservation. An evaluation survey involving 31 participants assessed the operability, functionality, satisfaction level with the visual experience, and changes in user interest. The survey results suggested that users were highly satisfied with both the operability and the realism of the visual experience. An increase in users’ interest in biodiversity preservation was also confirmed.
