This study aims to generate forest 3D point cloud maps that provide useful information for forestry operations using a laser scanner mounted on a forest survey multicopter. To achieve this, it is necessary to generate basic flight paths flown by the multicopter, generate routes to avoid trees and other obstacles, and search for waypoints to extend the area that can be measured by LiDAR. In this paper, algorithms for these tasks are proposed. These algorithms are essential for the multicopter to scan the target forest while flying over it. The effectiveness of the proposed method is demonstrated by numerical simulations.

Maintaining appropriate blood glucose levels within an acceptable range is essential for patients with type 1 diabetes. Zone model predictive control seeks to maintain its output within a given range and is often employed to keep blood glucose within the acceptable range. However, the use of a linear time-invariant model for blood glucose control results in poor accuracy for long-period predictions. Hence, previous studies implemented an input penalty function in order to correct errors in the calculated insulin dose caused by inaccurate model prediction. Parameters of the input penalty function were optimized to improve control performance for specific patient populations under one-meal scenarios, and it might not keep performance for each patient or other scenarios. In contrast, we construct a learning method of an input penalty function under the control of each patient. The learned input penalty function improves the control performance of each patient on various meals.

Neurosurgical procedures require utmost precision and skill to preserve cerebral vessels during surgery. Unfortunately, inexperienced physicians are susceptible to failure in this regard due to the delicate nature of the subarachnoid space where these vessels are located. The most critical step in cerebral vessel preservation is to apply appropriate tension to the arachnoid membrane. Using a neurosurgery simulator is expected to help train vessel preservation techniques. However, previous surgical simulators often did not consider the subarachnoid space due to the challenges involved in creating 3D models of this complex and intricate structure. To address this challenge, a method for creating a brain model that includes the subarachnoid space was proposed. In the proposed model, a brain and vessel model created from medical images of a patient is wrapped in a soft cloth-like material by conducting a simulation, and the molded cloth is used as an arachnoid model. For the intricate nature of arachnoid trabeculae, characterized by an indeterminate number and diverse shapes, adjacent trabeculae are united into a bundle and approximated as a single cylinder. The arachnoid trabecular models randomly place in the subarachnoid space. The efficacy of this approach in generating the arachnoid trabecular model has been substantiated through simulation-based verification. Furthermore, a calculation method based on the finite element method for neurosurgery simulation using the created subarachnoid space model is described, and a simulation of pulling the arachnoid is performed. This simulation demonstrated the potential to provide operators with an experience of the force required to pull the arachnoid membrane.

In recent years, the performance of rotary control mechanisms has been further improved. To achieve this, it is necessary to detect the rotation angle with high accuracy and to grasp the time-dependent variation of the bearing mechanism. By realizing runout detection in the XY direction perpendicular to the axis of rotation, shaft deterioration can be ascertained in advance and the life of the bearing mechanism can be estimated. In addition, by enabling runout detection in the Z-axis direction, deviation in the Z direction when a large workpiece is placed on the machine tool can be detected and machining errors can be compensated, enabling high-precision machining. This paper reports on research into high-precision detection technology for rotation angle and axis runout in the three directions of X, Y, and Z using a gear-type magnetic rotary encoder.