A method to compensate head movements for mobile eye tracker using invisible markers

Although mobile eye-trackers have wide measurement range of gaze, and high flexibility, it is difficult to judge what a subject is actually looking at based only on obtained coordinates, due to the influence of head movement. In this paper, a method to compensate for head movements while seeing the large screen with mobile eye-tracker is proposed, through the use of NIR-LED markers embedded on the screen. The head movements are compensated by performing template matching on the images of view camera to detect the actual eye position on the screen. As a result of the experiment, the detection rate of template matching was 98.6%, the average distance between the actual eye position and the corrected eye position was approximately 16 pixels for the projected image (1920 x 1080)

Oku-Noto Earthquake in 2023 : Damage to local industries, temples and shrines

2023年5月5日に発生した奥能登地震の被害調査を実施した．特に，奥能登地方を代表する地場産業や寺社の被害に注目した．距離減衰式にて珠洲市周辺の最大加速度や計測震度の距離減衰特性を整理した．距離減衰式による推定値は観測値より大きめの値を与えるものの概ね妥当であることを確認した．地場製造業での調査では老朽化した建屋，窯，土壁など耐震性の低い構造物が損傷した．寺社の被害調査からは重心が高い鳥居や鐘楼堂などが強い揺れのために倒壊した．鳥居，鐘楼堂などはいずれも土台との接合部で断裂し，倒壊に至ったことがわかった．We conducted a damage survey of the Okunoto earthquake that occurred on May 5, 2023. In particular, we focused on the damage to local industries that represent the Oku-Noto region and temples and shrines. We organized the distance attenuation characteristics of the maximum acceleration and measured seismic intensity around Suzu City using the distance attenuation formula. We confirmed that the estimate data by the distance attenuation equation are generally valid although they give larger values than the observed values. Surveys conducted by local manufacturing companies showed that aging buildings, kilns, and other structures with low earthquake resistance were damaged. Damage surveys of temples and shrines revealed that torii gates and bell towers, which have a high center of gravity, collapsed due to strong shaking

A novel method for unsteady flow field segmentation based on stochastic similarity of direction

Recent developments in fluid dynamics research have opened up the possibility for the detailed quantitative understanding of unsteady flow fields. However, the visualization techniques currently in use generally provide only qualitative insights. A method for dividing the flow field into physically relevant regions of interest can help researchers quantify unsteady fluid behaviors. Most methods at present compare the trajectories of virtual Lagrangian particles. The time-invariant features of an unsteady flow are also frequently of interest, but the Lagrangian specification only reveals time-variant features. To address these challenges, we propose a novel method for the time-invariant spatial segmentation of an unsteady flow field. This segmentation method does not require Lagrangian particle tracking but instead quantitatively compares the stochastic models of the direction of the flow at each observed point. The proposed method is validated with several clustering tests for 3D flows past a sphere. Results show that the proposed method reveals the time-invariant, physically relevant structures of an unsteady flow

A novel method of low-dimensional representation for temporal behavior of flow fields using deep autoencoder

A method capable of comparing and analyzing the spatio-temporal structures of unsteady flow fields has not yet been established. Temporal analyses of unsteady flow fields are often done after the data of the fields are reduced to low-dimensional quantities such as forces acting on objects. Such an approach is disadvantageous as information about the flow field is lost. There are several data-driven low-dimensional representation methods that preserve the information of spatial structure; however, their use is limited due to their linearity. In this paper, we propose a method for analyzing the time series data of unsteady flow fields. We firstly propose a data-driven nonlinear low-dimensional representation method for unsteady flow fields that preserves its spatial structure; this method uses a convolutional autoencoder, which is a deep learning technique. In our proposed method, the spatio-temporal structure can be represented as a trajectory in a low-dimensional space using the visualization technique originally proposed for dynamic networks. We applied the proposed method to unsteady flows around a two-dimensional airfoil and demonstrated that it could briefly represents the changes in the spatial structure of the unsteady flow field over time. This method was demonstrated to also be able to visualize changes in the quasi-periodic state of the flow when the angle of attack of the airfoil was changed. Furthermore, we demonstrated that this method is able to compare flow fields that are constructed using different conditions such as different Reynolds numbers and angles of attack