Masaharu Isshiki

A haptic virtual environment for molecular chemistry education

We attempted to produce an environment for education. Subjects such as mathematics or sciences are usually studied on a desk in a classroom. Our research goal is to allow students to study scientific contents more viscerally than existing studying methods by haptic interaction. To construct the environment, we have to make a user-friendly haptic interface. The study is described in two parts. The first part is defining what a useful haptic interface is. In this part, we focused on the grip of a haptic interface. SPIDAR-G is a haptic interface, which is manipulated by a grip with 8 strings.. Grip size is an important parameter for usability. We have found an optimal sphere size through SPIDAR-G usability testing. The other part is defining how teachers can use the interactive system with haptic interaction as a teaching aid. In this part , we focused on the interaction between two water molecules. First, we constructed an environment to feel Van der Waals force as well as electrostatic force with haptic interaction. Then, we observe the effectiveness of the environment when used by a class of students.

Solution of Inverse Kinematics by PSO Based on Split and Merge of Particles

This paper investigates the use of particle swarm optimization (PSO) with repeating splits and merges at predetermined intervals. After a split, there is no exchange of information between the particles, which belong to different swarms. Hence, even though one particle may be trapped in a local minimum, the others are not affected. If the other particles find a better solution, the trapped particle can escape the local minimum when the particles are merged. In order to verify the efficacy of the proposed method, we applied it to the learning of a neural network for solving the inverse kinematics problem of a manipulator with uncertain parameters. The back-propagation rule requires the Jacobian of the forward kinematics, but this cannot be calculated due to uncertainties. Because PSO does not require the derivative of the objective function, it is suitable for this problem. A simulation result shows that the proposed method can obtain more accurate inverse kinematics than either global best (gbest) PSO or local best (lbest) PSO.

3D Tsunami Run-up Simulation and Visualization using Particle Method with Gis-Based Geography Model

The 2011 off the Pacific coast of Tohoku Earthquake was one of the most powerful earthquakes on record in Japan and the huge tsunami caused by the earthquake inflicted extensive damage to the coastal areas of the Tohoku region. To form safe coastal areas, countermeasures against disaster should be developed considering not only tangible infrastructures including breakwater and bridges but also intangible measures including education on disaster prevention and the development of hazard maps. The tsunami run-up analysis is expected to play a role as one of the countermeasures against tsunami. In this research, we aim to establish a tool to effectively analyze the tsunami run-up in urban areas based on the Smoothed particle hydrodynamics (SPH) method. And then, we propose a series of pre-process procedures to develop a detailed geography analysis model that reflects the geography, elevation, and exterior shapes of buildings by referring to 3D location information and digital elevation model data obtained from a geographical information system. Finally, we established a photorealistic visualization method so that citizen can understand the tsunami phenomenon intuitively.

A realtime and direct-touch interaction for 3D woven cultural artifact exhibition

We propose a realtime and direct-touch interaction system for 3D woven cultural artifact exhibition. Our system enables to exhibit the cultural artifact at high-definition and enables realtime and direct-touch interaction based on our texture-based haptic modeling and rendering techniques. In our techniques, the reaction force is changed according to the pixel value of the 2D texture images of the 3D model surface on the screen.

Estimation of inverse model based on ANN and PSO with adaptively varying acceleration coefficients

We consider estimation of an inverse model for the uncertain systems. Multi-layered neural network (NN) can approximate any continuous nonlinear mapping, then we use NN in order to represent the inverse model. The the back-propagation rule, which is commonly used for updating the weights, requires the sensitivity function of the system. However, we can not calculate this function because of uncertainty of the systems. Hence, we apply particle swarm optimization (PSO) to update the weights. PSO is suitable for learning NN, because it dose not require the derivative of the objective function. This paper introduces a novel parameter automation strategy in order to overcome the premature convergence. The acceleration coefficients are adaptively varies with the distance between the particle and the gbest which is the best solution ever found by the swarm. We can maintain the diversity of the swarm and search a better solution around the gbest efficiently. We compare the proposed method with the basic PSO and TVAC PSO, which is proposed by Ratnaweera, through the simulation of the inverse kinematics problem. The proposed method can obtain the accurate inverse model than the basic PSO and TVAC PSO.