Herbaceous vegetation on riverdikes plays an important role in preventing soil erosion. The information on vegetation height is essential for the management to secure visibility of riverdikes for inspection. However, such information has been very limited or none to the managers. This paper aims to derive information of herbaceous vegetation height from the laser data acquired using UAV LiDAR and create a herbaceous vegetation height map on riverdikes. The proposed map clearly showed herbaceous vegetation height up to 3 m and its distribution over riverdikes. Highly accurate and dense point cloud enabled herbaceous vegetation analysis with spatial resolution of 5 cm. The map would provide valuable information for managers to form efficient vegetation management on riverdikes.

One of the most fascinating issues in modern condensed matter physics is to understand highly-correlated electronic structures and propose their novel device designs toward the reduced carbon-dioxide future. Among various developed numerical approaches for highly-correlated electrons, the density matrix renormalization group (DMRG) has been widely accepted as the most promising numerical scheme compared to Monte Carlo and exact diagonalization in terms of accuracy and accessible system size. In fact, DMRG almost perfectly resolves one-dimensional chain like long quantum systems. In this paper, we suggest its extended approach toward higher-dimensional systems by high-performanc computing techniques. The computing target in DMRG is a huge non-uniform sparse matrix diagonalization. In order to efficiently parallelize the part, we implement communication step doubling together with reuse of the mid-point data between the doubled two steps to avoid severe bottleneck of all-to-all communications essential for the diagonalization. The technique is successful even for clusters composed of more than 1000 cores and offers a trustworthy exploration way for two-dimensional highly-correlated systems.

In order to study quantum many-body problems, we develop two matrix diagonalization codes, which solve only the ground state and all quantum states, respectively. The target model in both codes is the Hubbard model with confinement potential which describes an atomic Fermi gas loaded on an optical lattice and partly High-Tc cuprate superconductor. For the former code, we obtain 18.692TFlops (57% of the peak) as the best performance on the Earth Simulator when calculating the ground state of 100-billion dimensional matrix. From these large-scale calculations, we find atomic-scale inhomogeneous superfluid state which is now a challenging subject for physicists. For the latter code, we succeed in solving the matrix whose dimension is 375,000 with locally 24.6TFlops (75% of the peak). The calculations reveal that a change from Schrodinger's cat to classical like one can be controlled by tuning the interaction. This is a marked contrast to the general concept.

In order to study a possibility of superfluidity in trapped atomic Fermi gases loaded on optical lattices, we implement an exact diagonalization code for the trapped Hubbard model on the Earth Simulator. Comparing two diagonalization algorithms, we find that the performance of the preconditioned conjugate gradient (PCG) method is 1.5 times superior to the conventional Lanczos one since the PCG method can conceal the communication overhead much more efficiently. Consequently, the PCG method shows 16.447 TFlops (50.2% of the peak) on 512 nodes. On the other hand, we succeed in solving a 159-billion-dimensional matrix by using the conventional Lanczos method. To our knowledge, this dimension is a world-record. Numerical results reveal that an unconventional type of superfluidity specific to the confined system develops under repulsive interaction.

This paper presents a development of the modeling of the human driving behavior based on the expression as hybrid dynamical system (HDS) focusing on the driver's collision avoidance behavior. The driving data are collected by using the three-dimensional driving simulator based on CAVE, which provides stereoscopic immersive vision. In our modeling, the relationship between the measured information such as the sensory information of range between cars, range rate and lateral displacement between cars and the output of driver of the steering amount are expressed by the piecewise linear (PWL) model, which is a class of HDS. Then, we solve the identification problem for the PWL model by using the combination of data clustering and support vector machine. By introducing the PWL model, it becomes possible to find not only coefficients in each submodel but also parameters in the logical (switching) conditions from the measured driving data. From the obtained results, it is found that the driver appropriately switches the 'control law' according to the sensory information. This enables us to capture not only the physical meaning of the driving skill, but also the decision-making aspect (switching conditions) in the driver's collision avoidance behavior.