Photonic-crystal surface-emitting lasers (PCSELs) are attractive for a wide range of applications such as material processing and remote sensing owing to their advantage of broad-area coherent lasing supported by a 2-D photonic crystal. Recently, we proposed double-lattice photonic-crystal resonators and achieved 10-W-class high-power and high-beam-quality (namely, high-brightness) operation. To further increase the brightness, it is important to understand the lasing mode behavior of PCSELs in detail. In this article, we experimentally investigate the change in the lasing mode depending on the current injection levels and compare the results with a theoretical analysis that accounts for the change in refractive index and gain distributions due to the current injection. We observed a transition from single-mode lasing to two-mode lasing at high injection currents. We develop a technique for resolving the near-field profiles of individual lasing modes and show that these profiles coincide with theoretical predictions. A comparison of experimental and theoretical results reveals that high-order modes appear due to a bandgap confinement effect induced by a change in the refractive index in the carrier injection area. To effectively remove this confinement effect and suppress the oscillation of high-order modes, we incorporate an in-plane heterostructure into the PCSEL.

Recomputation algorithms collectively refer to a family of methods that aims to reduce the memory consumption of the backpropagation by selectively discarding the intermediate results of the forward propagation and recomputing the discarded results as needed. In this paper, we will propose a novel and efficient recomputation method that can be applied to a wider range of neural nets than previous methods. We use the language of graph theory to formalize the general recomputation problem of minimizing the computational overhead under a fixed memory budget constraint, and provide a dynamic programming solution to the problem. Our method can reduce the peak memory consumption on various benchmark networks by $36\%\sim81\%$, which outperforms the reduction achieved by other methods.

The tsunami disasters that occurred in Indonesia, Chile, and Japan have inflicted serious casualties and damaged social infrastructures. Tsunami forecasting systems are thus urgently required worldwide. We have developed a real-time tsunami inundation forecast system that can complete a tsunami inundation and damage forecast for coastal cities at the level of 10-m grid size in less than 20 min. As the tsunami inundation and damage simulation is a vectorizable memory-intensive program, we incorporate NEC’s vector supercomputer SX-ACE. In this paper, we present an overview of our system. In addition, we describe an implementation of the program on SX-ACE and evaluate its performance of SX-ACE in comparison with the cases using an Intel Xeon-based system and the K computer. Then, we clarify that the fulfillment of a real-time tsunami inundation forecast system requires a system with high-performance cores connected to the memory subsystem at a high memory bandwidth such as SX-ACE.

Recently, power assist suits which support human physical activities have been researched. We are developing a power assist suit for workers in a nuclear power plant. If a disaster happens, the workers have to wear heavy radiation protective equipment. The power assist suit is supposed to support the wearer so that it reduces the load of the radiation protection equipment during an operation. This paper proposes a power assist control method based on motion prediction using 9-axis motion sensors. The power assist suit enables rapid power assist because the motion sensor can detect the start of walking motion in real time. A motion database consists of angles and angular velocities of wearer's chest, lower limbs, and joint angles of the power assist suit. The power assist suit recognizes the motion of the wearer based on the database. Then, it assists the wearer based on the estimation of the motion of the wearer. We conduct experiments to evaluate the proposed method.

Recently, two-wheeled inverted pendulum mobile robots have been popular. They support human locomotion and/or small goods transportation based on inverted pendulum upright controllers. The conventional inverted pendulum mobile robot controls to follow the fixed desired posture angle and wheel velocity. It is desirable to change the control parameters according to the user intention in order to offer comfortable operability of the robot. This paper proposes a user intention recognition system for a inverted pendulum mobile robot and shows experimental results.

Genetic Algorithm (GA) is modeling behavior of evolution in organic and known as one of method to solve Traveling Salesman Problem (TSP). However, GA obtains solution by overlaying generations because of being based on evolution in organic. Thus, it takes a long time to find approximate solution. While, Virus Theory of Evolution (VTE) can evolve by virus infection. VTE characteristic has sharing of information among same generation. If new algorithm is using both these characteristics of GA and VTE, convergence speed would be faster than GA. Thus, this study proposes Genetic Algorithm with Virus Infection (GAVI). GAVI algorithm is Virus Theory of Evolution (VTE) to be based on Genetic Algorithm (GA). We apply GAVI to TSP and confirm that GAVI obtains more effective result than GA.

In this paper, a method for automatic parking systems is proposed based on the approach of Kang-Zhi Liu, (2002) and the corresponding system package is developed. This method is composed of control laws for the steering angle and the driving velocity, algorithms for various kinds of parking tasks under steering angle arid parking space limitations. The system works pretty well in experiments subject to various parking situations.

In this paper, a set of the optimal state-space descriptions with minimum L/sub 2/-sensitivity is synthesized for continuous-time linear systems. First, an L/sub 2/-sensitivity measure is defined for continuous-time linear systems by using a pure L/sub 2/-norm. An expression is then developed for evaluating the L/sub 2/-sensitivity in continuous-time linear systems. Next, an iterative method is applied for obtaining the optimal coordinate transformation that minimizes the L/sub 2/-sensitivity measure. Finally, a numerical example is given to illustrate the utility of the proposed technique.