The DC microgrid system is one of hopeful candidate of the community-level power systems including renewable energy sources. This paper proposes an abstract system model of DC microgrid systems for fast and accurate simulation toward long-term virtual experiment of the DC microgrid system. We constructed the abstract system model based on the specification of the DC microgrid system. Experimental results show that the simulation results are valid comparing with the real measured data.
Drowsy driving accidents can be prevented if predicted in advance. The present work aims to develop a new method for detecting driver drowsiness based on the fact that the autonomic nervous function affects heart rate variability (HRV), which is a fluctuation of the RR interval (RRI) obtained from an electrocardiogram (ECG). The proposed method uses eight HRV features derived through HRV analysis as input variables of multivariate statistical process control (MSPC), which is a well-known anomaly detection method in the field of process control. In the proposed method, only one principal component was adopted in MSPC and driver drowsiness was detected through monitoring the T2 statistic. Driving simulator experiments demonstrated that driver drowsiness was successfully detected in seven out of eight cases before accidents occurred. In addition, the proposed method was implemented in a smartphone app for on-vehicle use.
The authors proposed a new differential evolution technique, Re-Labeling Differential Evolution (RLDE), which, in this paper, is refined and evaluated in the context of interactive solution of combinatorial optimization problems. Many of the practical design problems such as web page layout design and room lighting design are combinatorial optimization problems where the numerical evaluations are not available. The evaluation should be made by humans. There are two essential properties necessary for the solution methods to the above problems: (1) interaction between the methods and the users to extract human evaluations accurately without imposing too much burden on the users, and (2) abilities to solve combinatorial optimization problems. Interactive differential evolution (IDE) techniques possess the first property because they utilize pairwise comparisons but lack the second property, while interactive genetic algorithms have the second property but not the first one. RLDE is an extended algorithm of IDE so that it can solve combinatorial optimization problems. In differential evolution (DE), solution candidates are represented by numerical values. In combinatorial optimization problems, however, the numerical values are only labels to distinguish the components to be combined, and there are no structural relationships such as large/small and far/near among them which the DE relies on. RLDE collects information on the problem while it searches for the solution, and, based on the obtained information, re-labels the components so that the DE algorithm works more efficiently. RLDE was originally proposed as a technique for simple combinatorial optimization. In this paper, the authors extend RLDE to permutation-based combinatorial optimization. The performance of RLDE in terms of the burden on the users and the quality of the obtained solutions is evaluated and compared with the other techniques in numerical experiments.
This paper presents an adaptive Volterra filter implementation based on an exponentially-weighted a posteriori H∞ filtering algorithm. Its fast array form is immediately obtained by following the derivation of the fast multichannel RLS filter. Also the steady-state performance of the H∞ Volterra filter is analyzed by using an approximate expression for its excess mean-square errors. Several numerical examples show that the H∞ Volterra filter could achieve a balance between convergence and tracking capability when its attenuation parameter is chosen adequately.
It is conceivable to use a teleoperated robot as a method for exploring a disaster environment quickly while avoiding secondary disaster. According to conventional researches, the image captured behind from the teleoperated robot is useful as information provided to an operator for operating a teleoperated robot. In this research, a teleoperated method to provide the image from behind is proposed by using an autonomous robot. This method allows one image to include both the teleoperated robot itself and the environment around the teleoperated robot. Here, it is important to develop an algorithm that can navigate the autonomous robot to the position where the camera image can be obtained so that the operability is enhanced for the teleoperated robot at the front. This paper describes a method for determining the movement position for the autonomous robot in consideration of each robot's position and obstacles around the robot. A method for changing three modes (Follow/Back/Wait) is also described according to the situation. Finally, the movement position of the autonomous robot and the captured images actually provided by each robot are shown through the experimental results using real mobile robots to verify the usefulness of the proposed method.