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Research on Internet-based teleoperation has received increased attention in the past few years. In this paper, an Internet-based teleoperation system was implemented. In order to robustly transmit the surroundings and control information of the robot, packet-type data were used. The central problem in Internet-based teleoperation is data transmission latency or data loss. For this specific problem, an autonomous mobile robot with optimal two-layer fuzzy controller (2LFC) was introduced. When data transmission is failed, the robot automatically moves and protects itself,. In addition, a color detection system was implemented so that the robot can perceive an object and move to another object. The fuzzy controller was optimized by using the schema coevolutionary algorithm (SCEA), which finds an optimal solution. Using these technologies, the efficacies of the 2LFC, the SCEA, and the teleoperation system were verified. [ABSTRACT FROM AUTHOR]

There are several types of intersections such as merge-roads, diverge-roads, plus-shape intersections and two types of T-shape junctions in urban roads. When an autonomous vehicle encounters new intersections, it is crucial to recognize the types of intersections for safe navigation. In this paper, a novel intersection type recognition method is proposed for an autonomous vehicle using a multi-layer laser scanner. The proposed method consists of two steps: (1) static local coordinate occupancy grid map (SLOGM) building and (2) intersection classification. In the first step, the SLOGM is built relative to the local coordinate using the dynamic binary Bayes filter. In the second step, the SLOGM is used as an attribute for the classification. The proposed method is applied to a real-world environment and its validity is demonstrated through experimentation. [ABSTRACT FROM AUTHOR]

This paper presents a heuristic method for electroencephalography (EEG) grouping and feature classification using harmony search (HS) for improving the accuracy of the brain-computer interface (BCI) system. EEG, a noninvasive BCI method, uses many electrodes on the scalp, and a large number of electrodes make the resulting analysis difficult. In addition, traditional EEG analysis cannot handle multiple stimuli. On the other hand, the classification method using the EEG signal has a low accuracy. To solve these problems, we use a heuristic approach to reduce the complexities in multichannel problems and classification. In this study, we build a group of stimuli using the HS algorithm. Then, the features from common spatial patterns are classified by the HS classifier. To confirm the proposed method, we perform experiments using 64-channel EEG equipment. The subjects are subjected to three kinds of stimuli: audio, visual, and motion. Each stimulus is applied alone or in combination with the others. The acquired signals are processed by the proposed method. The classification results in an accuracy of approximately 63%. We conclude that the heuristic approach using the HS algorithm on the BCI is beneficial for EEG signal analysis. [ABSTRACT FROM AUTHOR]

We investigated the ability to suppress recall of visual images, using the think/no-think (TNT) paradigm. Participants were 27 male undergraduates (13 repressors, 14 nonrepressors) who watched video clips of a motorbike accident as we recorded their galvanic skin response (GSR). We then conducted the TNT paradigm using motorbike accident images. Both repressors and nonrepressors recorded higher GSR when watching the video clips than at baseline. Both groups showed greater suppression of imaginary memories in the no-think condition than they did in either the think or the baseline conditions. We found repeated attempts at no-think might be an effective strategy for suppressing imaginary memories and that there were no differences in the ability of repressors and nonrepressors to suppress memory in the imaginary memory condition. [ABSTRACT FROM AUTHOR]