日本人的模拟消防训练

国际消防界有一个共同的目标,那就是在同火灾作斗争中,既要拯救民众,也要保护自己的安全。日本人相信,加强消防训练,提高救援和自救本领,是减少火场伤亡的基本保障。于是日本人在模拟消防训练方面就别具特色。 模拟高层建筑火灾施救 日本人十分重视高层建筑火灾的预防及其救援工作。在日本的大城市中,每个消防署,都有一个消防训练塔,是为模拟高层建筑,用于救援训练与自救训练,用于消防挂钩梯和消防直升飞机和云梯车的作业训练。这种训练塔,一般都高于24米,

Characterization of Driving Style and the Influence of Distraction Based on Non-intrusive Driving Parameters

It is difficult to model human behavior because of the variability in driving styles and driving skills. However, for some driver assistance systems, it is necessary to have knowledge of that behavior to discriminate potentially hazardous situations, such as distraction, fatigue or drowsiness. Many of the systems that look for driver distraction or drowsiness are based on intrusive means （analysis of the electroencephalogram--EEG） or highly sensitive to operating conditions and expensive equipment （eye movements analysis through artificial vision）. A solution that seeks to avoid the above drawbacks is the use of driving parameters This article presents the conclusions obtained after a set of driving simulator tests with professional drivers with two main objectives using driving variables such as speed profile, steering wheel angle, transversal position on the lane, safety distance, etc., that are available in a non-intrusive way： （1） To analyze the differences between the driving patterns of individual drivers; and （2） To analyze the effect of distraction and drowsiness on these parameters. Different scenarios have been designed, including sequences with distractions and situations that cause fatigue. The analysis of the results is carried out in time and frequency domains in order to identify situations of loss of attention and to study whether the evolution of the analyzed variables along the time could be considered independent of the driver.

Simulating the Inverse Kinematic Model of a Robot through Artificial Neural Networks： Complementing the Teaching of Robotics

Teaching robotics necessarily involves the study of the kinematic models of robot manipulators. In turn, the kinematics of a robot manipulator can be described by its forward and reverse models. The inverse kinematic model, which provides the status of the joints according to the desired position for the tool of the robot, is typically taught and described in robotics classes through an algebraic way. However, the algebraic representation of this model is often difficult to obtain. Thus, although it is unquestionable the need for the accurate determination of the inverse kinematic model of a robot, the use of ANNs （artificial neural networks） in the design stage can be very attractive, because it allows us to predict the behavior of the robot before the formal development of its model. In this way, this paper presents a relatively quick way to simulate the inverse kinematic model of a robot, thereby allowing the student to have an overview of the model, coming to identify points that should be corrected, or that can be optimized in the structure of a robot.

Coupling Agent Based Simulation with Dynamic Networks Analysis to Study the Emergence of Mutual Knowledge as a Percolation Phenomenon

The emergence of mutual knowledge is a major cognitive mechanism for the robustness of complex socio-technical systems. It has been extensively studied from an ethnomethodological point of view and empirically reproduced by multi-agent simulations. Whilst such simulations have been used to design real work settings the underlying theoretical grounding for the process is vague. The aim of this paper is to investigate whether the emergence of mutual knowledge(MK) in a group of colocated individuals can be explained as a percolation phenomenon. The followed methodology consists in coupling agent-based simulation with dynamic networks analysis to study information propagation phenomena: After using an agent-based simulation the authors generated and then analyzed its traces as networks where agents met and exchanged knowledge. Deep analysis of the resulting networks clearly shows that the emergence of MK is comparable to a percolation process. The authors specifically focus on how changes at the microscopic level in the proposed agent based simulator affect percolation and robustness. These results therefore provide theoretical basis for the analysis of social organizations.

Self-assembly control and experiments in swarm modular robots

This paper presents a self-assembly control strategy for the swarm modular robots. Simulated and physical experiments are conducted based on the Sambot platform, which is a novel self-assembly modular robot having the characteristics of both the chain-type and the mobile self-reconfigurable robots. Multiple Sambots can autonomously move and connect with one another through self-assembly to form robotic organisms. The configuration connection state table is used to describe the configuration of the robotic structure. A directional self-assembly control model is proposed to perform the self-assembly experiments. The self-assembly process begins with one Sambot as the seed, and then the Docking Sambots use a behavior-based controller to achieve connection with the seed Sambot. The controller is independent of the target configuration. The seed and connected Sambots execute a configuration comparison algorithm to control the growth of the robotic structure. Furthermore, the simul- taneous self-assembly of multiple Sambots is discussed. For multiple configurations, self-assembly experiments are conducted in simulation platform and physical platform of Sambot. The experimental results verify the effectiveness and scalability of the self-assembly algorithms.