Granulated foam glass ceramic — a promising heat-insulating material

Environmentally safe, resource-conserving technology was developed for manufacturing thermal-insulating, granulated foam glass with closed pores. The heat-engineering characteristics of the granulated foam glass ceramic obtained allow recommending it as an effective thermal insulating material for high-temperature thermal units and residential networks.     

Deriving Trends in Historical and Real-Time Continuously Sampled Medical Data

Monitors in Intensive Care Units generate large volumes of continuous data which can overwhelm a database and result in information overload for the medical staff. Instead of reasoning with individual data samples of one or more variables, it is better to work with the trend of the data i.e., whether the data is increasing, decreasing or steady. We have developed a system which abstracts continuous data into trends; it consists of three consecutive processes: filtering which smooths the data; temporal interpolation which creates simple intervals between consecutive data points; and temporal inference which iteratively merges intervals which share similar characteristics into larger intervals. Storing trends can result in a reduction in database volume. Our system has been applied both to historical and real-time data.     

Reinforcement learning for a biped robot to climb sloping surfaces

A neural network mechanism is proposed to modify the gait of a biped robot that walks on sloping surfaces using sensory inputs. The robot climbs a sloping surface from a level surface with no priori knowledge of the inclination of the surface. By training the neural network while the robot is walking, the robot adjusts its gait and finally forms a gait that is as stable as when it walks on the level surface. The neural network is trained by a reinforcement learning mechanism while proportional and integral (PI) control is used for position control of the robot joints. Experiments of static and pseudo dynamic learning are performed to show the validity of the proposed reinforcement learning mechanism. © 1997 John Wiley & Sons, Inc.