THE VISUALIZATION AND ANALYSIS OF URBAN FACILITY POIS USING NETWORK KERNEL DENSITY ESTIMATION CONSTRAINED BY MULTI-FACTORS

The urban facility, one of the most important service providers is usuallyrepresented by sets of points in GIS applications using POI (Point of Interest) modelassociated with certain human social activities. The knowledge about distributionintensity and pattern of facility POIs is of great significance in spatial analysis,including urban planning, business location choosing and social recommendations.Kernel Density Estimation (KDE), an efficient spatial statistics tool for facilitatingthe processes above, plays an important role in spatial density evaluation, becauseKDE method considers the decay impact of services and allows the enrichment ofthe information from a very simple input scatter plot to a smooth output densitysurface. However, the traditional KDE is mainly based on the Euclidean distance,ignoring the fact that in urban street network the service function of POI is carriedout over a network-constrained structure, rather than in a Euclidean continuousspace. Aiming at this question, this study proposes a computational method of KDEon a network and adopts a new visualization method by using 3-D “wall” surface.Some real conditional factors are also taken into account in this study, such astraffic capacity, road direction and facility difference. In practical works theproposed method is implemented in real POI data in Shenzhen city, China to depictthe distribution characteristic of services under impacts of multi-factors

Learning to Navigate Cloth using Haptics

We present a controller that allows an arm-like manipulator to navigate deformable cloth garments in simulation through the use of haptic information. The main challenge of such a controller is to avoid getting tangled in, tearing or punching through the deforming cloth. Our controller aggregates force information from a number of haptic-sensing spheres all along the manipulator for guidance. Based on haptic forces, each individual sphere updates its target location, and the conflicts that arise between this set of desired positions is resolved by solving an inverse kinematic problem with constraints. Reinforcement learning is used to train the controller for a single haptic-sensing sphere, where a training run is terminated (and thus penalized) when large forces are detected due to contact between the sphere and a simplified model of the cloth. In simulation, we demonstrate successful navigation of a robotic arm through a variety of garments, including an isolated sleeve, a jacket, a shirt, and shorts. Our controller out-performs two baseline controllers: one without haptics and another that was trained based on large forces between the sphere and cloth, but without early termination.Comment: Supplementary video available at https://youtu.be/iHqwZPKVd4A. Related publications http://www.cc.gatech.edu/~karenliu/Robotic_dressing.htm

The Path and Enlightenment of Data-Driven Digital Transformation of Organizational Learning ——A Case Study of the Practice of China Telecom

This paper took China Telecom as a case. It has analyzed data-driven digital transformation in organizational learning, and summarized the methods and enlightenments of digital transformation