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It is important to extract a clear background for computer vision and augmented reality. Generally, background extraction assumes the existence of a clean background shot through the input sequence, but realistically, situations may violate this assumption such as highway traffic videos. Therefore, our probabilistic model-based method formulates fusion of candidate background patches of the input sequence as a random walk problem and seeks a globally optimal solution based on their temporal and spatial relationship. Furthermore, we also design two quality measures to consider spatial and temporal coherence and contrast distinctness among pixels as background selection basis. A static background should have high temporal coherence among frames, and thus, we improve our fusion precision with a temporal contrast filter and an optical-flow-based motionless patch extractor. Experiments demonstrate that our algorithm can successfully extract artifact-free background images with low computational cost while comparing to state-of-the-art algorithms.

The purposes of this study were to develop a novel material which was infused nanotechnology into electronics subjects to vocational high school students’with no background in nanotechnology. And then to investigate the cognitive load of them, after they learnedthenovel curriculum pattern. The researchers began todevelop a novel electricsmaterial which is consisted of four chapters includedseven sectionsby experts of electronics, nanotechnology and vocational education. Theexperimentswere conducted by using PPT pedagogy to teach the nanotechnology infused into electronicscourse between twodepartments ofvocational high schools. After four months and four times in one month experimental teaching, cognitive load scale questionnaireand K of KAP scale questionnairewere examined.Finally, the applications of this materialare illustrated that the performances of K scores weresignificant differencebetween electronic engineering department and information engineering department.

Landslides have caused extensive infrastructure damage and caused human fatalities for centuries. Intense precipitation and large earthquakes are considered to be two major landslide triggers, particularly in the case of catastrophic landslides. The most widely accepted mechanistic explanation for landslides is the effective-stress dependent shear strength reduction due to increases in pore water pressure. The Chashan landslide site, selected for the present study, has been intensively studied from geological, geophysical, geodetic, geotechnical, hydrological, and seismological perspectives. Our seismic monitoring of daily relative velocity changes (dv/v) indicated that landslide material decreases coincided with the first half of the rainy period and increased during the latter half of the rainy period. The geodetic surveys before and after the rainy period identified vertical subsidence without horizontal movement. The results from the multidisciplinary investigation enabled us to draw a conceptual model of the landslide recovery process induced by water loading. Where all sliding materials were stable (safety factor > 1.0), unconsolidated landslide colluvium and impermeable sliding surfaces trapped the seepage water to form a water tank, provided that compact forces were acting on the materials below the sliding boundary. The vertical force of compaction facilitates an increase in the cohesion and strength of landslide materials, thereby increasing the landslide materials’ stability. We demonstrated that the recovery process periodically occurs only under the combined conditions of prolonged and intense precipitation and the related stability conditions.