Crack Damage and Treatment of Low House on Expansive-Contractive Red Clay

Red clay distributes widely in China, it can be classified into two kinds: swell- shrinking and non-swell-shrinking red clay. According to the strong strength and low compressibility features of the Expansive-contractive Red Clay (ECRC), it can be used as an excellent natural ground. Sometimes its swell- shrinkage property may cause crack damage to the low house which based on the ECRC. This rupture is quite common and serious, the crack house became dangerous and had to be rebuilt. In China, the main preventive measures against the crack damage are: moisture holding, deep embedment, soil replacement method (sand cushion), sand surrounding and heat insulation, etc

Processing techniques of point cloud data on small-sized objects with complex free-form surface

The scattered point cloud data, which comes from such small-sized objects with complex free-form surface as shelled shrimp, is processed as error points removing, points filtering, holes filling, clouds segmenting, etc. using CATIA V5 R20. The principle of data processing and skills utilized in the operation mentioned above apply also to the small-sized objects similar to the shelled shrimp in reverse engineering in other fields, such as optical components, irregular parts, ears, nose, etc

Multi-Domain Active Learning: A Comparative Study

Building classifiers on multiple domains is a practical problem in the real life. Instead of building classifiers one by one, multi-domain learning (MDL) simultaneously builds classifiers on multiple domains. MDL utilizes the information shared among the domains to improve the performance. As a supervised learning problem, the labeling effort is still high in MDL problems. Usually, this high labeling cost issue could be relieved by using active learning. Thus, it is natural to utilize active learning to reduce the labeling effort in MDL, and we refer this setting as multi-domain active learning (MDAL). However, there are only few works which are built on this setting. And when the researches have to face this problem, there is no off-the-shelf solutions. Under this circumstance, combining the current multi-domain learning models and single-domain active learning strategies might be a preliminary solution for MDAL problem. To find out the potential of this preliminary solution, a comparative study over 5 models and 4 selection strategies is made in this paper. To the best of our knowledge, this is the first work provides the formal definition of MDAL. Besides, this is the first comparative work for MDAL problem. From the results, the Multinomial Adversarial Networks (MAN) model with a simple best vs second best (BvSB) uncertainty strategy shows its superiority in most cases. We take this combination as our off-the-shelf recommendation for the MDAL problem