Search

Rotating arc sensor is a key device for automation welding. The vibration has a big influence on signal's correct collection and reliable automatic welding. In order to solve the vibration problem and the dynamic balancing design with the restricted space, a bearing force analysis based dynamic balancing structure optimal design is proposed and implemented with the help of Pro/Engineer (PROE) and automatic dynamic analysis of mechanical systems (ADAMS) virtual prototype technology, in which three parameters of the counterbalance are considered. The method is suitable for the practical online adjustment. The simulation result shows that optimal design based counterbalance structure and parameters can satisfy the space requirement with lower vibration. The methodology provides a new idea for dynamic balancing design and adjustment of rotating arc sensor with adjustable rotation radius. [ABSTRACT FROM AUTHOR]

The volume of surveillance videos is increasing rapidly, where humans are the major objects of interest. Rapid human retrieval in surveillance videos is therefore desirable and applicable to a broad spectrum of applications. Existing big data processing tools that mainly target textual data cannot be applied directly for timely processing of large video data due to three main challenges: videos are more data-intensive than textual data; visual operations have higher computational complexity than textual operations; and traditional segmentation may damage video data's continuous semantics. In this paper, we design SurvSurf, a human retrieval system on large surveillance video data that exploits characteristics of these data and big data processing tools. We propose using motion information contained in videos for video data segmentation. The basic data unit after segmentation is called M-clip. M-clips help remove redundant video contents and reduce data volumes. We use the MapReduce framework to process M-clips in parallel for human detection and appearance/motion feature extraction. We further accelerate vision algorithms by processing only sub-areas with significant motion vectors rather than entire frames. In addition, we design a distributed data store called V-BigTable to structuralize M-clips' semantic information. V-BigTable enables efficient retrieval on a huge amount of M-clips. We implement the system on Hadoop and HBase. Experimental results show that our system outperforms basic solutions by one order of magnitude in computational time with satisfactory human retrieval accuracy. [ABSTRACT FROM AUTHOR]

Human-following robots are important for home, industrial and battlefield applications. To effectively interact with human, a robot needs to locate a person's position and understand his/her motion. Vision based techniques are widely used. However, due to the close distance between human and robot, and the limitation in a camera's field of view, only part of a human body can be observed most of the time. As such, the human motion observed by a robot is inherently ambiguous. Simultaneously identifying the body part being observed and the motion the person undergoing is a challenging problem, and has not been well studied in the past. In this paper, we propose a novel method solving the body part and motion identification problem in a unified framework. The relative position of an observed part with respect to the whole body and the motion type are treated as continuous and discrete labels, respectively, and the most probable labeling is inferred by structured learning. A fast part-distribution estimation is introduced to reduce the computational cost. The proposed approach is able to identify different body parts without explicitly building models for each single part, and to recognize the motion with only partial body observations. The proposed approach is evaluated using actual videos captured by a human-following robot as well as the synthesized videos from the public UCF50 dataset, originally developed for action recognition. The result demonstrates the effectiveness of the approach. [ABSTRACT FROM AUTHOR]

The vortex formed around the rolling ball and the high pressure region formed around the ball-raceway contact zone are the principle factors that barricades the lubricant entering the bearing cavity, and further causes improper lubrication. The investigation of the air phase flow inside the bearing cavity is essential for the optimization of the oil-air two-phase lubrication method. With the revolutionary reference frame describing the bearing motion, a highly precise air phase flow model inside the angular contact ball bearing cavity was build up. Comprehensive factors such as bearing revolution, ball rotation, and cage structure were considered to investigate the influences on the air phase flow and heat transfer efficiency. The aerodynamic noise was also analyzed. The result shows that the ball spinning leads to the pressure rise and uneven pressure distribution. The air phase velocity, pressure and cage heat transfer efficiency increase as the revolving speed increases. The operating noise is largely due to the impact of the high speed external flow on the bearing. When the center of the oil-air outlet fixes near the inner ring, the aerodynamic noise is reduced. The position near the inner ring on the bigger axial side is the ideal position to fix the lubricating device for the angular contact ball bearing. [ABSTRACT FROM AUTHOR]