This paper introduces a robot collision avoidance method using Kinect and global vision to improve the industrial robot's security. Global vision is installed above the robot, and a combination of the background-difference method and the Otsu algorithm are used. Human skeleton detection is then Keywords: robot safety, human body skeleton detection, Kalman filter, global vision, security controlCopyright © 2017 Universitas Ahmad Dahlan. All rights reserved. IntroductionSafety control strategy must be taken to avoid the collision between the moving robot and the operators in the working space [1]. Strategies used in safe human-robot collaboration (HRC) can be broadly divided into two categories: pre-collision strategy [2-4] and post-collision strategy [5]. The former strategy detects the danger before the collision and takes measures to prevent imminent collision. While the latter one requires higher real-time performance during the collision to suppress the impact force, and ensure the security of operators and robots. Therefore, pre-collision methods can normally achieve safer result in implementation of HRC. Researchers have been working on different pre-collision methods and presenting some important findings and solutions. As one of the pre-collision methods, sensors, such as ultrasonic [6] and photoelectric sensors, are installed on the robot to detect the man-machine position. The sensors identify the danger and then the robots will be immediately stopped. Such security strategy is very simple, and greatly reduce the reliability of collision avoidance and the work efficiency of the robot.The robot should follow the collision avoidance strategy at all run time. A more reliable safety strategy is essential. Sanderud [7] have presented proactive safety strategy based on a quantified measure of risk for human robot collaboration. The risk field is established based on an analysis of the human's movement and the consequence of a collision with different human limbs, combined with a likelihood analysis. Similarly, a simulation tool using real-world geometrical data was proposed to investigate different algorithms and safety strategies [8]. Kulić [9, 10] suggested a method of robot safe trajectory planning based on the mechanical principle of the minimizing danger index. However, the application of this method is limited by the large number of environmental data required.One of the most common and useful technologies used to detect intruding obstacles is robot vision. The robot vision has been developed in recent years, and could be a feasible solution in the collision avoidance strategy. A robot manipulator automatic path planning strategy based on 3D-TOF sensor was presented in peg-in-hole assembly process [11]. Similarly, a two fold strategy was presented to automatically generate safe path for robot trajectory based on data from TOF sensor [12]. Kuhn [13] used monocular vision to measure the human-robot distance in manipulator space and thus identified the risk. Yet this method is not conducive to th...
The burgeoning amount of textual data in distributed sources combined with the obstacles involved in creating and maintaining central repositories motivates the need for effective distributed information extraction and mining techniques. Recently, as the need to mine patterns across distributed databases has grown, Distributed Association Rule Mining (D-ARM) algorithms have been developed. These algorithms, however, assume that the databases are either horizontally or vertically distributed. In the special case of databases populated from information extracted from textual data, existing D-ARM algorithms cannot discover rules based on higher-order associations between items in distributed textual documents that are neither vertically nor horizontally distributed, but rather a hybrid of the two. In this article we present D-HOTM, a framework for Distributed Higher Order Text Mining. Unlike existing algorithms, D-HOTM requires neither full knowledge of the global schema nor that the distribution of data be horizontal or vertical. D-HOTM discovers rules based on higher-order associations between distributed database records containing the extracted entities. In this paper, two approaches to the definition and discovery of higher order itemsets are presented. The implementation of D-HOTM is based on the TMI [20] and tested on a cluster at the National Center for Supercomputing Applications (NCSA). Results on a real-world dataset from the Richmond, VA police department demonstrate the performance and relevance of D-HOTM in law enforcement and homeland defense.
In recent years, the country's gross national product has been on the rise. The gap between urban and rural areas has reduced, but there are still some problems that make the development of urban and rural areas not coordinated. Many rural areas are relatively backward in information, traditional farming methods are still used in agricultural production. To some extent, it has hindered the development of rural economy. the state have repeatedly proposed to narrow the urban-rural gap, achieve common prosperity, help farmers in poor rural areas to master advanced production technology, and then get rid of poverty. The training of rural talents with techniques needs to be carried out in stages, adjust measures to local conditions, train excellent technical trainers, establish an integrated link among counties, townships and rural areas, take full advantage of Internet technology. Through the above suggestions, let the rural problems develop towards a good direction. In this way, sustainable development in rural areas can be realized.
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