Operation Status Monitoring of Transmission Tower in Power System based on Data Fusion

This paper studies the application of data fusion technology in power system to solve some difficult problems in this complex energy system. A transmission tower identification and bird nest detection method based on corner, line, color and shape features is proposed. Through LSD (Line Segment Detection) and Harris corner detection method, the straight line segment and corner point in the image are extracted respectively. Combined with triangle method, the actual tilt angle of tower is measured; According to the nesting rule of birds in transmission towers, the basic unit segmentation algorithm of transmission towers is proposed, and the basic unit segmentation of transmission towers is realized by using the local maximum of the target pixel row statistical histogram. The algorithm proposed in this paper can effectively solve the problems of on-line measurement of tilt angle of transmission tower and on-line detection of bird's nest, which will lay a theoretical foundation for on-line monitoring of transmission tower status.

A Robust Method for Detecting Wind-Fallen Stems from Aerial RGB Images Using a Line Segment Detection Algorithm

Increased frequencies and windspeeds of storms may cause disproportionately high increases in windthrow damage. Storm-felled trees provide a surplus of breeding material for bark beetles, often resulting in calamities in the subsequent years. Thus, the timely removal of fallen trees is regarded as a good management practice that requires strategic planning of salvage harvesting. Precise information on the number of stems and their location and orientation are needed for the efficient planning of strip roads and/or cable yarding lines. An accurate assessment of these data using conventional field-based methods is very difficult and time-consuming; remote sensing techniques may be a cost-efficient alternative. In this research, a methodology for the automatic detection of fallen stems from aerial RGB images is presented. The presented methodology was based on a line segment detection algorithm and proved to be robust regarding image quality. It was shown that the method can detect frequency, position, spatial distribution and orientation of fallen stems with high accuracy, while stem lengths were systematically underestimated. The methodology can be used for the optimized planning of salvage harvesting in the future and may thus help to reduce consequential bark beetle calamities after storm events.

The Buffon needle problem for Lévy distributed spacings and renewal theory

What is the probability that a needle dropped at random on a set of points scattered on a line segment does not fall on any of them? We compute the exact scaling expression of this hole probability when the spacings between the points are independent identically distributed random variables with a power-law distribution of index less than unity, implying that the average spacing diverges. The theoretical framework for such a setting is renewal theory, to which the present study brings a new contribution. The question posed here is also related to the study of some correlation functions of simple models of statistical physics.

Line Segment Matching Fusing Local Gradient Order and Non-Local Structure Information

Line segment matching is essential for industrial applications such as scene reconstruction, pattern recognition, and VSLAM. To achieve good performance under the scene with illumination changes, we propose a line segment matching method fusing local gradient order and non-local structure information. This method begins with intensity histogram multiple averaging being utilized for adaptive partitioning. After that, the line support region is divided into several sub-regions, and the whole image is divided into a few intervals. Then the sub-regions are encoded by local gradient order, and the intervals are encoded by non-local structure information of the relationship between the sampled points and the anchor points. Finally, two histograms of the encoded vectors are, respectively, normalized and cascaded. The proposed method was tested on the public datasets and compared with previous methods, which are the line-junction-line (LJL), the mean-standard deviation line descriptor (MSLD) and the line-point invariant (LPI). Experiments show that our approach has better performance than the representative methods in various scenes. Therefore, a tentative conclusion can be drawn that this method is robust and suitable for various illumination changes scenes.

An Analysis of Zeno’s Paradox and Non-measurable Sets Based on Dialectical Infinity

The Problem of Continuity and Discreteness is the basic problem of philosophy and mathematics. For a long time, there is no clear understanding of this problem, which leads to the stagnation of the problem, because the essence of the problem is a problem of finity and infinity. The essence of the philosophical thought on which the mathematical definition of “line segment is composed of dots” is the idea of actual infinity, and geometric dot is equivalent to algebraic zero in terms of measure properties. In view of the above contradictions, this paper presents two solutions satisfying both the philosophical and mathematical circles based on the view of dialectical infinity, and the authors make a deep analysis of Zeno’s paradox and the non-measurable set based on both solutions.

Gird Based Line Segment Detector and Application: Vision System for Autonomous Ship Small Assembly Line

The shipbuilding industry demands intelligent robot, which is capable of various tasks without laborious pre-teaching or programming. Vision system guided robots could be a solution for autonomous working. This paper introduces the principle and technique details of a vision system that guides welding robots in ship small assembly production. TOF sensors are employed to collect spatial points of workpieces. Huge data amount and complex topology bring great difficulty in the reconstruction of small assemblies. A new unsupervised line segment detector is proposed to reconstruct ship small assemblies from spatial points. Verified using data from actual manufacturing, the method of this paper demonstrated good robustness which is a great advantage for industrial applications. This paper’s work has been implemented in shipyards and shows good commercial potential. Intelligent, flexible industrial robots could be implemented with the findings of this study, which will push forward intelligent manufacturing in the shipbuilding industry.