In the power system, the harmonic is one of the important concerns of power quality, which has a significant impact on the safe operation of power grids and the energy efficiency for users. When there are not enough s...
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In the power system, the harmonic is one of the important concerns of power quality, which has a significant impact on the safe operation of power grids and the energy efficiency for users. When there are not enough special harmonic monitoring devices installed on public transport, it is important to identify the harmonic source and assess the harmonic contribution based on the load data of the user's electricity meter. This paper proposes a data selection and matching idea based on change-point monitoring (CPM), canonical correlation analysis (CCA) and dynamic adjusted shapecontext (DASC) for determining the harmonic contributions of individual customers and utilities. In the proposed approach, the CPM is used to select the valuable data by detecting the violent fluctuation area of harmonic voltage and the DASC to determine the harmonic contribution of individual customers by matching the user load sequence and harmonic voltage sequence based on CCA, filtering the harmonic contribution from the utility side. Simulation data is used to evaluate the performance of the method and a real-world case study is also conducted. It is found that the proposed technique can be used to reveal the harmonic impact of the loads if data is selected resonably.
In order to solve the problem that it is difficult to accurately judge whether the Robotic Cow-Nosed Ray is similar to the real cow-nosed ray, the similarity evaluation method of the Robotic Cow-Nosed Ray is presented...
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In order to solve the problem that it is difficult to accurately judge whether the Robotic Cow-Nosed Ray is similar to the real cow-nosed ray, the similarity evaluation method of the Robotic Cow-Nosed Ray is presented in this paper. First, we collect images from different perspectives of the Robotic Cow-Nosed Ray and the cow-nosed ray, extracted their profiles and used Jitendra's sampling to ensure that the points were as uniform as possible to fully reflect the profile features of the cow-nosed ray. Second, we use the shapecontext operator to calculate the similarity evaluation matrix, and measure the matching degree of point pairs by normalized shortest distance. Last, calculate the similarity between each view through the shapecontext distance. The similarity of each view is weighted and summed to obtain the overall shape similarity of the Robotic Cow-Nosed Ray. The value of similarity is calculated based on the shape context algorithm, which points out the direction for the shape and structure optimization of the Robotic Cow-Nosed Ray.
The fusion between visible and infrared images captured by unmanned aerial vehicles (UAVs), both complementary to each other, can improve the reliability of target detection and recognition and other tasks. The images...
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The fusion between visible and infrared images captured by unmanned aerial vehicles (UAVs), both complementary to each other, can improve the reliability of target detection and recognition and other tasks. The images captured by UAV are featured by high dynamics and complex air-ground target background. Pixel-level matching should be conducted for the two different-source images, prior to their fusion. Therefore, an improved matching algorithm has been proposed that combines the improved Shi-Tomasi algorithm with the shapecontext (SC)-based algorithm. First, the Shi-Tomasi algorithm is employed to conduct feature-point detection in the scale space. The tangential direction of the edge contour where the feature-point lies is taken as its main direction, so as to guarantee the algorithm's rotational invariance. Then, this paper conducts the block description for the extracted feature-point within the n x n neighborhood of its edge contour to obtain its descriptors. Finally, a fast library for approximate nearest neighbors matching algorithm is adopted to match all the feature-points. And the experimental results show that, in the scene where the shape of the main target is clear, the algorithm can achieve better matching and registration results for infrared and visible images that have been transformed through rotation, translation, or zooming. (C) 2016 Society of Photo-Optical Instrumentation Engineers (SPIE)
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