Simulation of sports action recognition based on maximum spanning tree algorithm

Abstract The development and planning of optimal network reconfiguration strategies for electrical networks is greatly improved with proper application of graph theory techniques. This paper investigates the application of Kruskal's maximal spanning tree algorithm in finding the optimal radial networks for different loading scenarios from an interconnected meshed electrical network integrated with distributed generation (DG). The work is done with an objective to assess the prowess of Kruskal's algorithm to compute, obtain or derive an optimal radial network (optimal maximal spanning tree) that gives improved voltage stability and highest loss minimization from among all the possible radial networks obtainable from the DG-integrated mesh network for different time-varying loading scenarios. The proposed technique has been demonstrated on a multiple test systems considering time-varying load levels to investigate the performance and effectiveness of the suggested method. For interconnected electrical networks with the presence of distributed generation, it was found that application of Kruskal's algorithm quickly computes optimal radial configurations that gives the least amount of power losses and better voltage stability even under varying load conditions. Article Highlights Investigated network reconfiguration strategies for electrical networks with the presence of Distributed Generation for time-varying loading conditions. Investigated the application of graph theory techniques in electrical networks for developing and planning reconfiguration strategies. Applied Kruskal’s maximal spanning tree algorithm to obtain the optimal radial electrical networks for different loading scenarios from DG-integrated meshed electrical network.

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A Fast Two-Level Approximate Euclidean Minimum Spanning Tree Algorithm for High-Dimensional Data

Machine Learning and Data Mining in Pattern Recognition - Lecture Notes in Computer Science ◽

10.1007/978-3-319-96133-0_21 ◽

2018 ◽

pp. 273-287

Author(s):

Xia Li Wang ◽

Xiaochun Wang ◽

Xiaqiong Li

Keyword(s):

Spanning Tree ◽

Minimum Spanning Tree ◽

High Dimensional Data ◽

High Dimensional ◽

Tree Algorithm

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Efficient 3D stereo vision stabilization for multi-camera viewpoints

Bulletin of Electrical Engineering and Informatics ◽

10.11591/eei.v8i3.1518 ◽

2019 ◽

Vol 8 (3) ◽

pp. 882-889

Author(s):

Sharif Shah Newaj Bhuiyan ◽

Othman O. Khalifa

Keyword(s):

Stereo Vision ◽

Linear Order ◽

Spanning Tree ◽

Detection Method ◽

Corner Detection ◽

Geometric Transformation ◽

Image Stabilization ◽

Tree Algorithm ◽

Stabilization Process ◽

Key Points

In this paper, an algorithm is developed in 3D Stereo vision to improve image stabilization process for multi-camera viewpoints. Finding accurate unique matching key-points using Harris Laplace corner detection method for different photometric changes and geometric transformation in images. Then improved the connectivity of correct matching pairs by minimizing the global error using spanning tree algorithm. Tree algorithm helps to stabilize randomly positioned camera viewpoints in linear order. The unique matching key-points will be calculated only once with our method. Then calculated planar transformation will be applied for real time video rendering. The proposed algorithm can process more than 200 camera viewpoints within two seconds.

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Penerapan Bayesian Network dalam Memodelkan Kondisi Ekonomi Hijau Indonesia di Era Pandemi Berdasarkan Big Data

Seminar Nasional Official Statistics ◽

10.34123/semnasoffstat.v2021i1.1023 ◽

2021 ◽

Vol 2021 (1) ◽

pp. 1054-1064

Author(s):

Salwa Rizqina Putri ◽

Thosan Girisona Suganda ◽

Setia Pramana

Keyword(s):

Big Data ◽

Bayesian Network ◽

Data Model ◽

Spanning Tree ◽

Cross Validation ◽

Parameter Learning ◽

Maximum Spanning Tree

Untuk mendukung pertumbuhan ekonomi hijau Indonesia, diperlukan analisis lebih lanjut terkait aktivitas ekonomi di masa pandemi dan keterkaitannya dengan kondisi lingkungan. Penelitian ini bertujuan untuk menerapkan pendekatan Bayesian Network dalam memodelkan kondisi ekonomi hijau Indonesia di masa pandemi berdasarkan variabel-variabel yang disinyalir dapat berpengaruh seperti aktivitas ekonomi, kualitas udara, tingkat mobilitas penduduk, dan kasus positif COVID-19 yang diperoleh melalui big data. Model Bayesian Network yang dikonstruksi secara manual dengan algoritma Maximum Spanning Tree dipilih sebagai model terbaik dengan rata-rata akurasi 5-cross validation dalam memprediksi empat kelas PDRB sebesar 0,83. Model terbaik yang dipilih menunjukkan bahwa kondisi ekonomi Indonesia di era pandemi secara langsung dipengaruhi oleh intensitas cahaya malam (NTL) yang menunjukkan aktivitas ekonomi, kualitas udara (AQI), dan kasus positif COVID-19. Analisis parameter learning menunjukkan bahwa pertumbuhan ekonomi provinsi-provinsi Indonesia masih cenderung belum sejalan dengan terpeliharanya kualitas udara sehingga usaha untuk mencapai kondisi ekonomi hijau masih harus ditingkatkan.

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