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pruning algorithm for Voronoi skeletons

ELECTRONICS LETTERS 2010年第1期46卷 39-40页

作者： Beristain, A. Grana, M. Univ Basque Country GIC Fac Informat San Sebastian San Sebastian 20018 Spain

A robust and efficient binary image shape skeleton computation procedure in 2D, including a pruning procedure, is presented. The procedure is as follows: first the shape contour is subsampled along its arclength, then the Voronoi skeleton is computed from the resulting reduced contour set of points, and finally a novel two-stage pruning procedure is applied to obtain a simplified skeleton. Besides the information reduction, this pruning makes the skeleton more robust to noise. pruning can be done in real time thanks to some useful properties of Voronoi skeletons. Here it is proved that if the two end points of a Voronoi segment are inside the shape, then the entire segment is contained in the shape. A prototype implementation performs in real-time (60 frames per second) on an off-the-shelf computer. Testing it on an in-house but publicly available image database shows the stability and robustness of the approach.

关键词： Voronoi skeleton binary image shape skeleton computation computational geometry pruning algorithm shape recognition

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Improved pruning algorithm for Gaussian mixture probability hypothesis density filter

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Journal of Systems Engineering and Electronics 2018年第2期29卷 229-235页

作者： NIE Yongfang ZHANG Tao Department of Automation Tsinghua University Department of Strategic Missile and Underwater Weapon Naval Submarine Academy

With the increment of the number of Gaussian components, the computation cost increases in the Gaussian mixture probability hypothesis density(GM-PHD) filter. Based on the theory of Chen et al, we propose an improved pruning algorithm for the GM-PHD filter, which utilizes not only the Gaussian components’ means and covariance, but their weights as a new criterion to improve the estimate accuracy of the conventional pruning algorithm for tracking very closely proximity targets. Moreover, it solves the end-less while-loop problem without the need of a second merging step. Simulation results show that this improved algorithm is easier to implement and more robust than the formal ones.

关键词： Gaussian mixture probability hypothesis density(GM-PHD) filter pruning algorithm proximity targets clutter rate

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pruning algorithm for Multi-objective Optimization

Pruning Algorithm for Multi-objective Optimization

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10th International Joint Conference on Computer Science and Software Engineering (JCSSE)

作者： Sudeng, Sufian Wattanapongsakorn, Naruemon King Mongkuts Univ Technol Thonburi Dept Comp Engn Bangkok 10140 Thailand

ISBN: (纸本)9781479908066;9781479908059

Because of non-existence of an ideal single solution in Multi-objective optimization frameworks, the set of optimal solutions is required to be well spread and uniformly covering wide area of Pareto front. The decision maker (DM) still work hard to compromise the trade-offs solutions based on his/her preferences. In this paper, we proposed a pruning algorithm that can filter out undesired solutions and provides more robust trade-offs solutions to the DM. Our algorithm is called adaptive angle based pruning algorithm with bias intensity tuning (ADA). The pruning rationale is increasing the dominated area for the purpose of removing solutions that only marginally improves in some objectives while being significantly worse in other objectives. The extra angles are expanded from the regular dominated area. The bias intensity parameter () is introduced in order to approximate the portions of desirable solutions based on DM's opinions. We chose several benchmark problems with different difficulties including two and three objectives problems. The experimental result has shown that our pruning algorithm provides robust sub-set of Pareto-optimal solutions on several benchmark problems. The pruned Pareto-optimal solutions distributed and covered multiple regions instead of single region of Pareto front. In addition, it's clearly shown in bi-objective problems that the pruned Pareto-optimal solutions are located at knee regions of the Pareto front.

关键词： Multi-objective optimization pruning algorithm ADA pareto-optimal solutions

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Prediction of pre-miRNA with multiple stem-loops using pruning algorithm

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COMPUTERS IN BIOLOGY AND MEDICINE 2013年第5期43卷 409-416页

作者： Song, Xiaofeng Wang, Minghao Chen, Yi-Ping Phoebe Wang, Huating Han, Ping Sun, Hao Nanjing Univ Aeronaut & Astronaut Dept Biomed Engn Nanjing 210016 Jiangsu Peoples R China La Trobe Univ Fac Sci Technol & Engn Melbourne Vic Australia Chinese Univ Hong Kong Dept Obstet & Gynaecol Shatin Hong Kong Peoples R China Nanjing Med Univ Affiliated Hosp 1 Nanjing 210029 Jiangsu Peoples R China Chinese Univ Hong Kong Li Ka Shing Inst Hlth Sci Shatin Hong Kong Peoples R China Chinese Univ Hong Kong Dept Chem Pathol Shatin Hong Kong Peoples R China

In addition to experimental identification of pre-miRNAs, the computational prediction method is also becoming a hot research spot. Most existing prediction methods are usually excluding those pre-miRNAs with multiple loops. But as more and more miRNA have been identified, quite a number of miRNA precursor with multiple loops have been found. Therefore, determining how to effectively identify pre-miRNAs with multiple loops from the control dataset with multiple loops is an imperative problem. In this work, a pruning algorithm is presented to identify the main branch from the multiple stem-loops of pre-miRNA. A stack algorithm is employed to describe the secondary structure of pre-miRNA in four different patterns, and a recursive algorithm is employed to split the multiple stem-loops of pre-miRNA into several small branches, and to identify its main branch. Statistic results indicate that the information of the main branch can be represented as the whole sequence of pre-miRNA. Some features of main branch are extracted to describe pre-miRNA intrinsic features, and SVM classifier is implemented to recognize real pre-miRNA with multiple stem-loops. Based on training and testing on dataset from miRBase12.0, SVM classifier achieves sensitivity of 75.76% on RM-POS and specificity of 98.12% on RM-CDS, and specificity of 91.28% on RM-NCR. The obtained results indicated that the information of main branch after pruning can represent intrinsic features of pre-miRNA with multiple stem-loops. The proposed method in this work provides a powerful predicting method to recognize the real pre-miRNA with multiple stem-loops. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： MicroRNA precursor Multiple stem-loops pruning algorithm

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A pruning algorithm preserving modeling capabilities for polycrystalline data

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COMPUTATIONAL MECHANICS 2021年第6期68卷 1407-1419页

作者： Farooq, Harris Ryckelynck, David Forest, Samuel Cailletaud, Georges Marano, Aldo PSL Univ MINES ParisTech CNRS UMR 7633 MAT Ctr Mat BP 87 F-91003 Evry France Univ Cambridge Dept Mat Sci & Met Cambridge England

We are exploring the idea of data pruning via hyperreduction modeling. The main novelty of this paper is a lossy data compression/decompression approach for ploycrystalline data, which is based on a hyperreduction scheme that preserves data driven modeling capabilities after compression. We assume to know a mechanical model whose equations are satisfied by the data. It is shown that the proposed reconstruction of the data performs an oblique projection of selected original data. This is achieved by the solution of reduced mechanical equations. High resolution crystal plasticity finite element simulations demand computational and storage resources that are unusual, especially in cases where hundreds of grains are interacting under cyclic loading. The development of image-based modeling via computed tomography highlights the problem of long-term storage of simulation data by using data pruning. The present paper focuses on modeling cyclic strain-ratcheting as an example of numerical modeling that the proposed algorithm preserves. The size of the remaining sampled data can be user-defined, depending on the needs concerning storage space. The relevance of the pruned data is tested afterwards for statistics on the predicted strain, as if full finite element data were available. The proposed method is compared to the Gappy POD method, when no additional modeling step is expected after data pruning.

关键词： Model order reduction CPFEM Ratcheting Data compression pruning algorithm Material database

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A terminal distribution network black-start optimization method based on pruning algorithm considering distributed generators

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ENERGY REPORTS 2022年 8卷 237-244页

作者： Zhu, Yuhong Zhou, Yongzhi Wang, Zhe Zhou, Chenlong Gao, Bing Zhejiang Univ Polytech Inst Hangzhou Peoples R China State Grid Yongjia Power Supply Co Wenzhou Peoples R China

The main objective of the black-start in a terminal distribution network is restoring the regional distribution network in a blackout. Aiming at the problem that there are multiple optimization objectives in the process of black start, a black start model with comprehensive consideration is established, considering the minimum switching operation cost, maximum recovery efficiency and priority start of important load. Then, the constraints of voltage and frequency stability of the distribution network in black-start are put forward. Aiming at the problem that heuristic algorithm such as binary particle swarm optimization cannot get the global optimal solution, a black-start method based on pruning algorithm is proposed, which has higher efficiency than traditional algorithm as well as the ability to search the global optimal method. Finally, the efficiency of the above method is verified in the terminal distribution network of a coastal mountain county. (c) 2021 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Black-start pruning algorithm Distributed generators Distribution network

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THE COMBINED STATISTICAL STEPWISE AND ITERATIVE NEURAL NETWORK pruning algorithm

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INTELLIGENT AUTOMATION AND SOFT COMPUTING 2009年第4期15卷 573-589页

作者： Fnaiech, Nader Fnaiech, Farhat Jervis, B. W. Cheriet, M. ESSTT Tunis 1008 Tunisia Lab Imagery Vis & Artificial Intelligence Montreal PQ H3C 1K3 Canada

In this paper. we present a new pruning algorithm formed by combining the Statistical Stepwise Method (SSM) [1] with the Iterative pruning (IP) [4] algorithms. This proposed algorithm (SSIP) is used to simultaneously remove unnecessary neurons or weight connections from a given feed-forward neural network (NN) in order to "Optimize" its structure. Some modifications to the previous pruning algorithms published in [1] and [4] are also reported. Two versions of the combined SSIP are considered: In the first version, SSIP1, the modified IP is first applied to the given neural network in order to prune insignificant units, and their tire modified SSM is applied to the pruned network to remove unnecessary links. In the second version. SSIP2. the above procedure is applied to each layer in turn. working from the input layer to the output layer. The performances of the algorithms are compared using two real world applications, brain disease detection and texture classification. and the superiority of the SSIP pruning algorithm is demonstrated. This new algorithm can eliminate approximately 59% of the links in the initial oversized network in order to improve performance by approximately +39% and +26% for the sensitivity of learning, and generalization, respectively.

关键词： Feed-forward neural network (NN) pruning algorithm Iterative pruning algorithm (IP) Statistical Stepwise Method (SSM) SSM-Iterative pruning algorithm (SSIP)

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Neural Network pruning algorithm with Penalty OBS Process

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Journal of China University of Mining and Technology 2005年第1期15卷 52-55页

作者： MENGJiang WANGYao-cai LIUTao ShoolofInformation＆ElectricalEngineering ChinaUniversityofMining＆TechnologyXuzhouJiangsuProvince221008China

Aimed at the great computing complexity of optimal brain surgeon (OBS) process, a pruning algorithm with penalty OBS process is presented. Compared with sensitive and regularized methods, the penalty OBS algorithm not only avoids time-consuming defect and low pruning efficiency in OBS process, but also keeps higher generalization and pruning accuracy than Levenberg-Marquardt method.

关键词： generalization neural network pruning algorithm penalty method optimal brain surgeon CLC number:TP 183

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Sparselized higher-order neural network and its pruning algorithm

Sparselized higher-order neural network and its pruning algo...

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2nd IEEE World Congress on Computational Intelligence (WCCI 98)

作者： Li, WC Wang, YB Li, WJ Zhang, J Li, JY Hebei Univ Technol Tianjin Peoples R China

ISBN: (纸本)0780348605

In this paper, the fully-connected higher-order neuron and sparselized higher-order neuron are introduced, the mapping capabilities of the fully-connected higher-order neural networks are investigated, and that arbitrary Boolean function defined from (0,1)(N) can be realized by fully-connected higher-order neural networks is proved. Based on this, in order to simplify the networks' architecture, a pruning algorithm of eliminating the redundant connection weights is also proposed, which can be applied to the implementation of sparselized higher-order neural classifier and other networks. The simulated results show the effectiveness of the algorithm.

关键词： higher-order neural networks redundant connection weights sparselize pruning algorithm

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Scene Text Detection Based on Text Probability and pruning algorithm

Scene Text Detection Based on Text Probability and Pruning A...

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12th International Conference on Intelligent Computing (ICIC)

作者： Zhou, Gang Liu, Yajun Shi, Fei Hu, Ying Xinjiang Univ Inst Informat Sci & Technol Shengli Rd 14 Urumqi 830001 Peoples R China

ISBN: (纸本)9783319422978;9783319422961

As the scene text detection and localization is one of the most important steps in text information extraction system, it had been widely utilized in many computer vision tasks. In this paper, we introduce a new method based on the maximally stable extremal regions (MSERs). First, a coarse-to-fine classier estimates the text probability of the ERs. Then, a pruning algorithm is introduced to filter non-text MSERs. Secondly, a hybrid method is performed to cluster connected components (CCs) as candidate text strings. Finally, a fine design classifier decides the text strings. The experimental results show our method gets a state-of-the-art performance on the ICDAR2005 dataset.

关键词： Scene text detection Maximum stable extreme regions Text probability pruning algorithm

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