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Edge-pixels-based support vector data description for specific land-cover distribution mapping

JOURNAL OF APPLIED REMOTE SENSING 2015年第1期9卷 096034-1-096034-20页

作者： Shuai, Guanyuan Zhang, Jinshui Deng, Lei Zhu, Xiufang Beijing Normal Univ Coll Resources Sci & Technol State Key Lab Earth Surface Proc & Resource Ecol Beijing 100875 Peoples R China Capital Normal Univ Coll Resource Environm & Tourism Beijing 100048 Peoples R China

An edge-pixels-based support vector data description (EPSVDD) method has been developed for improving one-class classification accuracy. The proposed method was validated in two experiments: a simulated experiment and an actual experiment. In the simulated experiment, a ring segmentation search method was performed to segment the wheat spectral feature for deriving training samples of different spectral responses. As the training data moved from the center to the edge of wheat distribution, the hypersphere expanded and the overall accuracy (OA) simultaneously increased, highlighting the potential advantage of edge pixels in SVDD classification. In the actual experiment, edge training samples were manually acquired from geographical parcel boundary and minimum noise fraction (MNF) scatterplots for both wheat and bare-land classes. For the wheat class, EPSVDD yielded an improved classification with an OA of 92.71% and a producer's accuracy of 95.81%, which were higher than those of conventional SVDD method using typical training samples. Similarly, for the bare-land class, the OA of the EPSVDD was 92.53%, which was also significantly higher than traditional SVDD method. Then, SVDD classifications were carried out and repeated 10 times using different training set sizes. Mean OAs were almost higher than 0.9 with variance less than 0.03 using edge training samples, while highest OAs for wheat and bare land classes were 0.74 and 0.81, respectively, using random sampling method. The EPSVDD can effectively select the informative training sample for SVDD classifier to improve the accuracy of one-class classification. (C) 2015 Society of Photo-Optical Instrumentation Engineers (SPIE)

关键词： land cover one-class classification edge pixel support vector data description

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Remaining useful life prediction of industrial robot RV reducer with multiple deep networks and multicore support vector data description

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JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY 2024年第8期38卷 4037-4051页

作者： Ren, Guoai Wang, Zhihai Liu, Xiaoqin Song, Feng Kunming Univ Sci Technol Fac Mech Elect Engn Kunming 650500 Yunnan Peoples R China Kunming Univ Sci Technol Key Lab Adv Equipment Intelligent Mfg Technol Yun Kunming 650500 Yunnan Peoples R China

The remaining useful life prediction of Industrial robot RV reducer is challenging due to the strong redundancy, unstable degradation initiation point, and environmental interference that may obscure the key state information during long-term operation. To address this problem, this paper proposes a novel remaining useful life prediction method for robot RV reducer with multi-depth network and multi-kernel support vector data description. Firstly, the degradation features are constructed by the hidden layer node of deep belief network to reduce the interference and redundancy. Secondly, the first predicting time node is determined by multi-kernel support vector data description to locate the stable degradation initiation node. Then, the temporal convolutional network is applied to predict the remaining useful life in the degradation stage, which improves the prediction accuracy. Finally, the effectiveness of the proposed method is verified by the accelerated fatigue experiment of a self-made robot.

关键词： Deep belief network Industrial robot RV reducer Remaining useful life prediction support vector data description Temporal convolutional network

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A Density-focused support vector data description Method

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QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL 2014年第6期30卷 879-890页

作者： Phaladiganon, Poovich Kim, Seoung Bum Chen, Victoria C. P. Univ Texas Arlington Dept Ind & Mfg Syst Engn Arlington TX 76019 USA Korea Univ Sch Ind Management Engn Seoul South Korea

In novelty detection, support vector data description (SVDD) is a one-class classification technique that constructs a boundary to differentiate novel from normal patterns. However, boundaries constructed by SVDD do not consider the density of the data. data points located in low density regions are more likely to be novel patterns because they are remote from their neighbors. This study presents a density-focused SVDD (DFSVDD), for which its boundary considers both shape and the dense region of the data. Two distance measures, the kernel distance and the density distance, are combined to construct the DFSVDD boundary. The kernel distance can be obtained by solving a quadratic optimization, while support vectors are used to obtain the density distance. A simulation study was conducted to evaluate the performance of the proposed DFSVDD and was then compared with the traditional SVDD. The proposed method performed better than SVDD in terms of the area under the receiver operating characteristic curve. Copyright (c) 2014 John Wiley & Sons, Ltd.

关键词： support vector data description one-class classification density novelty detection data mining

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A new support vector data description method for machinery fault diagnosis with unbalanced datasets

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EXPERT SYSTEMS WITH APPLICATIONS 2016年 64卷 239-246页

作者： Duan, Lixiang Xie, Mengyun Bai, Tangbo Wang, Jinjiang China Univ Petr Sch Mech & Transportat Engn Beijing 102249 Peoples R China

In machinery fault diagnosis area, the obtained data samples under faulty conditions are usually far less than those under normal condition, resulting in unbalanced dataset issue. The commonly used machine learning techniques including Neural Network, support vector Machine, and Fuzzy C-Means, etc. are subject to high misclassification with unbalanced datasets. On the other hand, support vector data description is suitable for unbalanced datasets, but it is limited for only two class classification. To address the aforementioned issues, support vector data description based machine learning model is formulated with Binary Tree for multi-classification problems (e.g. multi fault classification or fault severity recognition, etc.) in machinery fault diagnosis. The binary tree structure of multiple clusters is firstly drawn based on the order of cluster-to-cluster distances calculated by Mahalanobis distance. support vector data description model is then applied to Binary Tree structure from top to bottom for classification. The parameters of support vector data description are optimized by Particle Swarm Optimization algorithm taking the recognition accuracy as objective function. The effectiveness of presented method is validated in the rotor unbalance severity classification, and the presented method yields higher classification accuracy comparing with conventional models. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Fault diagnosis Unbalanced datasets support vector data description Binary tree Mahalanobis distance

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Weighted support vector data description based on chaotic bat algorithm

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APPLIED SOFT COMPUTING 2017年 60卷 540-551页

作者： Hamidzadeh, Javad Sadeghi, Reza Namaei, Neda Sadjad Univ Technol Fac Comp Engn & Informat Technol Mashhad Iran Imam Reza Int Univ Dept Comp Engn Mashhad Iran

support vector data description (SVDD) is a support vector based learning algorithm for anomaly detection. In this method, the target is to form a boundary around the normalcy data by building a hyper-sphere. To gain noticeable accuracy, a control parameter is used to regulate the hyper-sphere volume. The value of this parameter depends on the data characteristics. Thus, there is no proper way to estimate it. On the other hand, the number of free parameters increases in the more improved versions of SVDD. In this paper, an evolutionary algorithm, Chaotic Bat Algorithm, is used with the aim of designing effective description of data. The proposed method, weighted SVDD based on Chaotic Bat Algorithm (WSVDD-CBA) is constructed based on a new weight and ergodicity of chaotic functions and automatic switching between global and local searches of Bat Algorithm (BA). To evaluate this method several experiments have been conducted based on 10-fold cross-validation over some data sets from UCI repository. Experimental results show the superiority of the proposed algorithm to state-of-the-art methods in the terms of classification accuracy, precision and recall rate measures. (C) 2017 Elsevier B.V. All rights reserved.

关键词： One class classification Weighted SVDD chaotic bat algorithm (WSVDD-CBA) support vector data description Switching search Ergodicitya

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Dynamic hypersphere based support vector data description for batch process monitoring

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CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2018年 172卷 17-32页

作者： Wang, Jianlin Liu, Weimin Qiu, Kepeng Yu, Tao Zhao, Liqiang Beijing Univ Chem Technol Coll Informat Sci & Technol Beijing 100029 Peoples R China

support vector data description (SVDD) is an efficient monitoring method that captures the spherically shaped boundary around the normal batch data and sets the control limit related to support vectors (SVs) for online monitoring. Using nonlinear transformation functions, SVDD constructs an irregular hypersphere in high dimensional space. When the batch process is complicated, the accuracy of monitoring will decrease with traditional control limit of SVDD. In this paper, dynamic hypersphere based support vector data description (DH-SVDD) is proposed for batch process monitoring. In training process, static hypersphere is built by the important SVs of training dataset. In testing process, dynamic hypersphere is built by the important SVs of combined dataset with current test sample and training dataset. If there is a significant change between these two hyperspheres, it means that the current test sample is an outlier. Thus, DH-SVDD has a relatively high monitoring accuracy because it fully considers relationship between the current test sample and the historical training dataset in high dimensional space. Comparison is conducted between the proposed DH-SVDD and traditional methods such as K-chart-SVDD, max limit SVDD and validation limit SVDD. The effectiveness of the DH-SVDD is also verified by a semiconductor etch process and a fed-batch penicillin fermentation process.

关键词： Batch processes support vector data description Online process monitoring Dynamic hypersphere

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Towards fast and parameter-independent support vector data description for image and video segmentation

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EXPERT SYSTEMS WITH APPLICATIONS 2019年第0期128卷 271-286页

作者： Slimene, Alya Zagrouba, Ezzeddine Univ Tunis El Manar Inst Super Informat Lab Rech Informat Modelisat & Traitement Vde Info Tunis Tunisia

Machine learning has become a pillar of today's expert and intelligent systems where a special attention has been drawn to unsupervised methods. support vector data description is one of the most interesting methods proposed in this context that addresses two main challenges: the absence of ground truth labels and the presence of outliers in data. It is well known that the ultimate problem has a great impact on a system performance since it typically prevents and limits its generalization and hence its failure. Even though the strengths of the support vector data description method, its application into multi class and large scale context, as computer vision systems, raises bottleneck issues due to its runtime complexity and mono-class context application. We propose, in this paper, an approach inspired from the movement behavior of particle swarm and relies on the use of core set paradigm to overcome the aforementioned drawbacks of support vector data description method. Indeed, since the proposed method uses some parameters corresponding to kernel function and swarm's movement formulation, we present and study in this paper some strategies to ensure a dynamic adaptation of these parameters. The main qualities of the proposed method is that it provides a fast scheme for clustering a set of points without neither a prior knowledge on the number of naturally occurring cluster in the data nor an assumption on clusters shapes. A quantitative and comparative performance assessment is carried out over the Berkeley and BuffaloXiph datasets. The results witness the efficiency of the method in terms of both classification accuracy and computational performance. These quantitative assessments reinforce the significance as well as the importance of embedding the proposed method in other intelligent systems application areas. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： support vector data description Swarm intelligence Unsupervised learning Image segmentation Video segmentation Clustering method

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Using a dynamically selective support vector data description model to discover novelties in the control system of electric arc furnace

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MEASUREMENT & CONTROL 2020年第7-8期53卷 1049-1058页

作者： Zhang, Jiong Wang, Yue Li, Qian Wang, Biao Shandong Univ Sch Mech Engn Jinan Peoples R China Shandong Inst Commerce & Technol Sch Informat Jinan Peoples R China Liaoning Vocat Coll Ecol Engn Dept Fundamental Teaching Shenyang Peoples R China Shenyang Med Coll Sch Med Informat & Engn Shenyang Peoples R China Shenyang Aerosp Univ Sch Automat Shenyang 110136 Peoples R China

As increasing data-driven control strategies are applied in electric arc furnace systems, the problem of novelty detection has drawn more attentions than before. The presence of outliers should be the main obstacle in practical applications for these advanced control techniques. To this end, this paper proposes a dynamically selective support vector data description model to discover novelties in electric arc furnace. In this model, support vector data description plays the role of base detector. Artificial outliers are generated with two objectives, one is to assist the dynamic selection, and the other is to optimize two parameters of support vector data description. Then clustering technique is used to determine the validation set for each test point. Finally, a probabilistic method is used to compute the competence of base detectors. In contrast to other novelty ensembles that have parallel structures, our ensemble model has a dynamic selection mechanism that could facilitate the mining of the potential of base detectors. Three synthetic and three real-world datasets are used to validate the effectiveness of the proposed detection model. Experimental results have approved our method by comparing it with several competitors.

关键词： Electric arc furnace novelty detection support vector data description ensemble learning

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Multi-Block Fault Detection for Plant-Wide Dynamic Processes Based on Fault Sensitive Slow Features and support vector data description

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IEEE ACCESS 2020年 8卷 120737-120745页

作者： Zhai, Chao Sheng, Xiaochen Xiong, Weili Jiangnan Univ Minist Educ Key Lab Adv Proc Control Light Ind Wuxi 214122 Jiangsu Peoples R China

This study proposes a multi-block fault detection method based on fault-sensitive slow features for large-scale dynamic industrial processes. Firstly, slow feature analysis (SFA) can effectively extract the process dynamic information. However, the slowest changing features may not contain more fault information. Thus, through the analysis of T-2 statistic in SFA-based process monitoring model, a fault sensitivity coefficient is defined as a new slow feature sorting criterion to select the most sensitive slow features to fault in each variable direction. Then, considering the unknown characteristics of the fault in the real-time monitoring process, the monitoring model is established for each dimension of variables based on the multi-block strategy. Finally, the support vector data description is used as a fusing method to integrate the statistics calculated in each sub-block to obtain an intuitive detection result. The effectiveness and superiority of the proposed strategy are demonstrated by the experiments on Tennessee Eastman benchmark process and an actual blast furnace ironmaking process.

关键词： Multi-block strategy slow feature analysis fault sensitivity coefficient support vector data description fault detection

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Multi-mode process monitoring based on a novel weighted local standardization strategy and support vector data description

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Journal of Central South University 2016年第11期23卷 2896-2905页

作者：赵付洲宋冰侍洪波 Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education(East China University of Science and Technology) Shanghai 200237China

There are multiple operating modes in the real industrial process, and the collected data follow the complex multimodal distribution, so most traditional process monitoring methods are no longer applicable because their presumptions are that sampled-data should obey the single Gaussian distribution or non-Gaussian distribution. In order to solve these problems, a novel weighted local standardization(WLS) strategy is proposed to standardize the multimodal data, which can eliminate the multi-mode characteristics of the collected data, and normalize them into unimodal data distribution. After detailed analysis of the raised data preprocessing strategy, a new algorithm using WLS strategy with support vector data description(SVDD) is put forward to apply for multi-mode monitoring process. Unlike the strategy of building multiple local models, the developed method only contains a model without the prior knowledge of multi-mode process. To demonstrate the proposed method's validity, it is applied to a numerical example and a Tennessee Eastman(TE) process. Finally, the simulation results show that the WLS strategy is very effective to standardize multimodal data, and the WLS-SVDD monitoring method has great advantages over the traditional SVDD and PCA combined with a local standardization strategy(LNS-PCA) in multi-mode process monitoring.

关键词： multiple operating modes weighted local standardization support vector data description multi-mode monitoring

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