Wood Board Defects Sorting Based on Method of Possibilistic C-Means Improved Support Vector Data Description

Fault signal is always a problem that interferes with the operation of fiber board gluing control system. This paper proposes a Possibilistic C-Means (PCM) improved support vector data description (SVDD) method to construct a multi-classifier for the fault diagnosis of fiber gluing control system. In the proposed method, SVDD is employed to construct a multi-classifier for the fault diagnosis in the gluing system. Because the original SVDD is sensitive to the noises and outliers, we use PCM to improve the accuracy of classifier and then conduct a series of experiments. The results of the experiments show that the method proposed can identify the actual situation accurately and provide a high identification rate up to 97%, which is better than original SVDD.

Power electronic circuits (PECs) are prone to various failures, whose classification is of paramount importance. This paper presents a data-driven based fault diagnosis technique, which employs a support vector data description (SVDD) method to perform fault classification of PECs. In the presented method, fault signals (e.g. currents, voltages, etc.) are collected from accessible nodes of circuits, and then signal processing techniques (e.g. Fourier analysis, wavelet transform, etc.) are adopted to extract feature samples, which are subsequently used to perform offline machine learning. Finally, the SVDD classifier is used to implement fault classification task. However, in some cases, the conventional SVDD cannot achieve good classification performance, because this classifier may generate some so-called refusal areas (RAs), and in our design these RAs are resolved with the one-against-one support vector machine (SVM) classifier. The obtained experiment results from simulated and actual circuits demonstrate that the improved SVDD has a classification performance close to the conventional one-against-one SVM, and can be applied to fault classification of PECs in practice.

Anomaly detection using improved deep SVDD model with data structure preservation

Support Vector Data Description (SVDD) methods have been successfully applied to hyperspectral anomaly detection. Unfortunately, the performance of SVDD methods suffers when noisy or non-informative bands are present in the data. If a set of sparse features could be identified for these techniques, the resulting data may improve SVDD performance while enjoying the benefits of decreased processing overhead. Although band selection has been investigated in previous efforts, this work builds on recent research that has resulted in the development of a theoretical framework for signal classification with sparse representation using L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> measures.

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.

Black rice is an important rice species in Southeast Asia. It is a common phenomenon to pass low-priced black rice off as high-priced ones for economic benefit, especially in some remote towns. There is increasing need for the development of fast, easy-to-use, and low-cost analytical methods for authenticity detection. The feasibility to utilize near-infrared (NIR) spectroscopy and support vector data description (SVDD) for such a goal is explored. Principal component analysis (PCA) is used for exploratory analysis and feature extraction. Another two data description methods, i.e., k-nearest neighbor data description (KNNDD) and GAUSS method, are used as the reference. A total of 142 samples from three brands were collected for spectral analysis. Each time, the samples of a brand serve as the target class whereas other samples serve as the outlier class. Based on both the first two principal components (PCs) and original variables, three types of data descriptions were constructed. On average, the optimized SVDD model achieves acceptable performance, i.e., a specificity of 100% and a sensitivity of 94.2% on the independent test set with tight boundary. It indicates that SVDD combined with NIR is feasible and effective for authenticity detection of black rice.

To improve the efficiency and usability of adaptive anomaly detection system, we propose a new framework based on Support Vector Data Description (SVDD) method. This framework includes two main techniques: online change detection and unsupervised anomaly detection. The first one enables automatically obtain model training data by measuring and distinguishing change caused by intensive attacks from normal behavior change and then filtering most intensive attacks. The second retrains model periodically and detects the forthcoming data. Results of experiments with the KDD’99 network data show that these techniques can handle intensive attacks effectively and adapt to the concept drift while still detecting attacks. As a result, false positive rate is reduced from 13.43% to 4.45%.

The paper aims to solve the problem of insufficient high risk data in risk assessment of R&amp;D projects. A one-class classification method called support vector data description (SVDD) is studied, and an intelligent risk assessment model based on SVDD with fuzzy regression information is also proposed. The model comes into being a new approach. Applying this approach, firstly verify the conversional risk evaluation indexes by fuzzy regression technique to develop a sensitive index system. Secondly the study uses the historical risk data referring to these indexes to train the SVDD one-class classifier. Unlike previously proposed intelligent methods of risk assessment, with this model the risk level can be distinguished only by training of low risk data. The results of its application on an example show that the method is feasible for risk assessment with the fuzzy high risk data.

Support vector data description (SVDD) is a well-known kernel-based one-class classification method that exhibits intrinsic regularization ability and robustness versus low numbers of high-dimensional samples. However, the efficiency of SVDD is limited by the cubic time complexity. To solve this problem, this paper first investigates the effect of selecting a reduced subset as the training set of SVDD, while guaranteeing the classification quality. To this end, a new heuristic sample condensed rule, termed HSC, is proposed to accurately identify those potential support vectors that characterize the classification boundary. HSC can consider both the spatial distribution and local density features of training samples, and focus on selecting samples very close to the decision boundary. When dealing with the local density computation, we introduce the idea of K nearest neighbors (KNN) to examine the density of samples in the neighbors of the object to be classified. Finally, a condensed but informative subset obtained by HSC will be applied to train SVDD breezily. The experimental results show that HSC-based SVDD sensibly improves over conventional SVDD, in terms of the size of the training set while guaranteeing a comparable classification quality. In addition, it is competitive over other improved SVDD classifiers in terms of training and testing time.

The effective extraction of impervious surfaces is critical to monitor their expansion and ensure the sustainable development of cities. Open geographic data can provide a large number of training samples for machine learning methods based on remote-sensed images to extract impervious surfaces due to their advantages of low acquisition cost and large coverage. However, training samples generated from open geographic data suffer from severe sample imbalance. Although one-class methods can effectively extract an impervious surface based on imbalanced samples, most of the current one-class methods ignore the fact that an impervious surface comprises varied geographic objects, such as roads and buildings. Therefore, this paper proposes an object-oriented deep multi-sphere support vector data description (OODMSVDD) method, which takes into account the diversity of impervious surfaces and incorporates a variety of open geographic data involving OpenStreetMap (OSM), Points of Interest (POIs), and trajectory GPS points to automatically generate massive samples for model learning, thereby improving the extraction of impervious surfaces with varied types. The feasibility of the proposed method is experimentally verified with an overall accuracy of 87.43%, and its superior impervious surface classification performance is shown via comparative experiments. This provides a new, accurate, and more suitable extraction method for complex impervious surfaces.

It is of critical importance to keep a steady operation in the blast furnace to facilitate the production of high quality hot metal. In order to monitor the state of blast furnace, this article proposes a fault detection and identification method based on the multidimensional Gated Recurrent Unit (GRU) network, which is a kind of recurrent neural network and is highly effective in handling process dynamics. Comparing to conventional recurrent neural networks, GRU has a simpler structure and involves fewer parameters. In fault detection, a moving window approach is applied and a GRU model is constructed for each process variable to generate a series of residuals, which is further monitored using the support vector data description (SVDD) method. Once a fault is detected, fault identification is performed using the contribution analysis. Application to a real blast furnace fault shows that the proposed method is effective.

While detecting anomalies in hyperspectral imagery with support vector data description (SVDD), large numbers of operation was run because of the high dimension character of dataset and the complexity of background and the high miss rate was discovered because of the interfered background by interior anomalies. This paper used incremental support vector data description (ISVDD) method that samples are divided many sub-sample, and incremental study is designed to simplify the computation. On every sub-sample study, optimization is needed according of the support vectors obtained from above sub-sample and current sub-sample data. By the experiment on the HYMAP data, computation complexity of the algorithm decrease obviously and the computation speed increase highly under the similar detection effect compared with SVDD algorithm.

Support vector data description (SVDD) is an effective algorithm for nonlinear process monitoring. However, the conventional SVDD method can't deal with the incipient faults well, which has the small fault amplitude and is easy to be overlapped by industrial noises. Aiming at this problem, an improved SVDD, called probability related SVDD in local variable field (LVPSVDD), is proposed to detect the incipient faults in nonlinear processes. Firstly, the method divides process variables into several local variable fields by hierarchical clustering, and the SVDD model of each variable field is built. Then the sliding window technology is applied to the SVDD distance statistics, and the probability distribution change in the sliding window is measured by Kullback Leibler divergence (KLD). For each local variable field, the corresponding probability related monitoring statistic is developed to replace the original distance statistic. Finally, the global monitoring statistic is obtained by integrating the monitoring results of all local variable fields with Bayesian inference strategy. The method is illustrated with simulation on the continuous stirred tank reactor (CSTR).

Non-native species threaten an ecosystem by interfering with ecosystem processes. Tamarisk is a problematic invasive species in the southwestern USA that has replaced a large amount of the native cottonwood species and altered the water regimes. Quantifying the spatial patterns at regional scale is essential for the in-time monitoring of invasion, given its fast-spreading characteristics. Suitability mapping is useful for estimating the potential distribution of a specific species at the regional scale, but is limited in providing real-time updates since it relies on the generation of related environmental factor products. One-class mapping techniques and public volunteered species occurrence locations together make remote-sensing imagery very suitable for the timely mapping of the spatial distribution of a species. However, the quality of volunteered locations has rarely been fully examined before using them as the ground truth for classification. This research develops a new method – Ensemble Support Vector Data Description (SVDD) – to incorporate volunteered data evaluation into one-class mapping. It can quantify the uncertainty associated with each volunteered sample based on a deviation index and improve the user’s accuracy of one-class mapping. This method is applied to map the distribution of tamarisk at the regional scale using Moderate Resolution Imaging Spectroradiometer (MODIS) imagery. Results indicate that this method can generate producer’s accuracy comparable to the original SVDD procedure, while providing higher user’s accuracy.

In this paper, we describe a method for video summarization that operates on a video segment level. We formulate this problem as the one of automatic video segment selection based on a learning process that employs salient video segment paradigms. We design an hierarchical learning scheme that consists of two steps. At the first step, an unsupervised process is performed in order to determine salient video segment types. The second step is a supervised learning process that is performed for each of the salient video segment type independently. For the latter case, since only salient training examples are available, the problem is stated as an one-class classification problem. In order to take into account subclass information that may appear in the video segment types, we introduce a novel formulation of the Support Vector Data Description method that exploits subclass information in its optimization process. We evaluate the proposed approach in three Hollywood movies, where the performance of the proposed Subclass SVDD (SSVDD) algorithm is compared with that of related methods. Experimental results show that the adoption of both hierarchical learning and the proposed SSVDD method contribute to the final classification performance.