Torsional analysis of the lower extremities has become an integral part of the decision-making process in treating neuromuscular problems. Solid knowledge of normal development of torsional relationships is essential for treating musculoskeletal problems. As a prerequisite, a normal reference, meaning an objective and quantitative standard of measurement, must be available for comparison prior ...

Active learning has been utilized as an efficient tool in building anomaly detection models by leveraging expert feedback. In active framework, a model queries samples to be labeled experts and re-trains the with data samples. It unburdens obtaining annotated datasets while improving performance. However, most of existing studies focus on helping identify many abnormal possible, which is sub-op...

In previous research the Support Vector Data Description is proposed to solve the problem of One-Class classification. In One-Class classification one set of data, called the target set, has to be distinguished from the rest of the feature space. This description should be constructed such that objects not originating from the target set, by definition the outlier class, are not accepted by the...

Data domain description techniques aim at deriving concise descriptions of objects belonging to a category of interest. For instance, the support vector domain description (SVDD) learns a hypersphere enclosing the bulk of provided unlabeled data such that points lying outside of the ball are considered anomalous. However, relevant information such as expert and background knowledge remain unuse...

To save time, cost and labor, there are many studies that have been conducted about the detection of faults in industrial processes. Most of the previous studies used only Independent Component Analysis (ICA) or Principal Component Analysis (PCA) for detection, but they cannot form close enough boundaries to reject outliers. This paper proposes an ICA-based approach to detect outliers in a proc...

Detection of early-stage liver diseases is a challenge in medical field. Automated diagnostics based on machine learning therefore could be very important for liver tests of patients. This paper investigates 225 liver function test records (each record include 14 features), which is a subset from 1000 patients’ liver function test records that include the records of 25 patients with liver disea...

In this paper we present a novel unsupervised feature learning network named C-SVDDNet, a singlelayer K-means-based network towards compact and robust feature representation. Our contributions are three folds: (1) we introduce C-SVDD encoding, a generalization of the K-means local encoding that adapts to the distribution information and improves the robustness against outliers; (2) we propose a...

Support Vector Domain Description (SVDD) is one of the best-known one-class support vector learning methods, in which one tries the strategy of using balls defined on the feature space in order to distinguish a set of normal data from all other possible abnormal objects. As all kernel-based learning algorithms its performance depends heavily on the proper choice of the kernel parameter. This pa...

Data-driven diagnosis methods for faults of proton exchange membrane fuel cell (PEMFC) systems can diagnose through the state variable data collected during operation PEMFC system. However, from system stack switching between different operating points easily cause false alarms, such that practical value is reduced. To overcome this problem, a fault method based on steady-state identification p...