The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
A tool for discovery of gait anomalies of elderly from motion sensor data is proposed. The gait of the user is captured with the motion capture system, which consists of tags attached to the body and sensors situated in the apartment. Position of the tags is acquired by the sensors and the resulting time series of position coordinates are analyzed with dynamic time warping and machine learning algorithms...
This paper compares three different approaches currently used in recognizing contact calls made from the North Atlantic Right Whale (NRW), Eubalaena glacialis. We present two new approaches consisting of machine learning algorithms based on artificial neural networks (NET) and the classification and regression tree classifiers (CART), and compare their performance with earlier work that employs multi-Stage...
In this paper we propose a two-level architecture ensemble classifier with incremental learning ability for solving the problem of limited experimental sample of the underwater vehicle machinery noise source. The first-level ensemble classifier aims to improve the generalization performance. The second-level ensemble aims to enable the classifier incremental learning. Experimental result shows two-level...
Learning from data sets that contain very few instances of the minority class usually produces biased classifiers that have a higher predictive accuracy over the majority class, but poorer predictive accuracy over the minority class. SMOTE (synthetic minority over-sampling technique) is specifically designed for learning from imbalanced data sets. This paper presents a modified approach (MSMOTE) for...
Based on SVM and incremental learning, this paper proposes a new method for recognition of underwater vehicle noise source on small samples. The new method may establish a classifier which structure is dynamic adjustable, and it can solve both Example- Incremental learning and Class-Incremental learning. The experimentation shows the generalization of classifier can be improved, and the classifier...
This paper presents a method based on classification techniques for automatic fault diagnosis of rolling element bearings. Experimental results achieved on vibration signals collected by an accelerometer on an experimental test rig show that the method can automatically detect different types of faults. Furthermore, the method is able, once trained on an appropriate representative set of basic faults,...
A simple yet effective unsupervised classification rule to discriminate between normal and abnormal data is based on accepting test objects whose nearest neighbors' distances in a reference data set, assumed to model normal behavior, lie within a certain threshold. This work investigates the effect of using a subset of the original data set as the reference set of the classifier. With this aim, the...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.