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.
The paper considers the problem of scaling the method of barrier certificates for data-driven validation of dynamical system models using a large number of collected trajectories. Construction of a barrier certificate requires solving a convex feasibility problem that consists of a set of affine constraints whose number grows with the size of the dataset. The time complexity of traditional methods...
This paper is concerned with black-box identification of nonlinear state space models. By using a basis function expansion within the state space model, we obtain a flexible structure. The model is identified using an expectation maximization approach, where the states and the parameters are updated iteratively in such a way that a maximum likelihood estimate is obtained. We use recent particle methods...
Optical motion tracking systems often require a lot of manual work to generate clean labeled trajectories. This can be a deterrent if the goal is the creation of large motion tracking datasets. Especially in the case of hand tracking, issues of occlusion (often self-occlusion by other fingers) make the post-processing task very difficult and time intensive. We introduce a fully automatic optical motion...
Dynamic Movement Primitives (DMPs)-originally a method for movement trajectory generation [1] has been also used for recognition tasks [2, 3]. However there has not been a systematic comparison between other recognition methods and DMPs using human movement data. This paper presents a comparison of commonly used Hidden Markov Model (HMM) based recognition with DMP based recognition using human generated...
We present a first implementation of a framework for the exploration of stylistic variations in intangible heritage, recorded through motion capture techniques. Our approach is based on a statistical modelling of the phenomenon, which is then presented to the user through a reactive stylistic synthesis, visualised in real-time on a virtual character. This approach enables an interactive exploration...
With the advent of ubiquitous computing and sensor technologies, indoor trajectory data of individuals can readily be collected to support analysis of their underlying movement behaviors. Different methods for activity recognition have been proposed where supervised learning algorithms are often adopted. In many applications like elderly care, the behaviors to be characterized are often not known...
The explosion of available positioning information associated with the inferred or user-declared semantics of the respective locations, already contributes in what is called the big data era, posing new challenges to the mobility data management and mining research community. In this paper, motivated by a series of challenges set in [11], we present a unified framework for the management and the analysis...
In order to design stable walking for a bipedal robot over uneven terrain, advanced control methods such as nonlinear control and receding-horizon control, and exact hybrid dynamics are needed. They are too complicated to be used in the many applications. In this paper, we use data mining techniques, locally weighted learning, principal component regression and regression clustering, and combine with...
The availability of massive volumes of trajectory data has made it convenient for the study of different types of movement behaviors. Among them, bi-directional movement behaviors exist ubiquitously in our daily life, from urban traffic to animal migration, and from sports to wars. To analyze bi-directional movement behaviors, people need to compare movements in two directions simultaneously for detecting...
Computational Maritime Situational Awareness (MSA) supports the maritime industry, governments, and international organizations with machine learning and big data techniques for analyzing vessel traffic data available through the Automatic Identification System (AIS). A critical challenge of scaling computational MSA to big data regimes is integrating the core learning algorithms with big data storage...
A friction modeling in zigzag and armchair lattice orientation of MoS2 has been demonstrated in this paper. Combing the assumption on the moving trajectories of the probe in both lattice orientations with two-dimension Tomlinson model, simulation of relationship between friction and orientation was smoothly performed with Matlab software. The lateral friction Microscopy(LFM) based experiment was conducted...
Trajectories are used in many target tracking and other fusion-related applications. In this paper we consider the problem of modeling trajectories as Gaussian processes and learning such models from sets of observed trajectories. We demonstrate that the traditional approach to Gaussian process regression is not suitable when modeling a set of trajectories. Instead we introduce an approach to Gaussian...
This paper provides a solution for anomaly detection in maritime traffic domain based on the clustering results presented in a previous work. That work created clusters for vessels moving close to shores by associating vessel movements with International Maritime Organization Rules (especially Traffic Separation Scheme Boundaries). In this paper, we show how three division distances with the clusters...
The validation and verification of cognitive skills of highly automated vehicles is an important milestone for legal and public acceptance of advanced driver assistance systems (ADAS). In this paper, we present an innovative data-driven method in order to create critical traffic situations from recorded sensor data. This concept is completely contrary to previous approaches using parametrizable simulation...
We introduce a new method for robots to further improve upon skills acquired through Learning from Demonstration. Previously, we have introduced a method to learn both an action model to execute the skill and a goal model to monitor the execution of the skill. In this paper we show how to use the learned goal models to improve the learned action models autonomously, without further user interaction...
Power semiconductor devices are the major cause of the power converter failures together with the electrolytic dc bus capacitors. In harsh operating environments, the power devices are subjected to various mechanical and electrical stresses, wear, and vibration that contribute to increased equipment failure rate, where a failed component can cause unexpected interruptions, serious safety issues, or...
A great deal of research efforts has been invested in temporal aspects of big data management during last years, with alternate fortune. This line of research aims at capturing, formally modeling and successfully exploiting all the time-dependent characteristics of the fundamental big data model ranging from state model to query model. Temporal big data management thus poses novel research challenges...
Web service application is becoming a popular and important software application on the web. With the advent of more and more service application using on the web, it is a crucial challenge to use an effective diagnostic technology to localize the service faults. To improve the diagnosis capability for service application software, we build a second-order hidden Markov diagnosis model by considering...
The proliferation of location-acquisition devices and thriving development of social websites enable analyzing users' movement behaviors and detecting social events in dynamic trajectory streams. In this paper, we firstly analyze the challenges in trajectory stream clustering, and then depict a three-part framework to deal with this issue, that includes i) trajectory data pre-processing for higher...
Existing methods have addressed the issue of detecting abnormal events at a smart home for medical care or security monitoring services extensively in the past decades. However, most of approaches use wearable sensors that require users to be equipped with the sensor devices at every moment. If the monitored users stop or pause the sensors, any abnormal events are not able to be detected. The use...
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.