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The recent build-up network of Automatic Identification System (AIS) equipped on vessels provides a rich source of vessel movement information. AIS is originally designed for automatically exchanging navigation information, such as their unique identification, position, course, and speed, with nearby vessels and terrestrial receivers to affect collision avoidance and safety control. The collected...
Credit card is a popular payment method and the transaction data keeps track of purchasing activities in people's daily lives. Extracting location of people's activities is an important task in many data mining problems because it may greatly help improve user experience and the service provided to people. Locating people from credit card transactions is equivalent to determining the location of every...
Mining trajectory data has been attracting significant interest in the last years. By analyzing trajectory data, we are able to discover the movement behavior and location-aware knowledge, and then develop many interesting applications such as movement behavior discovery, location prediction, traffic analysis, and so on. However, trajectory data mining is a challenge task because of the trajectory...
Recent research efforts in data stream management systems (DSMS) focus mainly on processing continuous queries over traditional data streams, and only a few addressed spatio-temporal continuous queries. OCEANUS presents an effort to extend TelegraphCQ DSMS with spatial support providing a platform for spatio-temporal streaming applications. Data type system that represents the formal basis for modeling...
Uncertainty is common in real-world applications, for example, in sensor networks and moving object tracking, resulting in much interest in item set mining for uncertain transaction databases. In this paper, we focus on pattern mining for uncertain sequences and introduce probabilistic frequent spatial-temporal sequential patterns with gap constraints. Such patterns are important for the discovery...
Nowadays, researchers tend to make use of GPS trajectories collected by companies, organizations, or volunteers. GPS trajectories on crowdsourcing websites are less widely used in researches but actually have great potential for various applications. Thus, this paper makes a comprehensive study on GPS trajectories on VGI websites and social websites. The volume, growth rate, and categories of the...
Spatial distribution of the dynamic network is a complex and dynamic mixture of its topology and geometry. Historically the separation of topology and geometry in mathematics was motivated by the need to separate the invariant part of the spatial distribution (topology) from a less invariant part (geometry). The geometric characteristics such as orientation, shape, and proximity are not invariant...
Fusion and mining of uncertain heterogeneous spatial data in the cyber-physical space are challenging problems especially to deal in a coordinated way with both topological and geometrical uncertainties. This paper explores opportunities to meet these challenges by generalizing the Dynamic Logic of Phenomena (DLP) and the Neural Modeling Field (NMF) Theory for geo-spatial data. The main idea behind...
In this paper, we develop a framework of trajectory search called pattern-aware trajectory search abbreviated as PATS). Given a set of trajectories, potential regions are extracted first and potential regions are viewed as popular regions interested by users. Furthermore, potential regions are organized as a region transition graph where each vertex is a potential region and edges capture sequential...
This work proposes a robust control framework to address the problem of practical tracking for a class of nonlinear systems described as hybrid automata. The framework reposes both on a suitable definition of the references to be tracked and on input-to-state stability properties of the feedback laws in order to guarantee a desired behavior of the hybrid automata in terms of robust transition between...
This paper proposes extensions to a recent control Liapunov function (CLF) based method for designing dynamical systems with trajectories that converge to the zeros of a nonlinear vector function f . Specifically, the CLF design method is extended to the case when the Jacobian of the vector function can be decomposed into a known part and a partially known part, for which certain norm bounds are known...
We present an algorithm for an event based approach to the global optimal control of nonlinear systems with coarsely quantized state measurement. The quantized measurements induce regions of the state space and the events represent the change of the system's state from one quantization region to another. We investigate the theoretical properties of the approach and illustrate the performance by a...
Mining trajectory databases (TD) has gained great interest due to the popularity of tracking devices. On the other hand, the inherent presence of uncertainty in TD (e.g., due to GPS errors) has not been taken yet into account during the mining process. In this paper, we study the effect of uncertainty in TD clustering and introduce a three-step approach to deal with it. First, we propose an intuitionistic...
In this paper, we propose a new iterative learning control (ILC) scheme, which is devoted to dealing with unknown parameters that are both time varying and iteration varying. In particular, we consider iteration-varying parameters that are generated by a second-order internal model. By incorporating the internal model into the parametric learning law, the ILC scheme can handle more generic nonlinear...
Assessing air traffic complexity on a mid term horizon can help to timely identify those safety-critical encounter situations that would require many tactical resolution maneuvers to be resolved. This is particularly useful in advanced autonomous air traffic management systems, where aircraft are responsible for self-separation maintenance. In this paper, we propose a new method to evaluate mid term...
Previous inverse optimal adaptive controllers (IOACs) have been developed that can handle structured (i.e., linear in the parameters (LP)) uncertainty for a particular class of nonlinear systems. A full-state feedback IOAC is developed in the companion Part I paper for Euler-Lagrange systems with an uncertain time varying inertia matrix. In this paper, an output feedback IOAC is developed to asymptotically...
This paper addresses the problem of tracking periodic references for uncertain linear systems subject to control saturation. Accordingly to the internal model principle, a control loop containing the modes of both the references and additive disturbances is considered. From this structure, conditions in a ??quasi?? LMI form are proposed to simultaneously compute a stabilizing state feedback gain and...
A recursive procedure is proposed to reduce a set of inequality constraints of high relative order to single constraints of relative order one for a class of uncertain, multi-input nonlinear systems. A modification of a nominal control signal on the newly constructed constraints guarantees robust constraint satisfaction. The resulting robust invariant controller is applied to the constrained flight...
A framework is developed which enables a general class of linear iterative learning control (ILC) algorithms to be applied to tracking tasks which require the plant output to reach given points at predetermined time instants, without the need for intervening reference points to be stipulated. It is shown that superior convergence and robustness properties are obtained compared with those associated...
A sufficient condition to solve an optimal control problem is to solve the Hamilton-Jacobi-Bellman (HJB) equation. However, finding a value function that satisfies the HJB equation for a nonlinear system is challenging. Inverse optimal control is an alternative method to solve the nonlinear optimal control problem by circumventing the need to solve the HJB equation. Inverse optimal adaptive control...
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