A comparative study of efficient initialization methods for the k-means clustering algorithm

K-means is undoubtedly the most widely used partitional clustering algorithm. Unfortunately, due to its gradient descent nature, this algorithm is highly sensitive to the initial placement of the cluster centers. Numerous initialization methods have been proposed to address this problem. In this paper, we first present an overview of these methods with an emphasis on their computational efficiency. We then compare eight commonly used linear time complexity initialization methods on a large and diverse collection of data sets using various performance criteria. Finally, we analyze the experimental results using non-parametric statistical tests and provide recommendations for practitioners. We demonstrate that popular initialization methods often perform poorly and that there are in fact strong alternatives to these methods.     

Reduction of matched and unmatched uncertainties for a class of nonlinear perturbed systems via robust control

The aim of this paper is to design a robust control for stabilization of a class of nonlinear perturbed system subject to matched and unmatched disturbances. Here, the concept of dynamic sliding mode control and the attractive ellipsoid method advantages are used to design a robust nonlinear control algorithm, which reduces considerably the perturbation effects. Hence, in finite time, the dynamic sliding mode control brings the system trajectory to a specific configuration. After this time, the controller reduces the perturbation effects by using the high‐gain control obtained in the attractive ellipsoid method. Thus, based on the solution of a specific matrix inequality, the feedback control of the system guarantees that the trajectory will be stabilized in the ultimate uniform bounded sense. To illustrate the theoretical results, a numerical example with a comparative study is introduced. Finally, the performance of the controller designed in this paper is tested on an electromechanical real‐time system.     

EPICS based low-level radio frequency control system in LIPAc

The IFMIF–EVEDA (International Fusion Materials Irradiation Facility – Engineering Validation and Engineering Design Activity) linear accelerator, known as Linear IFMIF Prototype Accelerator (LIPAc), will be a 9 MeV, 125 mA continuous wave (CW) deuteron accelerator prototype to validate the technical options of the accelerator design for IFMIF. The primary mission of such facility is to test and verify materials performance when subjected to extensive neutron irradiation of the type encountered in a fusion reactor to prepare for the design, construction, licensing and safe operation of a fusion demonstration reactor (DEMO). The radio frequency (RF) power system of IFMIF–EVEDA consists of 18 RF chains working at 175 MHz with three amplification stages each. The low-level radio frequency (LLRF) controls the amplitude and phase of the signal to be synchronized with the beam and it also controls the resonance frequency of the cavities. The system is based on a commercial compact peripheral component interconnect (cPCI) field programmable gate array (FPGA) board, provided by Lyrtech and controlled by a Windows host PC. For this purpose, it is mandatory to communicate the cPCI FPGA board from EPICS Channel Access [1]. A software architecture on EPICS framework in order to control and monitor the LLRF system is presented.     

Visual SLAM Based on Rigid-Body 3D Landmarks

In current visual SLAM methods, point-like landmarks (As in Filliat and Meyer (Cogn Syst Res 4(4):243–282, 2003), we use this expression to denote a landmark generated by a point or an object considered as punctual.) are used for representation on maps. As the observation of each point-like landmark gives only angular information about a bearing camera, a covariance matrix between point-like landmarks must be estimated in order to converge with a global scale estimation. However, as the computational complexity of covariance matrices scales in a quadratic way with the number of landmarks, the maximum number of landmarks that is possible to use is normally limited to a few hundred. In this paper, a visual SLAM system based on the use of what are called rigid-body 3D landmarks is proposed. A rigid-body 3D landmark represents the 6D pose of a rigid body in space (position and orientation), and its observation gives full-pose information about a bearing camera. Each rigid-body 3D landmark is created from a set of N point-like landmarks by collapsing 3N state components into seven state components plus a set of parameters that describe the shape of the landmark. Rigid-body 3D landmarks are represented and estimated using so-called point-quaternions, which are introduced here. By using rigid-body 3D landmarks, the computational time of an EKF-SLAM system can be reduced up to 5.5%, as the number of landmarks increases. The proposed visual SLAM system is validated in simulated and real video sequences (outdoor). The proposed methodology can be extended to any SLAM system based on the use of point-like landmarks, including those generated by laser measurement.     

Hydrodynamic Instabilities in Well-Balanced Finite Volume Schemes for Frictional Shallow Water Equations. The Kinematic Wave Case

We report the developments of hydrodynamic instabilities in several well-balanced finite volume schemes that are observed during the computation of the temporal evolution of an out-balance flow which is essentially a kinematic wave. The numerical simulations are based on the one-dimensional shallow-water equations for a uniformly sloping bed with hydraulic resistance. Subsequently, we highlight the need of low dissipative high-order well-balanced filter schemes for non-equilibrium flows with variable cut-off wavenumber to compute the out-balance flow under consideration, i.e. the kinematic wave.     

eTeacher: Providing personalized assistance to e-learning students

In this paper we present eTeacher, an intelligent agent that provides personalized assistance to e-learning students. eTeacher observes a student’s behavior while he/she is taking online courses and automatically builds the student’s profile. This profile comprises the student’s learning style and information about the student’s performance, such as exercises done, topics studied, exam results. In our approach, a student’s learning style is automatically detected from the student’s actions in an e-learning system using Bayesian networks. Then, eTeacher uses the information contained in the student profile to proactively assist the student by suggesting him/her personalized courses of action that will help him/her during the learning process. eTeacher has been evaluated when assisting System Engineering students and the results obtained thus far are promising.     

On model order priors for Bayesian identification of SISO linear systems

A method for the identification of single input single output linear systems is presented. The method employs a Bayesian approach to compute the posterior distribution of the model parameters given the data-set. Since this distribution is often unavailable in closed form, a Metropolis Hastings algorithm is implemented to draw samples from it. To implement the sampler, the inclusion of prior information regarding the model order of the identified system is discussed. As one of the main contributions of this work, a prior over the Hankel singular values of the model is imposed. Numerical examples illustrate the method.     

A practical approach for active camera coordination based on a fusion-driven multi-agent system

In this paper, we propose a multi-agent system architecture to manage spatially distributed active (or pan-tilt-zoom) cameras. Traditional video surveillance algorithms are of no use for active cameras, and we have to look at different approaches. Such multi-sensor surveillance systems have to be designed to solve two related problems: data fusion and coordinated sensor-task management. Generally, architectures proposed for the coordinated operation of multiple cameras are based on the centralisation of management decisions at the fusion centre. However, the existence of intelligent sensors capable of decision making brings with it the possibility of conceiving alternative decentralised architectures. This problem is approached by means of a MAS, integrating data fusion as an integral part of the architecture for distributed coordination purposes. This paper presents the MAS architecture and system agents.