Search results for " artificial intelligence"
showing 10 items of 1992 documents
Remote sensing image segmentation by active queries
2012
Active learning deals with developing methods that select examples that may express data characteristics in a compact way. For remote sensing image segmentation, the selected samples are the most informative pixels in the image so that classifiers trained with reduced active datasets become faster and more robust. Strategies for intelligent sampling have been proposed with model-based heuristics aiming at the search of the most informative pixels to optimize model's performance. Unlike standard methods that concentrate on model optimization, here we propose a method inspired in the cluster assumption that holds in most of the remote sensing data. Starting from a complete hierarchical descri…
Recognition of Falls and Daily Living Activities Using Machine Learning
2018
A robust fall detection system is essential to support the independent living of elderlies. In this context, we develop a machine learning framework for fall detection and daily living activity recognition. Using acceleration data from public databases, we test the performance of two algorithms to classify seven different activities including falls and activities of daily living. We extract new features from the acceleration signal and demonstrate their effect on improving the accuracy and the precision of the classifier. Our analysis reveals that the quadratic support vector machine classifier achieves an overall accuracy of 93.2% and outperforms the artificial neural network algorithm. Re…
Combining Benford's Law and machine learning to detect money laundering. An actual Spanish court case.
2017
Abstract Objectives This paper is based on the analysis of the database of operations from a macro-case on money laundering orchestrated between a core company and a group of its suppliers, 26 of which had already been identified by the police as fraudulent companies. In the face of a well-founded suspicion that more companies have perpetrated criminal acts and in order to make better use of what are very limited police resources, we aim to construct a tool to detect money laundering criminals. Methods We combine Benford’s Law and machine learning algorithms (logistic regression, decision trees, neural networks, and random forests) to find patterns of money laundering criminals in the conte…
A Hierarchical Detection and Response System to Enhance Security Against Lethal Cyber-Attacks in UAV Networks
2018
International audience; Unmanned aerial vehicles (UAVs) networks have not yet received considerable research attention. Specifically, security issues are a major concern because such networks, which carry vital information, are prone to various attacks. In this paper, we design and implement a novel intrusion detection and response scheme, which operates at the UAV and ground station levels, to detect malicious anomalies that threaten the network. In this scheme, a set of detection and response techniques are proposed to monitor the UAV behaviors and categorize them into the appropriate list (normal, abnormal, suspect, and malicious) according to the detected cyber-attack. We focus on the m…
Distributed Adaptive Control for Asymptotically Consensus Tracking of Uncertain Nonlinear Systems With Intermittent Actuator Faults and Directed Comm…
2019
In this article, we investigate the output consensus tracking problem for a class of high-order nonlinear systems with unknown parameters, uncertain external disturbances, and intermittent actuator faults. Under the directed topology conditions, a novel distributed adaptive controller is proposed. The common time-varying trajectory is allowed to be totally unknown by part of subsystems. Therefore, the assumption on the linearly parameterized trajectory signal in most literature is no longer needed. To achieve the relaxation, extra distributed parameter estimators are introduced in all subsystems. Besides, to handle the actuator faults occurring at possibly infinite times, a new adaptive com…
A distributed real-time data prediction and adaptive sensing approach for wireless sensor networks
2018
International audience; Many approaches have been proposed in the literature to reduce energy consumption in Wireless Sensor Networks (WSNs). Influenced by the fact that radio communication and sensing are considered to be the most energy consuming activities in such networks. Most of these approaches focused on either reducing the number of collected data using adaptive sampling techniques or on reducing the number of data transmitted over the network using prediction models. In this article, we propose a novel prediction-based data reduction method. furthermore, we combine it with an adaptive sampling rate technique, allowing us to significantly decrease energy consumption and extend the …
A finite element-based machine learning approach for modeling the mechanical behavior of the breast tissues under compression in real-time
2017
[EN] This work presents a data-driven method to simulate, in real-time, the biomechanical behavior of the breast tissues in some image-guided interventions such as biopsies or radiotherapy dose delivery as well as to speed up multimodal registration algorithms. Ten real breasts were used for this work. Their deformation due to the displacement of two compression plates was simulated off-line using the finite element (FE) method. Three machine learning models were trained with the data from those simulations. Then, they were used to predict in real-time the deformation of the breast tissues during the compression. The models were a decision tree and two tree-based ensemble methods (extremely…
Modeling of Human Posturokinetic Movements by a Linear Feedback System: Relations among Feedback Coefficients
2002
This study describes a method of modeling human trunk and whole body backward bending and suggests a possible neural control strategy. The hypothesis was that the control system can be modeled as a linear feedback system, in which the torque acting at a given joint is a function of the state variables (angular positions and angular velocities). The linear system enabled representation of the feedback system by a gain matrix. The matrix was computed from the kinematics recorded by a movement analysis system and from the joint torques calculated by inverse dynamics. To validate the control model, a comparison was made between the angular kinematics yielded by the model and the experimental d…
Intermittent cooling during judo training in a warm/humid environment reduces autonomic and hormonal impact
2018
Carballeira, E, Morales, J, Fukuda, DH, Granada, ML, Carratalá-Deval, V, López Díaz de Durana, A, and Stout, JR. Intermittent cooling during Judo training in a warm/humid environment reduces autonomic and hormonal impact. J Strength Cond Res 33(8): 2241-2250, 2019-The purpose of this study was to identify the effects of superficial cooling on physiological responses while training in a warm, humid environment during an international Judo training camp. Sixteen judokas (8 women and 8 men) participated in the experiment. Four high-level women and 4 men were randomly assigned to wear a cooling vest (vest group [VG]) during the recovery periods within a training session (i.e., 8 bouts of 5-minu…
Timing flickers across sensory modalities
2009
In tasks requiring a comparison of the duration of a reference and a test visual cue, the spatial position of test cue is likely to be implicitly coded, providing a form of a congruency effect or introducing a response bias according to the environmental scale or its vectorial reference. The precise mechanism generating these perceptual shifts in subjective duration is not understood, although several studies suggest that spatial attentional factors may play a critical role. Here we use a duration comparison task within and across sensory modalities to examine if temporal performance is also modulated when people are exposed to spatial distractors involving different sensory modalities. Di…