Search results for "TASK"

A novel clustering-based algorithm for solving spatially-constrained robotic task sequencing problems

2021

The robotic task sequencing problem (RTSP) appears in various forms across many industrial applications and consists of developing an optimal sequence of motions to visit a set of target points defined in a task space. Developing solutions to problems involving complex spatial constraints remains challenging due to the existence of multiple inverse kinematic solutions and the requirements for collision avoidance. So far existing studies have been limited to relaxed RTSPs involving a small number of target points and relatively uncluttered environments. When extending existing methods to problems involving greater spatial constraints and large sets of target points, they either require subst…

MFNet: Multi-feature convolutional neural network for high-density crowd counting

2020

The crowd counting task involves the issue of security, so now more and more people are concerned about it. At present, the most difficult problem of population counting consists in: how to make the model distinguish human head features more finely in the densely populated area, such as head overlap and how to find a small-scale local head feature in an image with a wide range of population density. Facing these challenges, we propose a network for multiple feature convolutional neural network, which is called MFNet. It aims to get high-quality density maps in the high-density crowd scene, and at the same time to perform the task of the count and estimation of the crowd. In terms of crowd c…

Using Inverse Reinforcement Learning with Real Trajectories to Get More Trustworthy Pedestrian Simulations

2020

Reinforcement learning is one of the most promising machine learning techniques to get intelligent behaviors for embodied agents in simulations. The output of the classic Temporal Difference family of Reinforcement Learning algorithms adopts the form of a value function expressed as a numeric table or a function approximator. The learned behavior is then derived using a greedy policy with respect to this value function. Nevertheless, sometimes the learned policy does not meet expectations, and the task of authoring is difficult and unsafe because the modification of one value or parameter in the learned value function has unpredictable consequences in the space of the policies it represents…

On the Influence of Affect in EEG-Based Subject Identification

2021

Biometric signals have been extensively used for user identification and authentication due to their inherent characteristics that are unique to each person. The variation exhibited between the brain signals (EEG) of different people makes such signals especially suitable for biometric user identification. However, the characteristics of these signals are also influenced by the user’s current condition, including his/her affective state. In this paper, we analyze the significance of the affect-related component of brain signals within the subject identification context. Consistent results are obtained across three different public datasets, suggesting that the dominant component of the sign…

2020

A soldier’s occupational physical task requirements are diverse and varied. However, the type of physical training that most effectively improves soldiers’ occupational task requirements has not been studied previously. The purpose of this study was to determine the important strength characteristics for soldiers during a repeated simulated military task course, and the type of training that may be effective to improve these abilities during a specialized military training period. Forty-two (n = 42) soldiers participated in the study. They were divided into three training groups; a soldier task-specific training group (TSG, n = 17), a strength training group (STG, n = 15), and a control gro…

Visualization of Exchanged Information with Dynamic Networks: A Case Study of Fire Emergency Search and Rescue Operation

2017

To perform emergency response activities, complex networks of emergency responders from different emergency organizations work together to rescue affected people and to mitigate the property losses. However, to work efficiently, the emergency responders have to rely completely on the data which gets generated from heterogeneous data sources during search and rescue operation (SAR). From this abundant data, rescue teams share needed information which is hidden in the abundant data with one another to make decisions, obtain situational awareness and also to assign tasks. Moreover, understanding and analyzing the shared information is a complex and very challenging task. Therefore, in this pap…

Ensuring the Reliability of an Autonomous Vehicle

2017

International audience; In automotive applications, several components, offering different services, can be composed in order to handle one specific task (autonomous driving for example). Nevertheless, component composition is not straightforward and is subject to the occurrence ofbugs resulting from components or services incompatibilities for instance. Hence, bugs detection in component-based systems at thedesign level is very important, particularly, when the developed system concerns automotive applications supporting critical services.In this paper, we propose a formal approach for modeling and verifying the reliability of an autonomous vehicle system, communicatingcontinuously with of…

Image-Evoked Affect and its Impact on Eeg-Based Biometrics

2019

Electroencephalography (EEG) signals provide a representation of the brain’s activity patterns and have been recently exploited for user identification and authentication due to their uniqueness and their robustness to interception and artificial replication. Nevertheless, such signals are commonly affected by the individual’s emotional state. In this work, we examine the use of images as stimulus for acquiring EEG signals and study whether the use of images that evoke similar emotional responses leads to higher identification accuracy compared to images that evoke different emotional responses. Results show that identification accuracy increases when the system is trained with EEG recordin…

Erratum: “Supplement 2 for the 2004 update of the AAPM Task Group No. 43 Report: Joint recommendations by the AAPM and GEC-ESTRO” [Med. Phys. Vol 44 …

2018

Right inferior frontal gyrus implements motor inhibitory control via beta-band oscillations in humans

2021

Motor inhibitory control implemented as response inhibition is an essential cognitive function required to dynamically adapt to rapidly changing environments. Despite over a decade of research on the neural mechanisms of response inhibition, it remains unclear, how exactly response inhibition is initiated and implemented. Using a multimodal MEG/fMRI approach in 59 subjects, our results reliably reveal that response inhibition is initiated by the right inferior frontal gyrus (rIFG) as a form of attention-independent top-down control that involves the modulation of beta-band activity. Furthermore, stopping performance was predicted by beta-band power, and beta-band connectivity was directed f…