Membrane computing models are parallel and distributed natural computing models. These models are often referred to as P systems. This paper proposes a novel multi-behaviors co-ordination controller model using enzymatic numerical P systems for autonomous mobile robots navigation in unknown environments. An environment classifier is constructed to identify different environment patterns in the maze-like environment and the multi-behavior co-ordination controller is constructed to coordinate the behaviors of the robots in different environments. Eleven sensory prototypes of local environments are presented to design the environment classifier, which needs to memorize only rough information, for solving the problems of poor obstacle clearance and sensor noise. A switching control strategy and multi-behaviors coordinator are developed without detailed environmental knowledge and heavy computation burden, for avoiding the local minimum traps or oscillation problems and adapt to the unknown environments. Also, a serial behaviors control law is constructed on the basis of Lyapunov stability theory aiming at the specialized environment, for realizing stable navigation and avoiding actuator saturation. Moreover, both environment classifier and multi-behavior coordination controller are amenable to the addition of new environment models or new behaviors due to the modularity of the hierarchical architecture of P systems. The simulation of wheeled mobile robots shows the effectiveness of this approach.

Neuro-architecture seeks to define and better understand the relationships between our psychological state and the artificial structures in which we spend most of our time, and incorporate that insight into the design. However, little is known about the subjective judgment of real architectural models and the cognitive processes involved in aesthetic appreciation of architecture. In the present study, we used real and computer-designed images of bedrooms to address the underlying neural representations of different images of the same object. Thirteen participants were asked to judge the arousal and valence of their own emotional experiences after viewing each image. Furthermore, we used EEG recordings to study the regions of the brain involved in the processing of both types of images. Our results show that there are significant differences in the brain processing of both types of images, especially at early stages, and suggest that realistic images are more pleasurable, which could influence aesthetic judgment. These results emphasise the importance of generating familiar, realistic and recognisable images to improve people’s acceptance.

Constructing and evaluating a comprehensive tool-chain with commercial off-the-shelf and proprietary tools for the deployment of model-based systems engineering (MBSE) is a challenging and complex task. Specifically, the lack of early assessment duri

Robust identification and tracking of the pupil provides key information that can be used in several applications such as controlling gaze-based HMIs (human machine interfaces), designing new diagnostic tools for brain diseases, improving driver safe

This research is intended to evaluate the suitability of a common-sense-based approach for providing causal explanations to power quality disturbances and, more specifically, to voltage-sage events, with the aim of improving the security and reliability of the electrical grid. Since voltage sags ar e one of the most common power-quality disturbance and may have a severe impact on sensitive loads and users, the case study presented is dedicated to find their causes as a prerequisite to prevent them. The main contribution presented in this work is in the knowledge management domain. However, the proposed architecture adopts a multi-layer approach that comprehensively faces all the necessary stages, comprising: measurement and data collection, filtering, distribution, homogenization, and integration up to the process of inference of the possible cause of the event. The architecture of the proposed system comprises three layers, namely: the information gathering, the information modeling and the information understanding layers. The proposed system has been evaluated using a synthetic dataset of simulated voltages sags. Information about weather conditions and different location features and environmental circumstances is aggregated to the electrical features of the voltage sag, in order to improve the inference of the external cause of the event. Even though a real-world dataset would be required to fully validate the proposed system and assess the benefits in a real scenario, the results obtained with the synthetic dataset are satisfactory and an overall accuracy above 90% of the cause identification is achieved.

A new thresholding algorithm is introduced for images of historical documents containing graphical elements as drawings. The major idea is to improve the binarization process such that the understanding of the drawings is not lost in the final bi-level image and the text areas are completely preser ved for further recognition. As quality is a subjective matter, the algorithm allows non-specialized user interference in order to achieve the expected quality of the final image. The proposed method achieved satisfactory visual results when compared to other state-of-the-art thresholding algorithms. As a consequence of the algorithm, there is also a black ink economy in the printing of the images if compared with a halftoning printing of a grayscale image. This algorithm ranked the fourth position at H-DIBCO 2016 (the handwritten documents version of the annual Document Image Binarization COntest).

Hausdorff distance measure is very important in CAD/CAE/CAM related applications. This manuscript presents an efficient framework and two complementary subalgorithms to directly compute the exact Hausdorff distance for general 3D point sets. The first algorithm of Nonoverlap Hausdorff Distance (NOHD) combines branch-and-bound with early breaking to cut down the Octree traversal time in case of spatial nonoverlap. The second algorithm of Overlap Hausdorff Distance (OHD) integrates a point culling strategy and nearest neighbor search to reduce the number of points traversed in case of spatial overlap. The two complementary subalgorithms can achieve a highly efficient and balanced result. Both NOHD and OHD compute the exact Hausdorff distance directly for arbitrary 3D point sets. We conduct a number of experiments on benchmark models and CAD application models, and compare the proposed approach with other state-of-the-art algorithms. The results demonstrate the effectiveness of our method.

Lip region tracking is of crucial importance to the better understanding of visual speech in a computer-aided system. This paper presents an efficient lip region tracking approach in virtue of incremental collaborative appearance model and regional particle filtering. Within this approach, we first learn an incremental weighted appearance model (IWAM) through adaptively updating the time-varying mean and eigenbasis by considering the temporal and spatial weights, and then discriminatively exploit an incremental sparse subspace model (ISSM) by considering the occlusions and background clutters. Accordingly, the collaboration of the IWAM and ISSM leads to a more reliable and flexible lip region representation. Subsequently, we propose a regional particle filtering for motion state estimation, by taking scale size and rotation estimation, reducing the computational load and alleviating the tracking drift into consideration. Furthermore, the affinely warped image patches corresponding to the rank-2-optimal states are employed to incrementally update the IWAM and ISSM synchronously. The extensive experiments conducted on different challenging sequences have shown that the improvement from the proposed framework compared to the state-of-the-art systems, are over 15%, 18% and 20%, on reducing the errors of center location, scale size and rotation angle estimations, respectively.

The control of Variable Speed Wind Turbines (VSWT) to achieve optimal balance of power generation stability and rotor angular speed is impeded by the non-linear dynamics of the turbine-wind interaction and sudden changes of wind direction and speed. Conventional approaches to design VSWT controller s are not adaptive. However, the wind shear phenomenon introduces a strongly non-stationary environment that requires adaptive control approaches with minimal human intervention, i.e. very little supervision of the adaptation process. Reinforcement Learning (RL) allows minimally supervised learning. Specifically, Actor-Critic is designed to deal with continuous valued state and action spaces. In this paper we apply an Actor-Critic RL architecture to improve the adaptation of the conventional VSWT controllers to changing wind conditions. Simulation results on a benchmark VSWT model under strongly changing wind conditions show that Actor Critic RL approach with functional approximation provide great enhancement over state-of-the-art VSWT controllers.

In this study, a novel approach, which is the combination of wavelet-based edge correlation (WEC), speeded up robust features (SURF) feature descriptors and feature point matching considering local constraints, is proposed to register the tissue slic