251,855 research outputs found
An overview of microclimate tools for predicting the thermal comfort, meteorological parameters and design strategies in outdoor spaces
Abstract
There are several outdoor microclimatic simulation software tools in use. The current research aims to identify some of the most prominent computer-based tools based on their capacity of predicting a significant number of variables and compare them in order to establish their differences. This article provides an overview of the applications of computational fluid dynamics in outdoor performance simulation, focused on three topics: general criteria, specific outputs, strategies, and elements can be investigated by the tool. The results have shown that ENVI-met tool is capable of predicting and simulating the set microclimate variables
Kinship Verification from Videos using Spatio-Temporal Texture Features and Deep Learning
Automatic kinship verification using facial images is a relatively new and
challenging research problem in computer vision. It consists in automatically
predicting whether two persons have a biological kin relation by examining
their facial attributes. While most of the existing works extract shallow
handcrafted features from still face images, we approach this problem from
spatio-temporal point of view and explore the use of both shallow texture
features and deep features for characterizing faces. Promising results,
especially those of deep features, are obtained on the benchmark UvA-NEMO Smile
database. Our extensive experiments also show the superiority of using videos
over still images, hence pointing out the important role of facial dynamics in
kinship verification. Furthermore, the fusion of the two types of features
(i.e. shallow spatio-temporal texture features and deep features) shows
significant performance improvements compared to state-of-the-art methods.Comment: 7 page
A review of the analytical simulation of aircraft crash dynamics
A large number of full scale tests of general aviation aircraft, helicopters, and one unique air-to-ground controlled impact of a transport aircraft were performed. Additionally, research was also conducted on seat dynamic performance, load-limiting seats, load limiting subfloor designs, and emergency-locator-transmitters (ELTs). Computer programs were developed to provide designers with methods for predicting accelerations, velocities, and displacements of collapsing structure and for estimating the human response to crash loads. The results of full scale aircraft and component tests were used to verify and guide the development of analytical simulation tools and to demonstrate impact load attenuating concepts. Analytical simulation of metal and composite aircraft crash dynamics are addressed. Finite element models are examined to determine their degree of corroboration by experimental data and to reveal deficiencies requiring further development
On the importance of nonlinear modeling in computer performance prediction
Computers are nonlinear dynamical systems that exhibit complex and sometimes
even chaotic behavior. The models used in the computer systems community,
however, are linear. This paper is an exploration of that disconnect: when
linear models are adequate for predicting computer performance and when they
are not. Specifically, we build linear and nonlinear models of the processor
load of an Intel i7-based computer as it executes a range of different
programs. We then use those models to predict the processor loads forward in
time and compare those forecasts to the true continuations of the time seriesComment: Appeared in "Proceedings of the 12th International Symposium on
Intelligent Data Analysis
A study on decoding models for the reconstruction of hand trajectories from the human magnetoencephalography
Decoding neural signals into control outputs has been a key to the development of brain-computer interfaces (BCIs). While many studies have identified neural correlates of kinematics or applied advanced machine learning algorithms to improve decoding performance, relatively less attention has been paid to optimal design of decoding models. For generating continuous movements from neural activity, design of decoding models should address how to incorporate movement dynamics into models and how to select a model given specific BCI objectives. Considering nonlinear and independent speed characteristics, we propose a hybrid Kalman filter to decode the hand direction and speed independently. We also investigate changes in performance of different decoding models (the linear and Kalman filters) when they predict reaching movements only or predict both reach and rest. Our offline study on human magnetoencephalography (MEG) during point-to-point arm movements shows that the performance of the linear filter or the Kalman filter is affected by including resting states for training and predicting movements. However, the hybrid Kalman filter consistently outperforms others regardless of movement states. The results demonstrate that better design of decoding models is achieved by incorporating movement dynamics into modeling or selecting a model according to decoding objectives.open0
Predictive biometrics: A review and analysis of predicting personal characteristics from biometric data
Interest in the exploitation of soft biometrics information has continued to develop over the last decade or so. In comparison with traditional biometrics, which focuses principally on person identification, the idea of soft biometrics processing is to study the utilisation of more general information regarding a system user, which is not necessarily unique. There are increasing indications that this type of data will have great value in providing complementary information for user authentication. However, the authors have also seen a growing interest in broadening the predictive capabilities of biometric data, encompassing both easily definable characteristics such as subject age and, most recently, `higher level' characteristics such as emotional or mental states. This study will present a selective review of the predictive capabilities, in the widest sense, of biometric data processing, providing an analysis of the key issues still adequately to be addressed if this concept of predictive biometrics is to be fully exploited in the future
Human Motion Trajectory Prediction: A Survey
With growing numbers of intelligent autonomous systems in human environments,
the ability of such systems to perceive, understand and anticipate human
behavior becomes increasingly important. Specifically, predicting future
positions of dynamic agents and planning considering such predictions are key
tasks for self-driving vehicles, service robots and advanced surveillance
systems. This paper provides a survey of human motion trajectory prediction. We
review, analyze and structure a large selection of work from different
communities and propose a taxonomy that categorizes existing methods based on
the motion modeling approach and level of contextual information used. We
provide an overview of the existing datasets and performance metrics. We
discuss limitations of the state of the art and outline directions for further
research.Comment: Submitted to the International Journal of Robotics Research (IJRR),
37 page
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