Discriminant incoherent component analysis

Face images convey rich information which can be perceived as a superposition of low-complexity components associated with attributes, such as facial identity, expressions, and activation of facial action units (AUs). For instance, low-rank components characterizing neutral facial images are associated with identity, while sparse components capturing non-rigid deformations occurring in certain face regions reveal expressions and AU activations. In this paper, the discriminant incoherent component analysis (DICA) is proposed in order to extract low-complexity components, corresponding to facial attributes, which are mutually incoherent among different classes (e.g., identity, expression, and AU activation) from training data, even in the presence of gross sparse errors. To this end, a suitable optimization problem, involving the minimization of nuclear-and l1 -norm, is solved. Having found an ensemble of class-specific incoherent components by the DICA, an unseen (test) image is expressed as a group-sparse linear combination of these components, where the non-zero coefficients reveal the class(es) of the respective facial attribute(s) that it belongs to. The performance of the DICA is experimentally assessed on both synthetic and real-world data. Emphasis is placed on face analysis tasks, namely, joint face and expression recognition, face recognition under varying percentages of training data corruption, subject-independent expression recognition, and AU detection by conducting experiments on four data sets. The proposed method outperforms all the methods that are compared with all the tasks and experimental settings

Synthesizing Training Data for Object Detection in Indoor Scenes

Detection of objects in cluttered indoor environments is one of the key enabling functionalities for service robots. The best performing object detection approaches in computer vision exploit deep Convolutional Neural Networks (CNN) to simultaneously detect and categorize the objects of interest in cluttered scenes. Training of such models typically requires large amounts of annotated training data which is time consuming and costly to obtain. In this work we explore the ability of using synthetically generated composite images for training state-of-the-art object detectors, especially for object instance detection. We superimpose 2D images of textured object models into images of real environments at variety of locations and scales. Our experiments evaluate different superimposition strategies ranging from purely image-based blending all the way to depth and semantics informed positioning of the object models into real scenes. We demonstrate the effectiveness of these object detector training strategies on two publicly available datasets, the GMU-Kitchens and the Washington RGB-D Scenes v2. As one observation, augmenting some hand-labeled training data with synthetic examples carefully composed onto scenes yields object detectors with comparable performance to using much more hand-labeled data. Broadly, this work charts new opportunities for training detectors for new objects by exploiting existing object model repositories in either a purely automatic fashion or with only a very small number of human-annotated examples.Comment: Added more experiments and link to project webpag

Dynamic behavior analysis via structured rank minimization

Human behavior and affect is inherently a dynamic phenomenon involving temporal evolution of patterns manifested through a multiplicity of non-verbal behavioral cues including facial expressions, body postures and gestures, and vocal outbursts. A natural assumption for human behavior modeling is that a continuous-time characterization of behavior is the output of a linear time-invariant system when behavioral cues act as the input (e.g., continuous rather than discrete annotations of dimensional affect). Here we study the learning of such dynamical system under real-world conditions, namely in the presence of noisy behavioral cues descriptors and possibly unreliable annotations by employing structured rank minimization. To this end, a novel structured rank minimization method and its scalable variant are proposed. The generalizability of the proposed framework is demonstrated by conducting experiments on 3 distinct dynamic behavior analysis tasks, namely (i) conflict intensity prediction, (ii) prediction of valence and arousal, and (iii) tracklet matching. The attained results outperform those achieved by other state-of-the-art methods for these tasks and, hence, evidence the robustness and effectiveness of the proposed approach

Traffic event detection framework using social media

This is an accepted manuscript of an article published by IEEE in 2017 IEEE International Conference on Smart Grid and Smart Cities (ICSGSC) on 18/09/2017, available online: https://ieeexplore.ieee.org/document/8038595 The accepted version of the publication may differ from the final published version.© 2017 IEEE. Traffic incidents are one of the leading causes of non-recurrent traffic congestions. By detecting these incidents on time, traffic management agencies can activate strategies to ease congestion and travelers can plan their trip by taking into consideration these factors. In recent years, there has been an increasing interest in Twitter because of the real-time nature of its data. Twitter has been used as a way of predicting revenues, accidents, natural disasters, and traffic. This paper proposes a framework for the real-time detection of traffic events using Twitter data. The methodology consists of a text classification algorithm to identify traffic related tweets. These traffic messages are then geolocated and further classified into positive, negative, or neutral class using sentiment analysis. In addition, stress and relaxation strength detection is performed, with the purpose of further analyzing user emotions within the tweet. Future work will be carried out to implement the proposed framework in the West Midlands area, United Kingdom.Published versio

Behavior prediction in-the-wild

In this paper, the problem of audio-visual behavior prediction in-the-wild is addressed. In this context, both audio-visual descriptors of behavioral cues (features) and continuous-time real-valued characterizations of behavior (annotations) are (possibly) corrupted by non-Gaussian noise of large magnitude. The modeling assumption behind the proposed framework is that naturalistic affect and behavior captured in audio-visual episodes are smoothly-varying dynamic phenomena and thus the hidden temporal dynamics can be modeled as a generative auto-regressive process. Consequently, continuous-time real-valued characterizations of behavior (annotations) are postulated to be outputs of a low-complexity (i.e., low-order) time-invariant Linear Dynamical System (LDS) when descriptors of behavioral cues (features) act as inputs. To learn the parameters of the LDS, a recently proposed spectral method that relies on Hankel-rank minimization is adopted. Experimental evaluation on a challenging database recorded in the wild demonstrate the effectiveness of the proposed approach in behavior prediction

The conflict escalation resolution (CONFER) database

Conflict is usually defined as a high level of disagreement taking place when individuals act on incompatible goals, interests, or intentions. Research in human sciences has recognized conflict as one of the main dimensions along which an interaction is perceived and assessed. Hence, automatic estimation of conflict intensity in naturalistic conversations would be a valuable tool for the advancement of human-centered computing and the deployment of novel applications for social skills enhancement including conflict management and negotiation. However, machine analysis of conflict is still limited to just a few works, partially due to an overall lack of suitable annotated data, while it has been mostly approached as a conflict or (dis)agreement detection problem based on audio features only. In this work, we aim to overcome the aforementioned limitations by a) presenting the Conflict Escalation Resolution (CONFER) Database, a set of excerpts from audiovisual recordings of televised political debates where conflicts naturally arise, and b)reporting baseline experiments on audiovisual conflict intensity estimation. The database contains approximately 142min of recordings in Greek language, split over 120 non-overlapping episodes of naturalistic conversations that involve two or three interactants. Subject- and session-independent experiments are conducted on continuous-time (frame-by-frame) estimation of real-valued conflict intensity, as opposed to binary conflict/non-conflict classification. For the problem at hand, the efficiency of various audio and visual features and fusion of them as well as various regression frameworks is examined. Experimental results suggest that there is much room for improvement in the design and development of automated multi-modal approaches to continuous conflict analysis. The CONFER Database is publicly available for non-commercial use at http://ibug.doc.ic.ac.uk/resources/confer/. The Conflict Escalation Resolution (CONFER) Database is presented.CONFER contains 142min (120 episodes) of recordings in Greek language.Episodes are extracted from TV political debates where conflicts naturally arise.Experiments are the first approach to continuous estimation of conflict intensity.Performance of various audio and visual features and classifiers is evaluated

Data-driven nonlinear MPC using dynamic response surface methodology

For many complex processes, it is desirable to use a nonlinear model in the MPC design, and the recently proposed Dynamic Response Surface Methodology (DRSM) is capable of accurately modeling nonlinear continuous processes over semi-infinite time horizons. We exploit the DRSM to identify nonlinear data-driven dynamic models that are used in an NMPC. We demonstrate the ability and effectiveness of the DRSM data-driven model to be used as the prediction model for a nonlinear MPC regulator. This DRSM model is efficiently used to solve a non-equally-spaced finite-horizon optimal control problem so that the number of decision variables is reduced. The proposed DRSM-based NMPC is tested on a representative nonlinear process, an isothermal CSTR in which a second-order irreversible reaction is taking place. It is shown that the obtained quadratic data-driven model accurately represents the open-loop process dynamics and that DRSM-based NMPC is an effective data-driven implementation of nonlinear MPC

Investigation of hydrostatic fluid forces in varying clearance turbomachinery seals

Varying clearance, rotor-following seals are a key technology for meeting the demands of increased machine flexibility for conventional power units. These seals follow the rotor through hydrodynamic or hydrostatic mechanisms. Forward-facing step (FFS) and Rayleigh step designs are known to produce positive fluid stiffness. However, there is very limited modeling or experimental data available on the hydrostatic fluid forces generated from either design. A quasi-one-dimensional (1D) method has been developed to describe both designs and validated using test data. Tests have shown that the FFS and the Rayleigh step design are both capable of producing positive film stiffness and there is little difference in hydrostatic force generation between the two designs. This means any additional hydrodynamic features in the Rayleigh step design should have a limited effect on hydrostatic fluid stiffness. The analytical model is capable of modeling both the inertial fluid forces and the viscous fluid losses, and the predictions are in good agreement with the test data

Sensitivity analysis of cost parameters for floating offshore wind farms: An application to Italian waters

Floating offshore wind farms represent the next frontier in wind power industry. However, the development of this technology is strongly dependent on its economic feasibility. There follows that the development of economic analyses is crucial to highlight the possible greater potential of floating offshore wind farms and to support their sustainability and technical value. In this context, the purpose of this paper is to present a sensitivity analysis of the main cost parameters for floating offshore wind farms, namely the distance from the coast, the distance from the closest port and the sea depth. It can give specific information on which parameters are more important, and how much they affect the total cost. To this aim, a comprehensive life cycle cost assessment of floating offshore wind farms has been developed. In this study the cost model has been applied to the Italian waters. The results shown should provide guidance on how to preliminary assess the quality of a given site for floating offshore wind farm installation, and should be helpful for future development of decision-making procedures in the offshore wind sector

Detailed study on stiffness and load characteristics of film-riding groove types using design of experiments

In the application of film-riding sealing technology, there are various groove features that can be used to induce hydrodynamic lift. However, there is little guidance in selecting the relative parameter settings in order to maximize hydrodynamic load and fluid stiffness. In this study, two groove types are investigated—Rayleigh step and inclined groove. The study uses a design of experiments approach and a Reynolds equation solver to explore the design space. Key parameters have been identified that can be used to optimize a seal design. The results indicate that the relationship between parameters is not a simple linear relationship. It was also found that higher pressure drops hinder the hydrodynamic load and stiffness of the seal suggesting an advantage for using hydrostatic load support in such conditions