Group Modeling: A Unified Velocity‐Based Approach

Crowd simulators are commonly used to populate movie or game scenes in the entertainment industry. Even though it is crucial to consider the presence of groups for the believability of a virtual crowd, most crowd simulations only take into account individual characters or a limited set of group behaviors. We introduce a unified solution that allows for simulations of crowds that have diverse group properties such as social groups, marches, tourists and guides, etc. We extend the Velocity Obstacle approach for agent‐based crowd simulations by introducing Velocity Connection; the set of velocities that keep agents moving together while avoiding collisions and achieving goals. We demonstrate our approach to be robust, controllable, and able to cover a large set of group behaviors.     

A Community Structure‐Based Approach for Network Immunization

We propose a community structure‐based approach that does not require community labels of nodes, for network immunization. Social networks have been widely used as daily communication infrastructures these days. However, fast spreading of information over networks may have downsides such as computer viruses or epidemics of diseases. Because contamination is propagated among subgraphs (communities) along links in a network, use of community structure of the network would be effective for network immunization. However, despite various research efforts, it is still difficult to identify ground‐truth community labels of nodes in a network. Because communities are often interwoven through intermediate nodes, we propose to identify such nodes based on the community structure of a network without requiring community labels. By regarding the community structure in terms of nodes, we construct a vector representation of nodes based on a quality measure of communities. The distribution of the constructed vectors is used for immunizing intermediate nodes among communities, through the hybrid use of the norm and the relation in the vector representation. Experiments are conducted over both synthetic and real‐world networks, and our approach is compared with other network centrality‐based approaches. The results are encouraging and indicate that it is worth pursuing this path.     

A Measurement‐Based Approach for Designing Fixed‐Order Controllers for Unknown Closed‐Loop Architecture

This paper presents a new technique to design fixed‐structure controllers for linear unknown systems using a set of measurements. In model‐based approaches, the measured data are used to identify a model of the plant for which a suitable controller can be designed. Due to the fact that real processes cannot be described perfectly by mathematical models, designing controllers using such models to guarantee some desired closed‐loop performance is a challenging task. Hence, a possible alternative to model‐based methods is to directly utilize the measured data in the design process. We propose an approach to designing structured controllers using a set of closed‐loop frequency‐domain data. The principle of such an approach is based on computing the parameters of a fixed‐order controller for which the closed‐loop frequency response fits a desired frequency response that describes some desired performance indices. This problem is formulated as an error minimization problem, which can be solved to find suitable values of the controller parameters. The main feature of the proposed control methodology is that it can be applied to stable and unstable plants. Additionally, the design process depends on a pre‐selected controller structure, which allows for the selection of low‐order controllers. An application of the proposed method to a DC servomotor system is presented to experimentally validate and demonstrate its efficacy.     

A Color‐Pair Based Approach for Accurate Color Harmony Estimation

Harmonious color combinations can stimulate positive user emotional responses. However, a widely open research question is: how can we establish a robust and accurate color harmony measure for the public and professional designers to identify the harmony level of a color theme or color set. Building upon the key discovery that color pairs play an important role in harmony estimation, in this paper we present a novel color‐pair based estimation model to accurately measure the color harmony. It first takes a two‐layer maximum likelihood estimation (MLE) based method to compute an initial prediction of color harmony by statistically modeling the pair‐wise color preferences from existing datasets. Then, the initial scores are refined through a back‐propagation neural network (BPNN) with a variety of color features extracted in different color spaces, so that an accurate harmony estimation can be obtained at the end. Our extensive experiments, including performance comparisons of harmony estimation applications, show the advantages of our method in comparison with the state of the art methods.     

A Practice‐Based Approach to Understanding Participation in Online Communities

The take up of online communities in organisations is often patchy. Previous studies, mainly within the cognitive tradition of thinking, explain such non‐participation either through features of technology or through individuals' motivational structures, and look at participation as a static, individualistic and functionalistic phenomenon. The aim of this article is to explore the insights on participation from a practice‐based approach (PBA). The paper draws upon empirical data from the use of collaborative technology in a Mexican University. The adoption of community technology is shown to be shaped by dynamic, collective, historical and contextual forces. Based on these findings, the value and the limits of the PBA, and a series of contributions and practical implications are discussed.     

A Phase‐Based Approach for Animating Images Using Video Examples

We present a novel approach for animating static images that contain objects that move in a subtle, stochastic fashion (e.g. rippling water, swaying trees, or flickering candles). To do this, our algorithm leverages example videos of similar objects, supplied by the user. Unlike previous approaches which estimate motion fields in the example video to transfer motion into the image, a process which is brittle and produces artefacts, we propose an Eulerian phase‐based approach which uses the phase information from the sample video to animate the static image. As is well known, phase variations in a signal relate naturally to the displacement of the signal via the Fourier Shift Theorem. To enable local and spatially varying motion analysis, we analyse phase changes in a complex steerable pyramid of the example video. These phase changes are then transferred to the corresponding spatial sub‐bands of the input image to animate it. We demonstrate that this simple, phase‐based approach for transferring small motion is more effective at animating still images than methods which rely on optical flow.     

Bounded‐Input Control of the Quadrotor Unmanned Aerial Vehicle: A Vision‐Based Approach

In this paper, a bounded‐input controller is designed for the quadrotor vertical take‐off and landing unmanned aerial vehicle (UAV). Visual information is used to localize the aircraft with respect to its environment and an image‐based visual servo scheme is developed to navigate the motion of it. The visual features are selected from perspective image moments and projected on a rotated image plane, which simplifies the controller design. The flow of the features is used as the linear velocity information, and the controller assumes angular velocity and attitude information available for feedback. To design the controller, the dynamics of the quadrotor are decoupled into two parts: translational dynamics and rotational dynamics. First visual data are used to design a bounded‐input controller for the translational dynamics, and then a saturated controller is designed for the rotational dynamics. The boundedness of the controller increases the chance of keeping the visual features in the field of view of the camera. Furthermore, the controllers also cope with the unknown depth of the image, and the external disturbances. The complete stability analysis of the overall system is presented to show that all states are bounded and the error signals converge to zero asymptotically. Simulation examples are provided in both nominal and perturbed conditions which show the effectiveness of the proposed theoretical results.     

A State‐Space Based New Approach To The Directional Interpolation Problem

Directional interpolation plays an important role in robust control, system realization and model reduction. Several solutions to various directional interpolation problems have been proposed. In this paper, we consider the directional interpolation problem in a general setting and present a statespace based new approach to solving the problem. The solution is simple, and its exposition is as self‐contained as possible. We describe all the (strictly) bounded real rational matrix functions that satisfy the directional interpolation requirements by means of linear fractional transformation. Moreover, we give a necessary and sufficient condition for the interpolating function to be unique and show that the unique interpolating function is an inner (a co‐inner). The main procedures used to generate the interpolating function consist of standard matrix operations consisting of easy numerical computations, so the present solution is significant from the numerical viewpoint as well as the analytical viewpoint.     

A Data‐Driven Approach for Sketch‐Based 3D Shape Retrieval via Similar Drawing‐Style Recommendation

Sketching is a simple and natural way of expression and communication for humans. For this reason, it gains increasing popularity in human computer interaction, with the emergence of multitouch tablets and styluses. In recent years, sketch‐based interactive methods are widely used in many retrieval systems. In particular, a variety of sketch‐based 3D model retrieval works have been presented. However, almost all of these works focus on directly matching sketches with the projection views of 3D models, and they suffer from the large differences between the sketch drawing and the views of 3D models, leading to unsatisfying retrieval results. Therefore, in this paper, during the matching procedure in the retrieval, we propose to match the sketch with each 3D model from historical users instead of projection views. Yet since the sketches between the current user and the historical users can have big difference, we also aim to handle users' personalized deviations and differences. To this end, we leverage recommendation algorithms to estimate the drawing style characteristic similarity between the current user and historical users. Experimental results on the Large Scale Sketch Track Benchmark(SHREC14LSSTB) demonstrate that our method outperforms several state‐of‐the‐art methods.     

Easy Matting ‐ A Stroke Based Approach for Continuous Image Matting

We propose an iterative energy minimization framework for interactive image matting. Our approach is easy in the sense that it is fast and requires only few user‐specified strokes for marking the foreground and background. Beginning with the known region, we model the unknown region as a Markov Random Field (MRF) and formulate its energy in each iteration as the combination of one data term and one smoothness term. By automatically adjusting the weights of both terms during the iterations, the first‐order continuous and feature‐preserving result is rapidly obtained with several iterations. The energy optimization can be further performed in selected local regions for refined results. We demonstrate that our energy‐driven scheme can be extended to video matting, with which the spatio‐temporal smoothness is faithfully preserved. We show that the proposed approach outperforms previous methods in terms of both the quality and performance for quite challenging examples.     

A Sensitivity‐Based Construction Approach to Variance Minimization of Markov Decision Processes

With a long‐run average performance as the primary criterion for a Markov decision process, variance measures are studied as its secondary criteria. The steady‐state variance and the limiting average variance along a sample path are discussed. The latter one is difficult to handle due to its special form. With a sensitivity‐based approach, the difference formula for the sample‐path variance under different policies is intuitively constructed and then the optimality equation is presented. Moreover a policy iteration algorithm is developed. This work extends the sensitivity‐based construction approach to Markov decision processes with non‐standard performance criteria. The difference between these two types of variance and bias criteria is illustrated with a numerical example.     

Optimized‐Based Stabilization of Constrained Nonlinear Systems: A Receding Horizon Approach

The problem of stabilizing constrained nonlinear systems while optimizing performance is investigated in this paper. The tool of weak control Lyapunov functions (WCLFs) is introduced to construct a tuning Sontag's controller where some adjustable parameters are optimized with respect to given performances in a receding horizon fashion. Two algorithms are presented and the corresponding closed‐loop systems with input constraints are proven to be stable in some regions by using the LaSalle's theorem and the properties of WCLFs. Moreover, the inverse optimality result of the controller is achieved. Finally, two open‐loop unstable examples are used to illustrate the performance and effectiveness of the results obtained here.     

Quadratic robust tracking control — A Polynomial Matrix Approach

A quadratic robust tracking problem is solved using a polynomial matrix approach. Because of the possibly unstable mode of the control sequence we propose a new quadratic cost function of error and control sequences to facilitate the optimization. The optimal controller makes the plant output track the reference sequence robustly and minimizes the proposed quadratic cost function. We also present the parametrized set of suboptimal controllers which yield finite costs so that there exists the central optimal controller in the set when the parameter is set to zero. The usefulness of our proposed cost function is demonstrated by simulation examples. Our design procedure has advantages over two other possible approaches: the LQ state-space approach and the Wiener–Hopf approach which may be tried to tackle the same problem. Our approach obviates the need to construct a dynamic observer compared to the state-space approach and it is computationally attractive compared to the Wiener–Hopf approach.     

A Learning‐Theoretic Approach To Model‐Set Identification

A new approach to model‐set identification is proposed based on an agnostic learning theory. The squared prediction error is estimated together with its uncertainty uniformly in some parameter region. Based on this estimation, a model set is constructed so as to include the best model. The proposed approach does not require assumptions on the true dynamics or the noise, neither does it need infinite number of input‐output data in order to justify its result. But it guarantees that the size of the identified model set converges to zero as the number of input‐output data increases. Improvement of the precision is considered on the proposed identification method. Generalization of the approach is discussed and a numerical example is presented.     

Ordered Weighted Averaging Operators 1988–2014: A Citation‐Based Literature Survey

This study surveys the ordered weighted averaging (OWA) operator literature using a citation network analysis. The main goals are the historical reconstruction of scientific development of the OWA field, the identification of the dominant direction of knowledge accumulation that emerged since the publication of the first OWA paper, and to discover the most active lines of research. The results suggest, as expected, that Yager's paper 1 (IEEE Trans. Systems Man Cybernet, 18(1), 183–190, 1988) is the most influential paper and the starting point of all other research using OWA. Starting from his contribution, other lines of research developed and we describe them.