Search

Eye movements can be an important cue to reveal consumer decision processes. Findings from existing studies suggest that the consumer decision process consists of a few different browsing states such as screening and evaluation. To reveal the characteristics and temporal changes of browsing states in catalog browsing situations, this study proposes a hidden semi-Markov-based gaze model, where the states in consumer decision processes are explicitly modeled as hidden states. To achieve a better understanding of consumer decision processes, eye movements are first encoded to a sequence of gaze features using semantic structure of digital catalogs. The proposed gaze model is trained and evaluated using gaze data which was collected from eight participants in an experimentally controlled catalog browsing situation. We analyze the estimated browsing states and demonstrate that the states can contribute to improve the performance of viewer interest estimation.

This paper explores how haptic interfaces should be designed to enable effective human-swarm interactions. When a single operator is interacting with a team of mobile robots, there are certain properties of the team that may help the operator complete the task at hand if these properties were fed back via haptics. However, not all team-level properties may be particularly well-suited for haptic feedback. In this paper, characteristics that make a property of a multi-agent system appropriate for haptic feedback are defined. The focus here is on leader-follower networks, in which one robot, the so-called leader, is controlled via an operator with a haptic device, whereas the remaining robots, the so-called followers, are tasked with maintaining distances between one another. Multi-agent manipulability, a property which describes how effective the leader is at controlling the movement of the followers, is proposed as one such appropriate property for haptic feedback in a human-swarm interaction scenario. Manipulability feedback is implemented using a PHANTOM Omni haptic joystick and experiments in which a team of mobile robots is controlled via a human operator with access to this feedback show that this is viable in practice.

The coordination and control of the agents in electrical networks is expected to have an important positive impact in future smart-grids. In the present paper we propose a distributed hierarchical coordination framework for power balancing, and the algorithms required to build such coordination architecture. This architecture is built such that a group of agents (a subtree of the hierarchy) can respond and adjust to deviations from a day-ahead plan for that group, without the need to coordinate with its upper levels of the hierarchy at every time step. The agents are arranged in groups by taking into account a measure of the flexibility of each group, thus allowing a partial de-coupling of the groups during the coordination, which reduces the required work of the central coordinator and gives robustness to the system. The assignment of agents to groups is formulated as a welfare maximization problem, a NP-hard problem for which an approximate solution can be obtained efficiently by using the sub-modularity and monotonicity of the involved functions. An electric vehicle charging coordination scenario is presented to exemplify the proposed architecture and methods.

The development of energy management systems (EMS) has attracted increasing attention during the last years. One of the main goals of EMS is to balance the power usage and generation, while also maintaining the quality of life (QoL) of the users. In this paper, a distributed coordination framework for on-line scheduling of appliances and control of the aggregated power demand of households is proposed. Each household consists of a set of appliances, and each appliance is modeled using a probabilistic generative model of its power usage profile, which can be updated through the day to indicate changes in user preferences. The coordination framework is formulated as a receding horizon distributed optimization, where the households' QoL, and the deviation from a scheduled usage are taken into account. The implemented distributed optimization can be seen as a negotiation among the households and a coordinator: the coordinator seeks to balance the aggregated power consumption by minimizing the deviation from a scheduled aggregated power usage, while each household tries not to deviate much from its preferred usage pattern.

This paper proposes a distributed scheduling algorithm of demands of multiple households/consumers in order to achieve a power balancing in total. Assume that an autonomous energy management system is installed in each household and that those households are capable of communicating with an aggregator. Then, it becomes possible to negotiate via the autonomous energy management systems to find an agreement point that takes both the users' demands and the aggregator's objective into account. The key aspect of the algorithm is that it enables us to encapsulate the particular control and objective in each household, and realizes the negotiation based on power profiles. We also show the proposed framework can be smoothly integrated with a probabilistic generative model of power profiles.

To analyze the performance of the formation system in response to inputs/disturbances from outside of the formation, we propose the notion of readiness, which describes the initial conditions of the formation in terms of a certain set of input space. A higher readiness means better initial conditions of the system with better performance in maintaining or recovering the original formation shape against the exogenous inputs/disturbances. Optimization method based on calculus of variations is used in the analysis for deriving the optimality conditions. Simulations modeled on both unicycle and car-like robots demonstrate the features and the potential applications of the proposed notion.

This paper unifies the previously defined instantaneous measures of responsiveness in multi-agent systems, namely those of stiffness and manipulability. These concepts are used for characterizing how a networked multi-agent system responds to perturbation of agents or to exogenous movements of its leader nodes. The resulting unified notion of a system's responsiveness provides us with a precise characterization of the effect of different choices of leader nodes and interaction topologies have on how easy or hard it is to control the network.

In the past few years we have developed an algorithmic framework for optimal control of switched-mode hybrid dynamical systems. Such systems are described by the equation x = f(x, v) (1) where x ∊ Rn, x 0 : = x(0) is given, v belongs to a finite set V ⊂ R, and f : Rn × V → Rn is a function that is continuously differentiable in x for every v ∊ V. Let L : Rn → R be a function, and consider the cost functional J, defined as J = ∫tf 0 L(x(t))dt, (2) for a given final time t f > 0. The problem that we consider is to minimize J.

In this paper, we address the problem of selecting leaders in a network by investigating how much instantaneous impact the leaders have on the remaining agents. As a measurement of the influence of leaders' inputs, we exploit the notion of manipulability, which is recently developed for leader-follower networks driven by a state-dependent weighted consensus equation. This paper first extends the manipulability index in order to measure the influence of leaders' inputs on the network centroid. We then demonstrate in simulation how the manipulability index is suitable for selecting effective leaders.

This paper applies a general computational technique for optimal control of switched-mode hybrid systems, recently developed by the authors, to voltage-regulation problems in a step-down DC-DC converter. Unlike existing techniques that are based on model-predictive control and a specific algebraic structure of the problem, the algorithms presented here are based on gradient descent with Armijo step size, and consequently can incorporate time-dependent state constraints in a natural way. The approach proposed in this paper is complementary to the extant MPC-based techniques, and it appears to compare favorably with some of the established works. Two problems are being addressed: one concerns pulse-width modulation and computes the optimal duty ratio, and the other computes an optimal switching schedule without a fixed cycle time.

We introduce the notion of manipulability to leader-follower networks as a tool to analyze how effective inputs injected at a leader node are in terms of their impact on the movements of the follower nodes, as a function of the interaction topologies and agent configurations. Classic manipulability is an index used in robotics for analyzing the singularity and efficiency of configurations of robot-arm manipulators. To define similar notions for leader-follower networks, we use a rigid-link approximation of the follower dynamics and under this assumption, we prove that the instantaneous follower velocities can be uniquely determined by that of the leader's, which allows us to define a meaningful manipulability index of the leader-follower networks.

We propose a novel method to estimate the object that a user is focusing on by using the synchronization between the movements of objects and a user's eyes as a cue. We first design an event as a characteristic motion pattern, and we then embed it within the movement of each object. Since the user's ocular reactions to these events are easily detected using a passive camera-based eye tracker, we can successfully estimate the object that the user is focusing on as the one whose movement is most synchronized with the user's eye reaction. Experimental results obtained from the application of this system to dynamic content (consisting of scrolling images) demonstrate the effectiveness of the proposed method over existing methods.

Human-machine interaction still lacks smoothness and naturalness despite the widespread utilization of intelligent systems and emotive agents. In order to improve the interaction, this work proposes an approach to estimate user's interest based on the relationships between dynamics of user's eye movements, more precisely the endogenous control mode of saccades, and machine's proactive visual content presentation. Under a specially-designed presentation phase to make the user express the endogenous saccades, we analyzed delays between the saccades and the presentation events. As a result, we confirmed that the delay while the user's gaze is maintained on the previous presented content regardless of the next event, called resistance, is a good indicator of the interest estimation (70% success, upon 20 experiments). It showed higher accuracy than the conventional interest estimation based on gaze duration.

We have developed a high-precision method that selects an appropriate model of a video image in order to track an unknown face in front of a large display. Currently, Active Appearance Models (AAMs) are used to track non-rigid objects, such as a faces, because the models efficiently learn the correlation between shape and texture. The problem with an AAM is that when it tracks an unknown face, excessive training data increases tracking errors because there is an intermediate model size beyond which the reduction in fitting performance outweighs the gains from any improved representational power of the model. To increases the accuracy with which an unknown face is tracked, we built clustered models from training datasets and select a cluster that includes a face which is similar to the unknown face. Our method of clustering and cluster selecting is based on the Mutual Subspace Method (MSM). We demonstrated the effectiveness of our method by using the leave-one-out cross-validation.

In this paper, we propose a novel approach to speaker detection by an integration of audio-visual information using the cue of timing structure. We first extract feature sequences of lip motion and sound, and segment each of them into temporal intervals. Then, we construct a cross-media timing-structure model of human speech by learning the temporal relations of overlapping intervals. Based on the learned model, we realize speaker detection by evaluating the timing structure of the observed video and audio. Our experimental result shows the effectiveness of using temporal relations of intervals for speaker detection.

In human communication, dynamics of communication, i.e. timing structure of utterances, nodding, gesture, pause and so on, plays a crucial role to realize smooth natural communication. We proposed a computational scheme named interval-based linear hybrid dynamical system (ILHDS, in short) for modeling complex dynamic events and conducted several experiments to explore characteristics of dynamic structures of human verbal and nonverbal communication based on ILHDS. In the paper, we describe the theoretical scheme of ILHDS followed by its practical applications