Soft robots, compared to regular rigid robots, as their multiple segments with soft materials bring flexibility and compliance, have the advantages of safe interaction and dexterous operation in the environment. However, due to its characteristics of high dimensional, nonlinearity, time-varying nature, and infinite degree of freedom, it has been challenges in achieving precise and dynamic control such as trajectory tracking and position reaching. To address these challenges, we propose a framework of Deep Koopman-based Model Predictive Control (DK-MPC) for handling multi-segment soft robots. We first employ a deep learning approach with sampling data to approximate the Koopman operator, which therefore linearizes the high-dimensional nonlinear dynamics of the soft robots into a finite-dimensional linear representation. Secondly, this linearized model is utilized within a model predictive control framework to compute optimal control inputs that minimize the tracking error between the desired and actual state trajectories. The real-world experiments on the soft robot “Chordata” demonstrate that DK-MPC could achieve highprecision control, showing the potential of DK-MPC for future applications to soft robots. More visualization results can be found at https://pinkmoon-io.github.io/DKMPC/.

In crowd evacuation scenarios, it is an effective way to ensure evacuation safety by recognizing real crowd emotions and then taking measures such as emotional infection to reduce crowd panic. However, factors such as distance, exposure, angle, and occlusion can lead to incomplete face expression information collection, thus failing to accurately identify crowd emotions. Therefore, it is still a very challenging problem to recognize individual emotions in the case of incomplete face expression information collection, and thus accurately identify crowd emotions during evacuation. To solve this problem, we propose a spatial-temporal consistency-based crowd emotion recognition method to accurately identify the real emotions of the crowd. First, for video frames that can capture the complete facial expression information, we use the residual network to accurately identify the individual emotion values in each frame. For video frames that cannot capture the complete facial expression information, we propose an individual emotion calculation model based on spatial-temporal consistency to calculate the individual emotion values in each frame. Second, we define the crowd panic level and obtain the real crowd emotion by calculation. Finally, we implement an end-to-end crowd panic emotion recognition system to verify our method. The experimental results show that the method can accurately calculate the crowd panic level, which is important for guiding crowd evacuation.

Abstract Precise energy management in distribution power system requires high‐precision time synchronization among large‐scale deployed devices. Multiple clock sources‐based time synchronization possesses advantages of reliability, high precision, and robustness, but still faces several challenges such as coupling between time synchronization error and delay, as well as different timescales between clock source and clock weight optimization. In this paper, a multi‐clock source time synchronization model is constructed and a problem is formulated to minimize the synchronization error and delay through jointly optimizing large‐timescale clock source selection and small‐timescale weight selection. A reinforcement learning‐based multi‐timescale multi‐clock source time synchronization algorithm named RL‐M 2 is proposed to solve the formulated problem from a learning perspective. Besides, a lossless switching method is proposed to address the switching problem for multiple clock sources. Simulation results demonstrate the superior performance of RL‐M 2 and the lossless switching method in time synchronization delay and error.

Automatic visualization generates meaningful visualizations to support data analysis and pattern finding for novice or casual users who are not familiar with visualization design. Current automatic visualization approaches adopt mainly aggregation and filtering to extract patterns from the original data. However, these limited data transformations fail to capture complex patterns such as clusters and correlations. Although recent advances in feature engineering provide the potential for more kinds of automatic data transformations, the auto-generated transformations lack explainability concerning how patterns are connected with the original features. To tackle these challenges, we propose a novel explainable recommendation approach for extended kinds of data transformations in automatic visualization. We summarize the space of feasible data transformations and measures on explainability of transformation operations with a literature review and a pilot study, respectively. A recommendation algorithm is designed to compute optimal transformations, which can reveal specified types of patterns and maintain explainability. We demonstrate the effectiveness of our approach through two cases and a user study.

Negative emotion will spread among the crowd, and make people irrational. Intervention can reduce crowd panic by isolating the source of transmission or spreading positive emotions, which is an effective measure to ensure the safety of the crowd. However, it is still a very challenging problem that how to use appropriate intervention to maximize the influence to infected individuals while minimizing the cost of the intervention. To this end, we present an intervention optimization method for crowd emotional contagion (IO-CEC) to adaptively intervene in negative emotional contagion. First, we build a stochastic events based emotional contagion model (SEEC) to capture the dynamic evolution characteristics of crowd emotional contagion. In this model, we combine the SIS infection model and the Markov stochastic process to study the dynamic change of the number of infected individuals. Second, we propose a Hawkes process based intervention model (HIM) to describe the intensity of intervention. By using the Hawkes stochastic point process, two intervention intensity functions are defined to quantify the infection rate and recovery rate. Then, in order to improve the efficiency of intervention, we formulate the intervention optimization as a problem of maximizing the utility and use a heuristic algorithm to solve it. Finally, we implement a crowd emotional contagion simulation system to visualize the results of emotional intervention. Experimental results show that our method is effective and has great significance for guiding realistic emotional intervention.

Sequential incentive marketing is an important approach for online businesses to acquire customers, increase loyalty and boost sales. How to effectively allocate the incentives so as to maximize the return (e.g., business objectives) under the budget constraint, however, is less studied in the literature. This problem is technically challenging due to the facts that 1) the allocation strategy has to be learned using historically logged data, which is counterfactual in nature, and 2) both the optimality and feasibility (i.e., that cost cannot exceed budget) needs to be assessed before being deployed to online systems. In this paper, we formulate the problem as a constrained Markov decision process (CMDP). To solve the CMDP problem with logged counterfactual data, we propose an efficient learning algorithm which combines bisection search and model-based planning. First, the CMDP is converted into its dual using Lagrangian relaxation, which is proved to be monotonic with respect to the dual variable. Furthermore, we show that the dual problem can be solved by policy learning, with the optimal dual variable being found efficiently via bisection search (i.e., by taking advantage of the monotonicity). Lastly, we show that model-based planing can be used to effectively accelerate the joint optimization process without retraining the policy for every dual variable. Empirical results on synthetic and real marketing datasets confirm the effectiveness of our methods.

ABSTRACT Large‐scale group performance animation has been an important research topic because of its diverse range of applications including virtual rehearsal and film production. Animating hundreds of virtual actors as what the director wishes is a tough task. In this paper, we address this challenge by introducing an optimization method that generates large‐scale group performance by deducing a small‐scale one with fewer actors. We introduced group motion bigraph technique and transformed the motion‐deducing problem into a constrained optimization problem. A solving process is then presented to automatically obtain the motion of the large group with velocity constraints. Moreover, an interactive system of constructing the group motion bigraph has been implemented, which provides flexible edit and control on deducing group motion. The animation results show that our method is competent for deducing large‐scale group performance from only several motion clips performed by small groups. Copyright © 2013 John Wiley & Sons, Ltd.

ABSTRACT We present a rigid‐motion‐inspired method for animating liquid characters in this paper. Our method allows an animator to control the motion of liquid characters with motion capture data that is widely used in rigid body animation. It animates the most visual interesting part of liquid character, that is, to preserve character's shape as well as produce enough liquid details. To this end, we build a two‐layer model to represent the character by two coaxial layers: the rigid kernel and the liquid shell. Different control paradigms are used for the two layers instead of applying homogeneous force that is common in previous approaches. By embedding the control algorithm to the Navier–Stokes equations, we compute the fluid velocity that drives the motion of the liquid character. Results show that the method is easy and intuitive to use while incurring little additional cost.Copyright © 2013 John Wiley & Sons, Ltd.