Pedestrian trajectory prediction in high-density and dynamic environments remains a significant challenge due to the limitations in modeling complex group interactions and evolving pedestrian-environment dependencies. This article presents KG-PTP, a knowledge graph-driven trajectory prediction framework that incorporates a dynamic trajectory knowledge graph (DTKG), a direction-aware clustering algorithm (D-DBSCAN), and an internal–external behavior modeling component (IN-EX). The DTKG enables semantic integration of heterogeneous spatial–temporal information and supports real-time updates. D-DBSCAN dynamically groups pedestrians based on motion direction and density, while IN-EX captures both individual motivations and external environmental influences. Experimental evaluations on ETH pedestrian dataset (ETH) and UCY crowd dataset (UCY) datasets demonstrate that KG-PTP achieves 12.3% and 15.7% reductions in average displacement error (ADE) and final displacement error (FDE), respectively, compared with state-of-the-art baselines. Furthermore, the proposed framework is validated through crowd evacuation simulations, confirming its applicability to public safety scenarios.

Task planning focused on household robots represents a conventional yet complex research domain, necessitating the development of task plans that enable robots to execute unfamiliar household services. This area has garnered significant research interest due to its extensive applications in robotics, particularly concerning household robots. Nevertheless, the majority of task planning methodologies exhibit suboptimal performance regarding the success and efficiency of completing household tasks, primarily due to a lack of cognitive capacity of household items and home environments. To address these challenges, we propose an efficiency-driven adaptive task planning approach based on hierarchical item-environment cognition. Initially, we establish a multiple semantic attribute-based priori knowledge (MSAPK) framework to facilitate the attributive representation of household items. Utilizing MSAPK, we develop a long short-term memory (LSTM) based item cognition model that assigns relevant attributes and substitutes to specified household items, thereby enhancing the cognitive capabilities of household robots at the attribute level. Subsequently, we construct an environment cognition model that delineates the relationships between household items and room types, enabling household robots to locate target items more efficiently. Through hierarchical item-environment cognition, we introduce a strategy for adaptive task planning, empowering household robots to execute household tasks with both flexibility and efficiency. The generated plans are evaluated in both virtual and real-world experiments, with promising results affirming the effectiveness of our proposed methodology.

In emergencies, negative emotion has a significant impact on decision-making during crowd evacuation. Psychological studies suggest that negative emotion in decision-making has a double-edged sword effect. Excessive negative emotion has adverse impacts, such as causing crowd chaos and congestion. Conversely, moderate negative emotion has a positive effect by speeding up crowd movement. However, current researches mainly focus on one aspect which is how to reduce the negative effects of negative emotion on crowd evacuation, while overlooking the benefits of negative emotion. How to fully explore the double-edged sword effect of negative emotion and regulate negative emotion to improve the efficiency of crowd evacuation is still an open issue. To achieve this, we propose the NegEmotion model which considers the positive impact of negative emotion on crowd evacuation, and regulates crowd emotion by controlling knowledge spreading according to Siminov’s psychological principle. In this model, the knowledge spreading network (KSN) and the stress emotional contagion network (SECN) are constructed. Based on these networks, we study the evolution process of knowledge spreading and stress emotional contagion, respectively. Next, we formulate the emotional regulation as an optimization problem to maximize the efficiency of crowd evacuation. Then, a heuristic algorithm is used to solve for the optimal emotional regulation strategy. Finally, a crowd simulation system is implemented to verify the effectiveness of our NegEmotion model. The experimental results show that our method is effective to improve the efficiency of crowd evacuation.

In crowd evacuation research, the knowledge contained in crowd evacuation is very complex and is multisource. Crowd evacuation scenarios restrict pedestrians’ movement decision-making, and the movement states of the crowd imply the movement characteristics. However, the existing studies on crowd evacuation navigation approach cannot make full use of the complex and multisource crowd evacuation knowledge, which reduces the effect of the evacuation navigation. To solve this problem, a new crowd evacuation navigation approach based on multisource knowledge is proposed. First, we collect relevant data on crowd evacuation using an image sensor network and establish a crowd evacuation knowledge graph to organize and store this data. Second, the explicit knowledge of scene structure and crowd movements is represented based on the crowd evacuation knowledge graph. Then, a deep-learning-based tacit knowledge model (DLTKM) is designed to extract the tacit knowledge of different groups and scene entities. Finally, a new crowd evacuation navigation approach based on wireless sensor network and related knowledge representations is designed to plan evacuation paths for evacuees. The experiment results show that this approach can plan reasonable evacuation paths for pedestrians, and improve the efficiency of crowd evacuations.

Crowd evacuation simulation can provide guidance for public emergencies or mass casualties which is easy to cause crowding and stampede. Deep reinforcement learning is an effective method for the path planning of crowd evacuation simulation which can reduce the dependence on data and has strong generalization. However, current methods of deep reinforcement learning ignore the consideration of panic emotion which could result in weak authenticity since the panic emotion has a significant impact on crowd evacuation. To address this problem, we propose a novel deep reinforcement learning for the panic crowd evacuation. First, a quadrant based crowd network is constructed to guide the movement of agents according to the change of panic degree in different quadrants. Second, a method of quantifying panic degree is proposed. Through the mean field equation, the dynamic evolution of agent states is realized, and the panic degree in different regions is quantified. Finally, a panic crowd evacuation model based on multi-agent deep deterministic policy gradient (MADDPG) was established. The computated panic degree is introduced into the reward function to recommend a path for multiagent to avoid panic. Experimental results show the effectiveness of our method.

There are two types of research on abnormal crowd behavior detection: micro modeling methods (e.g., crowd pose) and macro modeling methods (e.g., optical flow methods). In crowd evacuation, however, micro methods fail to solve the occlusion problem, while macro methods fail to address issues such as poor real-time performance and non-adaptive motion target detection. To address these issues, we propose a method that combines macro and micro methods to detect abnormal crowd behavior. Firstly, we extract the two-dimensional poses of moving people using the human pose estimation algorithm (OpenPose) and get the corresponding micro motion features. Secondly, we create an optical flow map of the video using the dense optical flow algorithm (Farneback) and a scene activity map by comparing the optical flow sizes between two consecutive frames. Then we derive the entropy change curve, which represents the macro motion features. Finally, we fuse micro and macro motion features and put the fused hybrid feature vectors into our classification network to train our model, and then find the transition time from micro to macro abnormal states in crowd evacuation videos. The experimental findings demonstrate that our proposed method can more accurately and quickly identify the abnormal behaviors that appear in evacuation scenes.

Skeleton-based action recognition task has been widely studied in recent years. Currently, the most popular researches use graph convolutional network (GCN) to solve this task by modeling human joints data as spatio-temporal graph. However, a large number of long-term temporal motion relationships cannot be effectively captured by GCN. Thus, recurrent neural network (RNN) is introduced to solve this defect. In this work, we propose a model namely graph convolutional network with long time memory (GCN-LTM). Specifically, there are two task streams in our proposed model: GCN stream and RNN stream, respectively. The GCN stream aims to capture the spatial motion relationships as well as the RNN stream focuses on extracting the long-term temporal patterns. In addition, we introduce the contrastive learning strategy to better facilitate feature learning between these two streams. The multiple ablation experiments have verified the feasibility of our proposed model. Numerous experiments show that the proposed model is superior to the current state-of-the-art method under two large-scale datasets including NTU-RGBD and NTU-RGBD-120.

Numerous cameras deployed in venues can collect video data, which can be analyzed to help evacuate people in emergency situations. Most surveillance videos can provide data support for pedestrian trajectory prediction. However, data-driven prediction method do not consider the impact of personalization on pedestrian trajectories, which results in ignoring individual personalization. How to accurately analyze the differences among pedestrians and yet accurately predict pedestrian trajectories is a challenging problem. To solve the above problems, we propose an singular value decomposition (SVD) based pedestrian trajectory prediction method, which introduces matrix decomposition to pedestrian trajectory prediction for the first time. Thus, This method analyzes the impact of pedestrian personalization on motion trajectories by mining the interaction patterns between pedestrians and environment. Firstly, we propose an information collection method based on environmental semantics, which can extract scene information from historical video data to construct an environmental information matrix. Secondly, we propose an SVD-based individual environmental feature preference method, which uses singular value decomposition methods to mine the data and analyze personalized pedestrian motion patterns to construct a personalized individual preference matrix. Finally, we built an personalized trajectory prediction method to predict pedestrian movement trajectories. The experimental results show that the method can not only analyze the effect of personalization on pedestrian movement but also accurately predict the pedestrian movement trajectory.

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.

BACKGROUND: Protein-protein interactions (PPIs) are crucial in various biological functions and cellular processes. Thus, many computational approaches have been proposed to predict PPI sites. Although significant progress has been made, these methods still have limitations in encoding the characteristics of each amino acid in sequences. Many feature extraction methods rely on the sliding window technique, which simply merges all the features of residues into a vector. The importance of some key residues may be weakened in the feature vector, leading to poor performance. RESULTS: We propose a novel sequence-based method for PPI sites prediction. The new network model, PPINet, contains multiple feature processing paths. For a residue, the PPINet extracts the features of the targeted residue and its context separately. These two types of features are processed by two paths in the network and combined to form a protein representation, where the two types of features are of relatively equal importance. The model ensembling technique is applied to make use of more features. The base models are trained with different features and then ensembled via stacking. In addition, a data balancing strategy is presented, by which our model can get significant improvement on highly unbalanced data. CONCLUSION: The proposed method is evaluated on a fused dataset constructed from Dset186, Dset_72, and PDBset_164, as well as the public Dset_448 dataset. Compared with current state-of-the-art methods, the performance of our method is better than the others. In the most important metrics, such as AUPRC and recall, it surpasses the second-best programmer on the latter dataset by 6.9% and 4.7%, respectively. We also demonstrated that the improvement is essentially due to using the ensemble model, especially, the hybrid feature. We share our code for reproducibility and future research at https://github.com/CandiceCong/StackingPPINet .

With the aid of graph convolution neural network and transformer model, human action recognition has achieved significant performance based on skeleton data. However, the majority of existing works rarely focus on identifying fine-grained motion information (i.e., “read”, “write”, etc.). Furthermore, they tend to explore correlations between joints and bones ignoring the angular information. Consequently, the recognition accuracy for fine-grained actions with most models is still less desired. To address this issue, we first attempt to bring angular information as a complement to familiar joint and bone information, while learning the potential dependencies of the three kinds of information using graph neural networks. Based on this, we propose a self-attention-enhanced graph neural network (SAE-GNN), which consists of a kernel-unified graph convolution (KUGC) module and an enhanced attention graph convolution (EAGC) module. The KUGC module is devised to effectively extract rich features in the skeleton information. The EAGC consisting of a multi-scale enhanced graph convolution block and a multi-headed self-attention block is designed to learn the potential high-level semantic information in the features. Besides, we introduce contrastive learning in the two blocks to enhance feature representation by maximizing their mutual information. We conduct extensive experiments on four publicly available datasets, and results show that our model outperforms state-of-the-art methods in recognizing fine-grained actions.

To incorporate the cooking logic into ingredient recognition from food images is beneficial for food cognition. Compared with food categorization, ingredient recognition gives a better understanding on food cognition, by providing crucial information on food compositions. However, there exist situations in which different food are made of different ingredients, thus it is necessary to incorporate cooking logic into ingredient recognition to achieve a better food cognition. Based on this point, our paper proposes a sequential learning method to guide a neural network based (NN-based) model on producing ingredients following the corresponding cooking logic in recipes. Firstly, in order to make a maximum utilization of visual features from images, a double-flow feature fusion module (DFFF) is proposed to obtain features from two image-based, visual tasks (food name proposal and multi-label ingredient proposal). After that, fused features from DFFF, together with original image features, are feed into a bidirectional long short time memory (Bi-LSTM) based ingredient generator to produce sequential ingredients. To guide the sequential ingredient generation process, reinforcement learning is employed by designing a hybrid loss related to both the common and personality traits in ingredients for optimizing the model ability of associating images and sequential ingredients. In addition, sequential ingredients are utilized in a backward flow by reconstructing food images, so that sequential ingredient generation can be further optimized in a complementary manner. In experiments, the results demonstrate the superiority of our method on driving the model to allocate more attention to the correlation between images and sequential ingredients, and produced ingredients are comprehensive and logical.

Traditional crowd evacuation simulation methods focus on the analysis of individual behavior in the crowd, but ignore the analysis of potential group properties in the process of crowd motion, which reduces the visual realism of the crowd evacuation simulation process. During crowd motion, crowds cause unconscious self-organization due to common destinations and social relationships, thus forming groups. Considering the influence of group properties on crowd motion is important to improve the visual realism of crowd motion simulation. To address this problem, we propose a crowd motion modeling method for group properties analysis (GPA-CMM). First, we build a data-driven group properties quantification (DGPQ) model to describe the characteristics of group motion accurately. In the model, we divide the crowd into several groups, extract motion properties of the crowd, and quantify the features of intra-group stability and inter-group conflict. Then, in order to analyze their influences of group properties, we build a stability and conflict based crowd motion analysis (SC-CMA) model. Finally, we implement a crowd simulation system based on SC-CMA, visualize the results of the theoretical analysis in a graphical way. The experimental results show that the method can simulate crowd motion more realistically.

As crowd sensing tasks become more complex, it is increasingly needed for participants to form teams to collaborate interactively in order to complete tasks more efficiently. It is valuable to fully understand user behavior, the organizational principles of crowd collaboration teams and incorporate them into the user recruitment process. In fact, there is a two-way process of subjective and objective selection for building a team in crowd sensing. From the participant’s perspective, the willingness of users to participate in the task is a subjective factor; from the platform’s perspective, the trust relationship between users is an objective factor. However, existing work has not well recognized the value of including collaborative teams in the crowd sensing recruitment process and has not considered the willingness and trust relationships between participants simultaneously, which results in poor quality of service (QoS) among recruited users. To address this problem, we propose a willingness and trust-based collaborative team recruitment method (WT-CTRM). First, we construct a willingness network (WN) and a trust network (TN) to describe the interaction of willingness and trust among the collaborated users respectively. Based on these networks, we then predict the unknown willingness and trust relationships between users for collaboration by using graph convolutional networks (GCNs). Finally, we build collaborative teams for the user recruitment process of crowd sensing based on the consensus constraints of the predicted willingness network and trust network. Simulation results show that the recruitment scheme significantly improves the QoS in task scenarios with collaborative requirements.

With the continuous urban expansion, the safety of public places has attracted more and more attention. Once an accident occurs and people distrust their surroundings, it will cause congestion, trampling and other events. Individual trust value is particularly important in crowd evacuation. However, it is very difficult to evaluate the individual credibility with existing methods. We propose a blockchain-based crowd-sensing trust management mechanism (B-CSTM) to address this problem. First, we provide a message credibility evaluation mechanism that can evaluate the credibility of messages sent by individuals on spot. Second, we developed a trust calculation method that uses a distributed Evacuation Perception Unit (EPU) to calculate individuals’ trust values by perception scores. Finally, we build a blockchain-based trust management mechanism model that uses blockchain to store and query trust values. Through experimental analysis, we visualize the trust results in the evacuation scenario. It is shown that the mechanism is feasible for the management of trust values (collection, calculation and storage, etc.) in crowd evacuation.

The surveillance videos captured by cameras deployed in large-scale places can provide help for path planning of crowd evacuation in emergencies. However, most surveillance videos are not allowed to be shared in order to avoid privacy disclosure. Thus, it is difficult to obtain the video data of the whole scenario for path planning which greatly reduces effectiveness of path planning. How to provide global path planning for crowd evacuation while ensuring privacy protection is a challenging problem. To solve this problem, we propose a federated learning based path planning method for crowd evacuation. In this method, the potential field model are introduced into the federated learning framework to provide the privacy protection since only the potential field information is aggregated between the camera terminal and the central server, instead of the surveillance videos. First, we construct the local potential field for the surveillance scene of each camera by extracting the scene information and the crowds information. Second, the central server periodically performs the global potential field information aggregation and integrates these information to plan the optimal global path dynamically for crowd evacuation. Finally, we implemente a simulation platform to simulate the process of crowd movement and visualize the dynamic change of the potential energy field. Experimental results prove that the proposed method can provide privacy-preserving path planning for multi-camera evacuation scenarios.

Achieving autonomous navigation of indoor robots in a mapless environment is a long-standing research problem. Deep Reinforcement Learning (DRL) is widely used for robot navigation by virtue of learning through interaction with the environment. However, the large number of trials for training need lengthy computation times. To address this issue, we propose an innovative DRL model with long-time memory capability for mobile robot’s mapless navigation, which can achieve end-to-end navigation based only on laser-ranging data and target location. The long-time memory capability is realized by introducing a memory module based on the special structure of the Long Short-Term Memory (LSTM). The memory module ensures that the model derives information from previous navigation experiences and thus optimizes the model’s decision-making. In addition, to enable the model to explore the environment more effectively, we design a novel dual-noise mechanism consisting of Gaussian noise and Ornstein-Uhlenbeck noise. Extensive experiments are conducted on the Gazebo simulation platform and validate that the proposed approach can generate a smoother navigation path and exceed the state-of-the-art performance with less computation cost.

Summary Siamese‐based trackers have made great progress in visual tracking community, however, the shared structure of network between classification and regression tasks limits the ability of the trackers to obtain more robust classification prediction and more accurate regression prediction. In this paper, we propose an effective visual tracking framework (named Siamese Disentangled Tracking‐Head, SiamDTH), which disentangles classification and regression in Siamese‐based network for visual tracking from two aspects: feature decoupling and differentiated tracking‐head. First of all, we gather the features of receptive fields with different scales and ratios, and decouple the correlation features through two different styles of feature fusion mode for classification and regression respectively. Moreover, we design the differentiated tracking‐head structure in the sibling head for discriminately handling the parallel classification and regression tasks on visual tracking. Extensive experiments on visual tracking benchmarks including VOT2018, VOT2019 and OTB100 demonstrate that our proposed SiamDTH achieves state‐of‐the‐art performance with a considerable real‐time speed. Our source code is available at: https://github.com/xl0312/SiamDTH .

Crowd emotional contagion is affected by physical cyberspace and virtual cyberspace at the same time. In addition, the individual personality also plays an important role in crowd emotional contagion. However, few existing works have investigated the two factors jointly. To solve this problem, we propose a personalized virtual and physical cyberspace-based emotional contagion model (PVP-ECM) to simulate the process of crowd emotional contagion. First, we construct an individual emotion model and propose the personalized emotional contagion rules to consider the influences of the individual personality on crowd emotional contagion coupling the virtual and physical cyberspaces. Second, we use mean field theory to derive an evolution process and obtain mean field equations for the PVP-ECM. Then, we solve the PVP-ECM numerically using the finite difference method. Third, we construct a personalized BA scale-free network for the PVP-ECM simulation to further verify the stability of the model. Finally, we perform the PVP-ECM simulation and implement a simulation system to visualize the results of our theoretical analysis. The experimental results show that our approach can simulate the process of crowd emotional contagion more realistically. The proposed method can provide guidance for coping with the personalized crowd emotional contagion of public emergencies.

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.

In emergency evacuations, crowds often become congested and stampeded because of extreme panic, resulting in injuries and fatalities. Safety officers can spread positive emotions and reduce crowd panic by issuing information or appeasement, which is an effective way to ensure evacuation safety. However, how to deploy safety officers appropriately and maximize positive emotional contagion to cover the largest number of chaotic individuals is still a challenging problem. To solve this problem, we propose an IoT-based positive emotional contagion (IoT-PEC) method for crowd evacuation. First, we employ IoT technologies to capture the attributes of crowd movement, such as position and velocity. Based on these attributes, the crowd chaos can be quantified by calculating the macroscopic and microscopic entropies. Second, we propose an entropy-based anisotropic emotional contagion model (E-AECM) to achieve the nonuniform positive emotional contagion considering the influence of chaos on the propagation rate. Third, we formulate the problem of safety officer placement as an optimization problem to maximize the positive emotional contagion on the chaos individuals. Finally, we implement a simulation system for crowd evacuation to visualize the results of positive emotion contagion. The proposed method can provide guidance for emergency response management.

In a crisis situation, negative emotions often spread among the crowd, and they have adverse impacts on human decisions, resulting in stampedes and crushes. Safety officers are often dispatched to scenes of emergencies because their positive emotions can calm the crowd down and avoid serious accidents. However, how to utilize the positive emotional contagion to maximize the “calm-down” effect remains a challenging problem in crowd evacuation. In this paper, we present an approach for optimizing positive emotional contagion in crowd evacuation. First, a computational model of positive emotional contagion is proposed to describe how safety officers calm a crowd down. To capture important influential factors for positive emotional contagion, such as the trust relationships among the individuals involved in a crisis situation and the variations of emotional contagion speed, we construct a trust-based emotional contagion network (Trust-ECN) and a heterogeneous emotional contagion speed computation model (HECS-CM). Based on these models, the emotional contagion process can be analyzed in a parametric way, and the infection probability for each individual in a given time window can be computed analytically with a continuous-time Markov chain (CTMC). Second, a maximization problem of emotional contagion is formulated. Since this optimization problem is NP-hard, an artificial bee colony optimized emotional contagion (ABCEC) algorithm is used to solve for the optimal positions of safety officers. We demonstrate the effectiveness of our method on both synthetic and real-world data at different scales. Finally, we implement a crowd simulation system to visualize the results of our theoretical analysis in a graphical manner. The proposed method can provide guidance for emergency response management.

Summary Small social groups based on kinship or friendships are ubiquitous in human crowds. Therefore, the effect of social groups on crowd evacuations and that of crowd evacuations on social groups must be investigated. To simulate the group phenomenon when an emergency occurs, we propose an improved social force model that takes into account the social group relationship among the population, and based on our proposed model, a novel grouping algorithm predicated on non‐uniform binary grid partitioning is put forward. The approach initially maps the individuals into the plane space, and then it adopts top‐down binary grid partitioning iteratively until the divided grid contains only the individuals with relations; then, the values of the relation and density of the non‐empty grid cells are calculated, and the grids are sorted according to these values. After sorting, selecting, merging, and forming the core grids, the other grids are merged to the core grids. We have compared the algorithm with the hierarchical classification algorithm and the grid‐based algorithm. The results show that the accuracy, speed, and scalability are all advantages. We also establish a simulation platform to illustrate the proposed grouping algorithm and the improved social force model for crowd evacuation simulation.

Summary Fluid simulation is an important research topic since it is widely used in virtual training, education, computer games, and digital entertainment. Generating fluid simulation results with high degree of visual realism is challenging in these applications. We present a visualization approach for fluid simulation in this paper. Our method allows students to understand the complex natural phenomena so as to solve the fluid engineering problems intuitively. In this study, an SPH (Smoothed Particle Hydrodynamics)‐based multi‐resolution method is proposed for fluid simulation. The method can keep the accuracy while improve the efficiency significantly. We construct a multi‐resolution fluid model from bottom to top by combining fluid computation and surface reconstruction. We first compute multi‐resolution particles with a novel adaptive SPH model in the bottom layer. Next, surface particles are extracted and pre‐processed to provide samples for surface extraction. Then, the multi‐resolution surface reconstruction model is built in the top layer. Meshes from different reconstruction resolutions are merged to obtain the final fluid surface. Experimental results show that our approach can effectively represent multi‐scale fluid details and efficiently produce visual‐pleasing fluid simulation results.

This paper presents a multi-agent cooperative reinforcement learning approach in cooperative design system. For effectively speed up the learning process, this approach adopts dynamic niche technology grouping design agents, and selects the optimal design agent in every groups. The selected agents make reinforcement learning via interaction with designers and carry on cooperative learning each other, and then spread the learned knowledge in respective groups. The radius of the niches and selected design agents are dynamically adjusted during cooperative reinforcement learning process.

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.

Task assignment problem is one of the important research topics in a multi-agent system. It is desired to assign each task to a suited agent with a minimum total cost. For the advantages of memory, multi-character, local search and the solution improvement mechanism in artificial bee colony (ABC) algorithm, a task assignment approach based on ABC in a multi-agent cooperative design system is proposed in this paper. Experimental results demonstrate that the optimal solutions obtained by the ABC algorithm are better than genetic algorithm and particle swarm optimization on solving some task assignment problems.

In order to inspire and assist designers to create novel 3D animation modelings, an improved cellular genetic algorithm with evolutionary rules is proposed in this paper and applied in 3D animation modeling design. In this algorithm, ACIS rule expressions are used for deforming the initial 3D animation modeling created by Maya or 3D Max, and human-computer interaction which used expert knowledge to determine fitness function values of individuals is adopted to evaluate generated modelings. Tree-structure encoding is used for the generation and evolution of ACIS rule expressions and an evolutionary rule based on expert knowledge is introduced to reduce the number of human-computer interactions. Besides, a prototype system, called 3D Animation Modeling Design System, is developed based on the proposed algorithm. The experimental results show that the proposed algorithm can automatically and efficiently generate a series of creative 3D animation modelings and reduce the number of human-computer interactions to some extent.

This paper presents a multi-agent cooperative system for crowd animation. It analyses related work about crowd animation first. Then, a multi-agent crowd animation system architecture is introduced, which offers a promising framework for dynamically creating and managing agent communities in widely distributed environments. Next, a role modeling approach based on dynamic self-adaptive genetic algorithm and NURBS (Non Uniform Relational B Splines) technology is presented. Following, a group of fishes modelling example is illustrated for showing the modeling process in the system. Finally, the current work is summarised and an outlook for the future work is given.

In order to generate novel 3D animation modellings automatically, an interactive genetic algorithm HAIGA based on C/S mode was proposed. In HAIGA, HSF synergy technology and ACIS rule were brought in. The ACIS rule expression was expressed by three binary trees, which were used to scale 3D entities unevenly in the x-axis, y-axis and z-axis direction separately. New rule expressions, which were used to generate new modellings by evolving automatically based on the existing 3D animation modellings , were generated by selection, crossover, mutation on binary trees. A prototype system in which 3D animation modellings could evolve automatically was developed. Experiments in the system show that the proposed method can support cooperative design effectively to generate a series of novel 3D animation modellings.

Character Modeling is becoming more and more difficult in animation industry today. Lots of designers are usually involved to cooperatively accomplish a character by computer networks or the Internet. This paper presents a Multi-Objective evaluation based Cooperative Character Modeling System(MOCMS), which can evolve various character models to generate creative ones based on multi-objective evaluations. The objectives are designed to embody different personalities, including qualitative and quantitative aspects. The former are given by different cooperative designers while the latter are calculated automatically by computers. This can incorporate qualitative and quantitative evaluation in a formal manner. Case study demonstrates that the proposed method can evolve character models according to the designers' intentions and shorten design time.

Design optimization is a multi-modal and complex problem which can not be treated successfully in scalar optimizer, such as evolutionary techniques. For typical genetic algorithms, most generalizations and variations do not spread to vector valued fitness functions. In this paper, we first propose a lattice based evolution technique with vector valued fitness function. Then we present a evolutionary searching paradigm for the design optimization problem. We present a simple Markov analysis of the proposed genetic algorithms.

In this paper, based on the discussion of some important issues related to cooperative design, and the analysis for niche technology, a group classifier algorithm and a sharing learning algorithm in a multi-agent cooperative system are put forward. The aim is to use socio-cultural perspectives and niche technology for supporting design reuse and share in a cooperative design system.

The purpose of this paper is to give a topological view to the spatial clustering focusing on density based clustering with its variations. We propose the definition of cluster topology; define clusters as various kinds of topological connected sets. More in this paper, the topological cluster forming algorithms are presented. Then we reform some typical algorithms with the new topological view. Examples are given to show the theories.

Multi-objective collaborative design synthesis and its evaluation is usually viewed as a multi-objective optimization problem (MOP). In this paper, it is formulated firstly and then, a hybrid Multi-objective Evolutionary Algorithm (h-MOEA) is proposed by introducing ideas from evolutionary computation, which is suitable for solving the MOP in design synthesis and its evaluation. Furthermore, a design synthesis and its evaluation method supporting multi-objective collaborative design, composed of many iterative steps, is developed and the h-MOEA is encapsulated as a black-box optimization tool to generate design solutions, namely the Pareto optimal set. And also, a Web-based experimental prototype is developed to verify the proposed method. Finally, a case study has been done to evaluate the effectiveness of proposed methods.

Based on the standard particle swarm optimization (PSO) algorithm together with the widely used dynamic niche technology, this paper presents a new variation combined with the dynamic niche sharing technique on the basis of traditional PSO algorithm. We proposed a cooperative particle swarm optimization model with cooperative multi-population. Applications are given on creative conceptual architectural design

Lots of geographically distributed designers are usually involved to accomplish a complex design task and creativity and amenity of artifacts are more and more emphasized, so researches on cooperative creative conceptual design system are valuable both theoretically and practically. This paper firstly identifies some major requirements of cooperative creative conceptual design system. Then, creative genetic algorithm based on simple genetic algorithm is proposed to serve as creative mechanisms and methods. The design and development of prototype TripleCDS is described and some typical design images are shown, so the creative mechanisms and methods and prototype are validated

In this paper, we present a novel computational approach for stimulating creativity of designers. The tree structured genetic algorithm is used for generating 2D sketch shapes and 3D images. This approach is illustrated by an artwork design example, which uses general mathematical expressions to form 2D sketch shapes for artistic flower vases and the combination of general and complex function expressions to form 3D images of artistic flowers. It shows that approach is able to generate some solutions for supporting creative thinking of designers.

Design is a complex problem solving and knowledge refinement process. Learning is a part of this process that can improve computer based design support systems by using the knowledge representing the experience and expertise of designers. Learning from past design examples, and learning new knowledge during the process of design are closely related activities that must be supported by future computer supported design systems. The paper analyses real design activity and proposes a model of design activity. Then it presents the software architecture of a design agent with learning mechanism. Subsequently, it introduces the knowledge representation in a design system. Finally, the paper presents a sharing learning process in a cooperative design system.