The analysis and prediction of agent trajectories are crucial for decision-making processes in intelligent systems, with precise short-term trajectory forecasting being highly significant across a range of applications. Agents and their social interactions have been quantified and modeled by researchers from various perspectives; however, substantial limitations exist in the current work due to the inherent high uncertainty of agent intentions and the complex higherorder influences among neighboring groups. SocialMOIF is proposed to tackle these challenges, concentrating on the higher-order intention interactions among neighboring groups while reinforcing the primary role of first-order intention interactions between neighbors and the target agent. This method develops a multi-order intention fusion model to achieve a more comprehensive understanding of both direct and indirect intention information. Within SocialMOIF, a trajectory distribution approximator is designed to guide the trajectories toward values that align more closely with the actual data, thereby enhancing model interpretability. Furthermore, a global trajectory optimizer is introduced to enable more accurate and efficient parallel predictions. By incorporating a novel loss function that accounts for distance and direction during training, experimental results demonstrate that the model outperforms previous state-of-the-art baselines across multiple metrics in both dynamic and static datasets.

federated learning (FL) enables decentralized devices to collaboratively train models without sharing raw data. However, when client data is highly heterogeneous, conventional FL often suffers from poor model performance and personalization. To address this, clustered FL (CFL) has emerged as a promising solution, grouping clients with similar data distributions to jointly learn better personalized models. Yet, most existing CFL methods rely on predefined thresholds or fixed numbers of clusters, which limits their adaptability to real-world, dynamic environments with diverse and evolving client data. This study introduces FedCDC, a novel CFL framework that leverages graph clustering to dynamically identify client communities without prior knowledge of clustering structure. Specifically, we propose a client similarity identification algorithm based on Louvain community detection community detection clustering (CDC), which constructs a similarity graph using model inference results and performs modularity-optimizing clustering. Furthermore, the graph-based approach captures high-order structural relationships among clients, enabling more precise and stable clustering even under severe data heterogeneity conditions. Extensive experiments across some benchmark datasets demonstrate that FedCDC consistently outperforms state-of-the-art (SOTA) baselines. In the challenging Dir(0.1) setting on CIFAR-100, FedCDC achieves accuracy gains of 12.75% over FedAvg, 23.33% over PerFedAvg, and 3.42% over FLIS(DC). More broadly, this work bridges graph theory with FL, introducing a scalable and interpretable way to form client communities. It provides a solution for real-world deployments of FL systems that are both accurate and adaptive, particularly in complex environments.

Cloud Manufacturing (CMfg) serves as a pivotal platform, seamlessly integrating enterprise resources and consumer demands, thus playing a central role in task scheduling and service allocation. However, the dynamic nature of cloud environments, especially during service interruptions, necessitates systems capable of promptly responding to real-time changes and demands, presenting formidable challenges for task migration. Traditional methods often struggle to adapt swiftly, leading to sub-optimal real-time decision-making. To address these challenges, we introduce the DRL-GNN-FL SynergyNet model, a hybrid solution that prioritizes both time efficiency and cost-effectiveness. This model leverages advanced technologies, including Deep Reinforcement Learning (DRL), Graph Neural Networks (GNN), and Federated Learning (FL). DRL enables rapid adaptability and real-time decision-making, GNN effectively processes complex network structures, and FL facilitates distributed learning and knowledge sharing. This comprehensive approach effectively tackles the intricacies of task migration in CMfg environments. Experimental results demonstrate the model’s exceptional performance, adaptability, and scalability in real-time scenarios, showcasing its potential as a practical and effective solution to the dynamic task migration challenges encountered in CMfg. The DRL-GNN-FL SynergyNet model offers a promising avenue for enhancing the resilience and responsiveness of cloud manufacturing systems.

The mechanical antenna (MA) is a potential solution for the extremely-low-frequency (ELF, 3–30 Hz) transmitter enabling industrial informatization. It can have the advantages of miniaturization and high efficiency compared to conventional transmitters. However, the current ELF MA has an issue with both high inertia and a long delay in symbol switching. Not only does this lower the data rate, but it also causes unnecessary power consumption in operation. This article proposes a compact ELF transmitter based on a heterogeneous architecture of piezoelectric cantilevers and oscillating electret, as well as a relevant information transfer program. The actuator fabricated from composite piezoelectric material exhibits a rapid dynamic reaction. Merging this with efficient spectrum utilization increases the data rate and reduces power consumption. A proof of concept demonstration conducted at a frequency of 26.4 Hz attained a data rate of 21 bit/s while consuming a mere 1.52 W of power. In addition, increasing the charge density of the electret can expand the transmission distance without requiring extra power consumption, thus adapting to more challenging applications such as Underwater Internet of Things, Through-the-Earth communication, pipeline inspection, and underground detection.

Pedestrian trajectory prediction has been broadly applied in video surveillance and autonomous driving. Most of the current trajectory prediction approaches are committed to improving the prediction accuracy. However, these works remain drawbacks in several aspects, complex interaction modeling among pedestrians, the interactions between pedestrians and environment and the multimodality of pedestrian trajectories. To address the above issues, we propose one new trajectory distribution aware graph convolutional network to improve trajectory prediction performance. First, we propose a novel directed graph and combine multi-head self-attention and graph convolution to capture the spatial interactions. Then, to capture the interactions between pedestrian and environment, we construct a trajectory heatmap, which can reflect the walkable area of the scene and the motion trends of the pedestrian in the scene. Besides, we devise one trajectory distribution-aware module to perceive the distribution information of pedestrian trajectory, aiming at providing rich trajectory information for multi-modal trajectory prediction. Experimental results validate the proposed model can achieve superior trajectory prediction accuracy on the ETH & UCY, SSD, and NBA datasets in terms of both the final displacement error and average displacement error metrics.

Recently, graph convolutional network (GCN) has become one of the most popular and state-of-the-art collaborative filtering (CF) methods. Existing GCN-based CF studies have made many meaningful and excellent efforts at loss function design and embedding propagation improvement. Despite their successes, we argue that existing methods have not yet properly explored more effective sampling strategy, including both positive sampling and negative sampling. To tackle this limitation, a novel framework named ITSM-GCN is proposed to carry out our designed Informative Training Sample Mining (ITSM) sampling strategy for the learning of GCN-based CF models. Specifically, we first adopt and improve the dynamic negative sampling (DNS) strategy, which achieves considerable improvements in both training efficiency and recommendation performance. More importantly, we design two potentially positive training sample mining strategies, namely a similarity-based sampler and score-based sampler, to further enhance GCN-based CF. Extensive experiments show that ITSM-GCN significantly outperforms state-of-the-art GCN-based CF models, including LightGCN, SGL-ED and SimpleX. For example, ITSM-GCN improves on SimpleX by 12.0%, 3.0%, and 1.2% on [email protected] for Amazon-Books, Yelp2018 and Gowalla, respectively.

Pedestrian trajectory prediction using video is essential for many practical traffic applications. Most existing pedestrian trajectory prediction methods are based on fully connected long short-term memory (LSTM) networks and perform well on public datasets. However, these methods still have three defects: a) Most of them rely on manual annotations to obtain information about the environment surrounding the subject pedestrian, which limits practical applications; b) The interaction among pedestrians and obstacles in a scene is little studied, which leads to greater prediction error; c) Traditional LSTM methods are based on the previous moment and ignore the correlation between the future and distant past states of the pedestrian, which generates unrealistic trajectories. To tackle these problems, first, in the stage of data processing, we use an image semantic segmentation algorithm to obtain multi-category obstacle information and design an end-to-end “Siamese Position Extraction” model to obtain more accurate pedestrian interaction data. Second, we design an end-to-end fully convolutional LSTM encoder-decoder with an attention mechanism (FLEAM) to overcome the shortcomings of LSTM. Third, we compare FLEAM with several state-of-the-art LSTM-based prediction methods on multiple video sequences in the datasets ETH, UCY and MOT20. The results show that our approach generates the same prediction error as the best results of the state-of-the-art method. However, FLEAM has more potential for practice application because it does not rely on manually annotated data. We further validate the effectiveness of FLEAM by employing manually annotated data, finding that it generates much less prediction error.

Future pedestrian trajectory prediction offers great prospects for many practical applications such as unmanned vehicles, building evacuation design and robotic path planning. Most existing methods focus on social interaction among pedestrians but ignore the fact that heterogeneous traffic objects (cars, dogs, bicycles, motorcycles, etc.) have significant influence on the future trajectory of a subject pedestrian. Also, the walking direction intention of a pedestrian may be referred by his/her facial keypoints. Considering this, this work proposes to predict a pedestrian's future trajectory by jointly using neighboring heterogeneous traffic information and his/her facial keypoints. To fulfill this, an end-to-end facial keypoints-based convolutional encoder-decoder network (FK-CEN) is designed, in which the heterogeneous traffic and facial keypoints are input. After training, FK-CEN is evaluated on 5 crowded video sequences collected from the public datasets MOT-16 and MOT-17. Experimental results demonstrate that it outperforms state-of-the-art approaches, in terms of prediction errors.

Recently, convolutional neural networks (CNN) have been widely used in image denoising. But with most CNN denoising methods, all the channels are treated equally and the relationship between spatial locations are neglected. In the letter, we propose a novel channel and space attention neural network (CSANN) for image denoising. In CSANN, we concatenate the noise level with the average and maximum values of each channel as the input and propose a convolutional network to learn the relationship between channels. Meanwhile, we combine the noise level map with the average and maximum values of each spatial locations as the input and use a convolutional network to learn the relationship between spatial locations. Moreover, we combine them as an attention network and introduce it into the main CNN and symmetric skip connections, which makes channels related to attention network play different roles in the subsequent convolution and offsets the performance degradation caused by using a single convolution kernel in spatial locations. In addition, the use of symmetric skip connections and resnet blocks avoid the vanishing gradient problem and the loss of shallow features. Experimental results show that, compared with some state-of-the-art denoising algorithms, the experimental results of CSANN have better visual effects and higher peak signal-to-noise ratio (PSNR) values.

Future pedestrian trajectory prediction offers great prospects for many practical applications. Most existing methods focus on social interaction among pedestrians but ignore the factors that heterogeneous traffic objects (cars, dogs, bicycles, motorcycles, etc.) have significant influence on the future trajectory of a subject pedestrian. Also, the walking direction intention of a pedestrian may be referred by his/her pose keypoints. Considering this, this work proposes to predict a pedestrian's future trajectory by jointly using neighboring heterogeneous traffic information and his/her pose keypoints. To fulfill this, an end-to-end pose keypoints-based convolutional encoder-decoder network (PK-CEN) is designed, in which the heterogeneous traffic and pose keypoints are modeled as input. After training, PK-CEN is evaluated on manifold crowded video sequences collected from the public dataset MOT16, MOT17 and MOT20. Experimental results demonstrate that it outperforms state-of-the-art approaches, in terms of prediction errors.

Pedestrian trajectory prediction is vital for transportation systems. Generally we can divide pedestrian behavior modeling into two categories, i.e., knowledge-driven and data-driven. The former might bring expert bias, and it sometimes generates unrealistic pedestrian movement due to unnecessary repulsive forces. The latter approach is popular nowadays but most existing neural networks, including fully connected long short-term memory (LSTM) networks, use a 1D vector to model their input and state. The shortcoming is that these works cannot learn spatial information about pedestrians, especially in a dense crowd. To tackle this, we propose to use tensors to represent essential environment features of pedestrians. Accordingly, a convolutional LSTM is designed and deepened to predict spatiotemporal trajectory sequences. As the tensor and convolution can learn better spatiotemporal interactions among pedestrians and environments, experimental results show that the proposed network can estimate more realistic trajectories for a dense crowd in evacuation and counterflow.

Pedestrian evacuation simulation is vital for urban civil engineers. Although there exist many works addressing the issue of emergency evacuation, only a few study the phenomenon of people actively squeezing to pass through an exit. To model this behavior, a three-circle model is adopted to represent the shape of pedestrians. Active rotation torque (ART) is proposed to model the active rotation behavior of pedestrians turning their torsos in the desired direction. This torque occurs either when a pedestrian is not facing his velocity direction, or when he wants to pass through a bottleneck. The equation of ART is designed and regressed with real pedestrian experiments, in which a gyroscope was used to measure the angle of torso rotation. The proposed torque model is then applied to manifold scenarios with various door widths and different safety separation belt settings. Then, both microscopic and macroscopic indexes, including evacuation time, rotation angle, and crowd density, are obtained to show that the proposed model can simulate both non-competitive and competitive pedestrian behaviors near exit bottlenecks more accurately than the circular social force model. Thus, the evacuation time of the exit can be predicted more precisely, which helps to design optimal multi-exit assignment strategies.

An image degraded by noise is a common phenomenon. In this letter, we propose a novel adaptive fuzzy switching weighted mean filter to remove salt-and-pepper (SAP) noise. The process of denoising includes two stages: noise detection and noise elimination. In the first stage, pixels in a corrupted image are classified into two categories: original pixels and possible noise pixels. For the latter, we compute the maximum absolute luminance difference of processed pixels next to possible noise pixels to classify them into three categories: uncorrupted pixels, lightly corrupted pixels, and heavily corrupted pixels. In the second stage, under the assumption that pixels at a short distance tend to have similar values, the distance relevant weighted mean of the original pixels in the neighborhood of a noise pixel are computed. For a nonnoise pixel, retain it as unchanged; for a lightly corrupted pixel, replace it with the weighted average value of the weighted mean and its own value; and for a heavily corrupted pixel, change it to be the weighted mean. Experimental results show that compared to some state-of-the-art algorithms, our method keeps more texture details and is better at removing SAP noise and depressing artifacts.

Mesoscopic traffic simulation is an important branch of technology to support offline large-scale simulation-based traffic planning and online simulation-based traffic management. One of the major concerns using mesoscopic traffic simulations is the performance, which means the required time to simulate a traffic scenario. At the same time, the GPU has recently been a success, because of its massive performance compared to the CPU. Thus, a critical question is "whether the GPU can be a potential high-performance platform for mesoscopic traffic simulations"? To the best of our knowledge, there is no clear answer in the research area. In this paper, we firstly propose a comprehensive framework to run a traditional time-stepped mesoscopic traffic simulation on CPU/GPU. Then, we design a boundary processing method to guarantee the correctness of running mesoscopic supply traffic simulations on the GPU. Thirdly, the proposed mesoscopic traffic simulation framework is demonstrated to simulate 100,000 vehicles moving on a large-scale grid road network. In this case study, running a mesoscopic supply traffic simulation on a GPU (GeForce GT 650M) gives 11.2 times speedup, compared with running the same supply simulation on a CPU core (Intel E5-2620). In the end, this paper explains the theoretical limitation of running mesoscopic supply traffic simulations on the GPU. In conclusion, regardless of high system complexity, the proposed mesoscopic traffic simulation framework on CPU/GPU provides an innovative and promising solution for high-performance mesoscopic traffic simulations.

Optimal deployment of simulation virtual machines is an important issue in Cloud Simulation. Challenges involve resource cost prediction for simulation tasks as well as host physical machine selection for simulation virtual machines. In this paper we propose a novel approach using knowledge curves (i.e., curves as knowledge base) to solve this problem. First we present a resource cost estimation algorithm using empirical load curves synthesis, and then discuss a deployment target host selection algorithm by curves matching. This approach can provide a promising solution for intelligent deployment of virtual machines in Cloud Simulation. In addition, the proposed approach will be increasingly precise and effective as curve knowledge base increases.