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Skeleton-Based Human Motion prediction With Privileged Supervision

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2023年第12期34卷 10419-10432页

作者： Dong, Minjing Xu, Chang Univ Sydney Sch Comp Sci Fac Engn Sydney NSW 2008 Australia

Existing supervised methods have achieved impressive performance in forecasting skeleton-based human motion. However, they often rely on action class labels in both training and inference phases. In practice, it could be a burden to request action class labels in the inference phase, and even for the training phase, the collected labels could be incomplete for sequences with a mixture of multiple actions. In this article, we take action class labels as a kind of privileged supervision that only exists in the training phase. We design a new architecture that includes a motion classification as an auxiliary task with motion prediction. To deal with potential missing labels of motion sequence, we propose a new classification loss function to exploit their relationships with those observed labels and a perceptual loss to measure the difference between ground truth sequence and generated sequence in the classification task. Experimental results on the most challenging Human 3.6M dataset and the Carnegie Mellon University (CMU) dataset demonstrate the effectiveness of the proposed algorithm to exploit action class labels for improved modeling of human dynamics.

关键词： Dynamics Training Task analysis Predictive models Encoding Recurrent neural networks prediction algorithms Human motion prediction motion classification privileged learning skeleton-based human pose

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Adjacency Matrix Deep Learning prediction Model for Prognosis of the Next Event in a Process

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IEEE ACCESS 2023年 11卷 11947-11955页

作者： Razo, Martha Darabi, Houshang Univ Illinois Mech & Ind Engn Dept Chicago IL 60607 USA

prediction of the next event is important for organizations to improve and optimize their system process to achieve organizational goals. Existing predictive models are limited since they use discovery algorithms that might not be able to conserve the sequences of events as reported in the event log. Discovery algorithms alter the sequence of events in two ways, either the algorithms generate an additional sequence of events not found in the event logs or remove the order of events. Since prediction relies on these process algorithms, the prediction model can suffer and produce underperforming results. Models that do not use discovery algorithms, such as deep learning models, ignore completely the sequence of events. To overcome these limitations, we propose a new algorithm called AXDP (Adjacency Matrix Deep Learning prediction Model). AXDP predicts the next event of a process using graph theory techniques, specifically adjacency matrices and predicts using the power of deep learning models. AXDP has a major advantage, in that sequence of events is conserved, resulting in better prediction of the next event. When testing AXDP on eight publicly available datasets, AXDP outperforms what we believe to be the most recent and best predictive models that exist for the prediction of the next event for six of the eight datasets.

关键词： Predictive models Deep learning prediction algorithms Eigenvalues and eigenfunctions Recurrent neural networks Transforms Task analysis process mining event logs adjacency matrix max eigenvalues

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Beyond the Limits of Predictability in Human Mobility prediction: Context-Transition Predictability

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2023年第5期35卷 4514-4526页

作者： Zhang, Chao Zhao, Kai Chen, Meng Shandong Univ Sch Software Jinan 250100 Peoples R China Georgia State Univ Robinson Coll Business Atlanta GA 30302 USA

Urban human mobility prediction is forecasting how people move in cities. It is crucial for many smart city applications including route optimization, preparing for dramatic shifts in modes of transportation, or mitigating the epidemic spread of viruses such as COVID-19. Previous research propose the maximum predictability to derive the theoretical limits of accuracy that any predictive algorithm could achieve on predicting urban human mobility. However, existing maximum predictability only considers the sequential patterns of human movements and neglects the contextual information such as the time or the types of places that people visit, which plays an important role in predicting one's next location. In this paper, we propose new theoretical limits of predictability, namely Context-Transition Predictability, which not only captures the sequential patterns of human mobility, but also considers the contextual information of human behavior. We compare our Context-Transition Predictability with other kinds of predictability and find that it is larger than these existing ones. We also show that our proposed Context-Transition Predictability provides us a better guidance on which predictive algorithm to be used for forecasting the next location when considering the contextual information. Source code is at https://***/zcfinal/ContextTransitionPredictability.

关键词： prediction algorithms Urban areas Predictive models Trajectory COVID-19 Entropy Context modeling Limits of predictability entropy location prediction predictive algorithm human mobility

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A Novel Mobility Induced Channel prediction Mechanism for Vehicular Communications

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2023年第5期22卷 3488-3502页

作者： Peng, Fei Zhang, Shunqing Jiang, Zhiyuan Wang, Xianling Chen, Wen Shanghai Univ Shanghai Inst Adv Commun & Data Sci Shanghai 200444 Peoples R China Shanghai Jiao Tong Univ Dept Elect Engn Shanghai 200240 Peoples R China

The acquisition of channel state information (CSI) is a vital and challenging task in wireless communications, especially for high mobility scenarios with rapidly varying channels. Channel prediction, as an effective approach to acquire CSI, is considered a promising approach to improve the communication performance. However, most of the current prediction methods with the assumption of slowly varying channel components cannot cope with rapid variation. In this paper, we propose a novel channel prediction scheme to incorporate the mobility of both transceivers and scatterers. Based on the estimated mobility parameters, the component-wise channel extraction results are derived and then the channel frequency response is predicted. In addition, the prediction performance and the computational complexity of the proposed scheme are analyzed and evaluated, and the relationship between the mobility parameter estimation error and the channel component prediction accuracy is revealed as well. Through predicted results based on the simulated and measured data, the performance of the proposed scheme surpasses the conventional schemes with genie-aided information.

关键词： Channel estimation Wireless communication Predictive models Channel models Delays prediction algorithms Machine learning Channel prediction mobility parameter estimation parametric wireless channel model vehicular communications

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Intelligent Sports prediction Analysis System Based on Edge Computing of Particle Swarm Optimization Algorithm

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IEEE CONSUMER ELECTRONICS MAGAZINE 2023年第2期12卷 73-82页

作者： Huang, Yan Bai, Yijun Henan Univ Inst Phys Educ Sports Reform & Dev Res Ctr Kaifeng Peoples R China Luoyang Normal Univ Dept Phys Educ Guangdong Tianhe Branch China Telecom Luoyang Peoples R China

In recent years, the level of competition in my country's sports field has developed rapidly, and the public's attention to sports competitions has also increased. The forecast and analysis of sports events have therefore gathered the attention of a broad audience. The sports forecast news reports issued by various sports news media have become popular entertainment news topics by many readers. The research purpose of this article is to explore the research and design of intelligent sports prediction analysis systems based on particle swarm optimization algorithm. Under the background that artificial intelligence has been widely used in various industries, this article combines the predictive performance of the edge computing of the particle swarm optimization algorithm in artificial intelligence and the traditional sports event predictive analysis method to design an intelligent sports predictive analysis system. After understanding the methods and research status of sports events prediction analysis through literature research, this article conducts demand analysis and feasibility analysis on the intelligent sports prediction analysis system based on the main factors that need to be considered in sports prediction analysis and the current application of particle swarm optimization algorithms. According to the demand analysis, the functional modules of the intelligent sports predictive analysis system are designed, and a systematic test experiment is carried out on the predictive performance of the intelligent sports predictive analysis system for sports events. Experiments show that the prediction accuracy of the intelligent sports prediction analysis system for sports events is higher than that of traditional sports events prediction methods, which can reach about 89.6%, and it can better cater to the interests of readers who are concerned about sports events.

关键词： Sports Particle swarm optimization prediction algorithms Optimization Predictive analytics Task analysis Image edge detection

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Harnessing the Power of Ego Network Layers for Link prediction in Online Social Networks

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS 2023年第1期10卷 48-60页

作者： Toprak, Mustafa Boldrini, Chiara Passarella, Andrea Conti, Marco IIT CNR I-56124 Pisa Italy

Being able to recommend links between users in online social networks is important for users to connect with like-minded individuals as well as for the platforms themselves and third parties leveraging social media information to grow their business. predictions are typically based on unsupervised or supervised learning, often leveraging simple yet effective graph topological information, such as the number of common neighbors. However, we argue that richer information about personal social structure of individuals might lead to better predictions. In this article, we propose to leverage well-established social cognitive theories to improve link prediction performance. According to these theories, individuals arrange their social relationships along, on average, five concentric circles of decreasing intimacy. We postulate that relationships in different circles have different importance in predicting new links. To validate this claim, we focus on popular feature extraction prediction algorithms (both unsupervised and supervised) and we extend them to include social circles' awareness. We validate the prediction performance of these circle-aware algorithms against several benchmarks (including their baseline versions as well as node-embedding- and graph neural network (GNN)-based link prediction), leveraging two Twitter datasets comprising a community of video gamers and generic users. We show that social awareness generally provides significant improvements in prediction performance, beating also state-of-the-art solutions such as node2vec and learning from Subgraphs, Embeddings and Attributes for Link prediction (SEAL), and without increasing the computational complexity. Finally, we show that social awareness can be used in place of using a classifier (which may be costly or impractical) for targeting a specific category of users.

关键词： Social networking (online) prediction algorithms Feature extraction Blogs Representation learning Measurement Predictive models Dunbar's model link prediction social circles Twitter

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Short-Term Traffic Flow prediction of the Smart City Using 5G Internet of Vehicles Based on Edge Computing

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2023年第2期24卷 2229-2238页

作者： Zhou, Shenghan Wei, Chaofan Song, Chaofei Pan, Xing Chang, Wenbing Yang, Linchao Beihang Univ Sch Reliabil & Syst Engn Beijing 100191 Peoples R China

The paper aims to explore the performance of short-term traffic flow prediction of the 5G (5th Generation Mobile Communication Technology) Internet of Vehicles (IoV) based on edge computing (EC) for the smart city and to further improve the intelligence of the smart city. Aiming at the current emergency of traffic congestion and road congestion, the present work adds EC to the current vehicle network, and integrates a deep convolution random forest neural network (DCRFNN). Additionally, it implements a model for short-term traffic flow prediction of a 5G vehicle network based on EC and deep learning (DL), and analyzes its performance by simulation. The results reveal that the proposed algorithm has a lower average delay cost, and the average unloading utility is stable at approximately 70%. In the prediction performance analysis, the recognition accuracy of the proposed algorithm reaches 98.06%. It is at least 1.14% higher than that of the advanced convolution neural network (CNN) algorithm proposed by other scholars and achieves a faster convergence rate. Therefore, the constructed short-term traffic prediction model implemented has a high-quality prediction performance while also ensuring a better unloading performance. The results can provide an experimental basis for traffic flow prediction and intelligent development of the smart city.

关键词： Roads 5G mobile communication Smart cities Transportation Predictive models prediction algorithms Computational modeling Edge computing 5G Internet of Vehicles traffic flow unloading smart city

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SA-LSTM: A Trajectory prediction Model for Complex Off-Road Multi-Agent Systems Considering Situation Awareness Based on Risk Field

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2023年第11期72卷 14016-14027页

作者： Wang, Yuning Wang, Jiahao Jiang, Junkai Xu, Shaobing Wang, Jianqiang Tsinghua Univ Sch Vehicle & Mobil Dept & Inst Beijing 100084 Peoples R China

Autonomous Vehicles have wide-ranging applications in off-road environments. Off-road vehicular scenes can be abstracted as multi-agent systems, and trajectory prediction is a critical process for context understanding. Agents compete and cooperate other to pursue their individual targets, which makes trajectories complicated and changeable. Hence, in order to derive precise predictions, it is necessary to reason how agents interact with each other. However, current prediction algorithms lack a unified and appropriatemethod for spatiotemporal reasoning. Previous methods mainly rely on vehicle-lane constraints to capture features, which is only applicable in structured environments. To address this issue, this article proposes a novel off-road multi-agent trajectory prediction framework called SA-LSTM. This framework comprises situation awareness extraction and Long Short Term Memory (LSTM) prediction backbones. Based on the analysis of agents' movement patterns, we decompose actions into the maintenance of previous movements and the strategy variations in response to environment situations. In situation awareness extraction, risk field and pooling layers are applied to filter interpretable awareness. As for prediction backbones, proper LSTM networks are selected to adapt the task features. In short-term predictions, a residual mechanism is used to preserve physical inertia, while for long-term predictions autoregression process is applied. We also establish a multi-agent dataset based on human-manipulated chaseand-run tasks to train and validate the performance of SA-LSTM. Experiments show that compared with the best baseline model at each prediction length, SA-LSTM reduces themean absolute error by 7.27%, 32.99%, and 28.07% at the prediction time of 0.6, 3.0, and 6.0 seconds, respectively, proving better prediction accuracy.

关键词： Trajectory Task analysis Predictive models Cognition Multi-agent systems prediction algorithms Long short term memory Off-road environment multi-agent systems trajectory prediction LSTM situation awareness extraction

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Robust Human Upper-Limbs Trajectory prediction Based on Gaussian Mixture prediction

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IEEE ACCESS 2023年 11卷 8172-8184页

作者： Li, Qinghua Zhang, Lei Zhang, Mengyao Du, Yuanshuai Liu, Kaiyue Feng, Chao Qilu Univ Technol Shandong Acad Sci Sch Informat & Automat Engn Jinan 250353 Peoples R China Shandong First Med Univ Shandong Acad Med Sci Coll Artificial Intelligence & Big Data Med Sci Jinan 250117 Peoples R China Qilu Univ Technol Shandong Acad Sci Int Sch Optoelect Engn Jinan 250353 Peoples R China Jinan Engn Lab Human Machine Intelligent Cooperat Jinan 250353 Peoples R China

Accurate prediction of human motion trajectory can improve the security of human-robot cooperation. Due to the unstructured nature of collaborative workspace and the uncertainty of sensor sensing data, the trajectory prediction accuracy of traditional prediction algorithms is low, and the uncertainty is difficult to estimate. Aiming at the complex characteristics of human upper limb movement patterns, this paper proposes a robust upper limb end trajectory prediction algorithm. The robust Gaussian mixture model was used to model the trajectory of human upper limb movement, and the statistical values of the future trajectory were obtained by combining Gaussian mixture regression. The advantage of this algorithm is that the prediction result is not only the predicted value of the position, but also the probability distribution of all possible future motion trajectories of the upper limb. The position prediction information in a specific motion mode can be obtained by using probability and statistical distribution characteristics. The algorithm is tested on both public and private datasets. Experimental results show that this method can predict human trajectories well.

关键词： Trajectory Task analysis Robots prediction algorithms Statistics Predictive models Gaussian mixture model Human-robot collaboration trajectory prediction GMM GMR RGMTP

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Sufficient Conditions for Convergent Recursive Extrapolation of qLPV Scheduling Parameters Along a prediction Horizon

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2023年第6期68卷 3182-3193页

作者： Morato, Marcelo Menezes Normey-Rico, Julio Elias Sename, Olivier Univ Fed Santa Catarina Dept Automat & Syst BR-88040900 Florianopolis Brazil Univ Grenoble Alpes CNRS Grenoble INP Inst EngnGIPSA Lab F-28000 Grenoble France

predictive control (MPC) algorithms have long been applied to nonlinear processes. In a quasi-linear parameter varying (qLPV) setting, nonlinearities are included into bounded scheduling parameters, which are given as a function of endogenous variables;these scheduling parameters are a priori unknown along a future prediction horizon, which complicates MPC design. To address this problem, the literature points out two options: robust MPC approaches, considering the scheduling to be uncertain;or suboptimal ones that set values for these parameters along the horizon. With respect to the latter group, this article proposes an extrapolation algorithm that estimates the future values of the qLPV scheduling parameters for a fixed prediction horizon of N steps;the method is based on a recursive procedure using simple Taylor expansions. Sufficient conditions for convergent extrapolation are presented with regard to the form and class of the scheduling function and the robustness of the MPC. Different benchmark examples from the literature are presented to illustrate the algorithm, which is also compared to state-of-the-art techniques.

关键词： Scheduling Trajectory Processor scheduling prediction algorithms Extrapolation Real-time systems Computational modeling Estimation linear parameter-varying systems nonlinear predictive control optimization algorithms

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