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Multivariate time series prediction, especially in early fault warning for chemical processes, poses significant challenges. The advent of graph neural network (GNN) method has made breakthroughs in this domain by enabling the processing of topological data. However, the traditional methods suffer from the issue of over-smoothing and inability to capture intricate multi-scale spatio-temporal dependencies. Additionally, the existing graph structures fall short in describing the complex spatial relationships among multi-stage sensors, impeding their adaptability to dynamically evolving chain reaction scenarios. To alleviate these limitations, a novel Dynamic spatio-Temporal Network for early fault warning in chemical processes, named DSTN for short, is proposed in this paper. We extract the spatial and temporal features of the time series by the designed dynamic GNN and the improved Transformer network. Then, we integrate the spatio-temporal features through the residual network. DSTN has the following advantages: (1) A one-dimensional convolutional neural network is seamlessly incorporated into the Transformer architecture for bolstering its capacity to discern both global and local features within time series. (2) The continuous sliding window and mutual information methods are employed to construct a dynamic topology graph, and a K-order adjacency matrix is designed to rectify the inefficiencies in learning weights associated with convolution kernel parameters. (3) Multiple spatio-temporal modules interconnected via residual connection to adaptively fuse multi-scale features. Experimental results demonstrate that our proposed DSTN method outperforms existing methods in terms of both performance and interpretability in early fault warning of chemical processes. [Display omitted] • One-dimension convolution enhances Transformer's ability to learn global local feature weights. • The K-order adjacency matrix and sliding window are used to construct higher-order dynamic graphs. • The residual connects spatial and temporal features to improve the learning ability of the model. • The effectiveness and practicability of method are verified by two chemical process datasets. [ABSTRACT FROM AUTHOR]

• A behavioural multiparametric based pain assessment system is proposed. • Spatio-temporal features are used for facial expression based pain assessment system. • To learn body movement dynamics FCN based stacked BiLSTM model is implemented. • VGGish-SVM based model is proposed for sound based pain assessment system. • Decision level fusion is implemented to improve the performance of the system. • Introduced a new mutiparametric pain dataset which is collected from the hospital. Existing approaches for pain assessment are mostly based on facial expressions which cannot fully describe the expressiveness of pain. Although few researchers have also implemented multimodal approaches, they have not considered body movement for pain assessment. Besides, assessing pain in an unconstrained setting is challenging due to occlusions and poor illumination. Hence, to overcome these challenges we present a multi-stream framework for behavioural multiparametric pain assessment. To detect pain from face images a domain adaptation technique with a stacked BiLSTM network is implemented for joint spatio-temporal modelling. Further, a FCN-based-BiLSTM model is implemented to learn pain-related body dynamics. To learn pain-related features from pain sound VGG-based features are extracted and then a linear model is used for pain classification. Finally, a decision-level fusion approach is implemented to learn jointly from all pain classifiers. The proposed pain assessment system has achieved 92 % of accuracy after evaluating on self-created pain dataset. [ABSTRACT FROM AUTHOR]

In this paper, we propose a spatio-temporal contextual network, STC-Flow, for optical flow estimation. Unlike previous optical flow estimation approaches with local pyramid feature extraction and multi-level correlation, we propose a contextual relation exploration architecture by capturing rich long-range dependencies in spatial and temporal dimensions. Specifically, STC-Flow contains three key context modules, i.e. , pyramidal spatial context module, temporal context correlation module and recurrent residual contextual upsampling module for the effect of feature extraction, correlation, and flow reconstruction, respectively. Experimental results demonstrate that the proposed scheme achieves the state-of-the-art performance of two-frame based methods on Sintel and KITTI datasets. • A general contextual attention framework of multi-level features & complicated fusion. • STC-Flow to capture rich spatio-temporal long-range dependencies for flow estimation. • PSC, TCC & RRCU for the effect of feature extraction, correlation, and reconstruction. • Promising performance in Sintel & KITTI for two-frame based optical flow estimation. [ABSTRACT FROM AUTHOR]

This study presents a methodology for estimating passenger's spatio-temporal trajectory with personalization and timeliness by using incomplete Wi-Fi probe data in urban rail transit network. Unlike the automatic fare collection data that only records passenger's entries and exits, the Wi-Fi probe data can capture more detailed passenger movements, such as riding a train or waiting on a platform. However, the estimation of spatio-temporal trajectories remains as a challenging task because a few unfavorable situations could result into deficient data. To address this problem, we first describe the Wi-Fi probe data and summarize their common defects. Then, the n -gram method is developed to infer missing spatio-temporal location information. Next, an estimation algorithm is designed to generate feasible spatio-temporal trajectories for each individual passenger by integrating multiple data sources, i.e., urban rail transit network topology, Wi-Fi probe data, train schedules, etc. This proposed method is tested on both simulated data in blind experiments and real-world data from a complex urban rail transit network. The results of case study show that 93% of passengers' unique physical routes can be estimated. Then, for 80% of passengers, the number of feasible spatio-temporal trajectories can be reduced to one or two. Potential applications of the trajectory estimation approach are also identified. • This study extends personalization and timeliness into the SST estimation. • A new algorithm is developed by integrating structurally heterogeneous data sources. • The estimated trajectory describes detailed information both on-board and at stations. • Real-world analyses are conducted for the URT network with the largest route length in China. • This is the first attempt to find out all the routes and trains at the network level. [ABSTRACT FROM AUTHOR]