A5027329007- Face and Expression Recognition
- Traffic Prediction and Management Techniques
- Sparse and Compressive Sensing Techniques
- Video Surveillance and Tracking Methods
- Domain Adaptation and Few-Shot Learning
- Multimodal Machine Learning Applications
- Advanced Image and Video Retrieval Techniques
- Remote-Sensing Image Classification
- Advanced Graph Neural Networks
- Face recognition and analysis
- Image Retrieval and Classification Techniques
- Topic Modeling
- Text and Document Classification Technologies
- Anomaly Detection Techniques and Applications
- Advanced Vision and Imaging
- Transportation Planning and Optimization
- Advanced Clustering Algorithms Research
- 3D Shape Modeling and Analysis
- Tensor decomposition and applications
- Human Pose and Action Recognition
- Advanced Neural Network Applications
- Human Mobility and Location-Based Analysis
- Complex Network Analysis Techniques
- Remote Sensing and Land Use
- Image and Signal Denoising Methods
Beijing University of Technology
2016-2025
Lanzhou University
2023-2025
Guilin Medical University
2020-2025
Henan University
2025
China Tobacco
2024
Hezhou University
2024
National University of Singapore
2009-2024
Shanghai Maritime University
2022-2024
Jiangsu Normal University
2024
Perdana University
2012-2024
Traffic prediction is a core problem in the intelligent transportation system and has broad applications management planning, main challenge of this field how to efficiently explore spatial temporal information traffic data. Recently, various deep learning methods, such as convolution neural network (CNN), have shown promising performance prediction. However, it samples data regular grids input CNN, thus destroys structure road network. In paper, we introduce graph propose an optimized...
Grain boundaries (GBs) play an important role in the mechanical behavior of polycrystalline materials. Despite decades investigation, atomic-scale dynamic processes GB deformation remain elusive, particularly for GBs polycrystals, which are commonly asymmetric and general type. We conducted situ atomic-resolution study to reveal how sliding-dominant is accomplished at tilt platinum bicrystals. observed either direct sliding along or with atom transfer across boundary plane. The latter...
Traffic forecasting is a challenging problem in the transportation research field as complexity and non-stationary changing of traffic data, thus key to issue how explore proper spatial temporal characteristics. Based on this thought, many creative methods have been proposed, which Graph Convolution Network (GCN) based shown promising performance. However, these depend graph construction, mainly uses prior knowledge road network. Recently, some works realized fact network tried construct...
Traffic forecasting is attracting considerable interest due to its widespread application in intelligent transportation systems. Given the complex and dynamic traffic data, many methods focus on how establish a spatial-temporal model express non-stationary patterns. Recently, latest Graph Convolution Network (GCN) has been introduced learn spatial features while time neural networks are used temporal features. These GCN based obtain state-of-the-art performance. However, current ignore...
Metro passenger flow prediction is a strategically necessary demand in an intelligent transportation system to alleviate traffic pressure, coordinate operation schedules, and plan future constructions. Graph-based neural networks have been widely used problems. Graph Convolutional Neural Networks (GCN) captures spatial features according established connections but ignores the high-order relationships between stations travel patterns of passengers. In this paper, we utilize novel...
Graph convolutional networks (GCN) have been applied in the traffic flow forecasting tasks with graph capability describing irregular topology structures of road networks. However, GCN based methods often fail to simultaneously capture short-term and long-term temporal relations carried by data, also suffer over-smoothing problem. To overcome problems, we propose a hierarchical network merging newly designed Transformer (LTT) spatio-temporal (STGC). Specifically, LTT aims learn among while...
Dengue is an important medical problem, with symptoms ranging from mild dengue fever to severe forms of the disease, where vascular leakage leads hypovolemic shock. Cytokines have been implicated play a role in progression disease; however, their profile patients and synergy that continued plasma not clearly understood. Herein, we investigated cytokine kinetics profiles at different phases illness further understand cytokines disease.
Recently, Graph Convolution Network (GCN) and Temporal (TCN) are introduced into traffic prediction achieve state-of-the-art performance due to their good ability for modeling the spatial temporal property of data. In spite having performance, current methods generally focus on measurement road segments, i.e. nodes flow graph, while edges which represent correlation data different segments form affinity matrix GCN, usually constructed according structure network, but properties not well...
Cycloparaphenylenes (CPPs) represent a significant challenge for the synthesis of mechanically interlocked architectures, because they lack heteroatoms, which precludes traditional active and passive template methods. To circumvent this problem explore fundamental functional properties CPP rotaxanes catenanes, researches have resorted to unusual non‐covalent even labor‐intensive covalent approaches. Herein, we report ring‐in‐ring strategy that makes use surprisingly strong inclusion crown...
Traffic prediction methods on a single-source data have achieved excellent results in recent years, especially the Graph Convolutional Networks (GCN) based models with spatio-temporal dependency. In reality, various modes of urban transportation operate simultaneously. They influence and complement each other common space-time occasions, constituting system dynamically. Thus, traffic from multiple sources is ostensibly heterogeneous, but internally correlated. The typical single driven are,...
The traffic data corrupted by noise and missing entries often lead to the poor performance of Intelligent Transportation Systems (ITS), such as bad congestion prediction route guidance. How efficiently impute is an urgent problem. As a classic deep learning method, Generative Adversarial Network (GAN) achieves remarkable success in image recovery fields, which opens up new way for imputation. In this paper, we propose novel spatio-temporal GAN model imputation (STGAN). Firstly, design...
This paper introduces an L1-norm-based probabilistic principal component analysis model on 2D data (L1-2DPPCA) based the assumption of Laplacian noise model. The or L1 density function can be expressed as a superposition infinite number Gaussian distributions. Under this expression, Bayesian inference established variational expectation maximization approach. All key parameters in learned by proposed algorithm. It has experimentally been demonstrated that newly introduced hidden variables...
Traffic flow data has three main characteristics: large amount of noise and incompleteness, temporal spatial correlation, dynamic sequential property. Problems noise, loss incompleteness could decrease the prediction performance make it difficult for transportation system management. Inspired by recent work on low rank representation (LRR) mode decomposition (DMD), we propose a Low Rank Dynamic Mode Decomposition (LRDMD) model which solves aforementioned problems simultaneously. LRDMD...
Multiview subspace clustering has been demonstrated to achieve excellent performance in practice by exploiting multiview complementary information. One of the strategies used most existing methods is learn a shared self-expressiveness coefficient matrix for all view data. Different from such strategy, this article proposes rank consistency induced model pursue consistent low-rank structure among view-specific matrices. To facilitate practical model, we parameterize on matrices through...
Temporal characteristics are prominently evident in a substantial volume of knowledge, which underscores the pivotal role Knowledge Graphs (TKGs) both academia and industry. However, TKGs often suffer from incompleteness for three main reasons: continuous emergence new weakness algorithm extracting structured information unstructured data, lack source dataset. Thus, task Graph Completion (TKGC) has attracted increasing attention, aiming to predict missing items based on available...
Accurate prediction of origin-destination (OD) demand is critical for service providers to efficiently allocate limited resources in regions with high travel demands. However, OD distributions pose significant challenges, characterized by sparsity, complex spatial correlations within or chains, and potential repetition due the recurrence similar semantic contexts. These challenges impede traditional graph-based approaches, which connect two vertices through an edge, from performing...