19 research outputs found
Towards Unsupervised Graph Completion Learning on Graphs with Features and Structure Missing
In recent years, graph neural networks (GNN) have achieved significant
developments in a variety of graph analytical tasks. Nevertheless, GNN's
superior performance will suffer from serious damage when the collected node
features or structure relationships are partially missing owning to numerous
unpredictable factors. Recently emerged graph completion learning (GCL) has
received increasing attention, which aims to reconstruct the missing node
features or structure relationships under the guidance of a specifically
supervised task. Although these proposed GCL methods have made great success,
they still exist the following problems: the reliance on labels, the bias of
the reconstructed node features and structure relationships. Besides, the
generalization ability of the existing GCL still faces a huge challenge when
both collected node features and structure relationships are partially missing
at the same time. To solve the above issues, we propose a more general GCL
framework with the aid of self-supervised learning for improving the task
performance of the existing GNN variants on graphs with features and structure
missing, termed unsupervised GCL (UGCL). Specifically, to avoid the mismatch
between missing node features and structure during the message-passing process
of GNN, we separate the feature reconstruction and structure reconstruction and
design its personalized model in turn. Then, a dual contrastive loss on the
structure level and feature level is introduced to maximize the mutual
information of node representations from feature reconstructing and structure
reconstructing paths for providing more supervision signals. Finally, the
reconstructed node features and structure can be applied to the downstream node
classification task. Extensive experiments on eight datasets, three GNN
variants and five missing rates demonstrate the effectiveness of our proposed
method.Comment: Accepted by 23rd IEEE International Conference on Data Mining (ICDM
2023
Preparation and Properties of Mo Coating on H13 Steel by Electro Spark Deposition Process
From MDPI via Jisc Publications RouterHistory: accepted 2021-06-29, pub-electronic 2021-07-01Publication status: PublishedFunder: Jilin Science and Technology Development Project; Grant(s): 20200401034GX, 2020C029-1Funder: Fundamental Research Funds for the Central Universities; Grant(s): 45120031B094H13 steel is often damaged by wear, erosion, and thermal fatigue. It is one of the essential methods to improve the service life of H13 steel by preparing a coating on it. Due to the advantages of high melting point, good wear, and corrosion resistance of Mo, Mo coating was fabricated on H13 steel by electro spark deposition (ESD) process in this study. The influences of the depositing parameters (deposition power, discharge frequency, and specific deposition time) on the roughness of the coating, thickness, and properties were investigated in detail. The optimized depositing parameters were obtained by comparing roughness, thickness, and crack performance of the coating. The results show that the cross-section of the coating mainly consisted of strengthening zone and transition zone. Metallurgical bonding was formed between the coating and substrate. The Mo coating mainly consisted of Fe9.7Mo0.3, Fe-Cr, FeMo, and Fe2Mo cemented carbide phases, and an amorphous phase. The Mo coating had better microhardness, wear, and corrosion resistance than substrate, which could significantly improve the service life of the H13 steel
Controlling remote instruments using Web services for online experiment systems
Online experimentation allows students from anywhere to operate remote instruments at any time. This promising e-learning application is well positioned to use Web Services to conduct online experiment systems due to its interoperability and Internet compliance. We present a double client-server architecture for online experiment systems and the methodology to wrap the functions of instruments into Web Services. We propose that the instrument Web Services should be stateful services and we present the framework to manage the states of the instrument web services. We benchmark the performance of this system when using SOAP as the wire format for communication and propose solutions to optimize performance
Microstructure and mechanical properties of ZrC modified Ni60 hard-facing alloy fabricated by laser metal deposition
Crack-free specimens were prepared from ZrC-modified Ni60 alloy by laser metal deposition. The effects of ZrC powder adulteration on the mechanism of microstructure refinement were investigated. The results demonstrated that the adulteration of ZrC powder did not provide nucleation sites for the hardening phase. The ZrC powder was added to Ni60 alloy powder to reduce the content of columnar dendrites and the eutectics of laser metal deposition specimens. The transformation of the dendritic hardening phase into a massive hardening phase was induced. The fine NiâBâSi eutectic microstructure changed to a massive NiZr eutectic and peritectic microstructure with increasing ZrC powder mass fraction. The microstructure transformation mechanism constrained the initiation and propagation of cracks in the deposited specimens. The present research provides a method for improving the cracking defect of laser additive manufacturing NiâCrâBâSi system alloys and a theoretical basis for promoting the application of the alloy in additive manufacturing
Semantic and spatialâspectral feature fusion transformer network for the classification of hyperspectral image
Abstract Recently, transformerâbased networks have been introduced for the classification of hyperspectral image (HSI). Although transformerâbased methods can well capture spectral sequence information, their ability to fuse different types of information contained in HSI is still insufficient. To exploit rich spectral, spatial and semantic information in HSI, a novel semantic and spatialâspectral feature fusion transformer (S3FFT) network is proposed in this study. In the proposed S3FFT method, spatial attention and efficient channel attention (ECA) modules are employed for the extraction of shallow spatialâspectral features. Then, a transformerâbased module is designed to extract advanced fused features and to produce the pseudoâlabel and class probability of each pixel for semantic feature extraction. Finally, the semantic, spatial and spectral features are combined by the transformer for classification. Compared with traditional deep learning methods and recently transformerâbased methods, the proposed S3FFT shows relatively better results on three HSI datasets