184 research outputs found
Graph Convolutional Networks for Simulating Multi-phase Flow and Transport in Porous Media
Numerical simulation of multi-phase fluid dynamics in porous media is
critical for many subsurface applications. Data-driven surrogate modeling
provides computationally inexpensive alternatives to high-fidelity numerical
simulators. While the commonly used convolutional neural networks (CNNs) are
powerful in approximating partial differential equation solutions, it remains
challenging for CNNs to handle irregular and unstructured simulation meshes.
However, subsurface simulation models often involve unstructured meshes with
complex mesh geometries, which limits the application of CNNs. To address this
challenge, here we construct surrogate models based on Graph Convolutional
Networks (GCNs) to approximate the spatial-temporal solutions of multi-phase
flow and transport processes. We propose a new GCN architecture suited to the
hyperbolic character of the coupled PDE system, to better capture the
saturation dynamics. Results of 2D heterogeneous test cases show that our
surrogates predict the evolutions of the pressure and saturation states with
high accuracy, and the predicted rollouts remain stable for multiple timesteps.
Moreover, the GCN-based models generalize well to irregular domain geometries
and unstructured meshes that are unseen in the training dataset
A Damage Identification Approach for Offshore Jacket Platforms Using Partial Modal Results and Artificial Neural Networks
This paper presents a damage identification method for offshore jacket platforms using partially measured modal results and based on artificial intelligence neural networks. Damage identification indices are first proposed combining information of six modal results and natural frequencies. Then, finite element models are established, and damages in structural members are assumed by reducing the structural elastic modulus. From the finite element analysis for a training sample, both the damage identification indices and the damages are obtained, and neural networks are trained. These trained networks are further tested and used for damage prediction of structural members. The calculation results show that the proposed method is quite accurate. As the considered measurement points of the jacket platform are near the waterline, the prediction errors keep below 8% when the damaged members are close to the waterline, but may rise to 16.5% when the damaged members are located in deeper waters.publishedVersionNivå
Embedded Based Miniaturized Universal Electrochemical Sensing Platform
We created an embedded sensing platform based on STM32 embedded system, with integrated carbon-electrode ionic sensor by using a self-made plug. Given ration of concentration-unknown nitrate liquid samples, this platform is able to measure the nitrate concentration in neutral environment. Response signals which were transmitted by the sensor can be displayed via a serial port to the computer screen or via Bluetooth to the smartphone. Processed by a fitting function, signals are transformed into related concentration. Through repeating the experiment many times, the accuracy and repeatability turned out to be excellent. The results can be automatically stored on smartphone via Bluetooth. We created this embedded sensing platform for field water quality measurement. This platform also can be applied for other micro sensors’ signal acquisition and data processing
MULTI-VESSELS COLLISION AVOIDANCE STRATEGY FOR AUTONOMOUS SURFACE VEHICLES BASED ON GENETIC ALGORITHM IN CONGESTED PORT ENVIRONMENT
An improved genetic collision avoidance algorithm is proposed in this study to address the problem that Autonomous Surface Vehicles (ASV) need to comply with the collision avoidance rules at sea in congested sea areas. Firstly, a collision risk index model for ASV safe encounters is established taking into account the international rules for collision avoidance. The ASV collision risk index and the distance of safe encounters are taken as boundary values of the correlation membership function of the collision risk index model to calculate the optimal heading of ASV in real-time. Secondly, the genetic coding, fitness function, and basic parameters of the genetic algorithm are designed to construct the collision avoidance decision system. Finally, the simulation of collision avoidance between ASV and several obstacle vessels is performed, including the simulation of three collision avoidance states head-on situation, crossing situation, and overtaking situation. The results show that the proposed intelligent genetic algorithm considering the rules of collision avoidance at sea can effectively avoid multiple other vessels in different situations
Multi-scale diff-changed feature fusion network for hyperspectral image change detection.
For hyperspectral images (HSI) change detection (CD), multi-scale features are usually used to construct the detection models. However, the existing studies only consider the multi-scale features containing changed and unchanged components, which is difficult to represent the subtle changes between bi-temporal HSIs in each scale. To address this problem, we propose a multi-scale diff-changed feature fusion network (MSDFFN) for HSI CD, which improves the ability of feature representation by learning the refined change components between bi-temporal HSIs under different scales. In this network, a temporal feature encoder-decoder sub-network, which combines a reduced inception module and a cross-layer attention module to highlight the significant features, is designed to extract the temporal features of HSIs. A bidirectional diff-changed feature representation module is proposed to learn the fine changed features of bi-temporal HSIs at various scales to enhance the discriminative performance of the subtle change. A multi-scale attention fusion module is developed to adaptively fuse the changed features of various scales. The proposed method can not only discover the subtle change of bi-temporal HSIs but also improve the discriminating power for HSI CD. Experimental results on three HSI datasets show that MSDFFN outperforms a few state-of-the-art methods
BGGAN: Bokeh-Glass Generative Adversarial Network for Rendering Realistic Bokeh
A photo captured with bokeh effect often means objects in focus are sharp
while the out-of-focus areas are all blurred. DSLR can easily render this kind
of effect naturally. However, due to the limitation of sensors, smartphones
cannot capture images with depth-of-field effects directly. In this paper, we
propose a novel generator called Glass-Net, which generates bokeh images not
relying on complex hardware. Meanwhile, the GAN-based method and perceptual
loss are combined for rendering a realistic bokeh effect in the stage of
finetuning the model. Moreover, Instance Normalization(IN) is reimplemented in
our network, which ensures our tflite model with IN can be accelerated on
smartphone GPU. Experiments show that our method is able to render a
high-quality bokeh effect and process one pixel image in 1.9
seconds on all smartphone chipsets. This approach ranked First in AIM 2020
Rendering Realistic Bokeh Challenge Track 1 \& Track 2.Comment: accepted by ECCV workshop 202
Functional Group Induced Transformations in Stacking and Electron Structure in Mo2CTx/NiS Heterostructures
The two-dimensional transition metal carbide/nitride family (MXenes) has
garnered significant attention due to their highly customizable surface
functional groups. Leveraging modern material science techniques, the
customizability of MXenes can be enhanced further through the construction of
associated heterostructures. As indicated by recent research, the Mo2CTx/NiS
heterostructure has emerged as a promising candidate exhibiting superior
physical and chemical application potential. The geometrical structure of
Mo2CTx/NiS heterostructure is modeled and 6 possible configurations are
validated by Density Functional Theory simulations. The variation in functional
groups leads to structural changes in Mo2CTx/NiS interfaces, primarily
attributed to the competition between van der Waals and covalent interactions.
The presence of different functional groups results in significant band
fluctuations near the Fermi level for Ni and Mo atoms, influencing the role of
atoms and electron's ability to escape near the interface. This, in turn,
modulates the strength of covalent interactions at the MXenes/NiS interface and
alters the ease of dissociation of the MXenes/NiS complex. Notably, the
Mo2CO2/NiS(P6_3/mmc) heterostructure exhibits polymorphism, signifying that two
atomic arrangements can stabilize the structure. The transition process between
these polymorphs is also simulated, further indicating the modulation of the
electronic level of properties by a sliding operation.Comment: 10 pages, 5 figures,2 table
- …