Browsing by Subject "Deep Learning"
Now showing items 21-40 of 55
-
(2021-06-29)Safety-related properties like lower flammable limit (LFL), upper flammable limit (UFL), auto-ignition temperature (AIT) and flash point (FP) are crucial hazardous properties for fire and explosion hazards assessment and ...
-
Dimpler is one of the body languages that contributes to the emotion contempt when the action appears unilaterally, and to boredom. It is one of the subtle expressions that people did in everyday life. Although the universal ...
-
(2018-11-06)Convolution neural networks have become one of the dominating deep learning models, especially for computation vision tasks such as image classification and segmentation. Dense convolution filters are inefficient, due to ...
-
(2020-11-17)Deep learning techniques produce impressive performance in many natural language processing tasks. However, it is still difficult to understand what the neural network learned during training and prediction. Recently, ...
-
(2021-11-23)Remote sensing (RS), a critical technology for large-scale-monitoring Earth-observing systems (EOS), plays an important role in Earth science and other related fields where physical, biological, and chemical properties of ...
-
(2020-06-24)Deep neural networks (DNNs) have achieved great success on grid-like data such as images, but face tremendous challenges in learning from more generic data such as graphs. In convolutional neural networks (CNNs), for ...
-
(2022-08-17)Integrated reservoir studies for performance prediction and decision-making processes are computationally expensive. In this paper, we develop a novel linearization approach to reduce the computational burden of intensive ...
-
(2021-07-14)Artificial intelligence (AI) is revolutionizing various systems within the Architecture, Engineering, Construction, and Facilities Management (AEC/FM) domains. The rapid advancements in computational methods, engineering ...
-
(2020-03-17)Recent successes combine reinforcement learning algorithms and deep neural networks, despite reinforcement learning not being widely applied to robotics and real world scenarios. This can be attributed to the fact that ...
-
(2022-12-01)The use of fiber-optics in reservoir surveillance can bring valuable insights to fracture geometry and fracture-hit identification, stage communication, and perforation cluster fluid distribution in hydraulic fracturing ...
-
(2019-02-28)In many cases, research on reliability analysis focuses on searching the state space of the system for states that represent events of interest, like failure of the system not meeting the required demand for a specific ...
-
(2020-05-13)With the emergence of ubiquitous environmental monitoring systems in the past few decades, we are gaining unprecedented ability to collect vast amounts of spatiotemporal environmental data. However, it still remains a ...
-
(2019-05-23)Facial expression recognition is getting popular in the research community because of its extensive use in understanding human sentiments. Among various medium of human interaction uses in daily life, the facial expression ...
-
(2020-07-15)This work presents novel image acquisition methodology to improve power and performance metrics of image acquisition system. Given the slowing Moore’s law, ubiquitous mobile devices like smartphones and focus on multimedia ...
-
(The17th IBPSA Conference, Bruges, Belgium, 2021-09)
-
(2019-08-14)Deep learning is a machine learning technique that enables computers to learn directly from images, text, or sound in the same way that people do. It is a key technology which enables selfdriving cars and speech recognition. ...
-
(2021-08-13)Coupling between fluid flow and geomechanical responses such as deformation and failure is essential to simulate potential damage in subsurface environments and ground surface structures. This study using coupled multiphase ...
-
(2019-05-23)Many engineering problems have multiscale features. These problems usually require some model reduction since the computational cost of a fine-scale solution is extremely expensive. Existing model reduction methods such ...
-
(2022-07-27)In data-poor environments, it may not be possible to set aside a large enough test data set to produce accurate test-set error estimates. On the other hand, in modern classification applications where training is time and ...