275,392 research outputs found
VICARED: A Neural Network Based System for the Detection of Electrical Disturbances in Real Time
The study of the quality of electric power lines is usually known as
Power Quality. Power quality problems are increasingly due to a proliferation
of equipment that is sensitive and polluting at the same time. The detection and
classification of the different disturbances which cause power quality problems
is a difficult task which requires a high level of engineering knowledge. Thus,
neural networks are usually a good choice for the detection and classification of
these disturbances. This paper describes a powerful system for detection of
electrical disturbances by means of neural networks
Differentiable Sparsification for Deep Neural Networks
Deep neural networks have relieved a great deal of burden on human experts in
relation to feature engineering. However, comparable efforts are instead
required to determine effective architectures. In addition, as the sizes of
networks have grown overly large, a considerable amount of resources is also
invested in reducing the sizes. The sparsification of an over-complete model
addresses these problems as it removes redundant components and connections. In
this study, we propose a fully differentiable sparsification method for deep
neural networks which allows parameters to be zero during training via
stochastic gradient descent. Thus, the proposed method can learn the sparsified
structure and weights of a network in an end-to-end manner. The method is
directly applicable to various modern deep neural networks and imposes minimum
modification to existing models. To the best of our knowledge, this is the
first fully [sub-]differentiable sparsification method that zeroes out
parameters. It provides a foundation for future structure learning and model
compression methods
CoCalc as a Learning Tool for Neural Network Simulation in the Special Course "Foundations of Mathematic Informatics"
The role of neural network modeling in the learning content of the special
course "Foundations of Mathematical Informatics" was discussed. The course was
developed for the students of technical universities - future IT-specialists
and directed to breaking the gap between theoretic computer science and it's
applied applications: software, system and computing engineering. CoCalc was
justified as a learning tool of mathematical informatics in general and neural
network modeling in particular. The elements of technique of using CoCalc at
studying topic "Neural network and pattern recognition" of the special course
"Foundations of Mathematic Informatics" are shown. The program code was
presented in a CoffeeScript language, which implements the basic components of
artificial neural network: neurons, synaptic connections, functions of
activations (tangential, sigmoid, stepped) and their derivatives, methods of
calculating the network's weights, etc. The features of the Kolmogorov-Arnold
representation theorem application were discussed for determination the
architecture of multilayer neural networks. The implementation of the
disjunctive logical element and approximation of an arbitrary function using a
three-layer neural network were given as an examples. According to the
simulation results, a conclusion was made as for the limits of the use of
constructed networks, in which they retain their adequacy. The framework topics
of individual research of the artificial neural networks is proposed.Comment: 16 pages, 3 figures, Proceedings of the 13th International Conference
on ICT in Education, Research and Industrial Applications. Integration,
Harmonization and Knowledge Transfer (ICTERI, 2018
Artificial intelligence methods for oil and gas reservoir development: Current progresses and perspectives
Artificial neural networks have been widely applied in reservoir engineering. As a powerful tool, it changes the way to find solutions in reservoir simulation profoundly. Deep learning networks exhibit robust learning capabilities, enabling them not only to detect patterns in data, but also uncover underlying physical principles, incorporate prior knowledge of physics, and solve complex partial differential equations. This work presents the latest research advancements in the field of petroleum reservoir engineering, covering three key research directions based on artificial neural networks: data-driven methods, physics driven artificial neural network partial differential equation solver, and data and physics jointly driven methods. In addition, a wide range of neural network architectures are reviewed, including fully connected neural networks, convolutional neural networks, recurrent neural networks, and so on. The basic principles of these methods and their limitations in practical applications are also outlined. The future trends of artificial intelligence methods for oil and gas reservoir development are further discussed. The large language models are the most advanced neural networks so far, it is expected to be applied in reservoir simulation to predict the development performance.Document Type: PerspectiveCited as: Xue, L., Li, D., Dou, H. Artificial intelligence methods for oil and gas reservoir development: Current progresses and perspectives. Advances in Geo-Energy Research, 2023, 10(1): 65-70. https://doi.org/10.46690/ager.2023.10.0
- …