356,312 research outputs found
Plant Process Emulator
The purpose of this project is to provide the VCU Engineering Students with a training system to simulate the use of Industrial Automation systems. Students need a wide variety of training systems to adequately train and improve their knowledge of all the fundamentals of PLC systems. There are multiple companies that sell a very expensive training setup that can teach students about Proportional-Integral-Derivative (PID) control systems and mechanical systems but those systems cost too much (~$20,000+) for a small university or trade school to fund. The training system that was built provides the student with real world control and monitoring of physical plant attributes like fluid level control and temperature control. A Programmable Logic Controller (PLC) is used to instantiate the PID’s for both level control and temperature control. A level transmitter and a thermocouple act as the process variables and the solenoid valves and a heater act as the manipulating variables to adjust the level and temperature respectively. All components of the system work harmoniously together to simulate a physical plant process. The demonstrations run through this trainer show how the hardware and software work together to allow the operator control of the system. The goal is to allow students a chance to be exposed to different uses of PLC’s and PID’s.https://scholarscompass.vcu.edu/capstone/1192/thumbnail.jp
Dynamic modelling, validation and analysis of coal-fired subcritical power plant
Coal-fired power plants are the main source of global electricity. As environmental regulations tighten, there is need to improve the design, operation and control of existing or new built coal-fired power plants. Modelling and simulation is identified as an economic, safe and reliable approach to reach this objective. In this study, a detailed dynamic model of a 500 MWe coal-fired subcritical power plant was developed using gPROMS based on first principles. Model validations were performed against actual plant measurements and the relative error was less than 5%. The model is able to predict plant performance reasonably from 70% load level to full load. Our analysis showed that implementing load changes through ramping introduces less process disturbances than step change. The model can be useful for providing operator training and for process troubleshooting among others
NIO: Lightweight neural operator-based architecture for video frame interpolation
We present, NIO - Neural Interpolation Operator, a lightweight efficient
neural operator-based architecture to perform video frame interpolation.
Current deep learning based methods rely on local convolutions for feature
learning and require a large amount of training on comprehensive datasets.
Furthermore, transformer-based architectures are large and need dedicated GPUs
for training. On the other hand, NIO, our neural operator-based approach learns
the features in the frames by translating the image matrix into the Fourier
space by using Fast Fourier Transform (FFT). The model performs global
convolution, making it discretization invariant. We show that NIO can produce
visually-smooth and accurate results and converges in fewer epochs than
state-of-the-art approaches. To evaluate the visual quality of our interpolated
frames, we calculate the structural similarity index (SSIM) and Peak Signal to
Noise Ratio (PSNR) between the generated frame and the ground truth frame. We
provide the quantitative performance of our model on Vimeo-90K dataset, DAVIS,
UCF101 and DISFA+ dataset
Self-Organized Operational Neural Networks with Generative Neurons
Operational Neural Networks (ONNs) have recently been proposed to address the
well-known limitations and drawbacks of conventional Convolutional Neural
Networks (CNNs) such as network homogeneity with the sole linear neuron model.
ONNs are heterogenous networks with a generalized neuron model that can
encapsulate any set of non-linear operators to boost diversity and to learn
highly complex and multi-modal functions or spaces with minimal network
complexity and training data. However, Greedy Iterative Search (GIS) method,
which is the search method used to find optimal operators in ONNs takes many
training sessions to find a single operator set per layer. This is not only
computationally demanding, but the network heterogeneity is also limited since
the same set of operators will then be used for all neurons in each layer.
Moreover, the performance of ONNs directly depends on the operator set library
used, which introduces a certain risk of performance degradation especially
when the optimal operator set required for a particular task is missing from
the library. In order to address these issues and achieve an ultimate
heterogeneity level to boost the network diversity along with computational
efficiency, in this study we propose Self-organized ONNs (Self-ONNs) with
generative neurons that have the ability to adapt (optimize) the nodal operator
of each connection during the training process. Therefore, Self-ONNs can have
an utmost heterogeneity level required by the learning problem at hand.
Moreover, this ability voids the need of having a fixed operator set library
and the prior operator search within the library in order to find the best
possible set of operators. We further formulate the training method to
back-propagate the error through the operational layers of Self-ONNs.Comment: 14 pages, 14 figures, journal articl
Neural Operator Learning for Ultrasound Tomography Inversion
Neural operator learning as a means of mapping between complex function
spaces has garnered significant attention in the field of computational science
and engineering (CS&E). In this paper, we apply Neural operator learning to the
time-of-flight ultrasound computed tomography (USCT) problem. We learn the
mapping between time-of-flight (TOF) data and the heterogeneous sound speed
field using a full-wave solver to generate the training data. This novel
application of operator learning circumnavigates the need to solve the
computationally intensive iterative inverse problem. The operator learns the
non-linear mapping offline and predicts the heterogeneous sound field with a
single forward pass through the model. This is the first time operator learning
has been used for ultrasound tomography and is the first step in potential
real-time predictions of soft tissue distribution for tumor identification in
beast imaging.Comment: 4 pages, 1 figur
On the importance of cyber-security training for multi-vector energy distribution system operators
Multi-vector Energy Distribution Systems (EDS) are increasingly connected to provide new services to consumers and Distribution Network Operators (DNO). This exponential growth in connectivity, while beneficial, tremendously increases the attack surface of critical infrastructures, demonstrating a clear need for energy operator cyber-security training. This paper highlights the cyber-security challenges faced by EDS operators as well as the impact a successful cyber-attack could have on the grid. Finally, training needs are contextualised through cyber-attack examples
Asynchronous Optimization Methods for Efficient Training of Deep Neural Networks with Guarantees
Asynchronous distributed algorithms are a popular way to reduce
synchronization costs in large-scale optimization, and in particular for neural
network training. However, for nonsmooth and nonconvex objectives, few
convergence guarantees exist beyond cases where closed-form proximal operator
solutions are available. As most popular contemporary deep neural networks lead
to nonsmooth and nonconvex objectives, there is now a pressing need for such
convergence guarantees. In this paper, we analyze for the first time the
convergence of stochastic asynchronous optimization for this general class of
objectives. In particular, we focus on stochastic subgradient methods allowing
for block variable partitioning, where the shared-memory-based model is
asynchronously updated by concurrent processes. To this end, we first introduce
a probabilistic model which captures key features of real asynchronous
scheduling between concurrent processes; under this model, we establish
convergence with probability one to an invariant set for stochastic subgradient
methods with momentum.
From the practical perspective, one issue with the family of methods we
consider is that it is not efficiently supported by machine learning
frameworks, as they mostly focus on distributed data-parallel strategies. To
address this, we propose a new implementation strategy for shared-memory based
training of deep neural networks, whereby concurrent parameter servers are
utilized to train a partitioned but shared model in single- and multi-GPU
settings. Based on this implementation, we achieve on average 1.2x speed-up in
comparison to state-of-the-art training methods for popular image
classification tasks without compromising accuracy
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