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Squirrel: A Switching Hyperparameter Optimizer
[article]
2020
arXiv
pre-print
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Motivated by the fact that different optimizers work well on different problems, our approach switches between different optimizers. ...
Squirrel, or here, short, Squirrel. ...
In our Squirrel framework, we switched between the following components: 1. ...
arXiv:2012.08180v2
fatcat:bbokv2bcbza45hsdqt2vikpdeu
ML models for real-time hybrid systems
[chapter]
2021
XLII JORNADAS DE AUTOMÁTICA : LIBRO DE ACTAS
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A correct system design can be systematically obtained from a specification model of a real-time system that integrates hybrid measurements In a realistic industrial environment, this has been carried ...
An AC motor controller which must be able to maintain a constant air flow through a filter is one of these systems. ...
basis for obtaining a great improvement over other approaches currently more used for example, obtaining hyperparameters using Bayesian optimization (HPO) [1] . ...
doi:10.17979/spudc.9788497498043.752
fatcat:jrnpw6nlubegfgaubnmnsrcbme
Condition Monitoring and Feature Extraction of Stator Current Phasors for Enhanced Fault Diagnosis in AC Drive
2021
International Journal of Engineering and Advanced Technology
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Variable Frequency Drives (VFD) comprises of Voltage source inverter and a three phase squirrel cage induction motor. ...
Process industries have seen a paradigm shift from manual control to automatic control. ...
A diode bridge rectifier is used to obtain dc voltage. The dc link is required to maintain the dc voltage supplied to the inverter. IGBT switches are used in the VSI. ...
doi:10.35940/ijeat.a3173.1011121
fatcat:agqxza3vifgjjplkr7dggcsgyi
A Video Captioning Method Based on Multi-Representation Switching for Sustainable Computing
2021
Sustainability
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To reflect this characteristic explicitly using a deep learning model, we propose a multi-representation switching method. ...
The proposed multi-representation switching method makes it possible for the three components to extract important information for a given video and description pair efficiently. ...
The proposed method with the optimal single parameters, θ * s , is expressed as "Multi-Representation Switching with single parameter (MRS-s)" and the methods with the optimal ensemble parameters with ...
doi:10.3390/su13042250
fatcat:y5lvtjmdrjbtfigd3ooiu4rnne
Optimization-Induced Graph Implicit Nonlinear Diffusion
[article]
2022
arXiv
pre-print
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As a result, our GIND obtains significant improvements on both node-level and graph-level tasks. ...
More interestingly, we can induce new structural variants by modifying the corresponding optimization objective. ...
Switching the direction of an edge k = (i, j) is equivalent to multiplying a matrix E k to the left of G, where E k is obtained by switching the (k, k)-th element of an identity matrix to −1. ...
arXiv:2206.14418v1
fatcat:mnyu4qqpwbgllpzrzjkzcnrqgq
Deep convolutional neural networks for automatic coil pitches detection systems in induction motors
2021
Journal of Electrical Engineering
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A system that can detect the stator coil pitch according to the stator current behavior while the motor is running can significantly shorten this improvement process. ...
For this purpose, in this study, a deep convolutional neural network (DCNN) model that can automatically estimate IM stator coil pitch angle with an accuracy rate of 100% is designed and applied. ...
In one study, it was found that an IM performs higher when fed with variable PWM switching frequency by shortening the coil pitch in a double layer stator winding [14] . ...
doi:10.2478/jee-2021-0027
fatcat:mgolomzinbeahpgdjxnmhjhy44
A Biased Graph Neural Network Sampler with Near-Optimal Regret
[article]
2021
arXiv
pre-print
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And unlike prior bandit-GNN use cases, the resulting policy leads to near-optimal regret while accounting for the GNN training dynamics introduced by SGD. ...
To address these issues, we build upon existing work and treat GNN neighbor sampling as a multi-armed bandit problem but with a newly-designed reward function that introduces some degree of bias designed ...
GCN GAT Algorithm γ η ∆T γ η ∆T Thanos 0.4 0.01 1000 0.4 0.01 1000 BanditSampler 0.4 0.01 N/A 0.4 0.01 N/A
Table 5 : 5 The detailed sampling hyperparameters for Squirrel. ...
arXiv:2103.01089v3
fatcat:6qfdpolqp5ag7bt7qvdm3kufty
Thermal Monitoring of Electric Motors: State-of-the-Art Review and Future Challenges
2021
IEEE Open Journal of Industry Applications
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In order to optimize the dynamic performance limits of a motor during online operation, important motor temperatures must be known in real time. ...
In recent years, many promising contributions have been made to this field leading to a vast literature basis. ...
For many ML models, therefore, a hierarchical optimization task results on two levels [114] : On the top level, suitable hyperparameters of the ML model class have to be found and on the lower level, ...
doi:10.1109/ojia.2021.3091870
fatcat:gyp462sx4bgu3lb554eowwuhyq
An End-to-End: Real-Time Solution for Condition Monitoring of Wind Turbine Generators
2020
Energies
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We show how to create, deploy and run the collection of proposed models in a real-time setting using a laptop connected to a test rig via a data acquisition card. ...
Based on a sampling rate of 5 kHz, predictions are stored in an efficient time series database and monitored using a dynamic visualization framework. ...
From a Three-Phase Squirrel Cage Induction Motor, they obtained 2600 of healthy and 2600 of faulty bearing samples. ...
doi:10.3390/en13184817
fatcat:m6jmsckszzdevbdcwooiqegf3y
Mini Cheetah, the Falling Cat: A Case Study in Machine Learning and Trajectory Optimization for Robot Acrobatics
[article]
2022
arXiv
pre-print
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2021 pre-print arXiv:2109.04424v1 fatcat:4rkkpjlg5fe67lemim6jfb7pfa
Specifically, we explore how trajectory optimization and machine learning can work together to enable highly dynamic bioinspired behaviors. ...
We find that a reflex approach, in which a neural network learns entire state trajectories, outperforms a policy approach, in which a neural network learns a mapping from states to control inputs. ...
Once we are ready to drop the robot, a switch on the remote controller indicates to the robot that it should start trying to detect a fall. ...
arXiv:2109.04424v2
fatcat:wp2lmeklqfaxbnks3utp45s4le
Prediction of wind farm reactive power fast variations by adaptive one-dimensional convolutional neural network
2021
Computers & electrical engineering
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Link to publication General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of ...
Additionally, to set the hyperparameters of the designed adaptive deep network, the root mean square propagation (RMSProp) has been adopted from [23] to find the optimal values for hyperparameters. ...
Despite these advantages, SVC struggles with a major challenge caused by switching limitations, which leads to a time delay (about half-cycle). ...
doi:10.1016/j.compeleceng.2021.107480
fatcat:xqnjszr22vh4hpfcunhb5wxccq
Towards Neural Programming Interfaces
[article]
2021
arXiv
pre-print
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of programs by altering hyperparameters. ...
In this new paradigm, a specialized neural network (called a Neural Programming Interface or NPI) learns to interface with a pretrained language model by manipulating the hidden activations of the pretrained ...
Note to train NPI on the avoidance task we simply switch the objective in Equation (5) . ...
arXiv:2012.05983v2
fatcat:4tpggy33uzdu3myvq2twly3quy
Increasing Electric Vehicles Reliability by Non-Invasive Diagnosis of Motor Winding Faults
2021
Energies
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Phase current waveforms obtained from a real object as well as simulation results based on the field-circuit model of an induction motor were used as a diagnostic signal in the research. ...
The article proposes a proprietary approach to the diagnosis of induction motors allowing increasing the reliability of electric vehicles. ...
In the process of hyperparameter optimization, a nested five-fold cross-validation was used to avoid a "skewed" assessment of a concrete model with specific hyperparameter values (the model would be evaluated ...
doi:10.3390/en14092510
fatcat:iiqjwbo2ibapdicoky5boqtxbe
Image Shape Manipulation from a Single Augmented Training Sample
[article]
2021
arXiv
pre-print
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2021 pre-print arXiv:2109.06151v2 fatcat:uj7dlsbndzadrj5rvvp7zqkytqOther Versions
In this paper, we present DeepSIM, a generative model for conditional image manipulation based on a single image. ...
Our network learns to map between a primitive representation of the image to the image itself. ...
Optimization: During training, we sample random TPS warps. ...
arXiv:2109.06151v3
fatcat:pvc3q3siqjgxbltfwno4o2geuu
Efficient context-dependent model building based on clustering posterior distributions for non-coding sequences
2009
BMC Evolutionary Biology
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The selected model's fit is evaluated using a Bayes Factor, which we calculate via model-switch thermodynamic integration. ...
To obtain an optimal balance between the number of parameters in a context-dependent model and the performance in terms of model fit, we have designed two parameter-reduction strategies and we have shown ...
hyperparameter in the prior for t b in T. ...
doi:10.1186/1471-2148-9-87
pmid:19405957
pmcid:PMC2695821
fatcat:zjcfkg4f6rbl7kdl3hg373ljze
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