19 citations · 19 across the 9 of their papers we have counts for
9 papers
Analysis of Interpolating Regression Models and the Double Descent Phenomenon
Tomas McKelvey
A regression model with more parameters than data points in the training data is overparametrized and has the capability to interpolate the training data. Based on the classical bi…
Cold-Start Modeling and On-Line Optimal Control of the Three-Way Catalyst
Jonathan Lock, Kristoffer Clasén, Jonas Sjöblom +1
We present a three-way catalyst (TWC) cold-start model, calibrate the model based on experimental data from multiple operating points, and use the model to generate a Pareto-optima…
Undiscounted Control Policy Generation for Continuous-Valued Optimal Control by Approximate Dynamic Programming
Jonathan Lock, Tomas McKelvey
We present a numerical method for generating the state-feedback control policy associated with general undiscounted, constant-setpoint, infinite-horizon, nonlinear optimal control…
Design and comparative analyses of optimal feedback controllers for hybrid electric vehicles
Maryam Razi, Nikolce Murgovski, Tomas McKelvey +1
This paper presents an adaptive equivalent consumption minimization strategy (ECMS) and a linear quadratic tracking (LQT) method for optimal power-split control of combustion engin…
Improving Linear State-Space Models with Additional Iterations
Suat Gumussoy, Ahmet Arda Ozdemir, Tomas McKelvey +3
An estimated state-space model can possibly be improved by further iterations with estimation data. This contribution specifically studies if models obtained by subspace estimation…
Learning Hierarchical Feature Space Using CLAss-specific Subspace Multiple Kernel -- Metric Learning for Classification
Yinan Yu, Tomas McKelvey
Metric learning for classification has been intensively studied over the last decade. The idea is to learn a metric space induced from a normed vector space on which data from diff…
Building Efficient CNNs Using Depthwise Convolutional Eigen-Filters (DeCEF)
Yinan Yu, Samuel Scheidegger, Tomas McKelvey
Deep Convolutional Neural Networks (CNNs) have been widely used in various domains due to their impressive capabilities. These models are typically composed of a large number of 2D…
Transmission Strategies for Remote Estimation with an Energy Harvesting Sensor
Ayca Ozcelikkale, Tomas McKelvey, Mats Viberg
We consider the remote estimation of a time-correlated signal using an energy harvesting (EH) sensor. The sensor observes the unknown signal and communicates its observations to a…
Performance Bounds for Remote Estimation under Energy Harvesting Constraints
Ayca Ozcelikkale, Tomas McKelvey, Mats Viberg
Remote estimation with an energy harvesting sensor with a limited data and energy buffer is considered. The sensor node observes an unknown Gaussian field and communicates its obse…