相关论文: Conditional Nearest Level Modulation for Improved …

We present a robust adaptive beamforming algorithm based on the worst-case criterion and the constrained constant modulus approach, which exploits the constant modulus property of the desired signal. Similarly to the existing worst-case…

Sample-based learning model predictive control (LMPC) strategies have recently attracted attention due to their desirable theoretical properties and their good empirical performance on robotic tasks. However, prior analysis of LMPC…

This paper presents design and control of dual-switch non-inverting buck-boost converter (CBB). This converter is designed to simplify the compatibility of electronic ballast with simple and low cost LED drivers. The converter provides…

We introduce a constrained optimization framework for training transformers that behave like optimization descent algorithms. Specifically, we enforce layerwise descent constraints on the objective function and replace standard empirical…

Latent class model (LCM), which is a finite mixture of different categorical distributions, is one of the most widely used models in statistics and machine learning fields. Because of its non-continuous nature and the flexibility in shape,…

This paper presents a hybrid switched-capacitor converter (HSCC) with a novel multi-mode modulation (3M) scheme for wide-range voltage regulation in 48-V automotive power systems. By introducing a three-state operating sequence beyond the…

The standard normalization method for neural network (NN) models used in Natural Language Processing (NLP) is layer normalization (LN). This is different than batch normalization (BN), which is widely-adopted in Computer Vision. The…

Transformers trained in low precision can suffer forward-error amplification. We give a first-order, module-wise theory that predicts when and where errors grow. For self-attention we derive a per-layer bound that factorizes into three…

We propose a general model-free strategy for feedback control design of turbulent flows. This strategy called 'machine learning control' (MLC) is capable of exploiting nonlinear mechanisms in a systematic unsupervised manner. It relies on…

We show that Funnel MPC, a novel Model Predictive Control (MPC) scheme, allows tracking of smooth reference signals with prescribed performance for nonlinear multi-input multi-output systems of relative degree one with stable internal…

Transistor-level simulation plays a vital role in validating the physical correctness of integrated circuits. However, such simulations are computationally expensive. This paper proposes three novel reduction methods specifically tailored…

This paper introduces a novel approach to system identification for nonlinear input-output models that minimizes the simulation error and frames the problem as a constrained optimization task. The proposed method addresses vanishing…

The objective of this research is to mitigate vibrations in induction motors. To achieve this goal, a discontinuous pulse width modulation (PWM) control strategy based on carrier wave modulation is proposed for multilevel inverters. This…

We propose a new field-level emulator that bridges two simulators using unpaired simulation datasets. Our method leverages a flow-based approach to learn the likelihood transport from one simulator to the other. Since multiple transport…

Public Safety Power Shutoffs (PSPS) force rapid topology changes that can render standard operating points infeasible, requiring operators to quickly identify corrective transmission switching actions that reduce load shedding while…

A selection switch based cell-to-cell voltage equalizer requires only one dual-port dc-dc converter shared by all the cells. A cell-to-cell voltage equalizer is proposed that utilizes a capacitively level-shifted Cuk converter and…

This article is the second part of a paper series about interference in extremum (i.e., peak or valley) current-mode control, which applies to both fixed and variable switching frequency power converters. Specifically, this part presents…

In this paper, we propose an analog network coding scheme with differential modulation (ANC-DM) using amplify-and-forward protocol for bidirectional relay networks when neither the source nodes nor the relay knows the channel state…

This paper presents a new deep learning-based framework for robust nonlinear estimation and control using the concept of a Neural Contraction Metric (NCM). The NCM uses a deep long short-term memory recurrent neural network for a global…

Few-shot learning aims to learn representations that can tackle novel tasks given a small number of examples. Recent studies show that cross-modal learning can improve representations for few-shot classification. More specifically, language…