Documentos de Víctor M. Zavala | REDI

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Área de conocimiento: "Aprendizaje automático"

Subtipo de publicación

Conference Object(1)

Publisher

Journal of Process Control(3)

Computers and Chemical Engineering(2)

ACS Sustainable Chemistry and Engineering(1)

AIChE Journal(1)

Área temáticas

Métodos informáticos especiales(9)

Física aplicada(7)

Ciencias de la computación(4)

Química física(3)

Electricidad y electrónica(1)

Área de conocimiento

Ciencias de la computación(5)

Ciencia de materiales(3)

Optimización matemática(3)

Objetivos de Desarrollo Sostenible

ODS 9: Industria, innovación e infraestructura(15)

ODS 12: Producción y consumo responsables(11)

ODS 7: Energía asequible y no contaminante(6)

ODS 17: Alianzas para lograr los objetivos(3)

ODS 3: Salud y bienestar(3)

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Palabras Claves

Machine learning(5)

classification(3)

Dynamic penalty(2)

Liquid crystals(2)

A dynamic penalty approach to state constraint handling in deep reinforcement learning

Abstract: Deep reinforcement learning (RL) has emerged as a promising approach to solving sequential optimizat

Palabras claves:

Constraint handling, Dynamic penalty, Penalty approach, reinforcement learning

Lee J.H., Víctor M. Zavala, Yoo H.

A dynamic penalty function approach for constraint-handling in reinforcement learning

Conference Object

Abstract: Reinforcement learning (RL) is attracting attention as an effective way to solve sequential optimiza

Palabras claves:

Constraints, Dynamic penalty, Penalty approach, reinforcement learning

Lee J.H., Víctor M. Zavala, Yoo H.

A machine learning framework for the analysis and prediction of catalytic activity from experimental data

Abstract: We present a machine learning framework to explore the predictability limits of catalytic activity f

Palabras claves:

Catalysis, Data Analysis, High-dimensional, Machine learning, pbkp_redictability

Dumesic J., Huber G.W., Keane A., Smith A.D., Víctor M. Zavala

Bayesian optimization with reference models: A case study in MPC for HVAC central plants

Abstract: We present a framework for exploiting reference models in Bayesian optimization (BO). Our approach i

Palabras claves:

Bayesian optimization, HVAC Plants, MPC Tuning, Reference models

González L.D., Kumar R., Lu Q., Víctor M. Zavala

Convolutional neural nets in chemical engineering: Foundations, computations, and applications

Abstract: In this article, we review the mathematical foundations of convolutional neural nets (CNNs) with the

Palabras claves:

Chemical Engineering, convolutional neural networks, grid data

Jiang S., Víctor M. Zavala

Accurate Characterization of Mixed Plastic Waste Using Machine Learning and Fast Infrared Spectroscopy

Abstract: We present a combination of convolutional neural network (CNN) framework and fast MIR (mid-infrared

Palabras claves:

classification, IR spectra, Machine learning, plastic waste, real-time

Bar-Ziv E., Friis S., Høgstedt L., Jiang S., Long F., Víctor M. Zavala, Zinchik S.

Fast predictions of liquid-phase acid-catalyzed reaction rates using molecular dynamics simulations and convolutional neural networks

Abstract: The rates of liquid-phase, acid-catalyzed reactions relevant to the upgrading of biomass into high-v

Palabras claves:

Chew A.K., Jiang S., Van Lehn R.C., Víctor M. Zavala, Zhang W.

Inference of building occupancy signals using moving horizon estimation and Fourier regularization

Abstract: We study the problem of estimating time-varying occupancy and ambient air flow signals using noisy c

Palabras claves:

Air flow, Carbon dioxide, FOURIER, Moving horizon estimation, occupancy, regularization

Víctor M. Zavala

SAFE-OCC: A novelty detection framework for Convolutional Neural Network sensors and its application in process control

Abstract: We present a novelty detection framework for Convolutional Neural Network (CNN) sensors that we call

Palabras claves:

Computer Vision, deep learning, Novelty detection, process control

Coutinho L.D.J., Pulsipher J.L., Soderstrom T.A., Víctor M. Zavala

Machine Learning Algorithms for Liquid Crystal-Based Sensors

Abstract: We present a machine learning (ML) framework to optimize the specificity and speed of liquid crystal

Palabras claves:

Automated, chemical sensors, Fast, Liquid crystals, Machine learning

Abbott N.L., Cao Y., Víctor M. Zavala, Yu H.