Asok Ray
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View article: A perspective on the safety of large-format lithium-ion battery in underground mining operations: Thermal runaway and its mitigation
A perspective on the safety of large-format lithium-ion battery in underground mining operations: Thermal runaway and its mitigation Open
The use of lithium-ion batteries (LIBs)-operated underground mining machinery has gained tremendous momentum as a result of their numerous advantages. This includes their high energy density, long cycle life, high voltage delivery, low hea…
View article: Quality Improvement using Six Sigma Methodology for Pre-Cast Structures
Quality Improvement using Six Sigma Methodology for Pre-Cast Structures Open
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View article: Enhancing Overall Equipment Effectiveness (OEE) of a Selected Machine in a Light Manufacturing Factory in Bangladesh
Enhancing Overall Equipment Effectiveness (OEE) of a Selected Machine in a Light Manufacturing Factory in Bangladesh Open
Overall equipment effectiveness provides reliable and considerable performance indicators for machinery performance. This research aimed to measure the OEE of the selected machine, identify the reasons behind its poor performance, and take…
View article: Convolutional Neural Network for Risk Assessment in Polycrystalline Alloy Structures via Ultrasonic Testing
Convolutional Neural Network for Risk Assessment in Polycrystalline Alloy Structures via Ultrasonic Testing Open
In the current state of the art of process industries/manufacturing technologies, computer-instrumented and computer-controlled autonomous techniques are necessary for damage diagnosis and prognosis in operating machinery. From this perspe…
View article: A Unified Mixed Deep Neural Network for Fatigue Damage Detection in Components with Different Stress Concentrations
A Unified Mixed Deep Neural Network for Fatigue Damage Detection in Components with Different Stress Concentrations Open
The article presents a mixed deep neural network (DNN) approach for detecting micron-scale fatigue damage in high-strength polycrystalline aluminum alloys. Fatigue testing is conducted using a custom-designed apparatus integrated with a co…
View article: Assessment of Transfer Learning Capabilities for Fatigue Damage Classification and Detection in Aluminum Specimens with Different Notch Geometries
Assessment of Transfer Learning Capabilities for Fatigue Damage Classification and Detection in Aluminum Specimens with Different Notch Geometries Open
Fatigue damage detection and its classification in metallic materials are persistently challenging the structural health monitoring community. The mechanics of fatigue damage is difficult to analyze and is further complicated because of th…
View article: A Concise Tutorial on Functional Analysis for Applications to Signal Processing
A Concise Tutorial on Functional Analysis for Applications to Signal Processing Open
Functional analysis is a well-developed field in the discipline of Mathematics, which provides unifying frameworks for solving many problems in applied sciences and engineering. In particular, several important topics (e.g., spectrum estim…
View article: Feature Selection of Surface Topography Parameters for Fatigue Damage Detection Using Pearson Correlation Method and Neural Network Analysis
Feature Selection of Surface Topography Parameters for Fatigue Damage Detection Using Pearson Correlation Method and Neural Network Analysis Open
The global objective of this study was to investigate the best features of the surface topography for fatigue damage detection and classification. The presence of the stress concentration in valleys of the surface topography causes a grain…
View article: Spectral invariants of ergodic symbolic systems for pattern recognition and anomaly detection
Spectral invariants of ergodic symbolic systems for pattern recognition and anomaly detection Open
Despite tangible advances in machine learning (ML) over the last few decades, many of the ML techniques still suffer from fundamental issues like overfitting and lack of explainability. These issues mandate requirements for mathematical ri…
View article: A Data-Driven Framework for Early-Stage Fatigue Damage Detection in Aluminum Alloys Using Ultrasonic Sensors
A Data-Driven Framework for Early-Stage Fatigue Damage Detection in Aluminum Alloys Using Ultrasonic Sensors Open
The paper presents a coupled machine learning and pattern recognition algorithm to enable early-stage fatigue damage detection in aerospace-grade aluminum alloys. U- and V-notched Al7075-T6 specimens are instrumented with a pair of ultraso…
View article: Homeostasis and Homeorhesis: Sustaining Order and Normalcy in Human-Engineered Complex Systems
Homeostasis and Homeorhesis: Sustaining Order and Normalcy in Human-Engineered Complex Systems Open
This letter explores the notions of biological homeostasis and homeorhesis for construction of hybrid decision & control laws for human-engineered complex systems. The complexity may remain dormant during normal operations, but it becomes …
View article: Transfer learning of deep neural networks for predicting thermoacoustic instabilities in combustion systems
Transfer learning of deep neural networks for predicting thermoacoustic instabilities in combustion systems Open
The intermittent nature of operation and unpredictable availability of renewable sources of energy (e.g., wind and solar) would require the combustors in fossil-fuel power plants, sharing the same grid, to operate with large turn-down rati…
View article: Transfer Learning for Detection of Combustion Instability Via Symbolic Time-Series Analysis
Transfer Learning for Detection of Combustion Instability Via Symbolic Time-Series Analysis Open
Transfer learning (TL) is a machine learning (ML) tool where the knowledge, acquired from a source domain, is “transferred” to perform a task in a target domain that has (to some extent) a similar setting. The underlying concept does not r…
View article: Identification of Long-Term Behavior of Natural Circulation Loops: A Thresholdless Approach from an Initial Response
Identification of Long-Term Behavior of Natural Circulation Loops: A Thresholdless Approach from an Initial Response Open
Natural circulation loop (NCL) systems are buoyancy-driven heat exchangers that are used in various industrial applications. The concept of passive heat exchange in NCL systems is attractive, because there is no need for an externally driv…
View article: Early Detection of Fatigue Crack Damage in Ductile Materials: A Projection-Based Probabilistic Finite State Automata Approach
Early Detection of Fatigue Crack Damage in Ductile Materials: A Projection-Based Probabilistic Finite State Automata Approach Open
Fatigue failure occurs ubiquitously in mechanical structures when they are subjected to cyclic loading well below the material’s yield stress. The tell-tale sign of a fatigue failure is the emergence of cracks at the internal or surface de…
View article: Identification of Long-term Behavior of Natural Circulation Loops: A Thresholdless Approach from an Initial Response
Identification of Long-term Behavior of Natural Circulation Loops: A Thresholdless Approach from an Initial Response Open
Natural circulation loop (NCL) systems are buoyancy-driven heat exchangers that are used in various industrial applications. The concept of passive heat exchange in NCL systems is attractive, because there is no need for an externally driv…
View article: Neural Network-Based Automated Assessment of Fatigue Damage in Mechanical Structures
Neural Network-Based Automated Assessment of Fatigue Damage in Mechanical Structures Open
This paper proposes a methodology for automated assessment of fatigue damage, which has been tested and validated with polycrystalline-alloy (Aℓ7075-T6) specimens on an experimental apparatus. Based on an ensemble of time series of ultraso…
View article: Temporal Learning in Video Data Using Deep Learning and Gaussian Processes
Temporal Learning in Video Data Using Deep Learning and Gaussian Processes Open
This paper presents an approach for data-driven modeling of hidden, stationary temporal dynamics in sequential images or videos using deep learning and Bayesian non-parametric techniques. In particular, a deep Convolutional Neural Network …
View article: Data-driven Detection and Early Prediction of Thermoacoustic Instability in a Multi-nozzle Combustor
Data-driven Detection and Early Prediction of Thermoacoustic Instability in a Multi-nozzle Combustor Open
Thermoacoustic instability (TAI) is a critical issue in modern lean-burn gas-turbine combustors, which is induced by a strong coupling between the resonant combustor acoustics and fluctuations in the heat release rate. This instability may…
View article: On Singular Perturbation of Neutron Point Kinetics in the Dynamic Model of a PWR Nuclear Power Plant
On Singular Perturbation of Neutron Point Kinetics in the Dynamic Model of a PWR Nuclear Power Plant Open
This short communication makes use of the principle of singular perturbation to approximate the ordinary differential equation (ODE) of prompt neutron (in the point kinetics model) as an algebraic equation. This approximation is shown to y…
View article: Investigation of Melt Pool Geometry Control in Additive Manufacturing Using Hybrid Modeling
Investigation of Melt Pool Geometry Control in Additive Manufacturing Using Hybrid Modeling Open
Metal additive manufacturing (AM) works on the principle of consolidating feedstock material in layers towards the fabrication of complex objects through localized melting and resolidification using high-power energy sources. Powder bed fu…
View article: On Singular Perturbation of Neutron Point Kinetics in the Dynamic Model of a PWR Nuclear Power Plant
On Singular Perturbation of Neutron Point Kinetics in the Dynamic Model of a PWR Nuclear Power Plant Open
This short communication makes use of the principle of singular perturbation to approximate the ordinary differential equation (ODE) of prompt neutron (in the point kinetics model) as an algebraic equation. This approximation is shown to y…
View article: Early Prediction of Lean Blowout from Chemiluminescence Time Series Data
Early Prediction of Lean Blowout from Chemiluminescence Time Series Data Open
Lean combustion systems are usually employed in gas-turbine engines, particularly in land-based applications, to reduce NOx emission. These combustion systems are often susceptible to lean blowout (LBO), which can be detrimental for operat…
View article: A Dynamically Controlled Recurrent Neural Network for Modeling Dynamical Systems
A Dynamically Controlled Recurrent Neural Network for Modeling Dynamical Systems Open
This work proposes a novel neural network architecture, called the Dynamically Controlled Recurrent Neural Network (DCRNN), specifically designed to model dynamical systems that are governed by ordinary differential equations (ODEs). The c…
View article: Neural Network-Based Learning from Demonstration of an Autonomous Ground Robot
Neural Network-Based Learning from Demonstration of an Autonomous Ground Robot Open
This paper presents and experimentally validates a concept of end-to-end imitation learning for autonomous systems by using a composite architecture of convolutional neural network (ConvNet) and Long Short Term Memory (LSTM) neural network…
View article: State-Space Representations of Deep Neural Networks
State-Space Representations of Deep Neural Networks Open
This letter deals with neural networks as dynamical systems governed by finite difference equations. It shows that the introduction of [Formula: see text]-many skip connections into network architectures, such as residual networks and addi…
View article: Analysis of Fatigue Crack Evolution Using In-Situ Testing
Analysis of Fatigue Crack Evolution Using In-Situ Testing Open
The objective of the current work is to investigate the feasibility of an in-situ technique to characterize the evolution of fatigue failure in conventionally manufactured aluminum parts in real time. An in-situ fatigue testing setup integ…