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Found 31 papers, 1 papers with code
LLM-Integrated Digital Twins for Hierarchical Resource Allocation in 6G Networks
Next-generation (NextG) wireless networks are expected to require intelligent, scalable, and context-aware radio resource management (RRM) to support ultra-dense deployments, diverse service requirements, and dynamic network conditions.
Towards Efficient Real-Time Video Motion Transfer via Generative Time Series Modeling
We propose a deep learning framework designed to significantly optimize bandwidth for motion-transfer-enabled video applications, including video conferencing, virtual reality interactions, health monitoring systems, and vision-based real-time anomaly detection.
From Automation to Autonomy in Smart Manufacturing: A Bayesian Optimization Framework for Modeling Multi-Objective Experimentation and Sequential Decision Making
The proposed framework is compared with traditional DoE methods and two other multi-objective optimization methods.
Confidence Adjusted Surprise Measure for Active Resourceful Trials (CA-SMART): A Data-driven Active Learning Framework for Accelerating Material Discovery under Resource Constraints
In recent years, active learning, where a surrogate machine learning (ML) model mimics the scientific discovery process of a human scientist, has emerged as a promising approach to address these challenges by guiding experimentation toward high-value outcomes with a limited budget.
Digital Twin Enabled Site Specific Channel Precoding: Over the Air CIR Inference
This paper investigates the significance of designing a reliable, intelligent, and true physical environment-aware precoding scheme by leveraging an accurately designed channel twin model to obtain realistic channel state information (CSI) for cellular communication systems.
In-Situ Melt Pool Characterization via Thermal Imaging for Defect Detection in Directed Energy Deposition Using Vision Transformers
Directed Energy Deposition (DED) offers significant potential for manufacturing complex and multi-material parts.
A Data-Efficient Sequential Learning Framework for Melt Pool Defect Classification in Laser Powder Bed Fusion
Ensuring the quality and reliability of Metal Additive Manufacturing (MAM) components is crucial, especially in the Laser Powder Bed Fusion (L-PBF) process, where melt pool defects such as keyhole, balling, and lack of fusion can significantly compromise structural integrity.
Advancing Healthcare: Innovative ML Approaches for Improved Medical Imaging in Data-Constrained Environments
Healthcare industries face challenges when experiencing rare diseases due to limited samples.
A Comprehensive Approach to Carbon Dioxide Emission Analysis in High Human Development Index Countries using Statistical and Machine Learning Techniques
Reducing Carbon dioxide (CO2) emission is vital at both global and national levels, given their significant role in exacerbating climate change.
Exploring the Efficacy of Group-Normalization in Deep Learning Models for Alzheimer's Disease Classification
Group Normalization computations are accurate across a wide range of batch sizes and are independent of batch size.
Enhancing UAV Security Through Zero Trust Architecture: An Advanced Deep Learning and Explainable AI Analysis
In the dynamic and ever-changing domain of Unmanned Aerial Vehicles (UAVs), the utmost importance lies in guaranteeing resilient and lucid security measures.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
Empowering Healthcare through Privacy-Preserving MRI Analysis
Integrating DL within the Federated Learning (FL) framework has yielded a methodology that offers precise and dependable diagnostics for detecting brain tumors.
An Explainable AI Framework for Artificial Intelligence of Medical Things
The healthcare industry has been revolutionized by the convergence of Artificial Intelligence of Medical Things (AIoMT), allowing advanced data-driven solutions to improve healthcare systems.
Binary Gaussian Copula Synthesis: A Novel Data Augmentation Technique to Advance ML-based Clinical Decision Support Systems for Early Prediction of Dialysis Among CKD Patients
Early prediction of dialysis is crucial as it can significantly improve patient outcomes and assist healthcare providers in making timely and informed decisions.
An Augmented Surprise-guided Sequential Learning Framework for Predicting the Melt Pool Geometry
Our study introduces a novel surprise-guided sequential learning framework, SurpriseAF-BO, signaling a significant shift in MAM.
An unsupervised approach towards promptable defect segmentation in laser-based additive manufacturing by Segment Anything
To address these issues, we construct a framework for image segmentation using a state-of-the-art Vision Transformer (ViT) based Foundation model (Segment Anything Model) with a novel multi-point prompt generation scheme using unsupervised clustering.
Accelerating material discovery with a threshold-driven hybrid acquisition policy-based Bayesian optimization
The UCB-to-EI switching policy dictated guided through continuous monitoring of the model uncertainty during each step of sequential sampling results in navigating through the MDS more efficiently while ensuring rapid convergence.
Strategic Data Augmentation with CTGAN for Smart Manufacturing: Enhancing Machine Learning Predictions of Paper Breaks in Pulp-and-Paper Production
In this article, operational data is analyzed from a paper manufacturing machine in which paper breaks are relatively rare but have a high economic impact.
ML Algorithm Synthesizing Domain Knowledge for Fungal Spores Concentration Prediction
The pulp and paper manufacturing industry requires precise quality control to ensure pure, contaminant-free end products suitable for various applications.
Building Energy Efficiency through Advanced Regression Models and Metaheuristic Techniques for Sustainable Management
In the context of global sustainability, buildings are significant consumers of energy, emphasizing the necessity for innovative strategies to enhance efficiency and reduce environmental impact.
A Data Driven Sequential Learning Framework to Accelerate and Optimize Multi-Objective Manufacturing Decisions
This metric considers both the quality of the Pareto front and the amount of data used to generate it.
Multi model LSTM architecture for Track Association based on Automatic Identification System Data
For decades, track association has been a challenging problem in marine surveillance, which involves the identification and association of vessel observations over time.
A CNN-LSTM Architecture for Marine Vessel Track Association Using Automatic Identification System (AIS) Data
During the training process, it learns and builds the trajectory for each of these underlying vessels.
A Bi-LSTM Autoencoder Framework for Anomaly Detection -- A Case Study of a Wind Power Dataset
Anomalies refer to data points or events that deviate from normal and homogeneous events, which can include fraudulent activities, network infiltrations, equipment malfunctions, process changes, or other significant but infrequent events.
Wireless Sensor Networks anomaly detection using Machine Learning: A Survey
We first introduce the characteristics of WSNs and the challenges of anomaly detection in WSNs.
Applications of Federated Learning in Manufacturing: Identifying the Challenges and Exploring the Future Directions with Industry 4.0 and 5.0 Visions
In manufacturing settings, data collection and analysis are often a time-consuming, challenging, and costly process.
Guiding the Sequential Experiments in Autonomous Experimentation Platforms through EI-based Bayesian Optimization and Bayesian Model Averaging
Afterward, we apply BMA to the same dataset by working with a set of predictive models and compare the performance of BMA with the traditional BO algorithm, which relies on a single model for approximation.
Towards Futuristic Autonomous Experimentation--A Surprise-Reacting Sequential Experiment Policy
We discuss whether it is beneficial to trade off exploitation versus exploration by measuring the element and degree of surprise associated with the immediate past observation.
Graph Regularized Autoencoder and its Application in Unsupervised Anomaly Detection
In order to make dimensionality reduction effective for high-dimensional data embedding nonlinear low-dimensional manifold, it is understood that some sort of geodesic distance metric should be used to discriminate the data samples.
A Spatio-temporal Track Association Algorithm Based on Marine Vessel Automatic Identification System Data
The proposed approach is developed as an effort to address a data association challenge in which the number of vessels as well as the vessel identification are purposely withheld and time gaps are created in the datasets to mimic the real-life operational complexities under a threat environment.
Neighborhood Structure Assisted Non-negative Matrix Factorization and its Application in Unsupervised Point-wise Anomaly Detection
Dimensionality reduction is considered as an important step for ensuring competitive performance in unsupervised learning such as anomaly detection.
