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View article: Integration of LLMs for Multitasking Workload Prediction in Mixed Reality Environments
Integration of LLMs for Multitasking Workload Prediction in Mixed Reality Environments Open
Multitasking in mixed reality (MR) environments introduces unique cognitive demands, particularly in workload management. Accurate workload prediction is critical for optimizing user experience, safety, and performance in such settings. Th…
View article: Evaluating Large Language Models for Turboshaft Engine Torque Prediction
Evaluating Large Language Models for Turboshaft Engine Torque Prediction Open
Recent advancements in deep learning have introduced new opportunities for quality management in manufacturing, particularly through transformer-based architectures capable of learning from limited datasets and handling complex, multimodal…
View article: Comprehensive assessment of privacy security of financial services in cloud environment
Comprehensive assessment of privacy security of financial services in cloud environment Open
In recent years, the financial industry has become a disaster area for information leakage, which has serious implications for user privacy security. In the absence of risk identification and assessment, the risk will be difficult to preve…
View article: Transfer Learning with CLIP Model for Multitasking Workload Prediction in Mixed Reality Environments
Transfer Learning with CLIP Model for Multitasking Workload Prediction in Mixed Reality Environments Open
Multitasking has become an important part of our modern life, especially when interacting with new technologies such as mixed reality (MR). Predicting human workload in MR environments is crucial for optimizing the user experience. This st…
View article: Neural Network-Adaptive Secure Control for Nonlinear Cyber-Physical Systems Against Adversarial Attacks
Neural Network-Adaptive Secure Control for Nonlinear Cyber-Physical Systems Against Adversarial Attacks Open
The “insecurity of the network” characterizes each agent as being remotely controlled through unreliable network channels. In such an insecure network, the output signal can be altered through carefully designed adversarial attacks to prod…
View article: Full Ceramic Bearing Fault Diagnosis with Few-Shot Learning Using GPT-2
Full Ceramic Bearing Fault Diagnosis with Few-Shot Learning Using GPT-2 Open
View article: How Numerical Precision Affects Arithmetical Reasoning Capabilities of LLMs
How Numerical Precision Affects Arithmetical Reasoning Capabilities of LLMs Open
View article: Across the Spectrum: A Study of Autism in National Survey Data Using Machine Learning
Across the Spectrum: A Study of Autism in National Survey Data Using Machine Learning Open
Autism, a neurological disorder and developmental impairment, affects roughly 1 in 36 children in the US. However, relatively few machine learning algorithms, the majority being Logistic Regression models, have been used to predict autism …
View article: Overview and trend analysis of global hot spring research based on bibliometrics and knowledge graph visualization
Overview and trend analysis of global hot spring research based on bibliometrics and knowledge graph visualization Open
Examining utilization patterns, conducting compositional tests, investigating therapeutic mechanisms, and scrutinizing geographical disparities aid in enhancing the comprehension of hot springs for medical applications. This validates the …
View article: How Numerical Precision Affects Arithmetical Reasoning Capabilities of LLMs
How Numerical Precision Affects Arithmetical Reasoning Capabilities of LLMs Open
Despite the remarkable success of Transformer-based large language models (LLMs) across various domains, understanding and enhancing their mathematical capabilities remains a significant challenge. In this paper, we conduct a rigorous theo…
View article: Knowledge-driven AI-generated data for accurate and interpretable breast ultrasound diagnoses
Knowledge-driven AI-generated data for accurate and interpretable breast ultrasound diagnoses Open
Data-driven deep learning models have shown great capabilities to assist radiologists in breast ultrasound (US) diagnoses. However, their effectiveness is limited by the long-tail distribution of training data, which leads to inaccuracies …
View article: Few-shot Learning for Plastic Bearing Fault Diagnosis – An Integrated Image Processing and NLP Approach
Few-shot Learning for Plastic Bearing Fault Diagnosis – An Integrated Image Processing and NLP Approach Open
Plastic bearings have a wide range of industrial applications due to their many desirable properties such as lightweight, low friction coefficient, chemical resistance, and ability to operate without lubrication. Timely bearing fault diagn…
View article: Fusion with Joint Distribution and Adversarial Networks: A New Transfer Learning Approach for Intelligent Fault Diagnosis
Fusion with Joint Distribution and Adversarial Networks: A New Transfer Learning Approach for Intelligent Fault Diagnosis Open
Bearings and gears are important components in rotating machinery, and the diagnosis of faults in bearings and gears has always been an important topic. Currently, data-driven fault diagnosis is a better method. However, under actual worki…
View article: Few-Shot Learning for Full Ceramic Bearing Fault Diagnosis with Acoustic Emission Signals
Few-Shot Learning for Full Ceramic Bearing Fault Diagnosis with Acoustic Emission Signals Open
Full ceramic bearings are critical components in many full ceramic and oil-free food processing and medical equipment. Developing effective full ceramic fault diagnostic methods is important. Supervised deep learning approaches have been c…
View article: A dynamic mode decomposition based deep learning technique for prognostics
A dynamic mode decomposition based deep learning technique for prognostics Open
View article: Special Issue on Wind Turbine Prognostics and Health Management
Special Issue on Wind Turbine Prognostics and Health Management Open
WIND POWER generating capacity was 239 GW at the end of 2011, with a further 46 GW of installed capacity to be operational by the end of 2012. While only providing 2.8% of the energy produced in the United States, it is anticipated that by…
View article: A New Acoustic Emission Sensor Based Gear Fault Detection Approach
A New Acoustic Emission Sensor Based Gear Fault Detection Approach Open
In order to reduce wind energy costs, prognostics and health management (PHM) of wind turbine is needed to reduce operations and maintenance cost of wind turbines. The major cost on wind turbine repairs is due to gearbox failure. Therefore…
View article: Wind Turbine Prognostics and Health Management
Wind Turbine Prognostics and Health Management Open
Wind power generating capacity was 239 GW at the end of 2011, with a further 46 GW of installed capacity to be operational by the end of 2012. While only providing 2.8% of the energy produce in the United States, its is anticipated that by…
View article: Lubrication Oil Condition Monitoring and Remaining Useful Life Prediction with Particle Filtering
Lubrication Oil Condition Monitoring and Remaining Useful Life Prediction with Particle Filtering Open
In order to reduce the costs of wind energy, it is necessary to improve the wind turbine availability and reduce the operational and maintenance costs. The reliability and availability of a functioning wind turbine depend largely on the pr…
View article: Dynamic characteristics and reliability analysis of ball screw feed system on a lathe
Dynamic characteristics and reliability analysis of ball screw feed system on a lathe Open
View article: A Bayesian Optimization AdaBN-DCNN Method With Self-Optimized Structure and Hyperparameters for Domain Adaptation Remaining Useful Life Prediction
A Bayesian Optimization AdaBN-DCNN Method With Self-Optimized Structure and Hyperparameters for Domain Adaptation Remaining Useful Life Prediction Open
The prediction of remaining useful life (RUL) of mechanical equipment provides a timely understanding of the equipment degradation and is critical for predictive maintenance of the equipment. In recent years, the applications of deep learn…
View article: Multiple data centers intended for latency minimization using artificial intelligence algorithms
Multiple data centers intended for latency minimization using artificial intelligence algorithms Open
Lightweight deep learning data and algorithms center technology have recently advanced to the point where multiple inferences of models tasks can be executed simultaneously on limited data center resources. This allows us to work together …
View article: Static compression testing CFRP single-lap composited joints using X-ray μCT
Static compression testing CFRP single-lap composited joints using X-ray μCT Open
View article: Regularized Deep Clustering Method for Fault Trend Analysis
Regularized Deep Clustering Method for Fault Trend Analysis Open
Effective fault feature extraction is the key of fault diagnosis. In previous works, it is shown that some embedding methods and unsupervised deep learning methods have the ability to extract fault features from raw signals directly, such …
View article: Residual stress relaxation and duty cycle on high cycle fatigue life of micro-arc oxidation coated AA7075-T6 alloy
Residual stress relaxation and duty cycle on high cycle fatigue life of micro-arc oxidation coated AA7075-T6 alloy Open
View article: A domain adaptation model for early gear pitting fault diagnosis based on deep transfer learning network
A domain adaptation model for early gear pitting fault diagnosis based on deep transfer learning network Open
In recent years, research on gear pitting fault diagnosis has been conducted. Most of the research has focused on feature extraction and feature selection process, and diagnostic models are only suitable for one working condition. To diagn…
View article: Gear pitting fault diagnosis using raw acoustic emission signal based on deep learning
Gear pitting fault diagnosis using raw acoustic emission signal based on deep learning Open
Gear pitting fault is one of the most common faults in mechanical transmission. Acoustic emission (AE) signals have been effective for gear fault detection because they are less affected by ambient noise than traditional vibration signals.…
View article: Beyond Securities Fraud
Beyond Securities Fraud Open
In Morrison v. National Australia Bank, the Supreme Court sent a clear signal that the presumption against extraterritorial application of federal legislation must be construed seriously going forward. The Court then adopted a transaction-…
View article: Semi-supervised gear fault diagnosis using raw vibration signal based on deep learning
Semi-supervised gear fault diagnosis using raw vibration signal based on deep learning Open
View article: Gear Pitting Fault Diagnosis Using Integrated CNN and GRU Network with Both Vibration and Acoustic Emission Signals
Gear Pitting Fault Diagnosis Using Integrated CNN and GRU Network with Both Vibration and Acoustic Emission Signals Open
This paper deals with gear pitting fault diagnosis problem and presents a method by integrating convolutional neural network (CNN) and gated recurrent unit (GRU) networks with vibration and acoustic emission signals to solve the problem. T…