A5084179009- Electrical and Bioimpedance Tomography
- Advanced Multi-Objective Optimization Algorithms
- Metaheuristic Optimization Algorithms Research
- Advanced MRI Techniques and Applications
- Evolutionary Algorithms and Applications
- Stock Market Forecasting Methods
- E-commerce and Technology Innovations
- Advanced X-ray and CT Imaging
- Microwave Imaging and Scattering Analysis
- Particle physics theoretical and experimental studies
- Non-Invasive Vital Sign Monitoring
- Advanced Technologies in Various Fields
- Market Dynamics and Volatility
- Financial Markets and Investment Strategies
- Non-Destructive Testing Techniques
- Time Series Analysis and Forecasting
- Human Pose and Action Recognition
- Heart Rate Variability and Autonomic Control
- Computational Physics and Python Applications
- Advanced Data Processing Techniques
- Advanced Image Processing Techniques
- Industrial Technology and Control Systems
- Ultrasound Imaging and Elastography
- Advanced Technologies and Applied Computing
- Advanced Vision and Imaging
University of Connecticut
2024
Sun Yat-sen University
2024
Chiba University
2022-2024
Harbin Engineering University
2022
The integration of deep learning models into financial risk prediction and analysis has significantly transformed traditional approaches. While conventional quantitative methods often rely on simplistic metrics like maximum drawdown, the advent necessitates a more nuanced evaluation, emphasizing model's generalization ability, especially during market crises such as stock crashes. This paper explores critical aspects evaluating models' control capabilities in finance, underscoring importance...
With the digital transformation of logistics industry, smart algorithms have become a core technology to improve efficiency and reduce costs. This paper reviews development history traditional discusses key role technologies such as Internet Things, big data analysis, artificial intelligence, automation in innovation. It focuses on application intelligent path optimization, scheduling, mining prediction, warehousing. To solve problem inconsistency between training testing objectives, this...
With the digital transformation of logistics industry, smart algorithms have become a core technology to improve efficiency and reduce costs. This paper reviews development history traditional discusses key role technologies such as Internet Things, big data analysis, artificial intelligence, automation in innovation. It focuses on application intelligent path optimization, scheduling, mining prediction, warehousing. To solve problem inconsistency between training testing objectives, this...
The time-linkage property, which means that the optimization problem not only relies on current solution but also historical solutions, is common in real-world applications. Although theoretical studies multi-objective evolutionary algorithms (MOEAs) have been rapidly developed decades, there exists no analyses for MOEAs problems. This paper aims to take first step towards rigorously analyzing To be specific, we constructed a with property based benchmark COCZ problem, called COCZTL. For...
Electrical properties (EPs) of tissues facilitate early detection cancerous tissues. Magnetic resonance electrical tomography (MREPT) is a technique to non-invasively probe the EPs from MRI measurements. Most MREPT methods rely on numerical differentiation (ND) solve partial differential Equations (PDEs) reconstruct EPs. However, they are not practical for clinical data because ND noise sensitive and measurements noisy in nature. Recently, Physics informed neural networks (PINNs) have been...
The integration of deep learning models into financial risk prediction and analysis has significantly transformed traditional approaches. While conventional quantitative methods often rely on simplistic metrics like maximum drawdown, the advent necessitates a more nuanced evaluation, emphasizing model's generalization ability, especially during market crises such as stock crashes. This paper explores critical aspects evaluating models' control capabilities in finance, underscoring importance...
Electrical properties (EPs) are expected as biomarkers for early cancer detection. Magnetic resonance electrical tomography (MREPT) is a technique to non-invasively estimate the EPs of tissues from MRI measurements. While noise sensitivity and artifact problems MREPT being solved progressively through recent efforts, loss tissue contrast emerges an obstacle clinical applications MREPT. To solve problem, we propose reconstruction error compensation neural network scheme (REC-NN) typical...
This study unveils the In-Context Evolutionary Search (ICE-SEARCH) method, first work that melds language models (LMs) with evolutionary algorithms for feature selection (FS) tasks and demonstrates its effectiveness in Medical Predictive Analytics (MPA) applications. ICE-SEARCH harnesses crossover mutation capabilities inherent LMs within an framework, significantly improving FS through model's comprehensive world knowledge adaptability to a variety of roles. Our evaluation this methodology...
We propose a novel approach to improve multi-objective evolutionary algorithms by modifying crossover operations. Our uses modifiable cross distribution and virtual point rebalance the probability of all options. This design reduces runtime for typical pseudo-Boolean functions. Experiments analysis show our effectively optimizes bi-objective problems COCZ LOTZ in Θ(n) time during crossover, outperforming conventional (C-MOEA) which require O(n log n) steps. For tri-objective problem...
Motivation: The recent physics-informed neural network (PINN) for Magnetic resonance electrical properties tomography (MREPT) still reply on ground truth as boundary conditions back propagations. Goal(s): It is aimed to propose a PINN that uses only the residuals of an MREPT analytic model rather than data. Approach: A framework which aforementioned guide learning process network, enhancing accuracy and reliability reconstruction, was proposed compensate conductivity reconstruction errors...
Magnetic resonance electrical properties tomography (MREPT) is a promising technique for non-invasive, early cancer detection based on the of tissues. However, noise sensitivity and artifact problems have historically hindered clinical application MREPT. In order to address these limitations, researchers investigated several approaches, including data-driven neural network methods, modified analytical models, or combination both. methods face challenge balancing trade-off between reducing...