A5103123928- Geophysical and Geoelectrical Methods
- Geophysical Methods and Applications
- Seismic Waves and Analysis
- Underwater Acoustics Research
- Image and Signal Denoising Methods
- Blind Source Separation Techniques
- Medical Image Segmentation Techniques
- Neural Networks and Applications
- Seismic Imaging and Inversion Techniques
- Non-Destructive Testing Techniques
- Image and Object Detection Techniques
- EEG and Brain-Computer Interfaces
- Image Retrieval and Classification Techniques
- Sparse and Compressive Sensing Techniques
- Soil Moisture and Remote Sensing
- Advanced Algorithms and Applications
- Machine Fault Diagnosis Techniques
- Electromagnetic Simulation and Numerical Methods
- Earthquake Detection and Analysis
- Cancer Research and Treatments
- Magnetic Properties and Applications
- Photoacoustic and Ultrasonic Imaging
- Music and Audio Processing
- Geoscience and Mining Technology
- Geological and Geophysical Studies
Central South University
2016-2025
Ministry of Natural Resources
2020-2024
Guilin University of Electronic Technology
2024
Jimei University
2022
Tsinghua University
2015-2021
South University
2017
Beijing University of Chinese Medicine
2009
China-Japan Friendship Hospital
2005
Medical images edge detection is an important work for object recognition of the human organs and it pre-processing step in medical image segmentation 3D reconstruction. Conventionally, detected according to some early brought forward algorithms such as gradient-based algorithm template-based algorithm, but they are not so good noise detection. In this paper, basic mathematical morphological theory operations introduced at first, then a novel proposed detect lungs CT with salt-and-pepper...
Background: Our previous findings showed that miR-33 expressed abnormally in clinical specimens of melanoma, but the exact molecular mechanism has not been elucidated. Object: To determine miR-33's roles melanoma and confirm whether HIF-1α is a direct target gene miR-33a. Methods: First miR-33a/b expression levels were detected HM, WM35, WM451, A375 SK-MEL-1. Then lentiviral vectors constructed to intervene miR-33a cells. Cell proliferation, invasion metastasis detected. cells mice model was...
Audio magnetotellurics (AMT), as a commonly used passive geophysical technique, provides outstanding metal ore exploration capabilities based on the resistivity structure of earth. However, accuracy AMT in translating geoelectrical structures decreases when data collected mining areas are poor quality and contain complex anthropogenic noise, leading to distorted apparent resistivity-phase curves posing significant challenges for mineral exploration. To effectively denoise data, we develop...
Geomagnetic data are widely used in earthquake prediction, mantle conductivity imaging, and other fields. However, the problem of geomagnetic being contaminated by cultural noise is becoming increasingly serious. Existing denoising methods have shortcomings such as insufficient flexibility need for manual intervention. To this end, we modify U-net propose a new intelligent signal method based on network. The novel network not only combines advantages convolutional neural (DnCNN) U-net, but...
Audio magnetotelluric (AMT) is commonly used in mineral resource exploration. However, the weak energy of AMT signals makes them susceptible to being overwhelmed by noise, leading erroneous geophysical interpretations. In recent years, deep learning has been applied denoising and shown better performance compared traditional methods. current methods overlook characteristics signals, resulting reduced accuracy. To enhance matching features we propose a CBAM-based (Convolutional Block...
In order to obtain the deep geological structure of Jinding lead-zinc mine, and better understand environment for mineralization in part deposit, we have developed a new exploration mode on Controlled Source Audio-frequency Magnetotellurics method (CSAMT). Firstly, established geophysical model based overview mineral performed forward modeling using coupled finite-infinite element method. On one hand, verified effectiveness CSAMT results, another designed field acquisition parameters by...
Abstract Magnetotelluric (MT) method is widely used for revealing deep electrical structure. However, natural MT signals are susceptible to cultural noises. In particular, the existing data-processing methods usually fail work when data contaminated by persistent or coherent To improve quality of collected with strong ambient noises, we propose a novel time-series editing based on improved shift-invariant sparse coding (ISISC), data-driven machine learning algorithm. First, redundant...
Controlled-source electromagnetic (CSEM) data recorded in industrialized areas are inevitably contaminated by strong cultural noise. Traditional noise attenuation methods often ineffective for intricate aperiodic To address the abovementioned problem, we have developed a novel isolation method based on fast Fourier transform, complementary ensemble empirical mode decomposition (CEEMD), and shift-invariant sparse coding (SISC, an unsupervised machine-learning algorithm under data-driven...
The theories of empirical mode decomposition (EMD) and instantaneous frequency solution which are two parts Hilbert-Huang Transformation (HHT) discussed in the paper. We focus on using EMD to electrocardiogram (ECG) can be decomposed into a limited number intrinsic functions. Different thresholds used treat function achieve de-noising then compared with effect wavelet transform de-noising. Transform is demonstrated effective removing general noise ECG.
Novel fully discrete schemes are developed to numerically approximate a semilinear stochastic wave equation driven by additive space-time white noise. Spectral Galerkin method is proposed for the spatial discretization, and exponential time integrators involving linear functionals of noise introduced temporal approximation. The resulting very easy implement allow higher strong convergence rate in than existing time-stepping such as Crank-Nicolson-Maruyama scheme trigonometric method....
Prevailing methods of magnetotelluric (MT) data analysis determine the spectra using variations Fourier Transform (FT), which is based on principle signal stationarity. However, MT series are non-stationary random signals that do not meet basic requirements conventional FT. In recent years, Hilbert–Huang (HHT) has been regarded as a powerful tool for adaptive non-linear and signals. This paper proposes first time adoption new method data, focuses two aspects facilitated by applying HHT. The...
It is well known that a magnetotelluric (MT) signal with high signal-to-noise ratio an important prerequisite for correct interpretation of subsurface structures. However, MT signals collected in the environment strong cultural noise often are low data quality due to pollution, which seriously affects accuracy interpretation. As can be seen from time-domain waveform, highly energetic, diverse, and random. This means denoising methods should have applicability guarantee accurate effective...
A new technique is proposed for signal-noise identification and targeted de-noising of Magnetotelluric (MT) signals. This method based on fractal-entropy clustering algorithm, which automatically identifies signal sections corrupted by common interference (square, triangle pulse waves), enabling preventing the loss useful information in filtering. To implement technique, four characteristic parameters — fractal box dimension (FBD), higuchi (HFD), fuzzy entropy (FuEn) approximate (ApEn) are...
ABSTRACT Magnetotelluric is one of the mainstream exploration geophysical methods, which plays a vital role in studying deep geological structures and finding hidden blind ore bodies. The seriousness human electromagnetic noise causes large number abnormal waveforms time series measured magnetotelluric data, data can no longer objectively reflect underground electrical distribution. In this work, we propose processing method based on K singular value decomposition dictionary training. First,...
The establishment of monitoring and warning systems for landslide with potential instability risk is an economical effective way to ensure life safety reduce property losses. However, as the number landslides sensors increases, amount data generated close massive, traditional are gradually unable manage analyze acquired data. In this study, intelligent early system its application were developed based on microservice architecture. system, functions landslide's project management, reception...
The magnetotelluric (MT) data collected in an ore-concentration area are extremely vulnerable to all kinds of noise pollution. However, separating real MT signals from strong is still a difficult problem, and the quite distinct clean morphological features. By performing signal-noise identification prediction, we develop deep learning method denoise containing noise. First, use convolutional neural network (CNN) learn feature differences between samples massive learned features realize...
A novel automatic R peak detection algorithm that is based on EMD (empirical mode decomposition) and adaptive threshold technique presented. The peaks are then identified by computing the first applying a set of thresholds not limited to strict range. proposed was able detect with high sensitivity when compared other algorithms used in Physionet ECG database. Additionally, new can successfully for wide variety shapes.
The closest iterative point (ICP) algorithm is commonly used in medical image registration. However, due to its natural limitation, the processing time and registration accuracy need be further advanced. In this paper, by computing moments of reference floating images, centroids are computed thus initial translation parameters obtained. rotation angles acquired respectively second-order central moments, inertia matrix, Karhunen–Loeve transformation (K-LT) singular value decomposition (SVD)...