A5100710533- Seismic Imaging and Inversion Techniques
- Image and Signal Denoising Methods
- Seismic Waves and Analysis
- Blind Source Separation Techniques
- Gene expression and cancer classification
- EEG and Brain-Computer Interfaces
- Drilling and Well Engineering
- Neuroscience and Neural Engineering
- Geophysical Methods and Applications
- Stochastic processes and financial applications
- Genetic Mapping and Diversity in Plants and Animals
- NMR spectroscopy and applications
- Hydraulic Fracturing and Reservoir Analysis
- Genetic and phenotypic traits in livestock
- Hydrocarbon exploration and reservoir analysis
- RNA and protein synthesis mechanisms
- Ultrasonics and Acoustic Wave Propagation
- Genomics and Chromatin Dynamics
- Machine Learning in Bioinformatics
- Image and Video Quality Assessment
- Complex Systems and Time Series Analysis
- Bioinformatics and Genomic Networks
- Structural Health Monitoring Techniques
- Advanced Numerical Analysis Techniques
- Seismology and Earthquake Studies
Linyi People's Hospital
2024
Yangtze University
2024
Jimei University
2020-2024
Chinese People's Liberation Army
2024
Henan University
2018
GGG (France)
2012-2015
Communication University of China
2013
China National Offshore Oil Corporation (China)
2009-2012
China University of Geosciences (Beijing)
2010-2012
Brain Tumour Research
2010
To define copy number alterations and gene expression signatures underlying pediatric high-grade glioma (HGG).We conducted a high-resolution analysis of genomic imbalances in 78 de novo HGGs, including seven diffuse intrinsic pontine gliomas, 10 HGGs arising children who received cranial irradiation for previous cancer using single nucleotide polymorphism microarray analysis. Gene was analyzed with microarrays 53 tumors. Results were compared publicly available data from adult...
The detection of recorded epileptic seizure activity in electroencephalogram (EEG) segments is crucial for the classification seizures. Manual recognition a time-consuming and laborious process that places heavy burden on neurologists, hence, automatic identification epilepsy has become an important issue. Traditional EEG models largely depend artificial experience are weak generalization ability. To break these limitations, we propose novel one-dimensional deep neural network robust...
Receiver operating characteristic (ROC) curve is widely used to evaluate virtual screening (VS) studies. However, the method fails address "early recognition" problem specific VS. Although many other metrics, such as RIE, BEDROC, and pROC that emphasize have been proposed, there are no rigorous statistical guidelines for determining thresholds performing significance tests. Also comparisons made between these metrics under a framework better understand their performances. We proposed VS...
Brain-computer interface (BCI) technology bridges the direct communication between brain and machines, unlocking new possibilities for human interaction rehabilitation. EEG-based motor imagery (MI) plays a pivotal role in BCI, enabling translation of thought into actionable commands interactive assistive technologies. However, constrained decoding performance signals poses limitation to broader application development BCI systems. In this study, we introduce convolutional Transformer network...
We propose an adaptive spectrum-broadening method (ASBM) to improve the resolution of nonstationary seismic data. This assumes that a trace can be split into segments, each which considered as approximately stationary. construct set specific windows, called molecular-Gabor (MG) by solving optimization problem, such in MG windows is A time-frequency (t-f) transform, obtained from frame constructed using windows. For trace, we first transform it t-f domain, then and/or amplitude compensation...
Electroencephalogram (EEG) plays a pivotal role in the detection and analysis of epileptic seizures, which affects over 70 million people world. Nonetheless, visual interpretation EEG signals for epilepsy is laborious time-consuming. To tackle this open challenge, we introduce straightforward yet efficient hybrid deep learning approach, named ResBiLSTM, detecting seizures using signals. Firstly, one-dimensional residual neural network (ResNet) tailored to adeptly extract local spatial...
Magnetic resonance (MR) images are often contaminated by Gaussian noise, an electronic noise caused the random thermal motion of components, which reduces quality and reliability images. This paper puts forward a hybrid denoising algorithm for MR based on two sparsely represented morphological components one residual part. To begin with, decompose noisy image into cartoon, texture, parts MCA, then each part is denoised using Wiener filter, wavelet hard threshold, soft respectively. Finally,...
Epilepsy is a prevalent neurological disorder disease that threatens human health in the world. The most commonly used method to detect epilepsy using electroencephalogram (EEG). However, detection from EEG time-consuming and error-prone work because of different experience levels physicians. To handle this challenge, paper, we propose multi-scale non-local (MNL) network achieve automatic signal detection. Our MNL-Network based on 1D convolution neural involving two specific layers improve...
The methods used to detect epileptic seizures using electroencephalogram (EEG) signals suffer from poor accuracy in feature selection and high redundancy. This problem is addressed through the use of a novel multi-domain fusion method (PMPSO).
Epilepsy is a neurological disorder and generally detected by electroencephalogram (EEG) signals. The manual inspection of epileptic seizures time-consuming laborious process. Extensive automatic detection algorithms were proposed using traditional approaches, which show good accuracy for several specific EEG classification problems but perform poorly in others. To address this issue, the authors present novel model, named SeizureNet, robust signals based on convolutional neural network....
ChIP-Seq is a powerful tool for identifying the interaction between genomic regulators and their bound DNAs, especially locating transcription factor binding sites. However, high cost rate of false discovery sites identified from data significantly limit its application. Here we report new algorithm, ChIP-PaM, target regions in datasets. This algorithm makes full use protein-DNA pattern by capitalizing on three lines evidence: 1) tag count modelling at peak position, 2) pa ttern m atching...
This paper addresses the issue of underutilized software test data in current aerospace quality management. Through analyzing third-party evaluation process, proposes to optimize process and management enhance development quality. The an improvement framework based on analysis, which provides a reference scheme for Overall, emphasizes importance analysis improving comprehensive solution addressing problems identified
Functional mapping based on parametric and nonparametric modeling of functional data can estimate the developmental pattern genetic effects a complex dynamic or longitudinal process triggered by quantitative trait loci (QTLs). But existing models have limitation for QTLs with irregular characterized many local features, such as peaks. We derive statistical model QTL curves any form wavelet shrinkage techniques. The fundamental idea this is repeated splitting an initial sequence into detail...
Computational cost and storage requirement are the main obstacles that inhibit research practical application of full waveform inversion (FWI). We have developed a fast parallel scheme to speed up FWI on graphics processing unit (GPU),which is computing device, via CUDA(an acronym for Compute Unified Device Architecture), by NVIDA used as programming environment. In this scheme, avoid frequent low‐bandwidth data transfer between host memory device memory, almost entire task, including...
Abstract We present a mathematic expression of the changing wavelet model seismograms (CWMS). Based on this expression, we study content and structure spectra seismic traces produced in two cases, i.e. reflectivity does not meet white assumption wavelets change during traveling subsurface. And analyze why spectral‐whitening conventional deconvolution methods cannot yield good results these cases. Then based CWMS, propose new method to enhance resolution traces. Firstly, divide signal into...
Functional gene clustering is a statistical approach for identifying the temporal patterns of expression measured at series time points. By integrating wavelet transformations, power dimension-reduction technique, noisy data smoothed and clustered allowing new functional profiles to be identified. We implement idea dimension reduction into mixture model clustering, aimed de-noise by transforming an inherently high-dimensional biological problem its tractable low-dimensional representation....
In the Gulf of Mexico (GOM), steeply dipping three-way closures are familiar subsalt targets. However, they generally poorly imaged due to low illumination caused by complex salt geometry in overburden. The weak underlying signal is masked excess noise coming from migration artifacts, residual multiples or converted waves. As a result, signal-to-noise ratio (S/N) these areas. This paper presents specialized reverse time (RTM) technique, which combines deconvolution imaging condition together...
The main task of Cohen class's distribution is finding suitable kernel function to reduce the cross‐term interference. We focus on adaptive optimal‐kernel time‐frequency representation which adapts its with time, and interference can be greatly reduced while resolution effectively kept. conduct method multi‐component synthetic signal compare short‐time Fourier transform continuous wavelet transform. Then we give seismic attenuation characterizing based representation. 3D field example...
The biological and statistical advantages of functional mapping result from joint modeling the mean-covariance structures for developmental trajectories a complex trait measured at series time points. While an increased number points can better describe dynamic pattern development, significant difficulties in performing arise prohibitive computational times required as well structure high-dimensional covariance matrix. In this article, we develop model quantitative loci (QTL) that govern...
Developmental instability or noise, defined as the phenotypic imprecision of an organism in face internal external stochastic disturbances, has been thought to play important role shaping evolutionary processes and patterns. The genetic studies developmental have based on fluctuating asymmetry (FA) that measures random differences between left right sides bilateral traits. In this article, we frame experimental design characterized by a spatial autocorrelation structure for determining...