A5101788337- Geophysical and Geoelectrical Methods
- Geological and Geochemical Analysis
- Geochemistry and Geologic Mapping
- earthquake and tectonic studies
- Geological and Geophysical Studies
- Seismic Waves and Analysis
- Neural Networks and Reservoir Computing
- High-pressure geophysics and materials
- Advanced Memory and Neural Computing
- Geophysics and Gravity Measurements
- Seismic Imaging and Inversion Techniques
- Meningioma and schwannoma management
- Neurofibromatosis and Schwannoma Cases
- Neural Networks and Applications
- Land Use and Ecosystem Services
- Thermal Radiation and Cooling Technologies
- Remote Sensing in Agriculture
- Trigeminal Neuralgia and Treatments
- Remote-Sensing Image Classification
- Ergonomics and Musculoskeletal Disorders
- Soil Moisture and Remote Sensing
- Urban Design and Spatial Analysis
- Gaze Tracking and Assistive Technology
- Modular Robots and Swarm Intelligence
- Non-Destructive Testing Techniques
Jilin Province Science and Technology Department
2021-2024
Jilin University
2021-2024
Xuzhou Central Hospital
2023-2024
Jinan University
2024
Hunan University
2024
Jiangnan University
2024
University of California, Santa Barbara
2024
State Key Laboratory on Integrated Optoelectronics
2023
Jilin Medical University
2022
Anhui University
2021
The Shuangjianzishan deposit in Inner Mongolia is a typical Ag-Pb-Zn of the southern Great Xing'an Range. Proven reserves Ag, Pb, and Zn this have reached scale super-large deposits, with favorable metallogenic conditions, strong prospecting signs, high potential. This paper reports study involving integrated geophysical methods, including controlled-source audio-frequency magnetotelluric, gravity, magnetic, shallow-seismic-reflection to determine spatial distribution ore-controlling...
Magnetotelluric (MT) impedance estimation requires a high signal-to-noise ratio (SNR). When low-SNR data are processed, it is difficult to obtain robust MT response. In this article, based on the spectral characteristics of noise sequences, influence scale and waveform sequences estimates studied, multiscale denoising method for signals proposed. This applies improved complete ensemble empirical mode decomposition with adaptive (ICEEMDAN) decompose into different components, then, spectrum...
The natural electromagnetic field observed in magnetotelluric (MT) sounding is non-stationary, making it challenging to obtain reliable frequency spectrum information using Fourier transform. In practical measurements, long-duration observations of the signal are often required accurate low-frequency impedance, resulting significant technical difficulties and high economic costs. We provide a method for estimating MT impedance instantaneous obtained with variation mode decomposition (VMD)....
Traditional magnetotelluric (MT) impedance estimations are based on Fourier theory and carried out in the frequency domain, which has a strict stationarity requirement for analyzed signal. However, assumption cannot be satisfied when data possess low signal-to-noise ratio (SNR) and/or short observation period. These shortcomings can cause significant errors MT estimations, especially low-to-medium bands. The alternating direction method of multipliers (ADMMs) polarization analysis...
Abstract This study reports trigeminal schwannomas (TSs) in 13 cases whose cancer invaded the cavernous sinus (CS) middle cranial fossa (MCF). Seventy-eight patients who underwent surgical treatment Beijing Tiantan Hospital last 6 years were retrospectively analyzed and a literature review was conducted. The divided into 2 groups by definitive evidence of CS invasion during surgery. Group A included 65 cases. Six suffered from diplopia. Tumor size their MCF ranged 7 mm to 48 (mean: 23.5 ±...
In this study, we propose the first hardware implementation of a context-based recurrent spiking neural network (RSNN) emphasizing on integrating dual information streams within neocortical pyramidal neurons specifically Context- Dependent Leaky Integrate and Fire (CLIF) neuron models, essential element in RSNN. We present quantized version CLIF (qCLIF), developed through hardware-software codesign approach utilizing sparse activity Implemented 45nm technology node, qCLIF is compact...
In this study, we propose the first hardware implementation of a context-based recurrent spiking neural network (RSNN) emphasizing on integrating dual information streams within neocortical pyramidal neurons specifically Context-Dependent Leaky Integrate and Fire (CLIF) neuron models, essential element in RSNN. We present quantized version CLIF (qCLIF), developed through hardware-software codesign approach utilizing sparse activity Implemented 45nm technology node, qCLIF is compact (900um²)...