A5043273140- Infrastructure Maintenance and Monitoring
- Medical Image Segmentation Techniques
- Vehicle Noise and Vibration Control
- Geophysical Methods and Applications
- Cryptography and Data Security
- Colorectal Cancer Screening and Detection
- Engineering Applied Research
- Speech and Audio Processing
- Advanced Neural Network Applications
- AI in cancer detection
- Advanced Image and Video Retrieval Techniques
- Video Analysis and Summarization
- Genetic factors in colorectal cancer
- Acoustic Wave Phenomena Research
- Image Retrieval and Classification Techniques
- Gastric Cancer Management and Outcomes
- Underwater Acoustics Research
- IoT-based Smart Home Systems
- Cryptography and Residue Arithmetic
- Photoacoustic and Ultrasonic Imaging
- Advanced Data Storage Technologies
- Advanced Numerical Analysis Techniques
- Mobile and Web Applications
- Soil Moisture and Remote Sensing
- Food Supply Chain Traceability
Shanxi Province Hospital of Traditional Chinese Medicine
2024
East China Normal University
2018-2019
National Supercomputing Center in Wuxi
2015
Changchun University of Technology
2007
This letter investigates the inversion of rough surface parameters (the root mean square height and correlation length) from microwave images by using deep convolutional neural networks (CNNs). Training data for CNN are simulated numerically computational electromagnetic method. As is powerful in extracting image features, scattering field surfaces first converted to via interpolated fast Fourier transform then fed into CNN. In order reduce overfitting, regularization technique dropout layer...
ObjectivesEffective exclusion of low-risk symptomatic outpatient cases for colorectal cancer (CRC) remains diagnostic challenges. We aimed to develop a self-reported symptom-based decision-making model application in scenarios.MethodsIn total, 8233 at risk CRC, as judged by physicians, were involved this study seven medical centers. A was constructed using 60 symptom parameters collected from the questionnaire. Further internal and external validation cohorts built evaluate discriminatory...
Deep convolution networks (CNN) is applied to inverse the rough surface parameters, including root-me an-square height and correlation length, from microwave images. We employ computational electromagnetic method simulate training data for deep CNN. The simulated backward scattering converted into images as inputs An inversion network of neural with five cascaded convolutional-maxpooling layers two fully connected designed, feature extraction regression by using layers. results demonstrate...
Aiming at improving the acoustic quality of latch mechanism automotive side door, contact point car door slam with loudest sound is obtained. In view difficulty obtaining sufficient experimental instantaneous samples during process testing, a method combined FEM and BEM using ABAQUS LMS software utilized skillfully to obtain samples. The credibility simulation verified within an error range smaller than 12%. Finally, subjective-objective evaluation algorithm based on BP neural network...
By using a wireless remote networking, and cloud computing platform technology, this system can collect idle space time from every company or unit who has its isolated cold-chain transport system.Those information of refrigerated truck's time, spare route be opened to all other customers with such demands.By way, small quality scattered goods will distributed incidentally cheaply those daily big company.This greatly improve the utilization efficiency cold chain reduce cost business...
In this paper, we propose a fast method for optimizing multiple size parameters of honeycomb absorbing material including height and dip coating thickness, based on deep convolutional neural networks (CNN). The reflection coefficients (S <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">11</inf> ) are simulated to generate CNN training data as inputs the in paper. network consists six concatenated convolution-maximum pooling layers three...