Jing Xu

Affiliations:
  • Jiangsu University of Science and Technology, Marine Equipment and Technology Institute, Zhenjiang, China
  • China University of Mining & Technology, School of Mechatronic Engineering, Xuzhou, China


According to our database1, Jing Xu authored at least 11 papers between 2015 and 2020.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

Legend:

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PhD thesis 
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Links

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Bibliography

2020
m6ABRP: Predicting m6A-YTHDF2 Binding Regions via Sequence-based Properties.
Proceedings of the 9th International Conference on Bioinformatics and Biomedical Science, 2020

2019
Horizontal Bending Angle Optimization Method for Scraper Conveyor Based on Improved Bat Algorithm.
Algorithms, 2019

2018
Cutting Pattern Identification for Coal Mining Shearer through Sound Signals Based on a Convolutional Neural Network.
Symmetry, 2018

Cutting Pattern Identification for Coal Mining Shearer through a Swarm Intelligence-Based Variable Translation Wavelet Neural Network.
Sensors, 2018

2017
Image Enhancement for Surveillance Video of Coal Mining Face Based on Single-Scale Retinex Algorithm Combined with Bilateral Filtering.
Symmetry, 2017

Parameters Tuning Approach for Proportion Integration Differentiation Controller of Magnetorheological Fluids Brake Based on Improved Fruit Fly Optimization Algorithm.
Symmetry, 2017

Pressure Control for a Hydraulic Cylinder Based on a Self-Tuning PID Controller Optimized by a Hybrid Optimization Algorithm.
Algorithms, 2017

2016
Identification of Shearer Cutting Patterns Using Vibration Signals Based on a Least Squares Support Vector Machine with an Improved Fruit Fly Optimization Algorithm.
Sensors, 2016

A State Recognition Approach for Complex Equipment Based on a Fuzzy Probabilistic Neural Network.
Algorithms, 2016

2015
A Cutting Pattern Recognition Method for Shearers Based on Improved Ensemble Empirical Mode Decomposition and a Probabilistic Neural Network.
Sensors, 2015

Multi-Sensor Data Fusion Identification for Shearer Cutting Conditions Based on Parallel Quasi-Newton Neural Networks and the Dempster-Shafer Theory.
Sensors, 2015


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