What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Mathematical optimization
His primary areas of investigation include Mathematical optimization, Job shop scheduling, Evolutionary algorithm, Benchmark and Metaheuristic.
His Mathematical optimization study frequently links to adjacent areas such as Selection.
His Job shop scheduling research incorporates elements of Multi-objective optimization, Dynamic priority scheduling and Fair-share scheduling.
As part of his studies on Evolutionary algorithm, Xinyu Li frequently links adjacent subjects like Algorithm.
His studies in Benchmark integrate themes in fields like Imbalanced data, Decision tree, Convergence, Feature learning and Oversampling.
In his study, Genetic algorithm is inextricably linked to Tabu search, which falls within the broad field of Hybrid algorithm.
His most cited work include:
- A New Convolutional Neural Network-Based Data-Driven Fault Diagnosis Method (431 citations)
- A New Deep Transfer Learning Based on Sparse Auto-Encoder for Fault Diagnosis (274 citations)
- An effective hybrid genetic algorithm and tabu search for flexible job shop scheduling problem (176 citations)
What are the main themes of his work throughout his whole career to date?
Xinyu Li mostly deals with Mathematical optimization, Job shop scheduling, Artificial intelligence, Algorithm and Scheduling.
The Mathematical optimization study combines topics in areas such as Scheduling, Fair-share scheduling and Flow shop scheduling.
In his work, Evolutionary computation is strongly intertwined with Evolutionary algorithm, which is a subfield of Job shop scheduling.
His Artificial intelligence study combines topics from a wide range of disciplines, such as Machine learning and Pattern recognition.
His Optimization problem, Particle swarm optimization and Metaheuristic algorithms study in the realm of Algorithm interacts with subjects such as Electromagnetism.
His work carried out in the field of Deep learning brings together such families of science as Transfer of learning and Data mining.
He most often published in these fields:
- Mathematical optimization (57.50%)
- Job shop scheduling (29.00%)
- Artificial intelligence (23.00%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (23.00%)
- Mathematical optimization (57.50%)
- Deep learning (12.50%)
In recent papers he was focusing on the following fields of study:
His main research concerns Artificial intelligence, Mathematical optimization, Deep learning, Pattern recognition and Job shop scheduling.
He has researched Deep learning in several fields, including Data mining, Vision based, Information fusion, Feature extraction and Iterative reconstruction.
In general Pattern recognition, his work in Support vector machine and Mutual information is often linked to Image quality linking many areas of study.
His work deals with themes such as Scheduling and Local search, which intersect with Job shop scheduling.
The study incorporates disciplines such as Taguchi methods and Flow shop scheduling in addition to Scheduling.
His studies deal with areas such as Multi-objective optimization, Engineering optimization and Benchmark as well as Evolutionary algorithm.
Between 2019 and 2021, his most popular works were:
- A transfer convolutional neural network for fault diagnosis based on ResNet-50 (50 citations)
- A semi-supervised convolutional neural network-based method for steel surface defect recognition (33 citations)
- A Three-Stage Multiobjective Approach Based on Decomposition for an Energy-Efficient Hybrid Flow Shop Scheduling Problem (33 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Algorithm
His primary scientific interests are in Artificial intelligence, Pattern recognition, Deep learning, Mathematical optimization and Job shop scheduling.
His work in the fields of Noise reduction, Weighted average method, Feature learning and Discriminative model overlaps with other areas such as Image quality.
His research in Pattern recognition intersects with topics in Iterative reconstruction, Vision based and Generative adversarial network.
His Deep learning research is multidisciplinary, incorporating perspectives in Point cloud, Segmentation, Information fusion, Network complexity and Robustness.
His Mathematical optimization research includes elements of Sequence-dependent setup and Scheduling.
When carried out as part of a general Job shop scheduling research project, his work on Flow shop scheduling is frequently linked to work in Energy consumption, therefore connecting diverse disciplines of study.
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