What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Machine learning
The scientist’s investigation covers issues in Data mining, Artificial intelligence, Algorithm, Soft sensor and Principal component analysis.
His work carried out in the field of Data mining brings together such families of science as Information extraction, Probabilistic logic, Bayesian probability and Statistical model.
The Artificial intelligence study combines topics in areas such as Machine learning and Pattern recognition.
His biological study spans a wide range of topics, including Benchmark, Fault detection and identification and Nonlinear system.
His studies deal with areas such as Least squares and Kernel as well as Nonlinear system.
His research integrates issues of Support vector machine, Partial least squares regression, Residual and Kernel principal component analysis in his study of Principal component analysis.
His most cited work include:
- Review of Recent Research on Data-Based Process Monitoring (534 citations)
- Data Mining and Analytics in the Process Industry: The Role of Machine Learning (325 citations)
- Process Monitoring Based on Independent Component Analysis - Principal Component Analysis ( ICA - PCA ) and Similarity Factors (205 citations)
What are the main themes of his work throughout his whole career to date?
His primary scientific interests are in Data mining, Artificial intelligence, Algorithm, Nonlinear system and Fault detection and isolation.
Zhihuan Song has researched Data mining in several fields, including Support vector machine, Probabilistic logic, Bayesian probability, Benchmark and Principal component analysis.
His studies in Artificial intelligence integrate themes in fields like Machine learning, Soft sensor and Pattern recognition.
He has included themes like Mixture model and Partial least squares regression in his Soft sensor study.
His study in Algorithm is interdisciplinary in nature, drawing from both Mode and Dimensionality reduction.
His Nonlinear system research incorporates elements of Mathematical optimization, Kernel and Kernel.
He most often published in these fields:
- Data mining (34.32%)
- Artificial intelligence (32.63%)
- Algorithm (26.27%)
What were the highlights of his more recent work (between 2018-2021)?
- Nonlinear system (21.19%)
- Data mining (34.32%)
- Artificial intelligence (32.63%)
In recent papers he was focusing on the following fields of study:
Zhihuan Song focuses on Nonlinear system, Data mining, Artificial intelligence, Algorithm and Fault detection and isolation.
His research in Nonlinear system intersects with topics in Mathematical optimization and Benchmark.
Zhihuan Song combines subjects such as Face, Manifold, Probabilistic logic, Dimensionality reduction and Feature extraction with his study of Data mining.
His Artificial intelligence research is multidisciplinary, incorporating elements of Machine learning, Soft sensor and Pattern recognition.
The various areas that Zhihuan Song examines in his Algorithm study include Linear regression, Bayesian probability and Mode.
In his research, Process control, Subspace topology and Control system is intimately related to Principal component analysis, which falls under the overarching field of Fault detection and isolation.
Between 2018 and 2021, his most popular works were:
- Quality variable prediction for chemical processes based on semisupervised Dirichlet process mixture of Gaussians (27 citations)
- Nonlinear industrial soft sensor development based on semi-supervised probabilistic mixture of extreme learning machines (19 citations)
- Soft-Sensor Development for Processes With Multiple Operating Modes Based on Semisupervised Gaussian Mixture Regression (19 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Machine learning
Zhihuan Song mostly deals with Artificial intelligence, Nonlinear system, Mixture model, Soft sensor and Fault detection and isolation.
His Artificial intelligence study combines topics in areas such as Machine learning and Pattern recognition.
His Nonlinear system research is multidisciplinary, relying on both Uncertain data and Statistical model.
His Soft sensor study integrates concerns from other disciplines, such as Stochastic process, Supervised learning, Estimation theory and Parallel computing.
His work carried out in the field of Fault detection and isolation brings together such families of science as Reliability engineering and Relevance.
His studies in Deep learning integrate themes in fields like Image capture, Feature extraction and Data mining.
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