Composite material, Mathematical optimization, Nanotechnology, Nanoindentation and Internal medicine are his primary areas of study. His research ties Chemical vapor deposition and Composite material together. His Mathematical optimization study which covers Algorithm that intersects with Genetic algorithm.
Xiaodong Li has researched Nanotechnology in several fields, including Supercapacitor, Capacitance and Oxide. His Nanoindentation study integrates concerns from other disciplines, such as Indentation, Amorphous carbon and Elastic modulus. His work carried out in the field of Internal medicine brings together such families of science as Sclerostin and Endocrinology.
Xiaodong Li mainly focuses on Composite material, Mathematical optimization, Chemical engineering, Nanotechnology and Artificial intelligence. His Nanoindentation, Elastic modulus, Composite number, Indentation and Deformation investigations are all subjects of Composite material research. His study in Evolutionary algorithm, Evolutionary computation, Multi-objective optimization, Particle swarm optimization and Optimization problem is carried out as part of his studies in Mathematical optimization.
He does research in Particle swarm optimization, focusing on Multi-swarm optimization specifically. His Nanotechnology study frequently draws connections to adjacent fields such as Supercapacitor. His studies deal with areas such as Machine learning and Pattern recognition as well as Artificial intelligence.
Xiaodong Li mostly deals with Chemical engineering, Optoelectronics, Composite material, Graphene and Composite number. His study in Chemical engineering is interdisciplinary in nature, drawing from both Carbon, Anode, Lithium and Electrochemistry. His study in Digital image correlation and Compression falls within the category of Composite material.
His Graphene study frequently links to related topics such as Oxide.
His main research concerns Chemical engineering, Optoelectronics, Carbon, Perovskite and Graphene. His Chemical engineering research is multidisciplinary, incorporating elements of Battery, Anode, Ion, Lithium and Electrochemistry. His study in the field of Light-emitting diode also crosses realms of Optical power.
The study incorporates disciplines such as Cathode and Electrocatalyst in addition to Carbon. His Perovskite research is multidisciplinary, relying on both Grain boundary and Energy conversion efficiency. His Graphene research is multidisciplinary, incorporating perspectives in Composite number, Composite material and Oxide.
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Condition Monitoring and Fault Diagnosis of Electrical Motors—A Review
S. Nandi;H.A. Toliyat;X. Li.
IEEE Transactions on Energy Conversion (2005)
A Review of Nanoindentation Continuous Stiffness Measurement Technique and Its Applications
Xiaodong Li;Bharat Bhushan.
Materials Characterization (2002)
Freestanding three-dimensional graphene/MnO2 composite networks as ultralight and flexible supercapacitor electrodes.
Yongmin He;Wanjun Chen;Xiaodong Li;Zhenxing Zhang.
ACS Nano (2013)
Targeted Deletion of the Sclerostin Gene in Mice Results in Increased Bone Formation and Bone Strength
Xiaodong Li;Michael S Ominsky;Qing-Tian Niu;Ning Sun.
Journal of Bone and Mineral Research (2008)
Benchmark Functions for the CEC'2010 Special Session and Competition on Large-Scale Global Optimization
Ke Tang;Xiaodong Li;P. N. Suganthan;Zhenyu Yang.
Sclerostin antibody treatment increases bone formation, bone mass, and bone strength in a rat model of postmenopausal osteoporosis.
Xiaodong Li;Michael S Ominsky;Kelly S Warmington;Sean Morony.
Journal of Bone and Mineral Research (2009)
Flexible Zn2SnO4/MnO2 Core/Shell Nanocable—Carbon Microfiber Hybrid Composites for High-Performance Supercapacitor Electrodes
Lihong Bao;Jianfeng Zang;Xiaodong Li.
Nano Letters (2011)
Cooperatively Coevolving Particle Swarms for Large Scale Optimization
Xiaodong Li;Xin Yao.
IEEE Transactions on Evolutionary Computation (2012)
A non-dominated sorting particle swarm optimizer for multiobjective optimization
genetic and evolutionary computation conference (2003)
Locating and tracking multiple dynamic optima by a particle swarm model using speciation
D. Parrott;Xiaodong Li.
IEEE Transactions on Evolutionary Computation (2006)
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