What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Operating system
- Control theory
His scientific interests lie mostly in Control theory, Genetic algorithm, Fuzzy logic, Control engineering and Positioning system.
In the subject of general Control theory, his work in Machine control, Control theory and Adaptive algorithm is often linked to Laser and Hysteresis, thereby combining diverse domains of study.
His Genetic algorithm research includes themes of Evolutionary algorithm, Search algorithm, Heuristics and Operations research.
His work on Fuzzy control system and Soft computing as part of general Fuzzy logic study is frequently connected to Schema, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
The Control engineering study which covers Linear motor that intersects with Stability, Adaptive control and Lyapunov function.
In Control system, David Naso works on issues like Multi-agent system, which are connected to Mathematical optimization.
His most cited work include:
- Distributed Consensus-Based Economic Dispatch With Transmission Losses (259 citations)
- Compact Differential Evolution (183 citations)
- Genetic algorithms for supply-chain scheduling: A case study in the distribution of ready-mixed concrete (173 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of investigation include Control theory, Actuator, Control engineering, Control theory and Mathematical optimization.
His research in the fields of Adaptive control, Nonlinear system, Control system and PID controller overlaps with other disciplines such as Positioning system.
His Control engineering research is multidisciplinary, relying on both Control, Linear motor and Robustness.
His Control theory research incorporates themes from Automatic control, Optimal control, Linear system and Fuzzy logic.
In most of his Mathematical optimization studies, his work intersects topics such as Economic dispatch.
David Naso has included themes like Scheduling and Heuristics in his Genetic algorithm study.
He most often published in these fields:
- Control theory (43.33%)
- Actuator (27.78%)
- Control engineering (25.00%)
What were the highlights of his more recent work (between 2014-2020)?
- Actuator (27.78%)
- Control theory (43.33%)
- Nonlinear system (12.78%)
In recent papers he was focusing on the following fields of study:
David Naso focuses on Actuator, Control theory, Nonlinear system, Control engineering and Shape-memory alloy.
His studies in Actuator integrate themes in fields like Mechanical engineering, Elastomer and Electronic engineering.
David Naso merges Control theory with Positioning system in his study.
His research investigates the connection between Nonlinear system and topics such as Bistability that intersect with problems in Design choice.
His Control engineering research is multidisciplinary, incorporating elements of Discretization, Dielectric elastomer actuator and Energy management.
As a part of the same scientific study, David Naso usually deals with the Control theory, concentrating on Haptic technology and frequently concerns with Stability and Algorithm design.
Between 2014 and 2020, his most popular works were:
- Modeling, Identification, and Control of a Dielectric Electro-Active Polymer Positioning System (63 citations)
- Closed loop control of dielectric elastomer actuators based on self-sensing displacement feedback (50 citations)
- Scalable Real-Time Electric Vehicles Charging With Discrete Charging Rates (49 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Operating system
- Control theory
His primary areas of study are Actuator, Control theory, Nonlinear system, Positioning system and Control engineering.
His biological study spans a wide range of topics, including Capacitance, Biasing and Stress.
His Capacitance study also includes
- Electronic engineering that connect with fields like Linear regression, Electrical resistance and conductance, Field-programmable gate array and Algorithm,
- Recursive least squares filter together with Transducer and Black box.
His study of Robust control is a part of Control theory.
His Control engineering study focuses on Control theory in particular.
His work deals with themes such as Mechanical engineering, Linear motor, Realization and Sensitivity, which intersect with Dielectric elastomer actuator.
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