Sökning: "Lei Feng"

Visar resultat 1 - 5 av 8 avhandlingar innehållade orden Lei Feng.

1. 1. Surface characterization and manipulation of polyampholytic hydrogel coatings

   Författare :Feng-I Tai; Thomas Ederth; Bo Liedberg; Lei Ye; Linköpings universitet; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; Hydrogels; antifouling; charge-balanced material; polyampholytes; force measurements; polymer swelling; protein adsorption; patterning; plasmonics;

   Sammanfattning : This thesis is dedicated to building up fundamental knowledge about polyampholytic hydrogels, which are developed in our group for anti-fouling purposes. Charge-balanced polymers, where positive and negative charges balance each other, have emerged as interesting candidates for many applications in materials science. LÄS MER

3. 2. Learning-based Control for 4D Printing and Soft Robotics

   Författare :Qinglei Ji; Lei Feng; Lihui Wang; Xi Vincent Wang; Yong Chen; KTH; []
   Nyckelord :TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; 3D Printing; 4D Printing; Soft Robots; Machine Learning; Reinforcement Learning; Control; Production Engineering; Industriell produktion;

   Sammanfattning : Exploiting novel sensors and actuators made of flexible and smart materials becomes a new trend in robotics research. The studies on the design, production, and control of the new type of robots motivate the research fields of soft robots and 4D printed robots. LÄS MER

4. 3. Improving Fuel Efficiency of Commercial Vehicles through Optimal Control of Energy Buffers

   Författare :Mohammad Khodabakhshian; Jan Wikander; Lei Feng; Johan Ölvander; KTH; []
   Nyckelord :TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; Energy buffer; Optimal control; Hybrid electric vehicle; Engine cooling system; Equivalent consumption minimization strategy; Model predictive control; Machine Design; Maskinkonstruktion;

   Sammanfattning : Fuel consumption reduction is one of the main challenges in the automotiveindustry due to its economical and environmental impacts as well as legalregulations. While fuel consumption reduction is important for all vehicles,it has larger benefits for commercial ones due to their long operational timesand much higher fuel consumption. LÄS MER

5. 4. Computationally Efficient and Adaptive Energy Management Strategies for Parallel Hybrid Electric Vehicles

   Författare :Tong Liu; Lei Feng; Hans Johansson; Nikolce Murgovski; KTH; []
   Nyckelord :TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; Hybrid Electric Vehicle; Energy Management Strategy; Computation Efficiency; Value Function; Adaptive Learning; Processor-in-the- Loop Simulation; Elhybridfordon; Energihanteringsstrategi; Beräkningseffektivitet; Värdefunktion; Adaptiv Inlärning; Processor-in-the-loop; Machine Design; Maskinkonstruktion; Optimeringslära och systemteori; Optimization and Systems Theory; Industriella informations- och styrsystem; Industrial Information and Control Systems;

   Sammanfattning : Hybrid electric vehicles (HEVs) are irreplaceable in attaining sustainable development in contemporary society. Owing to the extra degree of freedom in supplying traction power, HEVs resort to appropriate energy management strategies (EMSs) to present their superiority over conventional internal combustion engine vehicles and pure electric vehicles. LÄS MER

7. 5. Towards safe and efficient application of deep neural networks in resource-constrained real-time embedded systems

   Författare :Siyu Luan; Zonghua Gu; Leonid B. Freidovich; Lei Feng; Umeå universitet; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; Machine Learning Deep Learning; Real-Time Embedded systems; Out-of-Distribution Detection; Distribution Shifts; Deep Reinforcement Learning; Model Compression; Policy Distillation.;

   Sammanfattning : We consider real-time safety-critical systems that feature closed-loop interactions between the embedded computing system and the physical environment with a sense-compute-actuate feedback loop. Deep Learning (DL) with Deep Neural Networks (DNNs) has achieved success in many application domains, but there are still significant challenges in its application in real-time safety-critical systems that require high levels of safety certification under significant hardware resource constraints. LÄS MER